main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing spacy and loading the model\n",
    "import spacy\n",
    "nlp = spacy.load('en_core_web_md')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id                                               para\n",
      "0   1  Until recently, the conventional wisdom was th...\n",
      "1   2  This mindset can create new opportunities for ...\n",
      "2   3  Elon Musk has finally unveiled a prototype of ...\n",
      "3   4  While the sort of new HomePod grabbed all the ...\n"
     ]
    }
   ],
   "source": [
    "#using pandas to read csv\n",
    "import pandas as pd\n",
    "df = pd.read_csv('text.csv')\n",
    "\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones.\n",
      "1 : But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do.\n",
      "2 : The most visible advances have been in natural language processing (NLP), the branch of AI focused on how computers can process language like humans do.\n",
      "3 : It has been used to write an article for The Guardian, and AI-authored blog posts have gone viral — feats that were not possible a few years ago.\n",
      "4 : AI even excels at cognitive tasks like programming where it is able to generate programs for simple video games from human instructions.\n",
      "\n",
      "Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones.\n"
     ]
    }
   ],
   "source": [
    "#Splitting paragraph into sentences\n",
    "paragraph=df.para[0]\n",
    "\n",
    "processed_para=nlp(paragraph) #processed_para is a spacy doc object\n",
    "# print(processed_para.sents)\n",
    "\n",
    "sentences=[]\n",
    "for i,sent in enumerate(processed_para.sents):\n",
    "    print(\"{0} : {1}\".format(i,sent))\n",
    "    sentences.append(sent)\n",
    "\n",
    "sentence=str(sentences[0]) #converting spacy span object to string\n",
    "print('\\n'+sentence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Until\t0\n",
      "recently\t6\n",
      ",\t14\n",
      "the\t16\n",
      "conventional\t20\n",
      "wisdom\t33\n",
      "was\t40\n",
      "that\t44\n",
      "while\t49\n",
      "AI\t55\n",
      "was\t58\n",
      "better\t62\n",
      "than\t69\n",
      "humans\t74\n",
      "at\t81\n",
      "data\t84\n",
      "-\t88\n",
      "driven\t89\n",
      "decision\t96\n",
      "making\t105\n",
      "tasks\t112\n",
      ",\t117\n",
      "it\t119\n",
      "was\t122\n",
      "still\t126\n",
      "inferior\t132\n",
      "to\t141\n",
      "humans\t144\n",
      "for\t151\n",
      "cognitive\t155\n",
      "and\t165\n",
      "creative\t169\n",
      "ones\t178\n",
      ".\t182\n"
     ]
    }
   ],
   "source": [
    "#Tokenisation\n",
    "doc = nlp(sentence)\n",
    "\n",
    "for token in doc:\n",
    "    print(\"{0}\\t{1}\".format(token.text,token.idx)) \n",
    "    #idx property gives index of word in sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<spacy.strings.StringStore object at 0x11fe104f0>\n",
      "1433653077910583464\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'creative'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How are tokens encoded in spacy?\n",
    "print(doc.vocab.strings) #uses a hashmap to store strings\n",
    "print(doc.vocab.strings['creative']) #convert string to hash\n",
    "nlp.vocab.strings[1433653077910583464] #convert hash to string\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Until \tPOS:ADP \n",
      "Description: conjunction, subordinating or preposition\n",
      "\n",
      "recently \tPOS:ADV \n",
      "Description: adverb\n",
      "\n",
      ", \tPOS:PUNCT \n",
      "Description: punctuation mark, comma\n",
      "\n",
      "the \tPOS:DET \n",
      "Description: determiner\n",
      "\n",
      "conventional \tPOS:ADJ \n",
      "Description: adjective (English), other noun-modifier (Chinese)\n",
      "\n",
      "wisdom \tPOS:NOUN \n",
      "Description: noun, singular or mass\n",
      "\n",
      "was \tPOS:AUX \n",
      "Description: verb, past tense\n",
      "\n",
      "that \tPOS:SCONJ \n",
      "Description: conjunction, subordinating or preposition\n",
      "\n",
      "while \tPOS:SCONJ \n",
      "Description: conjunction, subordinating or preposition\n",
      "\n",
      "AI \tPOS:PROPN \n",
      "Description: noun, proper singular\n",
      "\n",
      "was \tPOS:AUX \n",
      "Description: verb, past tense\n",
      "\n",
      "better \tPOS:ADJ \n",
      "Description: adjective, comparative\n",
      "\n",
      "than \tPOS:ADP \n",
      "Description: conjunction, subordinating or preposition\n",
      "\n",
      "humans \tPOS:NOUN \n",
      "Description: noun, plural\n",
      "\n",
      "at \tPOS:ADP \n",
      "Description: conjunction, subordinating or preposition\n",
      "\n",
      "data \tPOS:NOUN \n",
      "Description: noun, singular or mass\n",
      "\n",
      "- \tPOS:PUNCT \n",
      "Description: punctuation mark, hyphen\n",
      "\n",
      "driven \tPOS:VERB \n",
      "Description: verb, past participle\n",
      "\n",
      "decision \tPOS:NOUN \n",
      "Description: noun, singular or mass\n",
      "\n",
      "making \tPOS:VERB \n",
      "Description: verb, gerund or present participle\n",
      "\n",
      "tasks \tPOS:NOUN \n",
      "Description: noun, plural\n",
      "\n",
      ", \tPOS:PUNCT \n",
      "Description: punctuation mark, comma\n",
      "\n",
      "it \tPOS:PRON \n",
      "Description: pronoun, personal\n",
      "\n",
      "was \tPOS:AUX \n",
      "Description: verb, past tense\n",
      "\n",
      "still \tPOS:ADV \n",
      "Description: adverb\n",
      "\n",
      "inferior \tPOS:ADJ \n",
      "Description: adjective (English), other noun-modifier (Chinese)\n",
      "\n",
      "to \tPOS:ADP \n",
      "Description: conjunction, subordinating or preposition\n",
      "\n",
      "humans \tPOS:NOUN \n",
      "Description: noun, plural\n",
      "\n",
      "for \tPOS:ADP \n",
      "Description: conjunction, subordinating or preposition\n",
      "\n",
      "cognitive \tPOS:ADJ \n",
      "Description: adjective (English), other noun-modifier (Chinese)\n",
      "\n",
      "and \tPOS:CCONJ \n",
      "Description: conjunction, coordinating\n",
      "\n",
      "creative \tPOS:ADJ \n",
      "Description: adjective (English), other noun-modifier (Chinese)\n",
      "\n",
      "ones \tPOS:NOUN \n",
      "Description: noun, plural\n",
      "\n",
      ". \tPOS:PUNCT \n",
      "Description: punctuation mark, sentence closer\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Token attributes and Part-of-speech tagging\n",
    "for token in doc:\n",
    "    print(\"{0} \\tPOS:{1} \\nDescription: {2}\\n\".format(token,token.pos_,spacy.explain(token.tag_)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AI    \tORG\n",
      "Description: Companies, agencies, institutions, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Named entity recogntion\n",
    "for entity in doc.ents:\n",
    "    print(\"{0}    \\t{1}\\nDescription: {2}\\n\".format(entity.text,entity.label_,spacy.explain(entity.label_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Until recently, the conventional wisdom was that while \n",
       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    AI\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
       "</mark>\n",
       " was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones.</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from spacy import displacy\n",
    "displacy.render(doc,style='ent',jupyter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Until\n",
      "the\n",
      "was\n",
      "that\n",
      "while\n",
      "was\n",
      "than\n",
      "at\n",
      "it\n",
      "was\n",
      "still\n",
      "to\n",
      "for\n",
      "and\n",
      "[recently, ,, conventional, wisdom, AI, better, humans, data, -, driven, decision, making, tasks, ,, inferior, humans, cognitive, creative, ones, .]\n"
     ]
    }
   ],
   "source": [
    "#Stop word removal\n",
    "for token in doc: #displaying stop words\n",
    "    if token.is_stop:\n",
    "        print(token)\n",
    "\n",
    "new_doc=[token for token in doc if not token.is_stop] #removing stop words\n",
    "print(new_doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Until', 'recently', 'the', 'conventional', 'wisdom', 'was', 'that', 'while', 'AI', 'was', 'better', 'than', 'humans', 'at', 'data', 'driven', 'decision', 'making', 'tasks', 'it', 'was', 'still', 'inferior', 'to', 'humans', 'for', 'cognitive', 'and', 'creative', 'ones']\n"
     ]
    }
   ],
   "source": [
    "#Removing punctuation\n",
    "new_doc=[str(token).strip() for token in doc if not token.is_punct] #filtering punctuation and stripping words\n",
    "print(new_doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['until', 'recently', ',', 'the', 'conventional', 'wisdom', 'be', 'that', 'while', 'AI', 'be', 'well', 'than', 'human', 'at', 'data', '-', 'drive', 'decision', 'make', 'task', ',', 'it', 'be', 'still', 'inferior', 'to', 'human', 'for', 'cognitive', 'and', 'creative', 'one', '.']\n"
     ]
    }
   ],
   "source": [
    "#Lemmatisation\n",
    "new_doc=[token.lemma_ for token in doc] #lemmatisation\n",
    "print(new_doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"en\" id=\"808a33f0641b44aa8223b8f369c09350-0\" class=\"displacy\" width=\"5300\" height=\"662.0\" direction=\"ltr\" style=\"max-width: none; height: 662.0px; color: #000000; background: #ffffff; font-family: Arial; direction: ltr\">\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">Until</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"225\">recently,</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"225\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"400\">the</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"400\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"575\">conventional</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"575\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"750\">wisdom</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"750\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"925\">was</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"925\">AUX</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1100\">that</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1100\">SCONJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1275\">while</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1275\">SCONJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1450\">AI</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1450\">PROPN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1625\">was</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1625\">AUX</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1800\">better</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1800\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1975\">than</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1975\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2150\">humans</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2150\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2325\">at</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2325\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2500\">data-</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2500\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2675\">driven</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2675\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2850\">decision</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2850\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3025\">making</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3025\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3200\">tasks,</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3200\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3375\">it</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3375\">PRON</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3550\">was</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3550\">AUX</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3725\">still</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3725\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3900\">inferior</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3900\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4075\">to</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4075\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4250\">humans</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4250\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4425\">for</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4425\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4600\">cognitive</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4600\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
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       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4775\">and</tspan>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-808a33f0641b44aa8223b8f369c09350-0-26\" stroke-width=\"2px\" d=\"M4620,527.0 C4620,439.5 4750.0,439.5 4750.0,527.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-808a33f0641b44aa8223b8f369c09350-0-26\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">cc</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M4750.0,529.0 L4758.0,517.0 4742.0,517.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-808a33f0641b44aa8223b8f369c09350-0-27\" stroke-width=\"2px\" d=\"M4620,527.0 C4620,352.0 4930.0,352.0 4930.0,527.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-808a33f0641b44aa8223b8f369c09350-0-27\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">conj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M4930.0,529.0 L4938.0,517.0 4922.0,517.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-808a33f0641b44aa8223b8f369c09350-0-28\" stroke-width=\"2px\" d=\"M4445,527.0 C4445,177.0 5115.0,177.0 5115.0,527.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-808a33f0641b44aa8223b8f369c09350-0-28\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">pobj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M5115.0,529.0 L5123.0,517.0 5107.0,517.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "</svg></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising dependencies\n",
    "displacy.render(doc, style='dep', jupyter = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('the', 16), ('to', 12), ('.', 11), (',', 10), ('new', 9)]\n"
     ]
    }
   ],
   "source": [
    "#Frequency analysis\n",
    "from collections import Counter\n",
    "words=[word.text for word in nlp(df.para[3])]\n",
    "common=Counter(words).most_common(5) #returns top 5 most common words\n",
    "print(common)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nu690cNMG3ZZESahvRmQL5tb71KrFdFt9i+BD4jAZBfjdGtniDJadRgCWlaK2doHSxCksN5vkPObGiIIOob9PKtTPGrQGqZIAUR/ueH47GAb0blbx15qkpopM/NwZxj93Cm+xjgwigkqb1oVV1r97Yec+93s+WqMjiXHHNNoZy2HcJwGpXkD1ay9jZlOoMEaHEXE9MT7iVo/6t9/AKuWQrS6qTxzReoP6d97EzKeI6x20VASrderfeROrnAMhiDab6DCm9fKlrbx7/8YacWX7OXfevE640QCpkJ3EIozW6rT9kM75m8iOh2z2QCoaz7+LO10GxJZbQdgmm3/4AgCy1aPx3TeJG50DXrhC+T7usUNEaxtoKbesUmGaCMPAcBxSj5/FmhiHqzd2bC4MI4mh30XaWmtkq03vzXfRUUz+c58i2tgkuHL9np7LQXFg0o1VRECPtJknZWRx7BRFZ4ql3gV8dcCbRlIB5tfW2Hz7x/iVVUATNDZxS2OMnH0WO1/Cr61iWA75w4+QGp1m9YWv07jy5ha5hs0qdrbA2LnP0Lx+nqjTABLLtXnjPTqLl7HSOYpHH6e7dpPWzYtE3Rajjz6Hkysl61o2Y49/mqhdY+2VbxH3kkR+v7KM4bjMfu6vk5s/RW9z6a6p1r1DGAI0XHq9RyprMnnI5b1Xu2wuhTz2XI7JeYejZ9Pcet9H9aPbWg02GLtNSXncpjRm02nEROHDY2QZB/idCuXJ01h2hlR2BKUiWpUb5ErzpHJjdBrLuJky1ZXzP7MZHsOQEGeH0U8dRwUxzniO3LEJ2leS9KfM/Ai5U5NEjR5aKqyMgwpiVCyRXsTm81cY++xJVBARVDs4pQxR06Px1uLeB9aa3q0ao88dJ6x3sfMpiufmtjMbbBM7n8IZzWHYJu5YjqjlEbd9VDDY2gpXagOXozTReoNovbHzFGJJuHJXmpXWRJvNLV/ubdz5t+r4hHekgikvwL+600UXVVpEld0DsOr6eHetq8OY4GZSmKAjSbg65DqGoPXdH5P/wqcYPX6M4Mp1Oi++iux2aX3/J+See4b0Y2cIFhbx3nynH2QD58g8+c8+hzUxhpFKMfJ3fgvZaNL6/vOEN26hfR/V6YKUeO9ewnBscp/6ONHaBqp9cG47KA5MuqPuLCP2NPVojaudn6KQzKfPYop7jMVpjV9d6/tWE8KQoU/YqmE6KQzLBsBMZchOHSJoVPAqKzs+8KhTx9tYJDt1hPT47BbpyjAg9jpoJZFBDxn5xL1OMkKGPsIwEP39u6PT2LkSXmWZ9NjMjlM0bBdhGDiFUSw3Q+w92I1PZQx+7m+MkCmYeB3F81+r43Uk77/VJV82+ZX/epxuW/Gdf1tBxtBtSdYWA0J/N9m/8u0GX/qbo0wecnjhmw0Wr/gYliBTdulWfUQ/FUmG/Q/aFLhZC7+V+MpN20BGwwYRTei3kuIRJ022NEvod+jUlwi8Opn8JOncOIZh9Sv4/mohbvss/eHrTP21x5j+lSfwlmrc+vev4IzkAJBBjDuRZ+xTJ0BA5/omGz+4RFRPXAhrf36esNZl5BNHsbIu/kaLjTutXsBfb/V9ntvQUrPytbeY+fWnmP7q4wTrLVb+9E0yh0YByMyVmf6lc6RmSoTVDrO//hRRy2P1z87TfGfpQ7gzDwZh24k8gNYI2wLDQAf3nwu8F/yLl/EvXt61vPfG2/TeeHvgNuHCItWF4QNj54VXt/+Qku5rb9F97a0HPdWhODBjduMG9XCFWG8HuirBIqG+t4RopWJkcFe+pdZJ1ZsQ3HZQCdPCTOWQQQ8V736ACbnG2NntHE+t5NZ+tdbJFEnLrb8BRH//diYJFuWmj5EeneFuRJ1GEqgb4HezcgVSE7OgNZ0bl/a95m5L8q/+x9Vdy/2u4iffaPCTbzR2LF++FrB87Q63jRC4IxMgBGu31vjX//juYKDBoafHuPLjVTIlF8s1aG/4ZEdc7LTJ6JE8qxfqhD3J+IkC1YV+cMgUdKvBDhKOgg4y9rHdLOn8GN3mGnHkEfQaFMaOkS1Oo1SE39m/GOZnDfOzJiYNrv/Oj8jnBadPWay8G3E7EzLYaCF+8jpzFYvr1yW3rm1bmTPTBqm04voP32fzh8Mj4tUXB1dkhbUuC7/7k62/TTtN+80app0iWotY/N030Wgiv4PlpIn8wSXUHyhMA3tiHIRAeX6S0xxGCNcBpVFhiOE66H7Bh45jEAJrdIR4YxPhONiT48l0vdFERxGy2f4r5eP/MHBg0k0ZWWIVABFpI49tuLTi+/jw+gR7b9scfN8HWAQkfh2tJJ2lK9SvvDlwnajTQAaDIpkCK1cge/jUgUj3QSEMk8zcUTBMgsrart9jX27lCNppi1TewrQNxo4X0FrjZi0KUxnCXszIfJbWmkdhMoXpmPjt6i7SDb0W6fwEbrrE5q03AU3Qq2M7aTL5iSTbpHsfFUF/yTCM5B/AyIjBL/61FDduSILgjnJZA55+yqFYiLl6B+kahsA0HkKkvQ/bzZItz9GpL+KmSzjpAkpG+J1qQsQfIOnms9OMlk/T7ixTbVzZWi4sC/dEkpmh/ZC4UsUsFRC2TVxr4JQKyFoDI5tGdXuYI2X8y9cw8zlks4kzPwdKYY+PocZG0UFA7+33sI0U4+XTWFaatc13CKO/hAHlIcHKpxj5xBFyR8eovHiN9pUNModGiFsewebBZsQHJt2CPYavuoTSJ20WyFrF+yPdA0LLiLjXxsrkMWxn1+9WOocwLKLu/QVzol4brRUy9O65Oi7uNOktXic7f3zH8uzhU+SOnUFrTW/pOp3rlzDdFKXHPk71tR9iptLkjp3FW72F1orio8+AkpjpLO2rF+gtXQMN6elD5E+cxXBS9Jau0752ARCkZw5jZfMI06L+1otEzcQfli46FGfSFKcyZEdcClNpmms93KxFfbGLDBVBJwINkS8x7YQ8nKxFKm8TdrfJJQo6BH6LwsgRTMul10pI3u/VAUGmMEXot7aKJ34W8dnPODz7CQep4PmfBLz204hnnrb5wudSvPxqwPUbyeynkDf4r/+rDFLC934Q8PbbETdvSi5fiXdMcM49bvPlL7m8cz7iytWYkbLBl7/kMj1tkkoJfvp6yPe+H/DsJxyeespmesqkUlH8wR/2mJgw+fznXFKu4I03Q777/b5Z3T9AJj+B6WQwTQevvUlx8iSVmzuNAENYTE08yXj5NBrNrZUXabQWdqyTcsscmfssrp2n01tncfVlwmgwCUyMPsr89LNU6pepNa+j9W3XnehnCGiwTEQmjXBddBiiej3sqQl0Jo1IpTB0Epgyc1nMcgnL8zBSScaLMA2EYSD7LoZMapRDM58m5RZpd1cIm22yuSnKpWNEsUepeAQhDKrV96lULm6dz+joGcbHz2IYFs3mLTY2zqO1ZG7+M6wsv4oQMDv3HI3GAo3GDUZHT6OVpFK5MPC6ByFl5ghkd2jJ+o7n4JhMfvkMI584ijuex1tt0r1RofDINAJY+cY7BzrmvqRrCYdJ9yiT7nFGnDmkjtBasepf2W/TB4L0e/TWbzL+5OdJjU7jV1e3/Lp2rkh6fA4ZBXiVvVNchsGvrhL32v0c3QmC+iZ3mtTCMLeUyA6KsFmleeEN7EKJ9MQcYW0TFYWkpuaSfVo2zugkQX0TAeQOn2Tt+3+KlSuQO3qaoLKKmcpQOPMknWsXCBuVJG9Za4TtoKWkdekt0jNHKJw6R/W1HybX0ol4509vEXkxzdUea5cMps6WaK54OFmLGy9tJJawhsZKjziQNJd7GJYg8ncGw5SMCHt10nNPEvot/F5C7EG3ShR2yY8cZu36Czu2KY4dJ1OawbJSFMaOIgyD8UMfI1OcQkY+nfoSreoCoDFMh/G5J7DcLKadIp0bw7RTzD/yC8jIQ0Y+tbWLBL06AKnsKKWJk1hOBjuVR5gmudIMhx75BWTsEwVdNhffRKvtgeOLX0jx5lshly/HrK8n1/f++zFnH5HMzmz7W3M5we//a5/JSZMvfcFl4UZMs7X747t6NeaRR6ytbVMpwac/5fIH/7FHLOFLX3R56+2Iz3za5Z3zIUtLkieesKlUFb/2K2laLcX3Xw9ZW99+l4JujchrJ+9cXxXPtBzCXoMo2EmWQghymUnGRs4A0Oos0+oso9S2qy+fnWKsfAbXyWNaLisbrw+txuz2Nuj5Vdrd1TsIF3QY0n39rf5noNnKVey76uIt6UO9vY7quxJiSbi8mmQI3P45jkFKorhHp7dOGHUJ+1WNjp1lZvaTtFpLVCsXsO0cs3PPImVArXaFkdFTzM49y/r6OygZMDZ2FtctsrLyGulUmUxmDMMwyednEcKk21kjn5um292gYI/jmllC5WOK5DuOVUjayhMpn1hHxCrCEAYZq4RUETl7FNOwiVWIa2YJ4g6NaOes0sw4lJ6YZ/Hf/5Sxz5xI7oRMClKKZ6cH3+wB2Jd0Yx2y4l9GoWhGG/iyCygUQ8jINBON3Sh8IF+OiiPaNy+RmzvJ2LnPouMIv76BYTtJ6tf4LJV3X9zKOrjn/UcBlXd+wtSzv8jUs1+l+u5LxL0kiGSmsrilMXprNw9O6oZJdv44zsgEhuVguikMJ4WK7kypE1sWlNaaqFXHX1vELo6QO3IKYVpYmRyGZdO9deUOfYBEsziorBJU1jAcl8KZJ7f2qqXGa9zp95Ysvl4lU3bwmyFxP6iWzQsMQxLFCm1AMMRY7TRXaFau47U3tlKHQr9Nq5qk4LQqCzvWz40cYmTmbD+/WtCqLmDZaUoTpxLxG61p126htcS0HMrTZ7HsFCAIvCaB1yRfnu9bf5pOY5kw6KBlhJsuUZ46i2nZgNiSdCyMHuG2xkZ1+R3kHaT7e/+iyy99NcXjj9p89/sBr74W0ulq2h2FY2+bsK2WZnVNoRQ88bhNNmvQbO3OyOh5mlZL4Y5tE/bGhuTWkkSIpFrUcQSra5JPPedSqSq+9nWfKII//GOPr3w5xa//WppXXgn5zveS90EriRyQ/VFbeY9h/jSpkm8ql5nENJ0dpJvLTGKZDlHcHbjtnVirnGe9+t5uBTit0d7wGczOooY7lkfb5zHozHtehfeu/EdA7CD5OPbY3DhPtfo+tp2hUJwnk52gVrvK1NTHqNevsb72FqCIY5+jx77C5ua7eH6dTGYMYZi0WktkMmPYdgbTSuH7dVwzRyA7FJwJ/LhDM16n5EzjmBlM4RApn5Qp6EQ1bMPBMTMUnUlC5ZMy87hGBkvYu0hXmAZm2sFbaaDC/vsmwHSse6K6A7kXNJrN4FZi5aIQGBiYKHY/BHd2jvTxEwSLi3hXd0cZ7wVedYWN17/H2LnPMPXcL6Fl1Bccj6lffoP6xVcfSEC9ce0drHSW0oknmfvCX08CA0Yiau5XlvE2D25FW5kcuWOPsPa9P8HK5ik++kxiJ0iJMJOIrplKY7iZ/hb6ruq72yIqEoTATKWRXm87kKfkHS/03k/YTQlKIwLfC8kXIJWy6HYUTz2bIo401y5FTM1ZLFwJyWQNpNR4PU2uYOB1Fa59g+rCdeJIM3PIoFEVdDuapUvfG3i81esv0Ghcx6utYmcKKJmojKXKU3jVZQzbxc7kUTJGOC7X3v3a1oCiogDTzaAiHyudByEIW1Uyo7P0Kkv0vApX3/kjAAzTIg56SSB2D8xMm1y9GnPmtM2ZUxavvhby6FmbM6dsTAtOnUw+klRK8OwnHUZHDFrthFgfOWNx+rSFAE6fsrh+I+bYUYuzZ2yKRYNHzlj4vk6yCPX20zAMyGYEK6uS6zdibAtcF6YmTTY2JYaApz/mbJHuIMho70qoKOqhVEw2M45pOEQkBCuESTo9glQxQdDccx+3z/hO8vswMEjiMwq7RFG3fz4KrWIMw8I0HRw7S+A3oG/cBUELEBimje/XyeWmkDKk014hm53ETRUxTQffb+CKURwzQyeqEakAqSI6UZVYBXiyjSlMHCNDpAIiFQKCRriK0hJDWDT1Opax26Wpwphgs83Yp4/jlLNIL6L0xDzFJ+ep//Tmge/FwfN07yiCSJk5HCNFM9qdNpQ+cpTS575I+7VXdpFu2Kiw/uq36a3vPsHe+i1WX/rmVmEEkGQHLF0hbDfITh3CyuRRcb8MeGNxR5Cru3oDrRRhJ5mWBvUNNl7/7lbZr/S7rL/2bbqrC9u7j0Mq51+gu3KD1Ng0pptBy5jY7+LX1ggagzVN0zOHycwfxy6NUTjzJN7aItLrEXdaFM48iWHZCdECKvCIu21Gn/k8AKab2us2E7XqhI0q5ac+jfI9gtoGveWFPbe5G9mcwZlzLmtLMSPjJp22Ykyb2LZgYzXGcQWHj9l02wkR97qKG5cjpuct4kjjdTWrSzHHTttksgY3roRcfHuPFCDDwM4UiPwuqfIUWsaE3Qap0iSx3yE7No9WChkHGJaDisOtdQzbJV2eQkV+v1JVEHbq2JkCppsmVZ4CrXFyIwTNDZxsifbq1T2vP5MVFIoG167HnH83QgjIZAQ3FpIBOtMnx//4Rx7FgqBeV7xzPqLnaTIZwfXryXrptMAwIJMWLC5LVtckmYxgs6L40Y8DGg2FEPDjHwcUC4lyXc/TzM6YfPxph2pdkUpDqWjQ7ij+8I8fLI1KypAo7pHLTmFbKfz+J+k6eVynQM+rDNSBFsKkXDhCubgtHK/RdLqrbFSH+z/HyqfJZiZY23wbpSKK+Xky6XEs0yWWPp3uOq3OErHcPZA4do6p8XPYVmZrmVIxKxtvEIStrXMYRMZaS6QMse3s1jLLSoPWSSaNaDAx8TidzipB0MLzqhTy82glCcMOXdPAlx3kHZlWnmzhye34T5cGAJv+zgKKvSB7ERs/eJ+JnztN7uQEuRPjFB+bpX15jdprCwfez76k6xpZLGFjGS4ZIw9AxioRKm8g6Zr5AobtEKyu7PotbNfYfOuHA4/jV5bxh0zlw+YmYXNvUefe+q0d1WNhs0Ll7ee3/paBx+ZbP9q1nZYxvY1b9DYOXnkmvS7+6i3C6jpxr4MKA1TgUX/rJaxsMjCoMCDuNNFSUnv9eeziCMr36C5eI6pX0Ghqb70EQNzr0Dj/KtLromWMf+0N3NFxpuYcljdaiTZt5dpWACysVWi++9PhJyiSeo7RCZNMzkBraDcVtU1JvSLptjVSavJFgzDUpDMGh48n+cv5okFlPWR9OebkWQelIAz2tqxNy8HJjxD5HeKgi2HaqChILBfTQpgWMu4ihIFh2US9xBoL2zUyE4cwTAtwCTv1xNdrOTj5Mk6njmm7SXm2ZRO0a2TH99eb/f4PdpPAq6+FvPrazmUbG7vXe/2NiNff2OkMffPtiDff3rmsUtkm0JdeCZmfM8nnBIWCQCtBs6lotzULCxGv/fThSd0FYZtsepx0aoR2dw3QpNwijpWl0b5JMX9o1zZCCDLpUSZGH8WyUlhWGiEEaxtv70m6I6UTTI49RhR7ZDPjjJVPJz5jw0aqCD9osLb5DstrP93l1jBNh3LxWH+ASCf+UhlQbVzdIt1hUCqmVrvKyMgpWq0lpAwYn3gUz6sShp3EBWg4GIZFGHXo9TaZmHicRuMGWkt6ceNAgbF7hZaKxtuLBJttUtNFzJRN3PHpXNskauyj13AH9iXd2x6wsj2N1opI+xiIrXzXXSemdZKn1334lRw/KwjrFcL67syNoLpGUN2d0hXWNjD9TY6fcQhDTZTXjI6beL0NJp/OoqRmY3WD8qMWm+uC0Qkfy14inRGMP6ZZylkYRhOtYeSsw9ikpl7dxJp3KRRNLr8XUFnfni4265I3X/Zvu0gRBgS+TvQ/I42S8JPv9ohCzZX3Qgxzp05v4GvCUPPyD3uYlqDX3TuYGPtdalffSPKp+8IqWsY0b76LikOCdg2UIjd7kqjbwLAcOqvXUHFEr7KEZ6yAUkmudr83Xe3qG6goIGhXuP0WqiigvfLBBnDvFyurkn/1b3qkUgINdLuaWu3himMIYeAFdYKoQz47xWbtElpL0m4Zx8nS7qxQGkC6SsWsVc5TbVzDEAazk88wM/XMgY5pmi6HZz+D1orN2kUazQWEMBgtn2Rq/AkOzXwaz6+xUXkPfUecxw8aXLr2dUzTJp+d4uj8F3GdwvY5aYmMgzssXY2UUV8WFNbW3sAwLI4e+zKGMOl5FZaWXiSKehiGTRC2iCOfMGjTaa8yPf0M3c56f08fZF6woLdYp3dru5JOWEbSP/KAHTn2JV1fdQGBHa3Rk00iFZCzRnCM9MD141oVFfhJh4mPsAXDhELfsiyUTBauhjz5idSWoNPUnE2nnYQnq+uSTFZgWSZXL4acOOOwthyTzRnkCgbX3k+2jSJN4OtdTnwZs6+CfaO2/bIPQ7ej9/z9NrSSxN7ugGbcFyOi77tuL13CdLPEfmfLF3+nUtyObW/v7y6XvfwA1bseBFLC+sZDViC6GwI8v04YdchnZ5JiIi1Ip0YAg54/PHc6jj3iOLHGgqhz4CC30bcqr936Duub51F9X3CzvYjWmrmpT1AuHqPWuEoUb1t7WiuCMJnRWKa7qxVTq3mLdmt5i2Tj2Of69W9vFTFJGbC09CIrK69u7e+2TnMQNLl08Q+BpPFAu73M22/97o7A4gcBM+Mw9ZWz1F5bwFtpAAnhjj57DBVLai8fzFWxr+KGgYktXLpxA601lnAIZJdGuNuiA/CuXiFcXiZz5hGMdGbgOv85QkawvioJfM3Gaky+YHDt/YjFGxFLCxEX3gpYvhlx9UJIp62IIthck7SbinolUa83TKhXJZ2morIhMU2BEIIBRXM/k1BxRNRt/KV1j/6rD0EYtgmCJpn0GKZhY9tZUqkyPa9CHD/83GmNputtsFG9gNK3xek1YdSh1V5Ea0XKLWIY9r3tV6s+Sd4mf4GVyeEWR+9YRyJlgJQBSkvSozN9mQCNUttWcWIlBwP9ww8TZspm9FPHwLzjg9MJGY88ffjA+9nX0k2becacQ5jCxBQ2UkcYwqQaLlOPdpe2RpVNGs//kNJnv0Dp81+ke+HdxPoN77zBd0DrHSknfxmwRYq0kcMRKVwjjYmNIUwEBqBRKKSOiXVIpH0C1cNTXeQ9tKXwfc21S/sEUu6QKF25tU1M77y+2/f4zk99Tj7iEEdwL7ozJhZpI0mLsUUKW9gYwsLAAERytVoSExEqn1D38FSH6B7V5D7CB4OEBDcpF4+TcstoLUm5Rbq9jYEBrQc+nlZ4Xg05YN9SxSgVYwiLB9XFFYYgO3kEJ1vGb6zv/h2Bnc4Ttqv8pbWDEYBp3CVClLRkMg4gxH4b+64ZqYBWvMm4e5he3MRTbXJmmZSZG3jtqWMnSB06jEilKD73GdInTiJbrSQdawDpSs+j8qd/fGDmsITDlH2MkjXO9oPe3m9HNlgMLiLvnpfeBQOTEWuasjVF1ij2ScjFFs4WCW2TrkYRbxFvqHwC3aMlq9TjNdqyNjB97p5xD64or6u5+E6IMCD0997QwqZgjVMyJ8iaRVJGDke4WMLBEnY/BfAO0t01yHj0VIum3KQer92z3sbe5+ZwPPUktnFnF4Xt5xoqn9XwGm11b2pUgzDrnKJsTbKTILYtrdv/vxi8T1M+uKDPuDXPpHPkrqW3Cw5gM15iPTp49Pw2ur1NtI7JZScJghYpp8hm9SJSPnyRGa31DrfBXb9yTy/tPhAI0uVpJh//AgiD6pXXiL02VjpPYeYU6ZEpepVtASA7U6AwexonV8JK5WjeukBr+TJOrkRx/ix2Oodhu9QX3qG7voCTK1E+8gSG5dCtLNJevYqWkuKhRAQ+XZ4m8lo0b7237Rq782pjhfIiio/OsFG5DEpjpB0KZ6YIKgePYe1LuqH2CCOPkj1FM96gE9fBSSzgQcicOkPh459EWBbCMnGnZ2B6t6DMbcStNsL4k+3Cr70F6TGxKFtTzDjHB/7ekQ1WwitDRdZt4TJqzTLtHCNnjuCKdN+qHTZSC0zA7LtZIAtm8jKOWXMEzik6ssZKdI1atHpP1u+DYm9ZR4HdH6Am7SOJJW+kMbF26Ynu3MrEwMQSNi5poIhGo7Qk0gG+6rAe3WItvE6gD1Y+uRckMWP2HFmjNPC8QuXTkY0HJl2BwaxzkrI1te+6PdmmKXdWKN4rTGxGrGlmnJMD3y2tdf8Y945Obx2lJLnsVJLZYVh4QX3L3/pwoXcEyD5ICDN5NztrN8hOHqZ89BybF15Ahh692jLlo+cwbZfYTwiuOP8ICIP2ylXGz34GGQWAZuT40wStTcJOnfKxJ1BRiDAMxk59ks76AghBduIwMvTpbt4kN3EYrRX1hfOoKOzvZzfiXkjt1RvM/dbTlJ8+TNTySM+UMFIWq//0+YHbDMKBbeJ2XGEu/QhKxwgs1oPBakq9C+8SVQ5uJegwStprOCaFQ0Wyk1kq725gOia9zXtX5U8bOSzh7LLGbpP1IfcsJWsSCytpX3Kf0yIhBLZwsXDIGAVG7Vmq0Qo3grdpy/qH9qLuOi8Ejkgzbh9izjlF1iztS7QH2acpLExh4YoMeXOUWeckK+EV1qMFPNW+b/LVKDqyTtYoDfzdFi4pM4uIxAMRvGukSBuDDYW7UbTGEcGD2XBO/3hDs3zQtOT9iQb5QZMw7lDMJbmpQdgiCPslxX+FoZXEb23S3byJjHwmH/v8ViZM2K6j4p1kaJgOSkbEgUfUa/XLqhNdbSVj4qBH2G0SB12sdIHs5FHc/ChKSQTbYuZKxni1Fbzq3sVQOpKsf/ci/lqL4hNzWFmX9vtrbD5/BW/5IEUpCQ5MupVwkVZcwTHShMojUoNHA//WAv6thQOfwG2MnB7l0OcPk5nM4VU9jnz5GG/8L6/uv+FdEBhkjCI9tZ0LaAuXGecEx92n7prGPjgESTDLwGXSPkLeHOGK/zqVaOlDtXph22Uy755h1Jq9d63jA0AIgYlFzixxMv00E/YhFoLzbEbLqH1cOsPQklUm7aMDfxNCkBZZbOE+kFujYIxjHvB1zxklTGHtkDGFRJvYLqZBQNT0d7RPvxtOP04wDKH27kn8fyc0nc4aUxNPYBgWXW+TMPyrq9x1G0IYSW8+w0zys2W85zjSrSwydurjONkSnfUbfX8vdFavMXL8KfzmJu2Vq0TdJnY6T+x3WH7jW4TtWl/v4nZJoUYOkI8dBOlF1F5buKdiiLtxT/1CQuXRiWuYwiJvjdz3QQfByTnUrtRo3qiDun9fkQBy5rbGri1c5p0zHHefwhK7S/seJoQQZIwCp1LPMOUcxeTeIroPAhOLWecEZ9LPMmEf/kAI924IDErWJKdTn2TOOXXf19uWdfZ63q6RwxIPNljmrVEMcbBGhZZwBlreVsZl5Mk5Rp6cwykNTpm8DVu4pIzs0N87sj5cv+QAaHfXMA2XXGYC368TRvtrLvysQ2uFkytTPv4UpSPnaK9dBzROtkTp0FmcXJnC3GnSI4m70nTTKCmJeq2k6UC/K4yVyiJDn9jrYNouVjpP5Hfobi5SPvokoyeeoTj/SNJ+6iHALqYpPDrchXo3DlCRlsEUFpZwkuAZkDXLxDp4qNKOQcNn9JExRs+OY6Ys2iv3O3ILskZCupZwmHNOcch9FMtw7tuVcE9HF4K0meeo+zixDtmIbg10NbhWnoniKTr+Jo3eMll3hF5Q76fl3BsMLObdMxx2H9vzQ/+gkDJyHE09gSFMlsL3ifW9BXR6qolEYg2xAVL9IOf9QiDIm+V+sHB/GMIgb47s8rnqfuGHlXN39FkbdDx3n3PuyAfzwbZ7a4AilhIvqPe1FAafUzY9zmj5JIZhYxo2pcJhDMOikJ/lxOGvIFWElBGd3jq1xt4l1geBZaYoF4+RSY9gGg6pVJmUW8Q0bOann8Xza0gVEYYd6s3r+GETrTSd9RvEXgdhmHTWb9BZS3qUaZ0IG1Xef4U48NBKYroZnEyJoLVJHPSwswVKh89Ru/4mdjqPV19DxgGp8hSGZVO79gbVK6+RmziclKJHfqJMpzXNxYsPpF+cmiow8YXTtN7bXYU78P7st4LAwBAWI84cAkGsAxwjtSvZ+UHRuNEAIfDrPn7NZ/P84DzggyBjFhEIxq155t2zOCK1ZwXdQXFQv6hAkDGKHHYfoyubdFR91zqzI09gGjaW4VDvLjJROMNy/S2CexR4NjCZd09z2H0MVxwsL/perhn2v24hBC5pjriPARwoe+ROxDrEk+2hs6eUkcV+gFmKKzKkRPbA3XcNTPLm7nPRUiH9KLFy9xA0NzATf64YTPJaazqqfu8ZL3c0wQzCJmHU3SrH3VoFfdekQZDPz3J07gsIYSCEiWEkLdSz6XHSMyN9sRnFevXdeybdQX52284wPfEU5eKRJEjdPy7A5NjjaC1RWuH5VYKwiR82AU3YriVT/7sQ9ZpbpeO34eRHsdJZWouX6NVWKB16hMzYIexUDtNJU194h8jrMHryacy+yJQMejQXL+7af686vCWSVUjhjGQJ1luoSJI7MbHjPRJA9vg4Zubgs7wDVaQJ1cUx0vTiJpHyydujQyvSkjMRWOURck88SfrocaxiCWHZrP7ePyXa3AQhsEfGMIsFwpVllJ/09movtahfqaKVRsv7DwqkjBwT9mGOuI+REtmBpKG1JiYiUj4dVacta3iqQ6B6SGIEAks42MIlZ5YomhNkjAKWcJJ0sgMQUckcZ959hCvea8R3+XdTdoG15gVKmdkk+GWl+ylqB4eJxZxzmqPuORyR3vOcbouLxDoi1B5NWaErG3iqS6T9LQIwsfr+yDwFc5S8NYKN2w/GDT8/IQROn3iljlgOrxyYVJSWtFSVPINJ18IhbeQQGPcVoMybI9h7DLx3Q2CQM8uYWDsGD8O1UEHM+o+uErWG+5dNYZMbEhiEZJDpyYMHH6WKuL74PRaWfkws+x14I4/X3vlnwHZKl5Qhb134/WTZlmKZZrNygUZzn9Q0nRznTly/9T1uLj8/NBWtUr/My2/+f9FaEkbbQe+kBPhPBorv3Aml1VaV3CAI2wZD7Oy3JkC4LpHXImhuMv74pxEpl7Baofr+q4TdJlHcY+pjX04aX3bq1Bfe2jcrahgKj0wz88vnuPmvXibY7HD2//lV4ra/Y1dW2qF5YXfNwjAcwPGXvBrNaB3HSJMyc0QqoBcPF61w5w4x9mu/gTs7j46ifkNIa0t5CyB77gnKX/gSlW/8Ke2fvsLEE5M4eZeFbw/OijgohBCkyfJo+jMDg2a6n/vZljXWomtsRgfr8yYQ5MwRZuwTjNlzZIwCxh4klJyLwYxzgtXwGg25M+Hbj1rk3HFSdpGJ4mmUVvfkWhAYycCSegzX2NvCVVriqQ71eI316Ca1ePXAQS9XZBi3DzFhH6JojmMLdyi5CyFwRYZjqSfxVZfNeImDvOkKSVtWgZND95sxi/3gVnjP5JszyzgD3oXbaYUG5o5rEkLgiBRZs7gjw0BLhTOaZeroKJsvXqc3JGJtCousWRp6Pl3VvOegYFJtduc2emDbm0HdIqQKt7o43NMxpU8sh5+nUhFBuDtYrLV6KD7m1CPHMXJpui9ud9IQtk3q7HH8S9ep33ibntkg//OfofHGK8St5Fm1wiXqly8SryfuT2t8BMPIo5r37kKovbZA/Y2b6FjhjOTwluqc/39tS5OCoPjYDJNffuTA+zx4N2BnnrSR2xqdW/Em4QD9T7NQpPzFL2GPTdB97zzh2grp46dIHz22vZLWhKsrxO0W6WPHab/+GmE7xC2lyIxniAOJloqoe3/RfyGMgf40pRUdWWM5usp6eINAD0hJEwK7lJQixp1WIsZOQtZtWeWyrFOJlzjiPsaINbMv8ZpYTDvHaHg7SXetcYGZ8rnEIs7MstG6vEcS+m7kzFJiye8RIU/ya2M2olushFeox+v3NO0HCHSPpfASm9Etpp1jzDlnyBiFPa3qlJHlkPsoLVkdfI/vgkLRla2+nulgv2TGKGBiYdsZUlaOXlgnVmHiq+9PX7VWW+r/QdwBNAZmf9vd07+ObKC0pGRN7PotCabtJF100mBSdkPUHuImtnD2zFzwVOue/d7/OcDIZXAOzSBsi2h1A0wDa2KU1OOnkPUW0eoG1sQIOoyhL6geXL1J6tGTWy4zo5jDcB3iKAJDYE+OkfnEOWS7S3DxGgiB6vaQjTbO4RniajNpvz4MSqNVXw/CD9l8/go62jmDi5oe/uoHkDLmGsn0NZA9NBAPEZdIHz2OOzOHd+Uy1W/+KXGjjpHO7iRdIG7UUF4PZ3wiMf0FjD06Tn6ugPRjepUeN/78wZ36t6G0oh6vcj14h0a8PnTqK0yL9Mw8UauJME3Cyk6y1Ciq8TKxDjmdtg6UbD9iTeOI1A7rphfWuL7xEyzTTQIZ6uAfoYHJrHOKvDW653paK5bC97kZvId33+lJCQLd41ZwAV/1OJl6moxZ2HP9EWuKCfswi+FuH9ogJClUPTLDim6MfJIrbDik7KRgI4i75NxRQBBEbSzT7ccgTKqyh9KSlJElZQx2MXVkjVD7A0nXFg4Zo7hrOZA0Vh1qwAtSIrdn9khXtrdI1x4vkT4zj3d5iWiz0c/c+c8UQiBMA2u0hFnMo6MYI5vByKSxp8bQQYhwXdJPnCFaWkMOsLKFELinj6LjGNlsI2wbI+Wiul7SSKCUxzk0g/fO+7gnDqP8K3uT7h2Q3ZDVP9vdT9Ffa7KxR4fou3FgJ6IpbCzhYhkutuEMtUicyUmMTJrO+beIG7sDSLehPB8dRZjZ5INoL7a4/udXWX5xkbU3VqldeniZEVprOrLOZf+1/tR6b1+jlhLpdTGc4cGbpqxwwz9PqPa2TpOpapqiOb7rN6VjwriLVCGlzDzmALX6QShbk0zaR/f0UWo0K+FVFvx3H5hwt84XxUZ0kxvBO0T7WGq3K8BcsXdq1W3EOsRTw6d/aSOPISyivk/TECZ5d4y0XcLAwIubKBUTxJ2tQBEk/v1hGR0d1aA1pCrMwCJt5ndZyEbKJuqEhM3Bzz1xQ5WGXofUEb7ubM04pBcgOx6Zx45Q+srTpE7OHjjgZ2FziFOcFh8jx+AB4q8SzFwGa2IUI5fFGh8BIYhW1vHevADCwCzmiFY29iRJ2WgTb1STHGqpiKt1opUNgmu3iJbWiFY3MQo57Lkp4loT2by/xraGa2GmbRBJ7q633DjwtvfQOSJE6phAdRO9XD3Y0hWuizBN4to+1TZCJKZ+vx27EGCYyRggDIE0H15JY6QDLvuvHagCSMcRnWuX+loQe738mlq8ymL4Psfcc3sGmUyRWMSb8SKmYWOI3VPdsfxxvKixr8VrYDLvPIIrhneg0FqzGS+yEJzH1w83f1MhWY8WyBolDrlnh7pXbvthJ52j3Ar2784a6YCuajDK4HxHQ5hkjDyVaInNztUtV4RgKdFm1TEttY4GvKi55R/PGHncAaSbuJoa/XS1GOsucr1dlJE2snRUAwDphXRvVBl5ao7soTIbP94dUEtItzz0On3V68ul9s+j6+NdXsIq50gdmyH/7CNknzxO57X3CRZ2C7/cCQeXKXGYLHmaVOmw9xTXxKbMOONihixFTAx8PBp6kw2W8Rg8OFvYFBhhXEyTo4SNi0YREuLRpqlrVFgl4v4Fd4Rj4xyZQ/kBstnGnp0CQ2BkM5iFXF8YK8ZIOUl8yHX6/RgNhG1huA7SSoShhW0jXBvhWMmsRAiMdAphW6hOj3i9gnt8nuDyAtq/NzdPaqrAzK+co/DoLIZtEta7rH3rPaovX9+zWOZOHJh0A9UjZ45svVBSR3RlY9d6yvPQscQqjxIsLQ4/8MgIZjpDXK+htSY9nmXqE4lGaGYyi1/1eOvqg4ucKC25FbxHPd77Bb4Tt/24+0ESUYmWmLQP7/mhGZjkzHJCmKNPU84eQsqdg1YuPcFy/e19jzluz1HcIfazGz3VYjG4QFcd3M90L4h0wFp0nbI12T+XwbCwGbfmWQ8X9vXtJhH9FlqrgQOYQJAzymyySNyvhrw78Hg771XekYmRMQq7CBXAVx0i7RPrkK5sDLyOlJElbeS3SNdMO2Tny3RuVAkqXYQpdkXFb2c+DIOvOjtI1yrnyD93FiPl4F1eov3KRZyZUdyjU/uSbkTIJst0yO1LuCkyHBZnmOIQAkGIj0KRp8SImGCKQ1zSb9Jk5wzTxuGoOMsMR9FoIkIUMvGVk6NAmSlxBKlfZYNl7itFANBxTFypkz53GtnuIpttVKuDNVIi++mPES6uEVcbZJ46i5HLkHr0BMH7N7DGRjCyaVKPnkgC9ikHa6yMmcugg4jg8g3iaoPUqSOgwX//GrLTxTYMos174xcz4zD7G09hF9Is/cFPidoBmUMjTH/1cQAqPzmYO/TApCtVRGT4CAxMYQ5N7A5XV5DdLrlzT+AvXEO2d08ZzVyO7NnHsMojtN9+A5SmfrlK/XJiiTp5h7N/+/GDntqeaMkq69HN+y5R3Q891aQRr+/5od3WaXCNDEHUYWHzZbp3CU4fHv/kvs0CTSzG7UN7pocpLdmMFmnEG9jlLHHbRzgmqrf/QCIcCy0lTjlH3Av23KYj69TiFfLmyFBX021rt2RNsB4t7HlsjcbXXULtD803zpklBOLAn7VjpMkaxYH3qqdaRDrRae2oOkV2k65jZHYFKrVUmK6NDGLC+m4Xg2O4Q89/+xq3ByAdSXrnbxAsV0AqrNE80Xod1d0/uyEi5IbefxZhYjHNEWbE0WTA1LeokIgzZSgwJQ4xwgTHxWO8q18ivMNiHWGSWY4T0GNN36JBhRAfAwOXNDmKuCJNixoPpP2gNMGVBYIrCzsWe+/s9JV2nv8pPL/dqipaXsd7e2fcILi8cx/+u5fx3036NVpTY9hTE0TL6/eczWCmbLJHRrn2v/2I7kI/U+LCCsIQlM7NPXzSjXWS0yowca0yGatIM94tbOMv3MC/tUDm1BlGfv6reNevYZeT/Et3dg5ncor0sRNkzj5K3GzQu3QRtCI3m2fs0eTFd3IOwrq3nNVBkFqyEd2it4ev8EER6oCuaiG1xNyjzDQRi0lT69wkVsEuN8JG632iPdJzIMk3zRsje2ZMeKpD06liT5ZIH5+ke2EJq5TBu1XBGS+gwxgdK8x8mrjRxcy4aKmS2cbhMToXlknNjxKsN9HZFHHbw8y4RLWdU09JTFNWCJRH2hweqXdFmrw5OrQy704EqkegekNT4DJG8Z6qCh2RHhrw81QSzFJIOnJw7MEUFmkjh4mNJEJYBkbaJqz3kmnrAGSN0tDno7TCV90tf7iRTQGauN7GzCTZNtmnT9H8zuuo3sPTxnVJMS0OA5pVbnKTS1t5420a9HQLRzxNgTLjYo5lvZ22OSomAc0mK9zk0tYsIkGdTVYwtYn8SxJ4uieIJOVMNtuECzsLIrIli0eeK7J+w2Px0h6zMiF2teXRUu1ZLHM3Dky6rXiTVpwEHcpqZujLLLsdGj/+AVahQP5jz5A5fQZhJwGi0he+hLBtrFyeqNGg8cPvEW1u9K9FYFgJaUXdiBt/8eCZC15fA/aDsnIT6K2pqimGl+CaWNjCpTFgoAJo9vZv9140x/bMGtBa01MtehMBZtpFmEZSqFLIYBez2OUcwXKN/LnDIJKXJdxo4i/XMRwTq5BGBRGqnxLjTBaxSgkB3k26QFJcoTukGU66prDIGkUckdrXxRBoD1/3GHaFKSODJeyhsp13wzXSA5XFNLovzB6iUXRlc2C6WlJZWMAxUngqQkcSYQiKj0wR1ntEzd2DZNYsDi1yiXWwHdQUgtHf+DTqLp+ie3iS5nfeYJjV6JBiThwnw/Z1KSRL+lrf2tyNPGXSIkdb16nolV2FOm0aVPUaWVFgjCnWuLkV6LNxUEIRaP8uwt3GsOU/c9AQLa4SLe4uZMiWLJ760ijv/Kg2lHRVEOOvtZj5tSfZ/NFl4m5AZr7MyCePsvnDywO3GYQDk+6ke4yCPdav1HKphsNL58KVZTb/6A/IPfkUmTOPYpfLSN/DzOVQvR6tn75K5603CJYX++Lm0NvssvT8TWSkkrSZB5RJ0FrTlrWhVsydKJQOMz79ROITQmy1/RDCYHXxVTqtvQkxUB6h9kkxnHQNYe4Q3Ek7JWZKj5NyCoRxj43WZVq91aHWoCNS5MyRPUVlJDHNeJNI+bjFEsJKiNSdLBK3PWTbI2p0kb0AYRnIjo9VyuJEknC1ARrsUgZnvIBhWwSrNcqfPsPGn7058Hi3K/i01nvm7ab7GQSB3Id0+0GmYfszhEXGLBIcIJ/ZwCRnlAa6PiIVJsfp3+tQe3iqQ9bcnQGQMQs4IoVHG2ElRRRhrTe0YjJnloeSbqh9elttwDXRRoPOmzuNi8KnH9vnygSmtkiLLA4pHFJoodjUg+v+BYKCSGaaHVr0GDzrq7PJHCdIkSFNdstH7NFjVJvkKGDjPlCw7GcaB/CMxL2Q5T95k9lfe5Lj/+3nEKZB7IVs/vAy1ZcOXtR1cEs32iRQPQSCSAV4au9Ui6iySf0H36P5/I8wUmmMVBotY2S3g44SDd07m+ONPTaBnXVY+vFNrIzNmb95lnd/b//A0jBIYjqyTqg9hG2jo37nCiEwbAct5VavLq9XYWPlDcpjpxKd0/pNLCtFNj99oL5Lkmhf8RKB2DHtPDL2SRq9FTbbV0k5ReZGnuJq1BqqvZAycskHvQe5SR3RlJt4ixX85VpizSpN7/p6kuCtNShN89WrSXmlVIk1rDRaKWo/uoCOFfXnL4HWOBMFutfWUN5g364kxlddFApziNhKcu5ZUkaG5j4GkUImhQNE2OxOnzMwyBll6uyvy2EJm4I5NtAdEejujqyOUAf0VHMw6RqFxN0hk+yF1pUNUhP5gZ1fBUbiQx4kWt6vhNySHNXQ+N6bcNd+2i9d2PFd3I0Qj2u8i9BJ5eUZ8fSe6WICQaY/EwnxiRj8LD06/edok7qDdDf0MpNinnFmkSJmVd/EozN0P/eDdN6kNO5gOYI40rRrEd1GjNZgWoLiuEOmYKI1tCoh7Vry3eZHbGzXoLERoqTGdg2K4zbdRozfk4zNugQ9RaZgYTkCvyepr4bIOLm/6ZxJccLBsgTp/AEU6LTGW6pz9f/3Q8yMg5W2ido+0osOnLkA90C6QghSRi7xW5pgxMb+KmNSoqREBQE0G0N2DNmpHGNnx3EKLmiNlbZw8g8mwxgqn04/ep8/8zjS8/DXljDdFOnDx5GdNt3rl9FxRBR2icIuY1PnWF9+nV5nHSEM3HQZ2xk+dd66TC0PoDMgdlhAWms2WpeQKqLjb1JKz+zpr0wZ2YGksPM84kRcR2n0He2P7h4PtFTcPt0d6/XdCrpf7RM1PYK1xp7HDLWHQu5JukkbpOEpbneiJ9tEysc2dz9/gRgqdn43TGEPFK2BJG0rUNtWd9T3yw/Kw7jtHjEwUaGkt9igt9gYuN+0kRteJq01ge4S6G0rXZgGGjDc7dlL5uxhmhv1PS2v2++aJD6QfoOFg0LucivcicSCVRgYO7I9WlS5oS8yL04wK44zziwVVqnqdTo08Ok9kGC/mzX54t+eYu50FtsWSKl54zs13vxuFRnDqY8X+PRvTpLKJN/O5pLPd//FKtWVgGd/eYzZU1n+6H++SasaMXU0zS//wzle+JMNLr/W4u/+v4+zccsnlTVJ501MS/DN/32Jq2+0SedNPvc3JjnzbInQk3idmNLE3pxjpm0mv3KWzrVNvKU63mprzwFyGA5MuhNuIjId9NNdHlZveSEETs7BLadwCynyh4qAZunHtx5ov4n/LLEa04ePE3damNkcKvAxs3mcsQm8lVvIzvaL6PUqjE8/QeA1MEybVLpEq76w77GSZo57v3jijv9C4ro4NvEZ/KiNa2XJuCNMFc8Sq5BuUKHevXXHtgYpkdlTDzgR8Al3kMmD4iDBnFhHyWxgD3eQIZJWRwfRTPB0e2gTTIFBdp9KuNtIid2ZB5C8t4HuEd5BflJHeLI9tAw5Z5YxhbXvbCZtFDAH5GBD4vfsykRNa2v9U/NE1Sb5T5xB9rMV0mfmaf7wbQ403z0gbkta7nXvVf+LTjoDGncsV6xwA093GGeGEmPMiKNMMk+DClW9xibLBBy8hP1OjEw6PPHFEf78ny1z9Y0WhTGboKuIQ02maPH5355i5UqPF/5kA9sx+OV/NMenf3OCr/0vw9NRbyOdM8kWLb75O0v4Hckv/6M5nv75Ua6+0ebo4zkeea7Ei3+ywZXXW5z7QpkTT+39bgnTIH9ygvLHDhHWurSvbNB6b4XeYv2eyPeelK43g1t09/OR9oseGBLdvRtaaepXakS9i5iOSXOh8VDet1hHWwQkux06l94ld+ZxolqFsLKOO353g0KorL1LafQ4jpNHq5jq+gV63f1bD93P6da7t0jZyUOOpE+tc3PoR51E0Ye3frmNQHkPbTA8KJSWBzqmLVxMTOL9SFd1CHWARu++XpGkgd1dUn03bosTDcoiUFoSqO6OrhB3pqulBgRD80YZE3tff2bGyA/tTqF0vJXvextxrYVhW0lAcykJUjszD7c5ANy2jHfOtO6G0b/bSUe0nc9IIamyRkvXyFKgoEcYFzOMiEmKjFJghGv6/H0Rb7MScuOdNs/96jjlSYcLLzaorQZoDbmSxdisy7f++TLV5eTeX3ixwbO/Oo6T2j+7KegpLr/WYvlywgO3LnY58bHkm5s4lMZrS66/1aa+FnLtzTZPfnHv5xt3A2783otkDo2QPzlB6fFZxp47TnehQuWFq7TfP1gtwL6km7NGmHKPkTGLFO0JerKZNHGLVqmGuwNMpc9+gfSpM6z/299HdYeXn5qFIqXPfxHZ6dD40ffprXcxbAPTTU5JK40K7y8qetvqi/ofZtSsU/r4p9FRhDV/hKhZx8oXd1lncdSjtnERw7TRWqNUvGP6/TCx0bq8w6rSqC3Rjrv9yCbWnsI2t1E0x/lE7pcf7onuA0ekDqR1e1tXdT9+jnWIr9r9Iond2QQmNmkjT7hnep2gYI4N3b8nd/vNkyBeb2DJcMYsYhsuvty7nDpjFrAGWLpa636MobFjebhSRTgWcaOzNauQHe++pqx7ISbCSNqNDl3HwgGMPd0QESENKrSoUdErjOop5sUpJsU8Hd1gkSv3POj3WpJv/fNljj1R4LHPlHjiCyP85I/WefN7NdyMCQICf/sb9HsS0zKSeMVd+zJMMO3tj1rGik5j+1qU1Jhm8rtlC2SskP2AaBxpAm+fb11DWO0SVru0Lq7ijubIHhtn5pceR1jGwyPdQPaohIvAUj8xPSEEXw4uL7VGRkjNH0JY+zimlcSZnMKYc2j8+AeUjpc48WunKR0voyJF9WKFN//X1w50EXdDo4lUsK2I9s7rdK9cRCuJME2c0Qm8W9dRvZ3XkM1PMTr5KI6T26p/X196nV57vU+EybJ8ehwvaBCr4L7V/7PuKNOlR3HsHJH0qbSvUuvcHFggYQgTx9jbJ3pbjnC/9f6yYLBXx+WdaMs6sl/1dDcsYZExint20jUwKFqDSTcJmu0OAvuq2+9ZttuzawiTvFGmLYcXAFjCIS2ywzMXVDBQW8KeLFP6ytOJH11rVC+guvz8Q/MuaDS9fnmvSxobZ2AQLEMeA4MIH5+9XVQKRY8OPtcxtc0RzjAqpljW1+45fcxyBEFP8d4Lda692eKzvzXJY58tc+3tNu1qROAp5k5lqS4FCAPmT2fZvOUTeoqgp3AzBqmcideRjM6kKI1vGwBaDx+/WrWIdM6iOO7QqUeUxp19fbqQaC7Y+RTp+TLlp+YpPDJD7IU0D9g1Ag5AupH2aUT33xBwGFQUgZSY5XxS5z6WofLuBo1rdSrvbjD9ydn73rdG7xBksQolUlMzCCu5XH9lkXBAx+LJ2afptFaorJ3felpCJboIUgYokt5ttpnCNtP0wjq94P5KlQ+NPsNG6zKdoELaKTJTPkfXr+BHuwnBwMA5oHDMzyqSqf7BSLcja0gdD7SgTWEnft09VD/TZn5oVVik/YHFMqH28VVnYBmyQFCwxliNrg+15FIii2MMrxTsyNpAn6oQAtns4l9dRscyCWY+RENXo2npOgjIUSDT12m4GyXGMTDw6Q3VYLgbCoVP7w7tinvP85w+nuHRT5eIQ4WSMH44zeq1Hn5HEoeK179V4ZlfGGV02sVyBPNnsnzzd5J01cVLXc59ocxn/8YkzY2QudNZDPNg53DtrTaPPFfkC39risVLXcbnUtju3i4LI2Uz9xtPkj8zhZmy6d6osvAvX6J1YXVgNsswfPDdC4fAcNyEBPv3SAYxsWdiZWxGzoyRKj+IxaZ3ZBMUzn0M5fvILct28IOJI49eZ4NeZ5uQC+lpBIKUU8ALmxh9hTXLdumF++cAD0OsAurdW8QqIIw7jOaOMuxrExgDp63/qaKrEsGaQX7dxNWS3TMoVzAHN6HUWhMqb0cQbes31FbBhDMg06Jgju5Zgpy0FBr8zmo07QEtm27DyKZwZsdQUqH9EO/y8Bz4+0GbOp7ubhU/dHVrhwshR5ExMQVoKqzt0Fy+u3vGnTCxyPRlLD3dva94QqsS0qqETBxKo9FcfKnBxZebeO3k+33pTzdpbkbMnc4gJXz7/1hh4XwyKNy62OE7v7fCiacLpDImb3ynyvkf19m85RNHite/XWX9xvazXrzUI/CSd6a6HPDdf7HKo58pkS1YXHqlyfuvNamuDPfrGraBO56n+uI1mueX8ddaW1q794KHQrrC3JbSu20lCHNnp4jtlcFIpcg++jjW6Bhxo47Wmsb1BqZjYGcdxs9Nsvrawc31QdiRTSAErXffRHb2k3HTHDr+RXrdzS1fbqd6i3YvIeFYBZjCRgiDSHoH7i47CJbhcHr6ywRxB8fKkHZKzI8+g1QRbW+dzfaVO05fDJxq/6eKUPv4ukta53eNj7elMveqcCsYowObUCqSYNYwcritx+CwmzzTRh5buEOP6RqZgWSdQNORg2dEWil0ECWWklJDy4sfBAEea3qBw+IRpjkKiD65RmTIMyUOk6NIkxqbejtOY2ByTDyKj0dXNwnw+gQsSJGmzASTHAJgU68MHQQNAz71WZcvfikpdf7X/7LH9asJkTc3I178k+GuIq8jef3bVV7/9m7rXElQzR5jUcy//Vc97tap+skf7pzNXn+7zfW3t2c5y1d6LF85eLZP3Am58bsvEHcerEDkwUnXNJn47b+DmUkCEPb4OMK2mfybf3ur2mz3US3schkzX6D9ykuJ2EXDT5zj6126611i7/66RsDtdLY7PiylGP/yLxHVq0mp7LX38Zd3p6TVK1fwvQZabUfkO946QfDw1brWm5dwrOxWmlK9e2srgJZ0PdhGUljxnw/pQtLVoWzuzjCBJHiXMjIDK9xuq3wN8q1KLWnHw91BCen6aL27M8btFjxBPOiYYs/mmVLHdOXgAV+2ekSbDZzpUTBEInQzpJ+Xhc0Y0xTESNLLjhQ5CpiYHBGnmWCOmIgAj4pe2SpwkMSschNXZ5jiEPPiJOPMolFYOLik6NHhhn6P8I6WQALBOLOJH1gExMRbxGpi45JCo7mlr1BjbehgpjUsXI95OS34v/53Ob7/HZ/rD6k/wfSsyc99JcUf/DuPKPyAM3e0fmDChYdBuloTbqyTOXkKqzyCkc4kLW8mp4Z7sbVGxzHt11+j9fproBUTT0/j5BwWf3iTqPPg1S53vgCdi+cxs7kkkCYM4vbgD6DTWsH36himA1oRxwEyTm6ymxIoxUN7sNXODYQwMPodgTNumVZvDamiAS+vuCehl/8U0JIVNKcHXrUtUolGrtxdnJM1ikkF2V0baq2JdURLDS/o8VQHX/UoDhjfDEyK5hi1ePcMzBYuKZEbqqncVQ3kECe0NVrEniijwhgVhBjOcDeSgUlBjDDKVP99EMTExDrGIYXdJ8EQnw7NHXKPHl2u6Heossq4mCNHAQOTHm2W9XU2WNoKuN2GJOayfotRMUWWAg4uBjYahY/Hpl6myiptGnu2gdIalhYlrZai3RpeKv+zBuHaSaFR9HC1Wx6cdJWi/t2/oP7972CXRxj5xV8mc+oMtT//xh0+1J3QUURUqxLVqiCTabyKFKZj4hRcVCTRSiODh5OuFWxsC1xYxTLCGPxxpDKjTM48RSo7hlYxrfpNKuvvomSXk4+6pDIGm2sx60sRvnf/5GsZLimnQMYpU8rMkU2NEUZdOt4m8YBc0Nu5k3vZulonwjveQxYtf1joyuY9VS61twJPu5+VY6QG5tMC5M0ylnAHDlK+3qllezc0irasMWbP7dLgNTCGpqE5Ir1nT7SWrO2R5aIJ12roMEZ2PTJnDzPM1A3xuazfGnqc/RATssEyG3p3qqclHIQ20HdlH1RYoTJE1+F+MOyrmZkzmZszMQyobCpu3YoJ+59CJis4dNikXDYIAs3NBUm1MigoCVPTBmPjJteuxvS6D2AgWSapE3Oonk9w4+Cdfg+064e2J6WIqhWC5UVSh4/Qff8CsnnwabmKFCOnRkmPZIi8CL/uPXBVmplNFOfNTBYjlUT/U7OHiVsNosbuaeb41DnarWUWb/wIy84wNfs0mdwkrcZ1GjXJ0TGTU4+6yFizcjM6aP3HDpQyc5Sy81iGg2FYpKw89c4tblZf20NPV+9b9aXRrEbXWQh293D6oFGcOEG3vkw8oFHpbSgth1p7g+CrLqHyB8pGmsLCNTIDg2kZszg06NiK9+8c0lF9grx7H0JslfneXTFnG+6eHZk7srar4OA2ZLNLuLSJME1Sx6eRrR4PNX3hgBh152lHFXrygxG+3wvzh0z+wT/MkcsLlE5y9L/1DZ/nfxRg24Kf/2spPvsFl34TCJYXJf/m97usr23fUyHg0GGTv/V3MwgE/8c/7xyYdJ2j09hjJXQsCW6uIWstnOlRRMpBbm4HQFOPHMZIuxgph3C5QnhzDXt6FGd2HBXFhLfWkfX9ZWQfevZCXKuh/HtPMfNrHhvvbDu+w/aDuxjMTBa0Jn/2iUTgJo5xxidR3pAcYyuF160i4wAlI+LYRxjJ6Ds5a2HZgsvvBtQr92+Bp50yI9lDhNKj2r5G7AREMthTwPx2cv1BEN1ja++HgeLhx+j0NogeIJvjbigkXdUYSLoCgWtksIW7IxPBwCQ9pCosySDYv+9ekiO8+14n6noOGaOwK0f4tkD9IEgd01WtgVZ+6uQs4XKFuNElrrWJqq2kSOIurhAIis4kJTtxK9TDVdpxhZIzTcrIIpF9B5Rg1buCbaQYc+dxjAxSh6x5V4l0QMYskTKzpMwclnCoh6t04zolZ4q5zCN04hrtqEo9XBlYQPJB4Tf+RhrLhn/yP7YJQ82v/Waa3/pbGd55K2J03OArv5jiR9/3+cF3AyanTP7h/yXHV34hxb/6F/2K01gzM2PyW38rje9p/uDf9XYQ8p4wDApf+Bjh0gay3t7y5QvHJv3IEYQQxBuNZJlhYJXzpM4cQfUCovUaueceI1qr4U6NYBVzdF5+Dz2gYeadeOik6924jvqzr6F696YB0F3rEHkR6ZE0nZX2UOm8e8FtazZqNfGXbqJCn3T3OCoc7Axv1K4zc/g5Ar+BabmgIehdQgjI5U1sR5BKC7rt+48wb7av0PE3STtFcqkJRrKHUTpG6ZD15mXUgN5zCrVvy24BA9vOA5h2mpGZs2TKswhhsHH9Fbz2JsWJ42RLsxi2iwx91q69iIoDLDfL6OzjpPLj+N0qlZtvICOf3OhhRmYeRRgmnfoStaV3dlTsudlRipMnaW1ex29vkC3NMjr/JAhorl+mtXkdw7Aozz6KEAaZwhRep0Ll5hsoufP6tFZ0ZJ0xe27gNbliN+m6Io07tKuG6hc37A1fdQiVR0rs7iBsCZusubMwQ5BkUwxqCZTsr0ukBgyEQpA9dwzVC3BmR+lUmsSVwVamKWwmU8dphOvEyu83QxUU7Ynk+GaGblSn6ExQCZKArCdb+LLDiDtLyZ1m018gbeaZTp+kGizSlhVC7Scl0LKD1JJu3KQTJ63tPyy4LjzxpMPX/thj8VbyLr3wk5Cf/2qK8QmDqRkT24ZXXw7Z3FC0WooL70U8es7G7b/upiX4+/9tFiHgn/zzNmur9/B9ao1/+Rb29Bg6klv+23Bpg2h95/viX1nEHCnQff0S3qWb2GMlUmcOY7gORi6NsJuIlP3hk65sNem17119p3S8zJGvHCMzmePCvz7P7HNzvPf77zzQueior47/3lv9/9d03n93aLPVevUKgd8gnRkllgFeZ4PAb+GmBRurMaMTiVLRgyCWPm25Rsdfp9a9yWrjXbLuCKXMLJZpE8YDSFdLAuWxt/qcGFoQkMqNkipMUl9+lyjoEvYaCGGQLc9hWi4bN15lZPYxxuafYHPhp4zOPYHWitrSeQrjxxidO8fGjVcJvSaVxbex3SyFiWP47Sm69cQ/6GZK5MeO0K0vE3RrmJbLxLFPUrn5BpabpTR1htBrEfltCmPH6LXWEpKXEUoNGmgkbVUbqq2b6lu6dyJt5odanF3VJtinczP0iwlkbaD/1hIOOWNnW6akD1t+aFFEVzWHdE7W6Egy9l98ETOXJvexk0BSBrz5r7+/4/tRWhHILiPODJvBQn82o1Fa0o0b5HSZbtwgY5cSUSQBJWcaW7hkrRKR2jYyAtWjHq3h32HJ9mSTUPVoRxVa0f5aIw8TjiOwHQiD7euNQp140oQglRJoDbc/C62T3w1j+xaNjhmsrkjGJkxOnLLY3Ahvh4r2h9b4l24SLm+SffIUqUcO033lQuJGcGxUyknaWEWS7MdOYxUytF96D6RCdj3iSpPOaxchlsiOh+rsP9P8YIoj7qN2PDORpbnQREUKYQjc4mCr7X5gplJIJTGzOYxUemhreNN06HU36LYTvVbTcjEMi+NnTM48kSLwFM3awwnuaTSx9ImljxfWqbZvDA00KeItdbe9MKxoIOw1iIMOpZmzdKu3iPw2WsYoGRP5bfxOhU7tFqNz56g5aTLFKZxMiWx5Nnkpu1UQBvnRQ2TL8whh4KQLWHbiJxeGyej8OUKvRXP9fbSKcfNj5Ebm++XUAq2iLas4jnr0GisE3eGWp0bTky1iHQ1MxXJEejfpGrmhLd9bcQV5wJLtpC37Se52ot92X1g4xP1S2kSMaLg6VVc2iAeppmmofeMlnJlR7Iky3Xeu95fvrl1VxCz23iVtFpnLniVrlVn1kk4FSkvUbY0wrTGERcmZAg1XO68xn90pih6rcKArS2u9pyDOw4Bg63XYQrutuX415tyTNi++ECAlPH7OplHXVCsSx0k64Zx5xKJeU5RHDI6ftLh+NSbsj2W1quJ/+h/afPnnU/zWb2fodTVvvXHAmIthUPi5pxEpF9XpJcJDQpB79jGcuXGsiRKy1SW4vkL63HFU16P4lY/jvXcD7+ICnZfeJfeJswjTQK2s0H71faL23mllH05F2h7C2wBoTdSLKBwukZ3JM/74BH794fkmC09+gvZ7b1F4/GnMbI7OxXfo3biya73xqXPUNi8R+A1AUBo9QeDVufzuMrmCQehr/N4HE+TYK7If63jfPm9CCCxhkzZyu7QForDL+rWXcHNjzJz8DBpNY/V9DMMEO4UQBpaTIY58tIyJI4/WzWtUl5Jy6Nu/l6YeYfXy88jYZ/rU5+44eY3X2sBJF8iNHqG1eR0Zh/jdGrfe+SYy8hHCRGuF5aT7YkL7E2CkQzzVwjZ2W5224fZ1JpJIv0CQErmhLpaWrBxA8/j2utV+NdxOCCFwjDQpI0unPwU3+n3UBkFpSU+1iIf545UmWq8TV9sQDz83A5NRNylCCGS33wV52DeliXVIxiwy6s6RtUp048bwi+2jK+uUnWkMYdCOq4NdIveJUknwxMccDh02GRs3+NwXXMplgwvvRly/JvmDf+fx3/yjLP/gH+aIIs2x4xZ/9nWPRkMR+JpXXgr5ha+mOXPWplAUKAnf+ub2+WkN3a7mW9/0GBs3+K2/laFW67Bw/QDPWylqf/CDXYub33p517LNf/q1Xcu889fwzl/DdE2O/tIpzONlNt/aW2T/AyFdYVmkj5/EmZnBcFzYo5GiCnwaP/o+zRsNUqUU6ZE0pmOy+PyDZS7cCcNNJapiaHoLV5Nc4gHI5MapV2+TscZNFVAypNeBekUyMW3R2q/9wQcARYynOkgdY4rhj8wUNjmzvIt03XSJ8sxZQCBlhLxDSyNbmmHi+HOkc6NUl84j44B2ZYH82BGcVAEZB7QqNwi9FqHXpDxzBqUkxh3VhlorGuuXsZ0sxcmTxGEPr7VBr7HC1InPICMvsYI37i0jPtYhXdWkwG7STUg2i4mJJE5yZY3swFxZqSO6qnHglLVAdYc23HRFqk+6yWzpdlnyIITa78uLDh+oEz/i/u9UIWdx9EiaW4s91isrRMqnHq4QKp9IB0TKS1wP9AjMkHYMYyMp6p3rdMPkefdkA6mjHbKWtp1E/dvVBdxolomxNN01g1QK4jj596CwbEGpJEhnBH/0H3poYGzMJpsBkLx3PuKf/28dnnrGwTQFf/wHHq//NCQMIArgT/4g4slnYk4cy3P9qserr/S4uZDcs4XrMX/47z0MlSHsOPzxf+jyxc9PYhIihLdj0iBMwcxz89g5m956l8p7m0w8OYUwBMIQNK7VcHIO+cMltNJUzm+QGc/g1z2CZsDUMzN4NY/cTB7TMWjdatK83mDqmRlM1yQ3naN5ff9g8sMnXcOg8OynKHz8OezRkaQUeA9LN9xYp/n8DwlbARtvrRO0AoK6T3vx4aWuxI0a+bNP0Hr3TaxcfmgSexR55AozBF4d28lhOzm67TUMA8qjSSDtL6tEIdBJt4O9GlNawqZojrMR3dyxXMYBvdY6wjDpNVfp1JPafiUjvHYFr7VOr760tby1cZXIb2E5WbSKicMeKg7YXPgpTqaEVpLm2vtEfpJMv371RcJunaBTJQ57xGEPrWI2brxKpjAFQhAFHZRM/OrVW28SevuVZCekO0gR7DZSRhZDmH1xnNRQi7N3QH/u1v1C0lG1gaTriPQOkrWEM9S69tXOThGDIGyTwokxyk/MkpkuYGUcVCQJWz69pQabLy0Q1bsUDt9g+jGbtTDE8iXHJwxKpTpSwsZGh7Exg263x6QjOHIUXn35GrlDJutXJaPjgmNFG8/zKJUCGk1Nr2MyMWlw65Zkdtbkut/DCy4zc8jEbsU8+ymXzU1Fr6tYXVUUiwbLS/ducAgMvEaJH37TQaoYL26RtvJYhottpkhbVVwzy8Iln9UrSdm3H7exjRGydoBluBhelh9/Z4U3nQzNsI1t5Ci5Gfy4Q2s9y/e/GaK0JGuXWFlp8u0/dWgGJmmzTKQ8DGERyE5SjbrZJUOW4vERvEqPuc8dZu3VZbprHayMzdjjk/g1D2EZHPn5Y4TtkOZCg9iPmf7kLI1rdVIjaeqXq0w+PYPlWpRPjVI5v75nK6078dBJN3XoMPmnP4FVKuFdu0awukz66HGs8gid828l+Yhzh7FGR2m9+hKdN19HxzHFoyWO/uIJDMvASllsvLnGwneuP5Rzar37JmYmS9xqEjnOlm7t3dhceZPpQ88xNfdxlIppVK7Qba8l05eOIgw1nQfIXHgQeKpNR9ZJm8PFzA1MCuYorsjs0AiIwx6tu6xMw3TQQNCr7/pNyWgrQHYn/E4Fv7M77apT256V9JrbieSR36bp73aL9Jr79ziDpCKqp9pDLXzXyGBiERH0p/27O/9CUlI8rBvFICgtacoq4/ahXb9ZwtnynYMmbeSGtly/3bhzGMyMzeHffILZr57FKaYx7H6kVOukt91yg/b1KkG1S6WiWF6WOI5gdNTg7KM2liV4/1LEZz7ncvFCxMa6olAyUAo6HUU2a5PJKE6csFhZkXzyOYfz70QcOmTSamompwyESKbnhpnUKZXKJmEYEUXg9TTlssHIiEmrpdi/X/VuCGHgmllSVh5T2EgvImuP0IsbWNohbeWJlE/Bnep33Uh8ywJB2irRDjfQRppYh1iGgyUc8s44DX8F07BxjDRZa4R6cFt6VrPlbrJypMgRyC6BBCfnMvXMDIZl4BZTVLNJ0LF6cRNvs0fp5Ejy96VNDMNg/vOHWXttBQEYRuKMlqGkeaPO5ttrjJ4dJz9fpLPaZvP8BoUjpQPdk4dPuvOHsUolOm+9QeXPvgaxRPy1XyLtODR/8iNku42Zz1P63BfJnDxF9+23gKRPWvtWk5vfu4GTdznz248+NNIVpoVdKOGMJlqp4eY6ytv9MXi9KgtX/gLDsJIejjIJOpgWZHIGhaLB2tL9a0LcCdOwk2m6YSGEgdYarWX/2HLLBypVYh36qktLVRjTc7vEvbeuUwiyZokRa5rVaJ/upFoRBx3iD0C282EiUMMt/JSRSzQp9LYew93QWtOR9SEZBIOhULRldbDMY79IwhEpIu2TGRJEu10huFeHi/Kj0xz7ux/HdEy89TaNd1fx1tsJKYxmCWo9wloSQO10NJWKot1WzB82sWxBZVPRbGoqm4rxcZMwgEpFoZSmPGKQzwsmJg08TzMyarC+pqhVFem0YHTMwPc0li0oFA0mJzWOoymVBLm8QbejSKUEKyuSX/xqmt/73f0DuYZjkp0rETZ9gmqyviFMbCOFVBGSkKyddMaIVYhrZpE6xjGzRNJHiqQE3jRstJZIHW6lrzlGGtNwk21URM4ZA5J4gyTCMTN9y7mAZbi4ZoZQ9hhJzVP3k+GieLSEMAWb5zeYeGIycYsrjYr6mifNZGDOTecx7KSLjZaK9EQW0zUxbAMtFSpSW+piQcMnf7hIfjaPkz9Y8P+hk65ZKGLYDp133kIHyUXoKEKYJloqdBwT1+u0Xn0Zd/4Q+ac/TrC2ggwlqZE0o4+M4+QdTMdk/PEJuutdehsPVtpaeuY5hGEQ94l2mPaCYVhkc1MoJem2V7GdDFJGCBGSzRlkCyaFksna0oM7uvKZGcKoQzY9jmk4KB0ThE3S7gixDABNEHXoeBtoLdEomvEmPbtNziwN3W9KZBmz5qjFq0MVsQCUiqncGtxa/WcJgerhqc4Q0k0s3SSrIDfQGo5JXBTqgMUlCZJBztc90mK3i+F2wC7S4VCXRtTv0TfUjyxg8nMnsNI2vdUml3/nBdafvzY0P71WTQgT4PrVndP8pUW5ZbECfPtbyXf39T8dTPi3/aGDcPVKvHU8SPy9L70YEB3A1iicHOeZf/zr3Pyjt7n6uy+jpSZWAeu93UFrAC8+mAvR95LZ0krnvT3XawbJDMrrNAFB1i5T95e2nkFrocHoo+Pk5wt0VjuEzYDapQqy36HG2+hSv1Jl5PQYWmmu/NFFcjN5xp+Ywkpb1C5X6a52iIMYFSsa12pUL2ziFF1GHx3Hq/Tw6/u7sR466QrDACFQ3vbBVRRipFJ9CcgE0eYGstXGPXQYhCBo+AnRPjGJlTIJGj5jj0+g9foDk64wTeqvvrCvtGNp9ATF8lEMy+H6xa9THDmG36vT6y7RqEkmZq2HVqAZS49y/hBB2MGyHWKpcJ0CQhiknCIdb42uX9mR3tOUFdqyStYoDPVLCyEYsacZiadZi4anof1VQai9He3S74QlkummKZJc2UHwVfdA6XZ3I9IBXdkcSKq3q+EEnYF+32R7H0/tIQYuBNlDJQC6CzVaVzYfqCDoIXf42UJlU7F4a/+dC9Mgf2IcK/NgXbwfHjR+3EbeETQMmgGX/s3OMvnOyk73V/XdTarvbhe/1JoBtYuDKxlvfe8GANf+9P17OrOHTroqDNBSYmS3gw2q1wPDwCoWietJbqaWErTCzOYQQuA3fJoLDdKjGWIvImgGrL68ROw9uFWpY8nIp79IVK+CBu/WdYL13SIehfIRmvUbjE6eRWuF4+aRcYDhge0IXvlBl0bt4ZDYbTWxRucWTpBDqhDT6L+wAqLYQ6md1x7pgEq8zKg1s2cnCdfIMOucoiWrdO9qhvhXDZEO8FUXpdUu3+ltSUVPdYbmynqqg7+HxT8MtzMnxtjdwcQ1MtiGi1BJmtoghDrYm3QBK5dMR+NeRNz98KrA7gW9A6ZIGrZB6bHpD/hs7g1yQHXnzwIevvZCvYYKfNz5ebwryQgQ1RM9htzjTxIsLaKVwh6fwMznt6rGSkfLFI+UaFytoTWEreCh5er2blzGLo2g4xit1dAyYK1k4k5A4Lh5LDvd97sK8kWD0kiKKxeCh1IgoVTMZv19YhUQBwcP8mxGt5i2jzMqZoZGSwWCsjXJsdQ5Lnmv3FMQ6WcNGo2n2sQ6GDjQuEYGS9gDfataJ9veT1v6WIdbTVjvnlWYIkkTS5py7vbjaa0TC/3O3GqRWIMIgRBJr62twJkAwzYxnJ2+ehUr2KczgTANhCG203Y1iRvvoB0NhECYInmX7nidtEqCeXse30i2E4bALqQpnZ3aOifDtdDxTgNFK71rGQIMK2lAOfD3O1e1+teqQcUD2hqJ/jpCJH7a2+a/ccc1QnKPlE4E4/e5TXffX601SH1fHSNu46GTrr94C9lskjp0lNuOpmBpkXBjndzHnsbM54kadVKHj+DMzNJ5/adopYj9GGEZpEYzfWnHhzctDiobCNtFy5iwsj60Pfzm6ltMH3qWdG6S44/8Kq3mLbrtVU4/7jI6YRFHmsIgsdX7Oaeow/2oSUU64FbwHgVzZE9r1xAmk/YxpJZc9d8Y2KLmg4KBiS1cDGEObMZ4r/BUm1D7OOy+3tvkNyhXNiHO1oGLIu5EQvYdAu3tkpEUCNIiT9YsDXTzKCRd2dxu0iig/Ng0879+DrecwRnJYOdTOOXkeiY+fYyRp+Z2+AjibsiV332Zte8P9oeaKYvcsTGmPnec4iNTpCayoAX+RpvKT2+x+fIC3Vv1oR21DcckPVWgeHqC8rkZcsfGcEczSeyjE9BbaVJ7a4mNF2/grbZ2vapW1mHs2SOUH58hd7hMZq5Eaiy5T4d+/Ryzv/AId29UfWOJy7/zAv7G9gwgM1Pk7H/3RXKHyqx+7zLv/+8vDDxfgJP/1bNM//xpestN3vvH36e31Njxe/ZQmZN//znKj0/z9n//F9TPr5CZLjD2ySOMffwQmZkiwhT4lS6t9zdY/vMLtK8PVp4z0zaFU+NMfvo4xUcmcMdyaKXx1lpUf7rI5isL9BYbqAPkWN+Nh0664doq7TdfR3a7W6Srel1aL72AXSyRfewcwjBQUUS0vk7r1ZdBKZy8g5WyiLoRKlbE/sMrQig89hR2aQSUwuuLrHcu7tZ16HU3uX7pz3BTRZSKCIOEGN8/D7mCQaelCP2H5Ty7//1U4xVWo+vMO48MTVcCMIXJrHMSx0hxM7iQlMLeg7zivUD0tR/SRp6COca4cwhftXm39/wD77snkzzbnFne9VvaSMhv0H0ItPdA7hVfd+mp9kBCT5t5ctobmL4ndbSr3bozmiV/bDT5Q2uiloddcME0UJEkavo7DI24Gw4lTCvnMvfVsxz920/jFNNILyTquyeKj0xSfnKWmV94hIV/9war37+MHNCFJXuozJP/n18kO1dOtKu9kLgbojWkJvPkjo4y+dnjTH7uBBf/yQ93kZOVdRj/5GEKJ5OMIOlvH0P6EWFj9+wi7gS7LERhGtiFFE45g5Xd2x9sZm3ccoa4HSAGNKBM9uXijmRJT+WBGU7+/ecoPTqJDGJUKDEdi/RMkcxskdpbywNJ1867HPqNcxz57Y9hZRziXkjcCzGEoPz4DKNPzzPzC2e48W9eZ+2HV4Y+p2F4+MURWtN84ce7FntX3qcqBJkTJxGui2y36V15n2A1SecI2wFhK0hSM0xB7Dy89jR2sUzrndfJP/ZUYk0MmZZbVgopIwK/iZsu4aaKBH4TrTRhqDl6yuHtVz48i3EYFJLF4BIFc5SyNbXnuoYwmbAOkTEKrIcLVONlWrJ6X9bfrn1j4hoZskaRnFmmaI5RsMaSLgoIVu9jWj8Ige4Ram+g+E3KyA7N5gi1N7RNzoGOq3r9tuy7kRY5IsNnUDluIufY2F6gof7OChf+5x9uLRKG4LH/+5fJzBRpXlrn5h++vaMVjJaK3sqAztC2ycyXT3P07zyNlbKpvHqT2ltLifUoIDtfZuJTR8mfGOf4/+kThC2PzRdv7ArSRe2AsNYjbPi0r1XoLTcIql200qTGcox+/BDlx2conZ3i0F9/gov/5Ec7rLqw4bHw79/cIkqnnOHx/8dXsNIGlddusfzN93YRbNTyiZofTopi8f/P3n9HWZKm533gL3xcb9Pb8r6r2s9MjwFmBn5gRQgECBIkl06URK2WWu3ukc46rs7ZXWq5oo5AoyVAEYAIR/iBmRmM6Zn2trq7vEnv83oXPr79I7LSVN5blVmZ1dMzwNOnTnXdGzfiC/d+7/ea5zkzwMgPnMEoxFn8s+u052sEHRc1aRAbShPYPu2F3d1jsq4w8kNnmfyZp5AkifXXpqm8t4S93kSSZZKTefo/foTkkQLH//bzOJU25bfn9+VD7cnoSpqKNtyPt1ZGuD5y3CRs7j8jbN26gXX3dlQ+5vs7lvmtxSblGyXMjElnrX0ofLr34JbXSZ65gDk0Sui5WNPd21GLgxeolu+gqgaDo8/huk1KK1fw3HXSGQXXFQdmGTssdMIGM84VNMno6gFuhyTJpJQ8cTNFXzBGPVij5q/RDKpRe/EevF8JeaPVNvJm43KamJzClBMb/5/coePWqwHlUSAI6YSNbVLfW4jJSaQu90SIEDvsPLBs7mHwNmTZ7+nY7TiuEsnzyPcZXYHAx9tFBu5WOriVbWORJQJng0awZlG/uoJbf/iEnj7Vz8gPnUFLGqy/NsOt//kV2vO1zdirrCk075Y4/reeJ3W0yMRPPEH1vSW8xk5j51Y63PiXL0fGfb6G39n2vkkS5bfnOfNffIbchWEypwaIDaZpz28ZqdANaN7dyuqb/alNGXJ7rUn1g6VDoWd9VPS/cBSv5XDzX71E5d3FHeevJnXUuNHVG8+eHWTkh86i6AorL97hzr97g85ifev6GiqtqTLHfuE5khN5xn/iCaofLBM6e0/478noiiBEyacJ6k2Ix1CLWfyShjbcR9hsI3wfJZPCXVglrD84Y0sQRJUL90FL6ORPFUkMJLjxm1fpvzRI5dbDmf73gtbNK5gjEwTtFvbyAs5qd/mNeHKAZn2edG6STmsVVYtjmBlcZx2rE9I3pNNN4PjbAUFI2VtEl0yOmpe68r/eD0XSSCtFUkqefm0y6tvHwQ0tnNDCF+6mBywho0jKZpurJhkokoYqaRuf6Shoe259PCjaQSTLfr+ig4pOQt7NZRsQ0A72zrfQDQJBJ2ziCXcXe5kq3Tvu/WJsUcWE/xjCOJImU3x2nOSRAoHtM/sf3qM9u9NbC72A0ptzZM8NkpzIkzkzSOpogcrlxV3b1a/16AwUguZUicq7C2RO9aPENcz+5A6j+1GHljKZ/vdvs/bS1C7j77dc/C46jLKm0PfxI8RHM3gtl5nfvkxnvrZjm9DxWX9thtzFYRJjWbLnhkhO5Gnc2jsl5t5MiBBRBk9RIAyRk3FUIQjbHdS+PGoxQ9iyCC0b92FGdxskVUNSVULbIjmSwi51UA0FWVNIjXSvu3wUCN+nM3UL2TQj8p0eDFdBYJNMj5BIDjBz+8v0Dz+JJMkMj2tk8wqqBonU46W/2w8CfJbcO4Qi4Kh5ibi8W8X2fkiShISCKSUw5cQmJWC40T55TxTzXqzyniIBSB+age2GZlDZEGDcieh8usdVG12EK/eLTlDHFRbGfUk8qcf1EIQ0/IMftxvMYpLU8T5kXaE5VaZ+c7XrdoHl0Zqp4jZstJRB5tzQLqP7UAiwluqEXoCsyCix3oKZH0W41Q4r37i9L2/bHEyROlZA1hRaMxWat7tLw/ttl9Z0Ba/losQ0sucGD9/oSjEDFAUlnUD4PrKuEoYhQaONpGmEHQvCEH+9tucDAyTOnSd27ATrf/A70c3dEKbMHMniHoIi8D1kn32B5rX3SF14Cj1XpH75DazZ3W2ya0uXKQ5eYHXxnYiasFPBsev4XhCpAAuJ0sqHzzL2IIQELHl3cIXNcfNp0kq+Z+NEN0hIIEkPFL38KKATNvCFgxDJhxr/SPnXpRkcfKXUDhsbSTyxJ0VmgaARHs4K7X4YhQSx/uj8Q8cjf2m0J0NZbDgNYqNtub+3aKakyeiZGFrSQIlpyJqyWSaVmMhvlrk9lJ71I4b2fBW/s7/VhtmXxChE10p4AYVnxnrGas2BVOSMKhJmX+/r2w17Cy90bDpvbHVyePNbM2xQqu3rgNuhDw0TO3YcSZKoT9dIDKZAkkiPZ5j7xswj7/d+yLE4aiqNJCu071yPBCu7wGqXmL+7xa1ZWb8BQP+wSmnVp1ELPjLhhftR8hcILJ9J8zx5dShSEPguQqSZ1iCl5HmgQucGrD0wfO0FUb1tq6eCxa5xiiis8Tgg68qmx5m/NEr+Uncpo+0QQYhi7vZSow6yIrknhkmf6CM+ksHIJ1DjOrKuIKvKZs3rw0i5P4rwO96+C4QUU0UxVSRJovj8BMXnJx76m9ALUMz9GYVvqwmRdX2Ta9eu2sz++RQrby7i2/6hJtL8Rp3EyXN07t5ENntpaPXG2pLP2tIhEIs+ZlSDFRyrw5B2jEH9CAk5sy+v9zAR4u9sDjgEtIIKQpt4qMkViEPxcjePG9YI8JF3BTd2wxadB5LcHATSNo/TLrWwVpq7mxfuqbdv/C2EoH1fPSsSFJ8f5+jPPUP6ZD+SLNFZbtCaqeDWLALHI3QDkhN58k893LB/WJAVeS/zLcBGUm9/Vjda9W2QFa23sFc3nl+xY6Md1zcMQjrL+6uQ+bYaXUnb0OIAcifyqDGN9fdXUQyFic8fZfbPD4dlrHX9fdR0Fmd1CSUWY8937jsQnbDBrHOFir/EoH6UQe3ohsLChwM3tKn6y6z5c1T9nTFHOW5gHhmkc3W2x68fjFZQ3Yw5PxiCxmEa3aC6wdn7cKPbCqoI8Xj4LkI3ILCjyb92dYWpX3sr6sx6EAR4rZ2eqlFMcuyvP0f27CCdpToLX7xK5fIibq2Db3kIP0QEIUPfd+oj1dqrxLQot/SYELg+oRNdz8q7C0z/xjsP/5FgV2XIw3Ago5v++AvEjp985N+bI6MgyyiGSmo0jZ7SaczX0ZM6xfN9h2Z0/WYdv1nf+P+PZj92N0T0jvuPIft4VINVmlaVRfc2Q/pRiuoohhRHlhTkjQjuoybGouRbtOQPCQmET8MvUfIXqPmr2KITxV8RSIaGOTmAZGg4UyuouSSSrmIeGUQyNELbRYmbBG0LfSgPgcBbqyEnTOzZVYLqVmK2GVYJCbpKrG9HQHAoSbTN4wZlAuEixMNXSc2gTPiYSIacagd7vUnqWAEtbdKer3ZtfHgYik+PkZzI4bUcFv/sOjP/4XLXkidFU/bxjGyXaLgnhPbwCVIE4WbtvKT1ziyoKQMtE3ussWWn1MaptElM5NBSJq2ZykPbsB8FBzK6xtAIiTPnCO1InXS/kHUD4Vr0PTHA5A8cw8iYDDw9DMDSK/NdfxMSYikWrWyArOlR+df2tl5ZRs5msDtbdcRaoYjfbCDcPYQsJAk1lUZJJPFbDYLmw5fIofBpBVWUHjy3EOl9uXuqGZVQVQMBpNIjOFYN121thgkEAllWCAOfIHhwrM3HpRGUaFplpqT3SCsF8uoQGaUv4ieVNBRUFEnZII6WN5dYkV0VRHUNIaEICAgIhY8vPBxh0QqqNIISdX8dR1ibBNLbIRsaStJELaTxyw1kQ0dJxTY/E0GIdX2e0PUwjw5FyYmYjpKJI+vqjhYON7RZd+eIKQ+ubOmEzQ2Z8sOBK2xK/hIpxX7oGqnqrz42Zjd7rUnzTonC02OkjhXJnOrff1UCYPYnkRQZr2bRnq10N7gxjfSpfmRDhdbDY7rbeSJUU0PWFQLr4dfhXuedpEgYhQRqQu9K/pM9M0hs8OHVOQdBZ7lB826Z3IVh0if7SR/v21dVwl5xsPCCJBG6LvWXXyTcB2nLPSQvPY2aSrHy5iLWWgM1plG6us7GG98VnrCZkW+yPqiRPHue5d/4asRidm9Imk7y6AWac1s967lPf5b666/gLC3s4ZxkjKERUhefojNzl8Ybrz70J47ocNN+/eH73gMURSOVGUOEAaaZQZE14sl+RBgghEBRdcxYjnZzhWplb5pjAoEnHMr+EmV/CQmZuJwiJkeS5feIW+55wfcY+EMCAuHjC3dj0rBxwjZW2No7l4MU1Ta6y1GNZ9C2kGMGoevjLlcQfkjQ7ERdfwslhBAEDYvQcpC0nY9ngMcH1u5ux8cBU06iySbNjfKvG9argERCyQA8QrJMIq6kkZAeOdEWugHrr81QeGac9IkiR//6s4R+QPNOaTPssHEo1LiO2Z9CjWvUb67tIJLxGlE7rhLXI0OmKTuqINSkwdD3niB3IVJZ2NvYfJxqBy1tkjySJzGapXFn/aG+mFu3sVYaZM4OkhjL0vexSVa/dXertVaWSIxkGf7+U8SHektVHQZCx2ft5SkKT42SGM9x7BeeZep/fYvm3fLOiWnj+sYGUsiGSv3G2r64NQ8c0w3bLeqvvULY3n+Hmt4/gHLiNACN+Ua0ctjD4P16jdYHlzHHJ3d8rmaymOOTkS5bFyiJJObYBM7aCn6ljJrLEz92AkmWsZcWo5bkIKBz9xZKJrP1Q0nCGB6NKCgX5kCWSV24ROv6lb15z/uEBMiqAci0Wivki6fw3BZChJhmFhFGtJOPCkFIO6zTDveuQ5fMjpLOH6G+/AGutQ+9sXqHTn1m89/uYhRrdRd2L/93xHr3tjp9LJCQyGh9pNTCptEFkJHJ68MIoG3V9rVPGZmcNoQsKfv+7XY0bq6x8MUrHP9bHyN/cYQz//lnqLy/iL3SJLA9JFVBS5uYfQkSozm8hs2Vf/pVfH/rOa1eWcZvuRjFBEOfP0kYhLRmognQKMTJnBmk77kJ3IaNnu1NqrQdoRs1ZdxryDjy156h/NYcbt2OJLjiGvZ6m+oHSztDIkKw+tIUxecmifWnOPJXnyI2kqEzX0MIgdmXovDkCKkTfVirTWIDh1e/3w21q8vM/9EVjv+t5yk+N4GRT1B9fwlrtUHo+MiagpYyMfqTJEaz2Gstrv73X9sX8c3Bja5lPTKDcui6sJF02C9pRK/9hbZF6tLTNN7e6XlKuk7yiSchDLDnZ6PStItPYy8vIhsGiZOnEa6Du9a94Fw2TeLHTmAvLaD39WOOT9K+ee3Q7UIY+rSay0gbMj6e26Zavh1RDCLRaa8TBG4kn74NmhHxHbjOHioGJBljQ+nX9/ZgQCWZ3MBpRo59Gteu4dj1x8eafQ8H2H1SydNvTKLLMZBg0bpJ0y8zYBwBIKUWcMI2q840fugxbB4noWYJRMCqM0XLj7zypJrnaOIpJGQWrRsbIQuJrNaPIccJhM+idR1POGiSQZ8xQULJ4oQdluxb+MIlJqcYMo8jSyqKpNI5ABcERCVKy1+7BcDRv/YMmdMDpE70EVgeoRcgKTKKGdFGSpLE6ktTu65le7bM/BevcPTnn4k4GvpTuDULEYRoSQM1oVN6a56FL17l3D/+3j3FUUMvYOkrN8ieGyJ7ZoCBTx8jf3EkmggUCVlXWX91mubd0q44dPnNOZa+fJ2Jn7pI6kQf8dEsXtMGscU5vPSlG7h1i8m/culA1++h5+EGLH35BpIsceRnnyZ7dpD0qf7N6ysrMoqpIWlROd3Sl2/sMbm7hQMZ3faV97Hu3nlkby+0rMjwHhJCq4O9vEi6S5tx8ux5hOdTfenrhJaFmskRP3UGNV+Iupo0DVm/0X3HQuCVSoiJI+j9A8SOHMeemyXci4bJPiFEiOvu7OqzrcpDfzd89FP4bpvFuw9ffmt6nImzP0Rp8T0qK9f2MKgQ125it8s4dvPxG9wDYix+llV7CkOJk9UG8YWLBGTUPkJClu3bBCLAC11A0PBLNPwyWa2fvD6CHbQ2uRbWnTmy2gCD5jHmrWsokkoofNacaQr6KAPmURasG/QZEyiSxro7S1YbZNA8zqJ1g5HYaawgEhUdM8/sGKffdvAaNoHl7YurIuh4LH35BvWbaxSfmyB/cZj4cAY1aRDYHp3FOu25CrUPlqlcXsS/z8iFXsjc775HZ6HGwGeOkzpWREsZ+G2P+q111l+dpvTGLE65Q/NuCXMgjdhDlUTzdomr//1XGfj0MQpPjREbTKMYKl7LoT1Xo359dQcb2dZ1cJn6tbdo3F5n4JNHSUwW0JIGfsuh9MYsq9+6S/ntBZITOYY+dzJiQ+uW4AoFQceLrmmX4+wVfttl/otXqV1bofDMOPmLw8SGMlG82XJpL9RozVaofbBE+d1FhLe/GP6BjG5ng6S8Fz9tLyh6DBC0r36Au7q8i4tBkhVUM4HXOZhXsB3CD1DiCbRcHsdeAgmCRo3yV/6E0HE2iI97TwB+q4FbWid+/BRqJktj+m4kn/oRgKrFyBSO0KjM7Gl7I5YlnZugunJ9z8dYm3uL0uJlguCjqXCwHTIyIBEIHztoEYQugqjaouGXdsRUNcmkaEygShqGHI8MtCQjREgnqNP0S4TC48iGxxsS0PZrNP0yiqQybJ5Ck6ZIqwWSap60VkRCouqtoMkGmqyz5pRpB1Ua/rYytlDw1v/+D5DkiHB7P4QpsEE4c3ud1nSZud+5vCGTFX13j4D8XulXN3hNh5Wv32b15Snke78VIMIN4cWN3733f/8zkOU9jU8EIc27JVqzFaZ//Z2t8q4N0vDQD3qSlLs1i+U/v8naN6cwYzlUVafdKRF4HsILEKGgdsXmtX/wWwgEQRdFmdZclff+yZeQFCnKFdiPXlsfOj7166s0bq8z+9uXdxKZb17f4JFIfQ4WXngEonFJVkiPniIMfOqzV3BXdpPPaPE0hdMfY/mtP+26DzWTxRgeRU2mMMcmcFeW8es1tHwBfXAINZHEHD+CVy0TbIhQtq99gGzGSJ6/FHH5lku4lQqJU2fx6zVC18FdXSZ0XcyxCfRiPwDG8Cju2grC9/HKJRInTuNVygSNvcdDFdUkkR6MQgD3hQUC36FdW9wRFpAVg3iyiB7LIEkKvmdhtdZx7W3HlGTMWA4zkSeZHcGIZ4l7/RRHLm5u4nQqtGqLCBEiyxpmooARy5AfPIuiGaTyE5tLIxGGtOuL2J3KtkMo5PpPI2+LkTdKU11DGHosQyI9RLu+hCTJxJL9qJpBEHjY7Qp2p4y4j/NCkhTMRAEzUUBR76uBFYJOc41Oc29y7dtR8ZYYjp2k6ZeouEsbzQpRcjAQO1/EpJoloaS50niRAeMIaa3v3uBQJA0ZBVU2IrKdDXlwRVKRkFElnUB4hIR4wmPJvs2yfTsqlUNGlSJCHHmjOkSV1B2qxME+21S7QfghwQPUFh7421AgbP+BtRb7NlwChBcS9PD+FFkHSepaeSMCgexrTPZ/D5nMBG9d/td4zlb4S4RiJxva/QjFI5XQPQgHub698EhGV1Z1YoVhNDOFpKi012cJPYd4cQxZM7AqS3jtOomBIyi6idMoYVdXSA4eRdFjGOk+rOoyZm4II11A+B6ttRm0eAYz24+iGQ/spJINAyUWo3P7JkosjmxExf+yGUM2Y7RvXkPNZAg6LYImtG9cw6vXCNdWotioqkIYUn/tZeInTqLlC3i1KiAhyTJKMrWp5aakUlCKykb8Rp3QdfBqFQJ7b8kkWdUZPvpJ8kNnEWEQqQ4kiyiqgdWpUC/dxbUbm4ZM0xP0jz9DYegcmpFCQiIUAY3yNCszr9OqLQACWVbJFI9RGDqLmSig6UmSuTF0cysBWFm9RruxighcVD1OYegs6fwR4ukBFNUkP3iGdH4SiOTmF+9+a4fRlWWNvpFL6LE0ZjyHbma49tov4a7dr3ohkcqNc+Tcj7C+cBndTJPMjKCoBrKi0q4vszT1ErX1O5t1x/cM+uDk82h6AhH66LEsRiyD71k0q/Oszb35SEZXl2MEwiUUAaaSoBOYPSWLPOEiITFsniShZje85KgryZATDMdOklCyVL3lzRK6uJphJHaKpJKj5M4TCI+6t0pOH2Isdg5fuNS9NdpBnaZXpqiPkVLzGEoCz//orxQeJ/qKZwlDn7XSlYdv/F2KRzK6im6SHDxG4HQIPZfsxHms6gp6qoDXqZMZP0d7fZZ4YQSrukxy8BiKZpIcPEp7bRZJicIHejKL26yiJTJkx88jKWpkmOQHl6m4a6u7El5GSkXUV2h2KQtrX9+6wdbdrVIyv16l8dbuUq/WB5e7n3ciSejYuPfXBj8A2eIJBsafobp2g/WFy4RhQDo/wejJz9KuL7Jw62t4ThTDlWSV4sglBieep166S3X9W4jAJ5EZZmD8WRRFZ/bGl7DbZUToUy/fpdNcJZUbY+T4p2mUplmafmXz2J7bIgyimd/3LMrL16iV7tI38iTFkSdYm3ub6tqtja1D7M5O6r7Ad5i98SUUVWdw4nkGJp574LlqRor+0aeol+8yf/trBL5DOj/BwPizDE4+T6e1hrNxjFiyn6Ejn0DVTBZufw2nU8NMFhk++imMWJrF29+gVd8tHvowmHIkj151VxCEpLU+AhFQcudYc2Z21e92/DoL1s2oqsCpRSVyoUvNW8UTNhIydtCi5q0RCp91Z46WV0GWFDpBg5obPYcVdwkvtNGVOKEINoy8YNWZ2vSeG946/iOKJWq6xOC4ThhCuxnQP6LTqvvEEgqSDEtTDp3WR1v5WZIU+vvO0+mU/tLoPgoC16JTXsDrNBm4+FkkWcPM9uO2KkiKRiw3TLxvDEnRkFWV0CviWS1aK1OosRRGKo+ezKHFMoAgcC0Cz6W9NgOAni6SyOuohoJmKii6TLLPpLVuYyRUfDfE7fgkcgbluTbpQRO74SFyOpnhOE7TI57Xcds+q7eauJ0DcCfIMsnT54ifPoc1dbtnhUM3ZPuOgySxMvsm7XpUyN5prjAw/iyqFsdz25uqv5qeYGjyY7SbK8zf+irORmlRozqLqscZGH+W8vJV7HYlIulul7HbZRTVIAx8HLtOs9q9xTYM3E2vMZWfQIQ+Vmu95/YRBHY7KplyrIdXLCiySqtTYWnqlU2PvF1fxohlyfadRDdSm0Y3nuwjkR5gefpVqqu3CEOPdmOFZGaEoSOfwPcdAn//HAaGkkCSZMpudK0Tam4zKdb0d7cGhwTUKCHrJn6rgZ7Jkyicor04TcXZbfStoIHVpQIhJKDur8N9j5knHMruHurDHwJFlegb1vE9gapJ5Ps0Jk6ZxBIysiLRrAZ0Npn5pM24tCTJm6slEEhSVNUgRNi1XTnaXt7kIIi22a0AKUvKxj43fiNJG+X13fYbjSeZGMA0sth2FVneMj3RcbrnR6J9y9u26zIeSSaWGcBplQm/A1YSj2x0RRiJRwoRgghxmiUCz6K1fJfQd9FTeVQjTm3mfcLAR9F0MuPn0RJZVDOO77QRIqS5dAffboGA1MgJtHhmQzFVQlZl+o5GjGD5iWRkZPM6jWWL6nybZNEgNWBSWWijqDJGUiXdF6O62ObI80VKUy1UXcFMawczumFI+/YNOtN3EZ4bqV7sEVFpl9j0OO9du1AEyLISxXg3vktmopCNW7qNrOjEkhvxRSQ8p4Wi6sRT/ciKRvgRTGgJBK3qPO36EvdeDM9pYbXK9I0mkJUttqt7L1MYeFtxZRFuTkDyI9K5Nb0yWW2A48lnAUHLr1B1u5PW34NRGCAxdpzatbfIP/UpQtfBHBhl/dUvP9IYHgeEEHRaAfGUwvBk1LEYBlBZ8/G9kO2pgmL+FCNDz7JWukohf4KYmWdx+U2a7RVGh54nnR6hVLrB9NzXNq83SCQTg4wMPUs6NYqsaHhem7X1K6yuvY/nbzUgpVMjnDz2BRaX30AQMlC8gBnLI8KASvUOy6tv0+6UiMJgGn3Fs/QXz5GI9xMzc5hGhnzuxOb+SuXrTM99fWeSVgiSiUH6Jj5LKjmMIms4Tp3F5TcolW9sGnwkGSORo//Y85Sm38Lt1PBdC1WPbIyix0AIAt9FVnUCz0bRTFTNQAiB57QQwYdLZvVIT3boe7itKoFrIQKfTmmB9uoM6bEzZMbPYtfXaa1MoSfzZMbP4rZqNBZv4rZrpEdOEXoenfICkqyQ6BsjDHxay3ewyksk+icIXBu7ukpr3aZ4JIlVd2lXXYyESqfmEvoCt+PjtFUaKxaJvIGiySiKRLNsE8vorN1u0io5SDL4zsGrDITnbcrF7wf18gzZ/lP0jV6itPgeQoQkMsPoRpJS6S6Bt+XNmYk8iqLTP/YMxeGLu/blOe2I4Ogjym0aBj6u29rhtQgRIkJ/w1vZGrfVLmO1SuQGTtGqLeDYDcx4jnR+IipNs/aeqNwxBnxmO7tFRx8ESZaRVJXkxEncyhqtmZsUn/3eRzr+44JjCW68s62N/AHNI7KskkwOoSg67c46MRMmxz5Ds70UJTatKoMDl6jVZyhXo/BSKjXM6RM/gSzJ1OtzeH4H08wxMfYpEvE+pma+uml4ZVkhFsszNvJCtELprNFqr5KI9zM89AyGkebu9JewnTogcN0m1dpdHKfBQN8FWp1V1ktXN8fb7pQI70+yygrHj3w/ll2lUr2DqhhkM5OcPP4FfN+mUov4sBXVIDdylnT/MQA6tWVqi9cYvfD9LN/8Fv3HP4Znt2iVZolnBqgt36TvyDMouglINFbvUFu6jggDZE2mcCJHor97Q8jK5XXs2sFpLh/J6AauRXPx1ua/K7ffAqB887Ud21Vuv/nAfwO0V6d3/nsjvHAP06/3Ji5prFg0Vq1vW+fSXlBdvU46P8HQ5MfJ5I8QBC66maZemmJl9r54siQTBC6V1evUS7tJ1gE6jVXCD3lm3it6LVm7odNYYWXuDcZPfo4j538U166janHC0Gf+9lac+8NAYFvIqobZP0L9xruRx72P1cy3BQ955nUtycrqu8wufItC7iTnTv/H1Bpz3Jn6M3Q9yRNn/xrJ5CDl6i0URWds+ONoaowbt3+fSvUu97zUybFPMzz4DM3WMksrb23uX5F1VNVgevZlVtffJwx9DCPDyaM/QjY9QTzej+3UCUOfam2Kam2KXPYYhdwJWq1lFpYe3DZv6CmqtSlu3f1jPK8NSAz0XeDE0R+ir3hu0+gGnsX61FvEcyMsXf0qnh0lpJ12FTPVh6KZOK0yycIYdmOdWLoPWdWZe/eLmKkig6c+RXNtCt/tYKR0Lv2NMxz/vu48un/4D/6cxTf3HlrshY8oJfc+cMgGV0IiQ540eTQMfDyWmcHDI0OeDHkEghLLWLRJkSVFDhkZGYVV5rHZ8kh8z8K16zh2ncrqDXzfwnc7tGqLONbOxJW3IfluNddYm3uL72aEoYdr1fF9h9r6Hex2Cd93sFrrWM1VRPjhGT23UaFx+32QFOzyCpIsU7/x9od2/McBIULanRK+b9PplBAiwLLKuF5ro/wNNDXy6BLxflLJIZqtJaobBheie7Sy9h7Dg8+SyxxhrXQV399KRDZby5QqNzdDFK7botaYoVg4tbnvRx4/gqWVtzcMbvRJpXaXIPSImfmH/t5qRgY29B0C3yFZnKC5Fq2+PbuJCAPcdg1Fi+0q43zc+M43uocMBZVBxllhnjwDqKgIBDHi5OmnwiomCQYZZ47bpMiRo48lpkmSoY8h5tnyUjUjSX7wHJWV6yxNvfRAqsb2RrY+kR6KYlLu3pRsxUaSZHvM9IHbhxsJlm+jDIas6OQHzhD4LsvTr+yagD5MCN/DWtlitROw49/fiQhDjzCMwmGh8AlCb7M2NtyovLmXoIqZeVTVpNVa2dXS6nkdbKeKYaQx9NSm0RUixHHq24xi9Jnv2xvJuoOR5we+g2Xv7MT0fRuE2JGE2zhyVIu+rdbbbqyTGzlHqzQDoUDVYwS+g9upkypOIisaZqYf323vCm08bjzSWzd4qY9P/JdPES/sns1aq22+9f98k/Lt2kHHtgOyKnHmJ47z5N881/X76a/P8/a/+QC7frAEk4SEioaDhUNnozIzJEGaPoYxiCMjE+BvaGYJ2tSpsIaPxyDjO/cnqyiqgRnPE0v1b8Rwo4ck8N0dGXq7U2Vt/m2KwxcZOfYp1ubfwfcsJElG05OYiXwU/7yPMMWxavi+Qyo/QTw1gOdGy7Ew8LpWANjtdRTVIJ0/QqM8TeBHL2PgOzsSfrKsbnBASCiKBlIUQ1O1GEIEhKG/q+Fhz9dZkpFVHd1IEU8NbH5+Lwbse9ZjIwO/H7JukDp2juT4CcrvvoTfamD2j9Ca2WoLN9I6X/jFzxLLPSohvCDcaBpw2z523aG91qE+16Q6Xac+38RpuHgdn+AQeEiiTL/Y/sE2g7rTsCqKFjXhBLufFYHADxxUxUDeprosREgQ3v+uRdSeh5FzCAK3y/3fOIP79h8GHq31KUaf+AFa5Tkqs+/hdmpIkkSrPIeRyGPVVghcG7dTx8oMMPnsTxEGHqWZdwn2wj9yiHgko6saCon+OMn++K7vhBDI2uOQiJHQExqpoUTXb82McSis8gEBLeoc5SwtGqyzSIBPSECVde7wASHhpkLuBgEicO+R2DmGe6VZhaFz5PpPbnLOBp5DvXSX5elXaDei7HoYuCze+SaSJFMcvkjfyCV8z0ZRdWRFo1VbwGqX4T6ja7fLlBbfY3DyY5z7+N/Bc5qARGnpfZamXtphSAGalXkqqzfID5wmUzxK4FkEgcfC7a9RXb25uV3f6JOk8xMoqkk8PYgkKQwf+wyF4ScIfIfKynWqq9f3xRuweV1EiNUqkR84zcmn/+rmCyZCH7tdYXn6VcrLH3wohtfIDxArDhM4NrJuImiQPn5+h9GVZIlEf5xE8WDL5l5wWx6lW1VmX1pk6e3VTSN8uOGz7jvzAxcRBqjK7glFQkJVDMLQ3/ScPwzsh0RGhAHrU2+xPrUzJHf31V8HoFNdorqwVRe8dvd1KvVbCM8nqB0e1cBe8W1ZX8qqhCRB4AnMtIrd+OgkLSJPV6dNE5sOOgY2HTq0yCMYYgIfDxuLJrUH7kszkgwffQFF1Vm88+IGPaNAkmTMRD6qUJAkZq79yWYowbUbzF7/MtXVWySzI6h6nNB3cew6reo8dqu7LHS0RK+RyIwgywqBb9OszG56opIWLb2E5+J7HWau/gm5gdOYiUg92LXrWM0Ssm6gZrL4G+3TQeARhD5u6Q61UsTfq8TiGIPjSJvcDQK7XWZ19o3NEMkWBK36IkvTr2y2McuKTnH4PNm+41RWrtNprkZxQUlC1WLkB04zfvr76HTW8PSA0LYJGr1fDknXUfN5/FJpX+V8WzuQ8O2tUI5qxhGP0OJ+EOhJjeGn+hm61Ed9ocnsNxeZ+dYia1fL+F14Bg4TllXG8y1SyaHN+t570LQ4ppGjWr+L4x5U8y7yth+3bp862IeazyCCEHduCdnQUQeLSKqKO7eEkogRf/YiYbuDfe0O3ur6Y1GI6Dm+D+1IG1B0mZGzGZBg/W6LwVMplm806D+WQghBq+IQS2m0Sg6NtQ9fhTRGAhA4WMgoDDKOjYVNh2VmSJDZJD4RCOpUN1tHbSzW2WLyzxSO0Tf6FAu3v87KzM7KDlWLYcYLxJJFDDOzI34b+DbVtRtU13qwnnVBGHiUFt+jtPhe1+/NoZFIcG9+BgDHqrIys5ugXdaNzeXb6tzuahMAvX+ITO55yivbyn7qS0z36CCrrd+mtr7VCWjGcwxMPI9r1Xc0gdyD77YZPfFZkn0TdPIh3tr6A40ucKBVjt9uAoLE6DG0dA4RBHQWD0cqar+QZInseJrUzyQZeW6QO1+a5frv3zmUUqVeaHfWaTQXyOeOk88ep1y9zb3qhcGBSyBBtTazI4n2KPA2QkbxWBFF1ggek+ccf/Isoe3gr5aRTR3j2ARSzIQgJHbxDN7sIrJpENrOhkbjh0ve/KEbXREIZEWK6mfdgHhWR4+rDJ5KEYaCITlN4IY0Cy6NtcOXyngY4iRxcVlmFg2DLAVklKjwnwYtdr787W3/9nCosuWJmok8qmZuhg+2Q8BGY8n+llL7hixjDo+RefYFJFnGOXKC2hsvocTixI+fgjBEzeSov/kyoe+TPPsEimHSvHqZ0LaQNI340ZOYo+NIkkztrVc3IyiyYZI8cwF7aR6/USd59iJaNkfo2DTef3uTbOh+RBNOjnppCq9LsjAKV0QxSTkWI37+PLHjx2l/8AGhZZF44okN7mQb+/ZtYidPoiST+JUqSjpN7NQpZNMkdB06H3yANjCIPjyEVijSuXEd69pOdjWvVaNx5wpeq46smXiNCp3F6V3j+jChaDLFkzmSA3ESfTHe+Jfv4bYej5EKQ4/5xVdJJgY4fvQH6WucxfPamLE82fQEpfIN1st7oAB9CCyrTLuzRi57lNMnfgJrozOt2VpivXxtW6PGweDOLmIcHUdkXfxyFX1yBElVCToWBCFWuYq3vBb9WTx4Cdh+8aEb3TAQOO2osyyeNUgPmKT7Tdx2JEcThlFctL764Qa376FOhSxFTnEJkKhRwuHRxuLadQLfoTB4FqtV2qgykDZithdIF45QWnp/szX2HpRkGnNwmKDTxllf5Z5wnzk0gt9s4NWrSLIS8VRsiADea/m89zcQddqEIe7aCs7yAiIMaV19j9Cx0XIFjL4BGlcu0759A7/dBgTuyiLxY6c2SYS0Qj/xyePU3noF4XsErSay3ocST5C++DR+s4lXq6ImEphDIzSvvRcRA3V6V14EvoNrN0jnx0mkB7Fa61HYRZZJpIboH32KwHdp1ebRRo7jLi7ir68TP3cO69YttL4+6i++GPEx2zbu6gqJ/gtIqopsmmh9fTTfeANjdITYyZPIiSTe+jqyYSLsLh6jEHiNKm69HF3DMHwo/8d2hKEgsP2ezFqR7JwEctTYIitRt6WkSA9NOpkZg1M/dhRJkXj9F9/DbXZJFEsykhI9D4RRV5/rtTaNmBABrtci2Bbbd732FtOXJNG21rh+6/cYGX6eTGoURTVw3Raz899idf39HVUKYRjges3NBOx2BKGP4zS7xn+D0OXu9FcYH/0E2cwkqdQwnm/tqFIQCDzfwvPaXeP5ntfG8x5c1ePOLhLUW5hnjqKNDBJUGwTNFt78CqFlR+xqgBQzkTTtkZqeDoJvS0x35WYDJAkRCl779VkIBYtXa5vfS5K0YXw/XEiaiuPb3NFuIKkqYaez6YVKMRNh2SBLyPEYoeU8lE+3snqDZHaMwvATZPtO4jpNJCQ0M4Wi6NTWbrE89fKuh1eJJ0BWUNNZlEQK2YzhrCyCAElRMYfG0PJF/FolMjS6jt9uoSZTuJUSeqE/okacuUNoW4SuQ+jYiCDYVEUG8FstvNIawTYRz8CytuKikoRimgjfw6uUtrgXJAm92Edgd3DWVhC+h9+o07x6mfiRE4jAp/7uGz09XatdYmXmDYaPvsDJp34G26ohAh9VT6AZCQLPYu7Gl3CtBqrnETSbeOvrJJ56MsoFdNr45S0ehdCyd8Ry/VqNoF4jLOSRE0nwfcyJcYJOB2dxt5Cjni1iFodo3PkAEfoosQSpY+eoXXnjgff3Hjoli6u/fYvly93j7RAln7W4hp5QSfTFyR5JkxlNkeiPE8sZKHrvWlEtpnLiBybplG3e+9Vr+PbO504v9KEVioTtNk5plaqzyFtX/g0oMrJhYocd3rr+bwl9FzkWR5Jl3rvz69FEp2lomRx+u4UVNplrvYs39xXUTI6g0yb0XCRNQVbMiJAqCGi0lnj97f+pKxfH2voV1kvXNg2mJkedX94G0ZBll7l194/ZnnDezqfgeR1u3f0ikSOx8zyFCHnn/V/igaEAVcU8fwqtv0Douji3pwlyGcwzx9D6i9i3Z3BuTROUa5injoAQ2Nc/XG7sb4vRjVaPG8ZMMRD4yJpBYLeRFBU1kcFtVDjUOIsEcjKBrOsIPyDsdJBjMUQYIhs6QcdCG+jDW1lDzqRRi3nsm3dR0pEmk3FkDHdmgdC2MY4dwZ2ZRwQBsmkQNFsIZ7cH4rsdZq//GY3KbJQUU82Ib6IW8RPU1u90LemSJAk9V8CtlqPluuvuoLDUCn2AQEmmIQxQkilCz0NSddRUBjWZjkIDytaLHDoOet8A8WMnsWbvxSt3CoDKZgxzdAKtUMS0xgjaLYJWMyL8OXWOMAiwFyOCHGt+huaVd4kfOUFgdfDrNWTDwF6aJ3H8NEo80dPohoHH2sI7WK010oUjGLEsSBKd5ip2u0y9PIXVKiHHY0iahj40hJJK4q2tb0h2b7tWmoY+PIyaz6MNDcI9Se9728gyqApeuYJfKUdhiPtEVNV4EiPfv2NSiQ+O79noBk5A5U6d5Xf2Fw4zswYjzw4w8uwgI08PkB5NdhWClCQJI61z4gcmWL9aYu7V5R3XQInHkWQFJZ0mkcluLKXbiMDHbzZQ01kIw80JGiQkVcVv1glsCyUWJ/RcZN3AHJ/Ea9ZQkkkCz8Hs60cAaiKJCAJE4GPNThG6vWLMW+Q1EhI5cwRJUlhtb3WwPqwi5UHfP6jOHQDfp/P65R0fuc027tzOfIN97Tb2tdt8O/BYjK6eLhArxvDtNrKqgSCaZXWTwG4j6yaEIb7TIVYcwW2UiQ9OYpeX8Dst4gPj+J0maiKNrKgbBviA8R5ZQesvoubziCDAW1lDyWYQjo2az+Mur6KkU/hr5c0lqByPoY8MIsdMJNNEyWeROp3Iq5JlzBNHNjKii3hL3XlfA9+htHiZ0uLlPQ/Vb9ZpXn8fWdPxaxUEELRbqKk0IvAJ5qdBURGei2zG8Bo1AquD3GwAgqDVJHTdHZy/1vxMFCM141G2vlmnM31nh1qGJMuIwMdeWkB4HpKs4NUqtK5/gJYvomxsE7SatG9ei0Q6JTmiuZRlZDMiF2ndvIpfe3Czgwh9GpWZB6pdCM/Dnp5BTadAUelcv47wfazb214WSYIgwJmdAwFBq4UzM4PwfdzVNXR5g1XL91FzefTRURrfeHHncQI/qtpIpPHbTYxc/wNVRA4Lds3h7lfmmHt5ieGnBzjz48cYf2G4p9ebGU8x+elR1q5XsKvbjZ6EbBiEtoWSShE6dhTTtm1EGKKYJkGng5JIIkkSfqOOkkzhVcpohT6UWAw1lcGrlAidjQaEWBzN95HjCYJOCyWRwl6cRYknQdl7B5ckKaT1fhRULL9Ow10lZ46iySaW38AOWphKkoa7hqmk0GSdQPgk9AIIqNrzyJJC2hhAkXSqziK2/+GXeR0mHovRjRWG4O4UydETeK1aFEoIArREms7aPEamSOh7+E4nkuVpVqNldyKD8P1IatyMkxyNlqqh7+J3agcak6QoqIUCkqYSVNqYJ4/hzM6jFQtIuoaSjKPkMqjFHCgyaj6HcD3kmImcjGqD5ZiJJMuo+SzCcZB1ndD3H64ftU9sX+7v+Lz96CU7fr1Kq75lCAMvis/ef9zW1d3VD/bCLPbCTgrIzlTkudhzWwmn5ns7W2clTUXJ5/BXt5bdm2GaPUB4Hu7cHPebP6e1xcsgXBfr5k26wS9FIZH4+fMouh4ZnOpuvTm3XsVvNxn81BdAkgg9h9qNd/Y0xsOA1/GZfWmR2mwDWZUZ/+Rw11ivrMiMPDdI7kszLFe3rqm7voqzthxNeitLUSOE70WG2HWx5mcjjx9QEglkM4a1MIvfbiGCYDOuGboO1vwswvdxVhajsFK9CrKMX68SWBbShsrKXiFt/OeGFllzGFXWSWhFGu4KKb1IQuRRZZ22VyGuZTDV1GbiuumugSSTNgbRZB03sBmIH2OucfnxJp8fMx6L0ZX1GHoqB2GIrBmAROBHyx0tmUUEPopuoiUyaKksXqeJrOkoIoYSS6AnMrjxFKHnEro2wSGIVwrPw7oWvZzCdXEXlxGWhb9ejh7SIMBdXNkQ2ZTwS9VIomdpJfKmJAnCEOEHuEsrCM/DW4oyn6Hz4Ze2fZSgjQ7hV2oY41GWOLRthOeh5rJIhr4RJlAJm22MU8ewr95EyWZAlnBnFxH2/nlz9wq/WqX9zjtIqholartMaIHVonb1TVqzN5EUNSKqb37IbckC6nNNXvvFyyQG4hRP5O7vswEgPZqk70yetatlAjfcGH90TuF9aiZBeydpUNBpoyZTUQy+2QAh8O/z6L1KZMzDSq9nen9J5VAEtNwSNWeRtN5HWh+g5Veo2AuYapqYmsLfSLpJG6WXVWeRvtgRsuYIXschbfRjKAkcv71Lbuk7EY/F6PqdBs3FO4htN/RedvqelDhSxAHgNsqIMMRan4+MnxDY5WVEGGKXojiMCIOusa59QQjC1rY+cTe60cLbuonbM9tiw5B2m08f9N1fRKiFHAQh+tgw7vIacjyGkh/GuTuDMTmGHI+h5jLY1TrCspFUFX1siNBy8FdLBI/R6BKGBM2HrBA2JtXQ95GCIGrSiKfwW49GL3kQ1KYbfPCbN/nUf/UMamz36ykrMoMX+7n9Z7NYlf1dN+E6eJVIAfnDgyAg2AyXW0EDU0mS0HLIkozlNzDVFDE1Q0zLIESAE7RZ7dylGBsnqRXoeFU6Xo2Gs4oXOt/RXi48JqPbXp4mdHbPiPcURsWOz/wd3+3Y7jv72v6FgV+uoaSTBO0OSiqBX64SLi6jxGOEzVYUvy9XCS07Sjr6Pu78MqHtPLC07MOCmkiTPfM0aiK18YmEW1uncvnlD30soR+y/M4aq1dKjDw72HWb/PEMWlzF2h0p2QM+vJdKALbfisrwCGl5ZRruOjljmGJsko5XpWIv0Bc/StYYIgg9nKBFWh8goeUIRUjFXkCVdfLmGIXYBA13HSf48Gg/Hwcei9H9TpDM2A/0lEZ6JEVyMI6ZMdATKoquIMkSgRviWxsEJiWL+lxz3x7IYUI1FHLHMuSPZjFzBpIMbtOjudymfLtKp2w/9L0zUjr541myk2mMtI4EuG2P9nqH8q0azZX2jn14C0t4gJxKIptGFLLpAftaFAt+0Db3IGsyyf44qaEEsYKJmTFQTQVZlRGBwHcD3KZHp2LRXGrTWmnvKqfaC7R0DjWRovL+q5urs28nZ7FVsVm/XmHkmcGuIYbkQALVeDyFR7IiEe+LkRpKEi+YmFkDNaaiqDJCiIiwp+nSKds0l9s0l9sPaFMWNNyt5oO1TsS+t3xfImylvTsmX7K2cgVeaLH4kFWHYiikR5KkhxPECjH0pIaqK5GCsBPgNF3a6xa12QbttU6PJezeJiRJ1aNoY+Bj5AfwmrWuTmYv/CW1Yw8oukz+WJaRZwfoO50nOZQgljfRkxqaqSJrkS5U6If4ToDb8rDrDq3lNqVbVRbeWKF0o/JIRuB+JPpiXPjZ08TyxuZnnXWLd//dtR1dSsnBBOd/+gTDTw+QGklipDQkScLreHTKNtXpOne+PMvMiwub8cDtkFWJ/vNFTn3hKP1no3PW4xGzlG/7WFWH2myDmRcXuPOlWdz2zqLysNUmbB/cczUyOiPPDDD63CDp0RTxgomRNtCTGoouIysyIhQEfojX8XHqDp2SRWOpxdqVMnOvLtO6b2J4EITv4bcb+O3mvl6exwW37VGbbeA7Pqq5+xVVTQU9tTcaz71CS2iMPDvA6LODZMZSxIsxjIweGS9Dia65EISb19zdnOzWrpaZe2WJxmLrQ4+5aQmN0ecGGH9hmOxEmnghGrcWU1FUZXOi8NrR+9lcarN+o8Lcy0uUblZ2vAdhEJ3fw2AWBqLyO83AyPURuA61q3srL4S/NLq7IG+0X575yeMMXuwj0RdDT2g9e/sVXUHRFYyUTmooQfFUjtHnBznxg5OsflDi+u/fZe1qqauR2yv0lM7Rz42RGU1tflZfaHL3q/OUbkRrzORQgk/+46cZfX4QLb7zhTTSBkbaIDuRpv9ckeRAgvf//Q3EtgYUWZOZ/PQoz/y9C2THU7vKlvSkjp7Uo0TO6Ty5Ixku/8o12uvbjJQQjx4TksBMG4y/MMyJH5okfyxLLG+i9GCsk+Soq0szVeJ5k9yRDEN+yOSnRznz48e5/eUZbv7RFE5zL0xdEsnJ0yTGTuBbUTjEqaxRevNrj3YuB4UAp+nitr2uRleSJPSkdnDKACla1Yx9bIgTP3KEwvEs8bzZs2RNQkJWZFRDJZYzyU6mCS+GTHxyhFM/epS7X5nj1p9M0yk/fjUXRZMZvNTHub9ygv7zRRLFWPcaZzaek5hKvBgjdzTD8NMDHPv8OAuvL3Plt29Tn2tuesSBE2yQUvXuFJQNEzWeQs/kaS9MkRg7vq+x/6XRvQcJ4nmTMz9xnHM/fQIza26woe2PSEWSJLS4RmZcJT2SZPwTw1z73Ttc+e1bh/owanGN7GSa0o0KZs7g4//oyQfWeMI9esIYl/7GGeyaza0/nUEE0YBGnx/kk//108QLsQeesyRJxAomZ37yOEII3vnlqwcmY1F0mYELRS7+/BmGnxpAi0ccvt2W1g+CrMrEctGSOHskzfgLw7z3azdYfmf1gSsOt7rOyjf+gIiqM5o4Pow63QfBt4MHsospuoIkPfocF13zPi787ClGnhlAi2tI8v7192RFxswaGBmd3JEMk58Z5f1fv8HCa8uPjSsi3hfjzI8f4+xPHidejO2pnfoeovdTJTuZJj2SZOTZQV7/ny4z/9oygRPgWT5dGFp3ILA7pI+ew1qdx62uExsY3df4/9LoEsWxCidzPPm3zjH5qZEHGq69QpIkJFUilje59AtnyZ/I8s4vXaF0s7qnJczDoMVUshNpFEPh3H90grFPDO1p3JIUjenCz5yifLtG6WaV9GiS5/6Tiw81uNv3ocVUTv7QEdavV7jz5VlEIKKHX1VQTBW/4yJ6cRFsg2oqHPu+CZ77Ty6SHNjNz/wokKSIe3ns+SGy42ne//Ubkdfb6G5IQ98lsDuoyQxeZY3Q9za7/75dEKF4oEEVQfjIBlc1FY59/wTP/f0nSA5256feL+49E0OX+shOpLj2e3e4+tu3o/jpISIxEOfp/815zvz4sQNVNEmShKIr5I5m+Mx/+zyv/vN3mf7aPG7LI/CCB8bMnfIq5fdeRjFiaJk87X2SIz1eYsvvAEiKxMCFIh/7zy8x+enRPRvcMAgj47mHB1/RZCZeGOZj/+hJBi/1IasHJ1tXdIX0cIK+M3kmPz2KkdiSKhGhiEIHPcYmSRKZiTST3zOKntQ4+5PHyU2mdxncMLhPfeA+xPImk58Z3VRT0FIm2TMD5C8MoyWNnr+7B9VUOP4Dkzz/Dy/2VGC9H0IIwiDcM3F6aijBk79wlrM/dRzV7H5vtWSGzOknKT79GYzCAIoZJ3Py0p72/7igGgqq0ftZ9B6RY1c1FU780BGe/4eX9mxw78Vyw6D3M7UdsZzJEz93mkt/4wyx/OFNXlpc5Ym/eorTP3Z0TwY3elYe/Lzcc0Ke+bsXmPzMKIEbPDQUaBQGSB87T3xokvjgOHrm4Zpt2/EX3tPNTaZ5+u9eYPjpgZ43UghBp2SxeqVMfa5Bp2zjWz5CRC9HLGeSHklSPJMnM5ZCVrp0E6kyQ5f6eObvXuDVf/4u69fLBwo1yIpEZiLF6R8/Ru5oJqKeXO4w9/IStdkGoReSGklw9LPjpEeSu36vxzUGzhc58j1jjH18GGXjBbcbDsvvrlO6XsGq2hgZg9HnBhl8oth1Qhp+aoBEX4xOyYr4LXSFwPURD/HmZVXmyPeO8eTfPEe8r7uHLYTAbXlU7tapTNVor3Rwmi6BH6LqMlpci677qTy5o5meRipeiPHEz56mtdrh9p/O7PpeTaaRNQOnug6yghAhZrF7udaHBdVUu9bpQlRW5j6CqoSkSBz57BhP/sJZ4sXexjBwA2qzDUo3qzQWWlg1m9API282oZHoi1M4kaV4KoeZ6T65aqbK6R87htfxefuXrhA4B0woS3D8+yc49YUHG9zADajcrbN6pURzsYXb9qL3VFcwcwa5I2n6zhRIDSU28zSSJJEeSXLx589gVeyHcjOrsSReq05z6upmb8F+8Bfa6OopnSf/5rmeBlcIgVN3ufUn00x9fZ7Wchun6eJZ/mYsVJJBNVT0lE68aDLy7CBnf+r4pqHbbkw2De/fOc83/rvXscoHKy0rHM+Rm8ygmgrVqTqv/Y/vsnq1jNNwEaFAT2qsvF/ik//4aRID8Z2GTYLi6TzJwcSmBFJ7rcO7v3KNmRcXsMo2vhOg6ApzLy1y6RfOcuxz47uuUyxvUDiRpXSrGoUYZAkjG6PxEE9k8FKRJ37uNJnR5C6DK4QgcANmXlzg9pdmqc82sWsObseL9MMEIIOiRln8eMGk/2yB0z92jL6zha6x+FjB5OLPn6F0s0p16r7yIwGh52xSYuqZwmb9+LcDsiYT74uhxbu/nlbNwbP2b8SGn+rn4s+dJj2y+5pDZMzXr1e48Yd3WbtaplOxcZvujni4rEY5CzNrkB1Pc/SzY0x+zyhGOlppbd+vFo8Mb3O5zfU/uAM95mE1niJwrE3Sq24YOF/kzE8cx8waXZ8XBFSm6lz7ndssX16nvdbBbbmE/sZ7KoFiqpgZneRAgtHnBjn1o0dJDUfPviRLFE5kCQPRM3m7ebwwJD44jqwbhJ5L0GnRmu3eit71fPe85XcZJBlO/+gRjn52rOtFDryAlcvrvPO/XGP1/XW8TveXUITRUs+zfNprHSp3asy/uswTP3eao58d20oKbUBWZcZeGObCz5zirf/5gwPFd7UNT6hTsnj1n7/L/GvLm5MBRPW5cy8v8W5fnI//o0u7MuGxnEEsa4AUaXS9/ctXufEHd3YsrwI3oHSrypXfvEV2PE3xdG7H+UiSxMD5Ine+PIsa1whsn9Lb83jN3sm1zHiKJ372NMVTuV1ehQgF7bUOb/yr95l5cQG35e2osthEGI3NKgdYZZvqVIOF11c4/x+f5PSPHcPI6LvGmT+W5cm/eY6X/ulbO3hp3VoJs3+EzKlLxEeOEDgWpbe+8eCL/xgRyxr0ncohK91f/upUHc/aX5IqO5Hiws+conBy9zUH8Doe137vDpd/5Tp2zen5XIa+wGm4OA2X+nyTpXfXmPr6PM/+/SeifW9bbEiSRLxgcv5nTtJabTN/HzsagKybpI+e34irWzRndpOlmxmDUz96lOLpfNexh17I3MtLvP4v3qM+3yTskksQAnzLp2X5tFY6G2Vjizzz959g9PlBFC2q/94lNNwFbr1MZ3UOWdGQkPbdl/AXNqabO5Lhws+c3lxWb0fgBsy9vMRL//QtFl5b7mlwuyFwQ8q3qrz6z9/h2u/cxmvvfjkUVebMTx5n6Mm+A50DREbq5h9Ps/Le+g6DuzkeJ2DxjRVWr5R2fSdJEpIceYUr761z4w/vdo9nCVi9UqJ0s9L1GLljGWRVJvRC4sMZBr/nBHqm+/JVMRSOfX6ciU+N7DIqQgiqM3Ve/O9e59YfT2967HtB6Ic0l9u8/i/e48pv38LvspxVNJnR5wY4+r2jbJfpCuwO1Q9eY/6Pf5Xlb/wBS1/+Ley13by7HwqkaFIauNj92RBCsHa1vK/KANVUOPq57tccovK0y792nTf+5ft0StbeHQEBXttj9luLfOOfvEbp5u4WOUmWKBzPcvanTpAa3h3mCl2b1txNmtNX6Sx3SUhJMPxMPxOfHO7pgS69s8a3/t9vUp2qdzW43RA4AWvXKrz4/3idpbf3qVAjQhQjjtk3jJHv3xCp3TsO3dPV4xrHPj9O/7nCoe5XVmQGnigeyr4UXebUjx4l3md2XapUZxpc/tXrVO4+eu+9XXX44DdukhpJcuQzo7uW5WZa58xPHmf9RgW3+eilNXbNYfWD9Qe+hK21DmvXKgw/PdCzOuHuV2YJAwk9nUfWDdxGldDdCn+IQFC5W8ezfIyUvuO3ycEoPiaEwF5vorT0nsYyNZjg1I8e7fryd0o2V37zFkvvrO3Z2N6P0Au59ju3GXqyn5FnBnZ9Hy/EGPv4MHOvLtPZqDGWdQPFiOM1qwSdb2+LqRZTOfLZMdJdDBREK5L16+V9JdJSw0lO/vCRriG0wAuY+toc13/3zoEEMMu3a7z9S1f49P/xWRJ9O6tQJEli+Kl+Rp8b5OYXp3YZRlnT0VJ5QNBevLvjOzNtMPz0AIkuyuMA7ZLF5V+9r158H2ivW7z7v1ylcCJLvLC3ZK6e6yew26y8+CpqIkXu/PO096Gpd+hG18waPPkL5w57t/tCfjRGIqNSmrOwmrsfpHsF0t0SQ4ETcPtPZ1h9f7dnuF+0Vjtc+c2bDF3sI36fdLesygycLzL8ZD8z33x0r6qx2KS1+uCyHK/j0Vho4dvBZkhiO5ymy9q1CrJmoGeK+D3ias3lNr4TYKR2fq4nNdSYiu8TLR97GUwJxj4+RGYsteurMAhZenuV6W8sHLiLr1O2ufEHdxl+qn/XclSSJfrO5imezDG38aLqmQKxgTGqV14/0HEPA6PPD3L88xM9v1+/VqY61dhzEk1SJEaeHSQ7ke76fXWqzq0/nqZdOlgnnggFS2+vcufLs1z4q6d2TapmxmDyM6MsvLZMc3nn8yUpKrKq43d28+QmB+OMPDvY3VkQMP21edavVg6UlF6/XmHqa/Oc/+mTe9pehAFqPEVsYGyTAH4/+K4LL0gSjJ5NMXA8SWGs+8w1/NQAmdFU1xvZWGpz+0szj+xp3Y+V90ssvNld/C7RH2P0+cEHlgY9DK016z5C6y4QYFXtnnWq1ZkGTsNFkmTUWBI9lY/I5++DVbEJ3d0GUZYljJSOW7dpTpex15rdt1Nljn5uvOt175RsZr61GFVBHBAiFKxeKVGd6U52nRpMUDiZ25x0JUVFMWObKsjfLgxcKPLU3z5PrNA9NON1PObfWKGxuHdeZVmVOfa5sa6xUN/xmX99hbVrB6ukuQe3FYUaarPdxzf8ZB/ZyfSuxoPQdemszuJUdy7zJUUidzTTdZIGaK9H1Tq+5XHsuMozz2ucOauSzUmcOKly/KTKs89rHD+h8tQzGqfPqJw8pZLJSgwNb5k+tx3lPqzq3hLbTmkZp7KO2T+MrMdo3Lmyp9/dw3ed0RVAs+RixNWuxePxvhgDF4o9e9envz6PVT68/vvQC7n1x1OEQTeuA5m+swVyx7KPvH+rbGM3Ht4R5jY93FZ3o1ufqUftj4EfiV0qStdWJ6e5lQ3eAUnCzJpkTvSROzdE7uJI1zrdgfOF6KW7D0IIGgtNFl7brZr8qLBrDsvvdo/VyapM/5n8pnHzOy2UWIK+5z9P5szTZE4/RWL8xKGN5UFQdJlEf4xzP32Cz/w3z0XJxW7lc6Fg6d01pr82v6+W8uLJHLljma7fNZei5Ja/x0oISdMerBohoHSzytLbq12dFi2hMfHJkV0JXTWRRo0lkNT7PjcVhp/q7x7LFbB6pUxlqoamCj72gs74uMqRYyqnz2qMjit84pM6Y+Mq5y5oyJLEykpILidz7rxGPC7t2FdtpsHa1YeTMCFJBLZFe+Eu9Vvv0Z67hZbOPvx3289rX1t/B0DVZBCC269XuiYEsuMpckd2NwLARgLtlaXuhuUAKN+pUZ1uUDie3fG5JElkJ9Lkj2ceqW5XhAK74ewp0efbfs9le3OlQ+AFSLKOrOlIsoKs6bv4qn076CkYKsmC5nSFznIDLanjNnZ7DSPPDqJ3KYMK3JDVK+UDtxNvh2f5lG9Xe/bR545miGUNWsuRCrK/ocqhxqLY4WGXjEmyhKzJKJqMakYilbG8yfBT/Rz53jHyx7MoGyRK90MIQW22wdUNnoD9YOSZAbQu/A0iFNTnm3szNACKQvK5Z3GXlnCmZ3pu5jRc1q6WOfrZsV0xUkmSIm6QX1Z3xI+9Vg09lUPRY3TsLYUS1VAZuNA9jxMGIaUbFdprFjIwfdcnDOHuHZ+PfULng/c8bCt6VjsdQb0W0mqGrKwGfPp7DF5/dacD0i5ZlG5WGf/EcM86XUlRMQqDhJ6DWRiKZJ40jdjAOO25veutfdcZXc2U6T+WpLHmUF/d/RKnhpKkhronKWpzTdpd4qOJWD+aFsd26thOlZiZww/cHbLU90OSZOKxIo7bxLd8Vj9Y32V0AYy0TnYijWaq++4yCtwAr+3vyViHQdjV24aNsEEgAI/QtVGMOKG/OzEXdeB1P5gsy8iGQmI0i5Y28douwbayJlmT6TuT7xlHX7t68Bj6jrF6Ie3VDoHbvaUzOZhA30gIeo0q5Xe++cjH0hMao88PYmb1rt9LioSsKugJDSOjkyjESI0kyR/NbNa3PghW1eHKb99m7uWlh26788DQd66ArO2+5r4bUL5d26yu0QYGIpUPTUeEAX61imzGUJJJRODjLq9Ekj+ShNrXF/FTdDpo/f1Imoa3ukrQiMI5tZk6rdVO18RUajhJajCxWaMeKcsIfKeDou/cXk9qPUMLVtWmvtCMOuWAV15yN7ko/uj3o33PTAc7+CnuCcC8/oq7S/zXt3waiy18299FGLV1PSVUM46USBMfnsStrSMpGlKXUNyDcOhG13cCqtP1yBgcIiQZEv3xrt1V22E1fRauNjDiCvZ9Y1B0mcRADC3R/bSrU3U8e/e4B/qeoNVewd0wsrKsIYcPXpJJkkIhd4JK7S4dZ43Kne6VEPe6YYyMsX+j64X4XcbbDSIQPePUbstFBALZUJFUjdB3NxsFduyjV+eNBMggKTJqUkeE4a7SskQxFnE7dPEiAj+ksXD4VQO+G+JZflejq20UyksyCGTM4hBm3zDtudsEjo0aT+LW9jYRxPImF/7qqcMePgBOy+Xqb93i5h/d3XeewUjrJPvjdLmVBHZA5W5t89+xkycQYYg+NIhfb+AuLRE2WyjJBGqhECVHhUDr60MfGsJdWEArFtD6+ggdB62vQPONN8EPaC61ezb+SLJE/nh2y8OWJBQzgSQrSPLOgSYHE10nDACr4uyqWOj2eG7/TAhYXQmxOt2vo121sapOT6MrfI/2wgJKLIldWsZvN5AUBbN/rOv2vXDoRteuO1z+letUp2qHul9ZlTn5w0d44udOP3A7CcgNmZgplSAQVBa2boye0EgOJHqWTTUWWzvaFRXFIJueIJeewHEjpd24WaCYO0Wldhfft8ikJ0jE+lBkjVpzjnZnjYHieSRJJmbmgbuEXkhzqUXoh13LdpIDcYy0HnHA7gOhH+J3SVj1RI931rOC6IUOI09WMeKbQoZ7hYREaPt0Fuvo2diuNyA5lEBLdH+YQz+kuXL4ChKhHxL0qoSQwMyZSIqMliyQPnYePVvArZUQ1TKZ00+y/tpXDn1M+0Frpc0Hv3WLG79/d1+14veQGkzsas65h8ALdk103uoqSiqFXyojaRpKKoUci0XyS9kMSszEvHSRxosv4pUrpD/+PEo6jV+rIZvmhkJEgFVzsGoOIhRdqkei1vt7kFWN0LVx6+Vdz1xqKNEzt+k0Xew9Jr62o5fBBbAbbkQFuidE+xFCEDr7e3YP3eiGfkh9vkH5du1Q9yur8p4y27IiUV+Lbrhyn4FTY+oDCTjaa+37SI09Gs1FbKdOtT6F49SjSy0EmhpDkhXisQKhCKjX5ynmTqIqBrKsUW3MYBhbCQyn5WE3HOL53UuueN7s2fL5IIS+2HMxeC+IcIu4WQiBXVnBbzcJHoHMW4lrpI4VkRSJ9sJOzz5RjHUtV4OIIOXH//XnDq1i5B60uNazEgCimtioPTSOCAOcSqQ+iwRaonuJ1eOGCAVu22P58jpXfuMmy++tP3L9bKwQ69r8A9EqqbV63yQfhpH4qgjRCgVAYE/PIiciRyX0PNrvvYc+PIK7vELQbBG02tjTMwjXQfj3pLcEVsXqytYlSdIOoh0tlSM5egK3WcFrVuksz2x+d3+Z5Xa4LRe7RzXOo8JteQ9tOpFkGbM4iKRqtOfvoOgmycnT0bOzR3zXxXSHTiaZeCKD54a496mjqoaCmekeQxNCYNfdHXFPQYjnt/EDF8dpEGyolvqBvSmOFwQOjtukY5VQiufRtRS228Cyyvj+1vGDDWmZbkbXyBo9DdKDIMLwwDSR4bawg6yoqPE0YeATei7iISGUXftyfZxy9CIHzs6H18waPUvjFE2mcCL3CKM/GFQ9ilEK34tqLxNp9HQuUqhudS83exy4xzVh111q03Wu/8Fd5l9djsI+B7i9sayO2iWGfo81zKlvGa2g1SJ0HIJGg7DTwQsC1GIR8+gRhO8RWBZIEt76OpKqYh49ij09Q/zsGRIXn8BdWMCv1TdXOHbdjRyC+4tYpEgJ5R6stQUCux2pdoQ7J5dYtjdTnW8HeJ3D5ev1bb9rJ+MmJBmjOEj6xBPIqk6sOISkagT7kKSH70Kju3i9SSKv47T8jeTQFiIG+V5LXBFl9x/obEkYegrTyCJJMp7XQbAV6xQIbLdOJjWC71to6taMHnphzyXiPfmf/eIgQg07drKBe8ZWT+UIHWf/PeWagtmfRPgh8ozM9sdXT2iPdI6PFZKMBDjVdbR0DiM/QGLiJEG7SekAibWHQQhB4AQ4DReratNet6hO11l6Z42lt1YfKZTQDVpcQ+pBIxo4IcG2VVL73csAuIt7S9Y5M1GVQX21ew261/F6VgHpyW3voAij0EIXqHG1J5l44AYHZy7btc+wa335JkSIU1qnfvNdFCOOU1lFBAFee3+dqx8po5vOygxPaMzccjfLPfYLIWBtqk1hNEaztNNoRBIv3e9iGIQ9l7fl6i1CEd0MVTGw7AqhCJAkmVZ7hSBwCEOfSu0uzfYyiqIhyyq1xiyu29rYf2/9JVmRe5KbPPhkOVTJZElWCD2H0LX2rSCwsQdkTUFOGLsSILIuP5Qy79sF4Xs0716lvTCFrOkEVntfJWNux6N0s9qbsFtshHG8DT29tofTiBJBzaUW9fkWdtU+dPVrWe1eggZR8vJxIvC6c9jek8+RZB7qxffiWrjHk3uQVUDX/Ya9k81b24TYpRX0bBE1mUFCQonF98XV8ZEyutm8wrknY6wu+tiPQF0HkO43yI/ESOQiUcbVqW1xK+kBciSCnl5uqXpj8//b1jpta73rdpXaHQDWy7uZkuDB2f9uGeYPGyIMUWNJFMOkszq379+Hrk9ruoJveXitnUsuWfkIGl0J0qcuUb99AzWexCmvPpIwpVNzuPmHU8z3bO4QG7FzQeAF+HbQlTjoUXDxgsbwoMJXX7Rx71uYSDK9ZWdCgYRMXM8RhB6238BQkrvkzVXZwA8foX661/sk3fvzcIG3no7IITsbm7vdg9EFMLJF4iNHURMpQtchcKyPhtFNpGR+8D9KIyugaRKyAreuOAgB771u8YnPJZi941LoV7jwbIzKesDqgk//iMpP/kIWzwn58z9ssTy3v7iN0/JpVVxaFXdXIb8I6VmrKsnSY+3Pk6TeD9G9mfvbDRH6+O2NWOYjPNRKTEPWFbz11i6jIsLesZDACyNl4Q/5Evi2T3LsDNZapHPlVLdNpvuI3YSBwK45+5Km0TTw/a0GryAARQZ/429l4830/Si/BdG297b3vaiKa2xY5cwpjW++7Gw+Q/6GvxL6oieHraRICEJUSUdWFPzQIWX243cckkaRUAT4oUsxPkm5M4uuxJA2eBsVSaXj1eh41Z7nJyvd3ychIg91L5NOz3zFg5ynA2Ivd1w2Y/idJiIMsNcXiQ325srohsdmdFUVzLhEsxbSboZMHNPJFRVEGFWGFAdUOq2QZz4V59f/dRXbChkc0Qh9k298scnAiMrTn4jxxX0a3b7JOONPZPCckMqCxfr01osggrBnoFxWpT1rLskyxOISnfZuHStJAt2QcOydX0iKjKJ333/gHTwhdhiQNR2jMBidRHX/D3XoBmgpk8KTScpvzePWt0p6AjfoObGsXS3z5f/6m4dWvSAbKrKmROoVMiAgcPxdRDyeHdD3iTPknvgYajyFrG8lbrxGjfrNdw9lPN3wf/k/ZPm3/2uLH/+ROJIEX/maxTNPGfzxlzp89lMxnntGR1Ulvv4tiz/5kkU8LvMTPxLniXMaiirxu3/U5sWXtjzQdErmJ382Trka8Ju/Gz3zvtO7g1A1lIjaU5JRkAhFgC6byJKKpsQIRYDl1QnCqGY7Yw7hhy6qbLDevktSLzzQ6Cq60nNls9dYbK92Z4lIf1CSpUOteJEUqavqy/0IHRsviMKL8eEjSMr+zOhjDS94nqDVDHBdgedGM5ysSGi6hKZF3rBjC5r1AN+DwBeU1gLq1YB4Umb0yP5dz8XrTZJ5nU5jt+cUuGHPkhBZkTel1h92I01T4tOfNfnGn9vEYhKSHNX/JVMysgITR1SuXHbxPMG9xKaiyz3rVN3WhiLCtxmh62KtzQMQ7LP2EDZWC/fabu+rufTaXs+JRdFlrKpzaC9Q8VKB9PECnaVGtLIRUL2xhteFo2L9ta+QOf0kCHAqW55uYO2vZnq/qDVCjk6oHD+icuuOx3PPGFRrIUMDCj/xhRj/v3/XYnBA4Ye/L84bb7ucOKrx8ecMfuN32kxOqPz0jyd49Y0onpDLyvz1v5qgXAn5vS9u3Ten6RJ6u58rSYpako2kiexH3quuxBBEOQvHb6HKOhISbmAjREjTWScQLoqk4QU2bvDg50NP6btKNgEQ7Lnd221tSBLdbwelyKirpnJoSUeIeK734ni59TJ6poDXrCLrBnZpZl/H+VBjutVywJMfj2FbcQoDKtffc3CdkBc+n6RRC6LWvANm5AXgWAGTl7LMvb8zq+g7/gNveCxnIGvyQ2fiTkfg+xBPSJw+p+N7AiMm0WqENOohI2MqriO4dd3DdaKTUU2lZ8un23AOTGd4GBCB1zOTvBfIG+VJQkByMkfd9jZbga2623OVYaR1ZFUicA/H6BqFOJIiER9O015sIKsyiq7isfve++0GjVvvoWUKdBbudtnb48HN2x5Hj6gEAayVAl74mMlv/m6bXFYmn1M4fTKaoF99w8GyBIP9Mn0FmTOnos9fes3B36gOOH5UJQzhn/1iA2+bT2FX7Z7eoqzK6FmFyvxW7H65uTsXUepEPLH3e7U1+8GxbzOr96xW6VT2Fje3672rZ1RTRYurh2p0VfPBYqD3YBQGSE2cjsoKRYga25+i8mMzulZH8PZLHeyOIAwF5RWfpXkvKtFxBC//eZvZOy6lNZ/hcQ3PE1TXA1r1gHYrZGnOw2rvf8ktAZqhcPu1Cu3qzpvmdfwHNlgkBuIoezC6qbRE/6BC34BCLC7hOpDLywQ+tFoCSQIzJmEYEu2WQJIjPlG9R3thp+J0VZj4ToMIBXouhhrXsdeaO2ZPq2z1LPJXDZVYIbZBPnNw1G+XqF4NUJM6geMjIeE1e3cvea06Xnt/RDIHxc3bHp/9tMntKQ/bFgz2K7TbAscRLK0E/MbvtCmVQ2KmhOcLSpWQqVmfX/vNFrW6IBaT2OhFYHrW58Ytnx/5gTjTMz7r5ei9aa1ZPdvEFVUmOZCgMX/47deKoRDLmj11B/d6nzsPYPvTExpGSqdTOpjO4HZoca0378I2SMibPBS9M5W98diMrusI7l7fMnrL89HNf+ObO5cl1XLAwvRug+NYAZX1R/P+zIRCPJNg9S401rfG4HU8mivtnu24mbEUqqE8tCvFsQUvftWiUQsprQVIQBCCrktYHUF53cJzxWY2WdYUMmOpnjGu1mob5wDqER8VyJqCrKsEtk97sU6w7YVvLrU3lFl3s34pukxuMn1oRrc1V9vzttnzz9OavoaaSH+oEj1zCwHjoyq//8UOmYxMrRbSbocsLAe8+Y7D//X/lCXwBe++7/Hbv9/mg2seFy/o/JP/NksYwDdecvjDP43epVI55A//pMMXfjDO3/xrSf6/v9jADyKhUbvhdr/mhkJ2PMXSW93rbA+CRDGGmdstIAlRMrvag2/3fjSX2gjR3ayZaZ1Y3qQ63UBV4cgJjdvXu79DgyMKngvlh9gTI6X3pHzdjjDw8Jt13NoaIgz33b35oYYXVC2qZPA8gaJIm46QLG8IRV4wWF3yWV/xCXyQlShLux8IoNPwGD6V2mFwIbrhzaU2rdVOV+Kc4sk8ekqn8xCVXteFhdl7N/DhS2LVVHrKF4lQUJ9rHiqt4bcLoRfgtxwkVd4VI2qutGmvdug7nd/1FqmGwuDFvki48BCgadA/pLCyGOxik7of8aEJvEaFWP8obn2bxlcY7FsRYD+wbcE/+N+WqTdCFEXiW684NFshvg+//KstkkkJSYq261iCjhXwr36pSTwefd5uC4IAvvqizYuv2LRagt/83XbkAd+rXvBCyjerDF4o7mJ3U02VvjP5PdXL7heZ8RTJvu7yOqEXUL7ZOwG3Hc3liPVL75ILiRdjmyrWiirxxDMm+T6FynpAvqjgOgLLEiTTMpom4dghmbzM7B2v5zMR74t17RjdfRIhaiqLli0gwhCntIy9urCnc4IP2ehefNakf0hlac4jlpDRdQnXEYQC7t5wI/ljBc48YVBaC0hnZG58sL8HXwI0U6GyaHUtAK/NNqjNNLoa3URfjOKpHPW5xqE+iLGsyWAPocF2yaIyVf9IJNIOAkmWCByfygdLxAfSu0vGgoiEe/T5wV1LOMVQGLrUj5kxsOsHn3z6BxU+/0Mx/vA/dKiWH3wjfatF7tyzKPFkxCG8AbdRoXb1zQOP5UG4FwbAE1jbql0iI7t7Mm+1Ba32zs8tW8CGj2A7AtvZ+f3CGyuc+rGju4yuosnkjmVJDSVpLB5eiEFSJAonciSHusc5y3fqtNf3lqT1Oj6lW1WGn+zf9Z2ZNchOZlBNBQix2iGdtuDoKZ1OO2T8qEarGfLmSzajkypnLxq8/i2rp5KUaipkRlO79P+6we808ZpVjFwfIvDx9pkH+VBL8odGo5ctV1QwTInigEIqI3P3hkujFpDOKWTzCp12yMVnTeq1/Vu+VFFn8VqD2rLdtRawtdqhfKfWk51r8tO7RSQPiqFLfcRy3YlX6nNNatOPLoD5UYGkSJiFBMmxHOZgqisl39Jbq1E97v2/lSTSY0mGn90tJPkoWFsNKK+H2A9glLqH8tsv0l6axq2s016c2vxjrx+eisV+ICERV7OYSnce2XtIaHn6YkeJqQ8m5lm/XsbqEfdMDSV6OgOPikRfnP6z+Z5cIotvrjyY32AbfCdg/Vp3gybJEsVTOZIDCYJAsLYSIIRgddGnUQ25c8Nl+rZH/7BCuyl4+asdEL3J8+LFGPkT2T2FaLeEKf+Qyvuv7Ftl5EP1dN99zULTJVxXEI9LLM35OLagVQ8BieV5H8cO8TyBbkisLOw/Mzl0Msnse3VaFZd0327CjMAJWLm8xtHPjZEZ3f1gDz3ZT+5IhtIel0APgxZXOf6D3YunfSegdLNC/TFwyX7YCL2Q1kyF1lx1Q45994tVna5TulndpRYLkUrv5KdHWLm8duDkSODD7Zvenl6gwGrTvHMVO7WItbL/LrzHgb1IesuSQs4cQZLA8nuT89g1l/nXl7vKJMXzJiPPDjD/2jJW5eAJKUmW6DuT62nI7brD0ture17VBU7A6vslgp8OupLf953JkzuWoT7f5K1XDjb+9EiS/rP5PW37HSVMuTjnM3PHY2nO584Nj9vXXOamPBwnquO9c91lfjqK57781c4jlY6tz3Q48bE8Excz+D3KZZbfW6d8u9a1WD+WNzj7U8d79n3vF5OfGaV4uvvNbK93WHh95dCJO75dEKFA+CGB7XddZQRuyJ0vzXRth1Y0mbHnh5j41OiBr30Yws2r3gO5U7fDbzex9hGT2y/6YpOMpy5xLPM8k+mnGIyfRJE0RpJnOZl9gSPpZ0ioEctawRxnMHEKQ4mW54aS2Pzt8ezHyOjRaqDtVeh4tc1jJLUC/bFjSMiYSor+2DEMJUEYhEx9ba7rCkNWZUaeG+yqmvwoiOUMjn//JPG+7nHR5XfWqM3uXclYhJFMUbXHStDMGhz5nrE9qW88CFpcZewTQ3uWYP+uFKYsrweUVh/NEFWXbO68XuXWK5WdvAvb4NRdbv7R1AbJyG4mssnPjHLyR48eiBVLkiUGLhR54mdPYyR3PxSBF7L89hqLjyF7/FHGwpurLLyx2tXwxgoml/7GGSY/M9Kze+8gkOQoabeb9EjsyCbJmkH61KVDO66ppglFtGoLRYihJNCVOFV7meX2TUAibUTGtOFFTQjGBkOdKunkzBGW2zdpezUyxhCK1EWp2W+QM4dRZYO4lsVQEniBDQIqt2vc/fO5rk5Gsj/OhZ89RfFM7lGqnzahxlRO/PARJj890l3tuWJz98/ndqk9PAyNpTazLy11bd+XJImjnx1j/IXhnkRWD4MkQ//5Iqe+cGzPE4+sGXiNCrUb79KauR4pSe8DH0mju08a1x0QIpLssZo+gdd7Sp19aZHZlxZ3JcwkSSJejHHpr5/l5A8f2UlDt0couszgxT6e+4cXKZ7Odb2ZzaUWH/zmze8aL3evsMo2H/zGDdpru18+SZLIjKV44b96lpM/fKRnHHy/UGMq6ZEk4y+McPFvnKX/3JbYoZ4pIOsmscHxzT/x4UkSw0cO5dgRBE7QxgnbOEEkhGkocUaSZ+mLHyFjDKDJUSgsCF18sdMrtf0mlt/ACdpRN5nURWdOeFSdJQqxcXQlRtNbJ9wg17TrLjf+4A7VqdquyU6SJQaf6OP5//QSxZOPZnj1pMaJH5zk6b99bpfSL0Qt4DPfmGfxzZWenAuyDMnU7oN7bY/515ap3q139ZA1U+XZv3eBoacG9p2LicIheT7+XzzZk2f7vl8gGybx4UniQxOoZhwtnSM1+WA1m/vxkWIZ+zAhAsH7//4G/ecKuwi0JUkiO57imb93geRgnLtfmaM209hTm2q8GGP8hWHO/tRx+s8Wuhpc3wm4/vt3KN06nLjxdxJEKFh+d43rv3eHJ/7a6V3ZYkmSSPTF+MR/+TR9p/NMf3OByp0anXL3xGg3SIpELGeSGoqTHEyQncww9GQfQxf7aJcsytuue3z0GG5tnf6P/8Am4Y2sajsqGQ4D949cl+NoskmlM48mmxvUoRJpfYCklseT47TU8oZW3U7PQNq2nRvGaKkVLL9O1V7kaOY5Wl6ZhruTCW/9RpVrv3eHp//OBeL3qadIssToc4O88I+f5vKvXmf1g9KeShgVTSYzkebY58Y491dObgp97jjvUFC6WeXmF6cQlsPQiIJlCbK5yEC2WyGaLoGAiWMaV951UFWJeDLiNqmWQ0rXK8x8a5H0aHJ384IUiV2+8L97inf+7VUW3ljBrj587LGCydClfi7+/GkKJ7J7I9CRQEumMftHUXQD2TCRJAVnj1p69/AX1ugC1GabXP6Vazz/n10iObC7xCU1mODSXz/LwIUiC6+tsH69TGOxhVXZaq+UFAkzrZMcTFA4kWX4mQHGPtY7PiRCwcyLC9z6k+kPnVXrowK36XHzT6ZJDSc49n0TXTPdelLjzE8dZ/TjQ6xfK1O5W6c+36RTsnBaHqEXRlI7urLRh69iZnVi+RjxgklyME52Ih2Jfqb0nkvH+o230dJ5Ku+/SnP6OgCqGSd34WOHdr51ZwU/dHGCFqHwN7zWFpIkoco6NWeZllcBBLKk0HJLhCJEllTcsMO6NYNAYPkRAU0gfGRJoemVECJEkVRAIhD+xv7rm+GMewicgKmvzpOdSHP6R4/uMl6SJDH0ZD/JwTgLr6+w8n6Jyp0azaVWJIuz8awqukK8aJKdTNN/tsDIMwP0nyt07eQSQtCp2Fz5rVusXS0zMCBz4SmDuWmP85d0hIB2S7C04NM/qJDJRfW1sgJDIwq1iuCbX7XwLJ87X55l8Ili5NHeR0ojyRL5Y1k+9o+eZP6VJRbfXqN0o0Jrpb3ZXn+P+yQ9kqRwIsvghT5GPzZIoi+++WzYdQff8okVTJRugphC4FTXqd98B0U3o0k6DPHt/TX1/IU2uqEfMvPNReLFOBd//nRXQ6nFVMY+NsTghT6ayy2sioPbdiNO1FCgGAp6XMPMGiQH4sTyZs8XXISC+deWefffXXtoA8Z3O5qLLS7/ynW0hMbkp0e6PuSyIpMZTZEZTeHbPnbdwWlE1z4MwoglS5VR1Ig8XY+r6Ek9EmPcY3xOBAFeoxoRl29kof0goHbjnUM71/u9zntY7dzZ9VnZ3l1BUdkgl7H8BhZRpULFnt+xTULL0R87RiB86k53va5OyeL9X79BLG9y5HvGdiUsJVkiPZLi9I8nmPj0KO3VDlbVxuv4BG6AJEuopoKe0kn0xUgOxHsqsUBk6K/8xk2mvj4f0UxKEIaCvgEFISI6S8OUSCRl4nGJMBBoukQqJaGoEvY2JZfqVI33f/0m2ck08WJsl2cqyRKpwQSnf+wY458Yprncxqo5m63n93gVzLxJaiCBkdF3UK0GbsDMi4u01zqc/anjxPI9OBhCEbGLjR4jNjgORG3k9X08L3+hjS5EDF/Xfvc2YRDyxM+eitSC71cwlST0pLYjDLEZatgDt2ekgRUy/+oSb/+bK5RvV//CernbUZ2p88o/e5vADTj62bGIDrDHtVRNlaSpdl2RHASSpiNJEiIMkLXt1I7fWaEf22+x0rlFEHoEoncJU2O+xav//F1kRWLiU70nu0QxRmJDGFIIsfW87vF599oel3/1Old/5/am4VtfDXj9pcjZkGVpg9wqalEWQiCQ8D2BqoGiSDvUY0QIc68s8c4vX+W5//RixAjYZRyyKpMcTJAcTOwYtyRJXePV9/Ti5l9d5t1/d5VEf5xjnx9/oICt26jSmrkBSGjp7L5FTP/CG12IDO+V37pFc6nFU3/7PIUT2e7Li23YsyclBFY5ytxe/rVrtFY6f2lw70FAa6XDS//0LWozDU7/2DES/bGHXvtHPpyIJHO2Kyj3P/951EQaWTcIfQ8R+MiajlNe/bZLsO8HgfCw9tgz31pu863/15u0Vjsc//4JzKz5QB7ZXgZrF0Qk7V6bbfLBb9zgzlfmdhA5+R4062Jr4+0/7PnZFkIv5MYfTYEET/7CWeLF+IHHHbgBC6+v8PL/520aSy0CN3ioDHvoOXjN2ub/G7n9NZj8pdHdQOiFTH99gcZCiyd+7jQjzw6S6I89mnbZBtyWR+Vujet/cJfbfzrdk2bvLzqcusvlX7nGynvrnPrCUYYu9ZEcShzo2m+HEAKn7lKdrrPwxgrVma26z8r7r0aClKPHaM3dwm83MItDKLHdbeLfTeiUbN74F++xdqXMqS8cpf9cAT3V3XvcC0QoaK22WXh9leu/f4e1a+VDkyTaDt/yuf77d+mULM79lZMb8WR13+MWoaC91mH2pSXe/qUPNqtp2mtRSEWEoqdjpWcKJMaPAyDJKu4+V0V/aXTvQ/l2jVf+h3cY+9gQo88PMfhEkcxYas/lKCIUWFWb9RsVlt9ZZ+7lRSp364fKcP/diMANWXxzlfKdGkMX+xj92Ma1H08/ojx9lMSpzzWozTYp3ayw/M4a1en6jjJBr1FFTWYIA4/O4jQi8BG+R+GZ7z3Es/towuv43PnyDOvXK4x9Yojxjw/Tf76AmektfX4/Ai+kudxi5f0Scy8tsvDGyg5p98eBwAmY/voCtdkmk58ZZfyFYfpO5/fEhStCQadssXx5ndlvRmWjTmNrvKEvaCy2CP2waxccRF2MXqOGGk8SBg5upXvMvheknmKJgCRJXb+MF2MMXerrWpPndTyW3l47FOKSnYOB/NEMfWe6s3U1FpqsXSsfnjcpQSxvkptMUziepXg6vxnEN9N6dO5SVP7ltjysskVzqU3pdpXyrRq1mQbN5dahjEdPaYw8O7iLj9ezfNavlWnugRLRSOv0ny/uKhcSoWDxrZWudbP3QzEUxj4+tKvZIwxClt5a3Xfh+wPHm9HJTabJjKejvyfSpAYTxIsmekKLXggpMta+7eNZ/qZOWWu1Q2OxRX2uSWu1E1FnbsvA3w89WyR/6QUQgsC10RJpOivz1K68vnXuetQ0oxq7n3m37bF+rUxrdf9qGx8VSEpUI104kSV/LPqTGk6Q6IuhJzVkVUaEka6cXXNorbSpzTWp3KlRnalTnWo8kKv6cUExFPJHMxRP5+k/myd3JENyII6RMVBNBREKfCugU7FpLrZYv15m7WqZ0u0a7bVOV2+8cDJH/lhm10pr/tUlOmWb2MDYLmHKyuWXdmwrhOjpej+S0X0gFIXiZ74fc3QCr1Ki9OKXCZpbveHmyDiFz3w/sm5Qf+c1mlcvIx7Gv7cPSKpG4Xt+gNpbr+DXKg//wR4ga3LEtZnUUA1lS/9Jiup9Qz/SXvM6Pk7T7UnWfVBMfm6C9FiKm793G6fXpCbB5PeOo8U1pr86uzkWSZE4/ZMnGf/MGIET8No/e5PWUmvXb0c/NkxyOMndL03jPYRX+H4MPtlP4XSBO398d4f3cBBIsoQWV9ESGlpM3egok7eufyg270HghfiWj2f7+B2fwNvjhCfL6Ok8eq4PWdPx203s9SVC92AVJn3pk6SMPhaql3H93hNjX/okuppgpXaVIIyum4TMSP4iA5nTCBFyc/nPaTu7yV8KyUkSRh9L1Q/ww0OoiJGiih09pUfX29x2vUUkwhm4Ab7t47Y83FZvGaYPE5IiYaR1jKSGaqrRO6rcG3NIsPF+2nXnwGoTiYmTkWq2Gd8Upiy//Y0d2zzI6B5+eCEIqLzyDWJjkxQ+/X3Iqsp2k+qsLLL2p79L8Xt/EDWd3dCJPsSuLElC1o2DdDTuQuiFWBX7UEhBDoJ4MUZ6NP3AUIckSSi6umtpJALBnT+5y/I7q3zm//ZJ1B5L9nt1r92uX7wYIwwFdtXe7TVKEYl5N0/wIBCh2Hy5HxvCELdWiqSKJGlLfveAMNQEcSPftYNsCxKKpKLct40gZLl2lWp7ngtjP44id2/WkCQ12n+XG6ZvtBI/yODvgojCDocpg/NhQAQCu+rsqTHioPhIClOGtkXQ6X6jRRDgt1qE7uN5iYTnsvYnv/NY9v3RwIMXHyIU3P2zqa7feR0fu2L1ji8LmH1xvvt3wIkfPU57rcPdL00h/Pv2IWDpjWWW3vj2UCIeCvYhu36IB2WlvlubDKKWYNdvI3rpqAOl5h1Kzd31vgDDuQt4vsNS7X2EeDzt5rIMF8/pjAxHckNvvOPQ7kI0dGRCRVUjaSH/EOy5JMGp4xrHNnTmXn/bofoIVLCPgntsdJaiYBQGcRv7W1Hv3+hKElqugDk8ihJPIoIAe2keZ/lwWJq0bJ7Y+BFkM45Xr2IvzBC0WyRPX8BemiewO8QnjiF8H2t+BjWdQc8Xad+5QWzyOObgCKHv0rpxhaC1JQuSOHmOoFVHy/ehxBP4rQadqVuEduS9qpkssdFJ1GQaFBlCgbOyiLUwg+jylOgpncEnB0gOJRAhNOYbrL63RuAEJIeS9J0tYOZMQj+kdKNMaYMXtO98EUmJwhWpkSRW2Wb1vVU66xZIkOhP0H+hiJmPIQJB5XaF9SulTUMpyRJDzwxipA2chsPqe2u0V6IJzkjrHPuhYyiaTPlmmdX31/fM7aDGVE584TiKrlCbqrLy7tqmvlZyOMnIs0OMfXKUTskiXohhVSyW3lqhvdJGVmVOfOEYekKnPldn+e2VHZ6SkdbpO99HajSF1/ZY+2CdxtxWyCl/Mk/f2QKKoeK2XNavrNOYbx5q8lGVDXKJcWJ6NurucqvU2vP4oUdMz5CJDaOrcYQIadir1DuRdE86NhR5oopBXM/i+h2qnXkcL3q2DC1FNj664VUKWvY6tfbClqGUJHKJcVTFxA9squ15bK++Oabh3BNRd5m9SrU9v6uTrBdkSWU49wSqrNNySlTbswRh5MiYWppC8ij9qZN4gYWuxnD9NpXWDG5g0Z8+QaU1h+NH56DIOn3pE9TaC5tj2yskCY4f1fjeT5k8fcng5//eOrendjpUigw//H0xEnGZf/nLDZqtnfdVUyNXopsxlqToez/YuQCRJBgfVfneT8X4oc/H+Nv/WYnX3/5w1VdEEDySxNOjGd1sHnNojNCxUWJxEkeOs/blP8Jv1Pa9ux2DyebJffwzSLKC32lhDo1g9A9Sf+c1EqfOAeCsr5C59Byh5+JWSsQnjqIXB2jfuYEIfBAhuec/hbO0sMPoZp/9BMJzccslECHJU+eQdZ3Ge2+jxOJknnweWdPxGjUSR06gGCb2ykJXx1IxFM7+zBnyx7PUZxoIBCIMKV0vE7gByeEExbNF3LaLntQZfm6I1/+Ht2ivtBl+boiR54ZZ+2CdMBAMPjlAaiTJjd+7hdfyiPfH6Tvfh9ty0RI6ox8f5q1ffGeT6Dx/PI/Xjpbbg0/2k53McO03r2PXnKjLxw0Yfm6I5FCC8q3q3gl1hCBwfAaf7Cd3LEP5VnVL1HDDAVR0ZTNGFgbbvUKB7wT0P5Gl73yR8q3KptHV4ipHvm+S4pki7dU2qaEE/U/0cfXXr1OfqRMvxrjw82dpLbUJvIDkYILWcovG/OEJRcqSyljhadLxIdpOaeOeChrWClLoYWppMvFhvMBBUwwKqaPcXP4qllsln5ygL3WCemeJUPjkEuMkjAJz5bfwAhtDTZKNj+AHDoqsU0wd53bwdVp21BWWMvvwAwcvsMglxkiafcyW3tjwYAWh8MknJ4gbeZr2Ku6e3cDot6nYGKnYAE1rddPo3vtekhUIJYQIoz8bn/elT6HIBguVdzbGOMB4/mlaVvdOtgchCOC3/6DNe1dc/s3/WOy6TSjgxZftqOHB2f1Cfe57YqysBlzuohIzNKDw7FMGr75hs1basrphCF/+usVblx2evrT3aot7UItFjIlxZF3HnppCSaXwymWCWp3EpSew70xhHD2CEo/jrq4Stlro4+NImkbY6WDfvk3YebTE4f6NbhhiL83jllYJbRvZjDH4Yz+NMTB0YKObOH4aJZGk8q2v4tUqxMaPkH3mE9Hx1lfR8gUCq03oe0iqimLGUDN53FL0sNjzMzirS6SfeHrXviVJInAcqq9/E+F5ZJ//FPHJEzTefwctm0cvDlB/93U603cIWg1SF57GLa1Fhvw+9J0vMvqxYd7+V+9Sul6O4pmyFAkvBoLyjQqN2QZuy0WNaXzq//wJCidymx6postMfXma5lKL8U+NcvT7jrD81grrV0vU7ta4utbBbbkousIn/5uPUzhV2DS6IgyZ/uoslTtVhp8Z5P/f3pnH2HVfdfxz93vfvs2+eRnHjpesTduYNEvTJiVVQmlZBKhQSAVNkZCKRFX+okKqUKsihEr/qCigQgulpSoQoFvSQFKaLomdxHbs8XiZ8YxnfW/edu+7+738ccdvPPHYiRPXFer7SPPHvPfufu+553d+53zPDY/sojhZZPG5Jby2x8l/m0YvaKQqVyc3FzghJx8/haRJVF6RIWIumpz6xmlGDw6zdHiZqa+fJAqibupVFKyHNGKYuGds07K58TwDN/cz89Q5Fn60iFE2uP1DtzB65zDNmSZGxSA/nufIF1+mNddCSSsEHf9VvVxtME/kBvj1V49X5o0h+nK7OL38TNeDFQSRIHSJiWnby9heAz90kASZ/WMPkzMGsb0k/1IUJJaax+m4NcrZHQwXD5A2KzQ6c1hujdnqj/BDB1GQ2TfyEHljqGt04zhmpTVFq7NIMT3GaPl2cvoAVfMMYeQxv/ZCIseovTYB7QtEcchC/SUEREqZzSL5jt9ioXGUUmYbbWeFc7Xn1z3o5JyutqYZyO/hfP1F4jiklBmn4zVwgtf/ortSVCaO4ehlmkYKAjzyrhRP/Le9pdEdH5V56J0GR497m4zuxsqvfl8FTUMbHyMOAgLTJLV/P351FT2Xwz03h1wuo1gd5FIRd2YWbWyMyLZRR0exDh9GGx1FHR7GOXX66jfO6zG6ooTWP0Rmz36UfBFBUVAKFUTt6h7yVyJIMmq5D79ew6/XiDwXd3mRyPdQiiW86jLZPfsJWg2CZgMpm0XKZFBLZazpYxsrusxFiIIA+/y5rvcb1Gvow6MAhK4LUYhaqhA06ijlfoJWk8jderhSmixir9nUTtQuaZ0uSALFHXkm7p0gM5RG0iTyYzmUi9KsmjMtrGUL3/Kpn2kiyAJaXkMQBfITWba/YzvZ4UzSsXVHgfMXxUlb823aCya+6dOYaRGF8aaSxStlo7wmLhfujeKu7GwUxZfpIXfpwqmKzvCbhynuLHLgN/YhCKDmNBozTRCSmvr5Hyzwlo/cQW2qxtwz86y+XEWQRFI7+pBSGl61jTZYIPKC9a7XAnJGR9QV3JUWxDHCehWbpKsELRvzxALxugZrRu/DDx1a9hJBtPmaCgik9QoD+T0YSgFRlEhrFRRpw3uyvDUcv0kQuVhOoiilyilAwFALDBX2k1ILiKJMRuun0dkItXW8OrbXSJZ1a0RRgKpsLryIfwIlinEccWGIkoQ6Nrax2jrJUGEfpfQEtlcnZwyx3DxBGF774fnEmMxnPlmmv0/i8W92+Ku/btFsJdflvrt0Hv3NLG+7U+fON+s89miOqWmfz36+xfGTPh//WIH77zHYPi6zd4+K48R85esWf/vFNlvI624ikxb4tfdlePB+A1mGYyd8vvCPbabPBIi6jjoyjGQYhG2ToNUirDfQtm3DuHE33vkF5Hwefft2RE1PRoGmSVCr4S0sIJdKiKmtG2++Fq7a6Cr5ApX73oV16ji17z1J7HuM/PIH3pAAMrC+/PqQ6EJ1yXpv+dgPcFeWKB28D3l1GW9tFdlzUCsDSJkc7uprGBbFMbHnXvRvfGGj+LUVOmdPkbv5TaS278JdWWTt+9/tCqC8kiiIEbp97zeT6ktx22O3cv4HC7z090fw2j73f+reTT+VtI1jFKWkOUscxahZlds/fBurR6t8/9M/xK273PeJuze5EaK8oU8gikln2OtSeNGtYb/K5QSB5myTFz7/4iZJxdBNxEwiL+LQ5w6TH8+x7e0T3P7YrUz/52nOPDGLNlSkfXQOOWsgqjL6SBGv2qZ9dB5jvAwN0IYK66OvOtl9ozSen8EYKSDIYtfoxnGMgLDlvityil2D91E3Zzm++g38wObmiV/a9JsLKl7J4YiAQEyEJCrsGrwP01nhxMK38IIO+8d+gYsNXJK5sLFscptfn8m69VmASz4PIo9q+zSDhb3UrXNdb/8nYfxn5wJ+8f3LfPQP8pRK0qYeZU8/6/Djwy5f+bt+vvRVk6/9e4coBn9dB/vP/qLBc4ddfu8DOT768TXOzgQEQfyqBhfg3Q+meMe9Op/48wZWJ+ZDH8jykQ/n+aM/WcNyHLyFRYgi/OUVQtMkjkLUsVG00VGc6dOE7TZSLod94gSR6yHnskipVDc09Ua4aqMryDKCouBWVyAMMEYnkHL5LX544WJvcacLF/2tEwcB7vIi2T370QaG8GqrGGMTCKKEX68RdiziMEApVXCOHCJybDJ7DiSfX2RM112hq7IOgiShlMqYx4/QeP5Z4lepYV87ucbku7Yz9KZBlg8vg5AIstg1B0kRUQyF5kyL0IvoP9BH+hUiLYVteYo7CjRmm1T2lgm9CKfuJLmGWY3G2SahHVLZWyYznEE4vHEs+fEspckioR9SvrGMIAjJJFz38IWNc7Dlwa4bgMuejAvnb/PHcRzjdwLSA2m0rEbohoRekKhHXbyswKZzby1beC2P0q5S4pn7IWpOw2t7BE6AUTZQUgpO3eXk46dIVVIUtuUQRXDO11ErWeIwJmjZBJZD0HYIHR+/bqGUMjjza8RRBIKAeXyBoNnBEYWuwQVoOysMlw5QSm+jZs4AIIkKXmAhCCKyqGG6VcIoIJcaxlDym85QSiuT1ftoOzE5YwjiGM+3EBBRJAPTqRJEPlljkJRapO0sbSyrFsnq/YSRR84YRBBlbH9zT7Mr90S70rdXeMaICSMfXc2iSgZB5BHFQdcDXjNn6M/tRsnpWG4N5xX7dC25XFJIGCYdjKMIfJ9L4r2uC54HYRTjbtHp+Eo8/GCKIIQ7bktGLIoq8NY7NAxdwOp4iJ0a8uAIgjqKv7CIOz+Pt7hE1LEJGw0gRrphDHVsjLDVJmw28FeW0UoG/mr1DaUVXrXRDTsWzvk58jffQXr7rkQar76RtK0NDpPetRdtYAilUKL0tnfgr1VpHTlEaJlk992CPjSCMboNrW8QyUhjz53FPH4E69QUSr5I4fY7kwdJkrBOT+EuLyRvpWYdOZPtGmGlWKYzm8RVlEKZzO59KKUycr5A8a1349VWMU8cxV16lRnGdSORmtyNUiwR+T7e6hLW6SlC69KmkbWpNc58Z4adD+5g/K5RojCmNddier0oYOWlVSZ/fgdDtw8S+eElRQhu22P7O7ch6zJaTmX+Bwu05lrEMSw+t8iOB7bRf1MfoRcmVTMXebLt8yYjbx1m4r5x9ILG4qEl6mcaCKLA2M+NMHjbIP0H+pANmdt+9xbWpteY+tdpsqNZJu4eIzeeIzOQ5sD799Oaa3Hu6TnWpusMvyWZ4KvsLWOUDG794M00zjY4/i9TSdVODPPPzjP50E7e9NitNGabzHx3FnPRYuCWfsYOjlDeUyY7kuWW37mJ1lyL41+donWuzZnvzDB6cITiZJEoTF4wM0/O4tQdMkMZJh/agaSIxHHy8jr7nbMEboDs2ljzlxoDSZcJGhbO+a1r3l8Z5207yyzUjzKQv5FydkciQuTVWai/RBh61K1zDBX2U0yPE0U+tt/kYm8mCB36c7sZLOxDlQxq5tkkVBCHrJlnGSzcSD41TBQFuEF73bAl2H6TUnqCvuwkipyiYc3RdpIWTZXsTkqZ7RSMYWRJZ2f/PZjuCnO15zHUAv25G0hpZXQlz7bKW7DcGivtadr2EqX0BOXsTvLGIJqSY+fA27DcKnO1Q90MiFr7DCPFm5kcvAfLXWO5eRzbawBJNwrLrVLKTKwXVlzfmf+fNAP9EvVGRH8lCTstr4R8+WsWtp1ITCqyh39uCnfFxBjKIY/kkFWLsN5EKygY/Rl0qngLNWRJgNBH12K0GyosPbN1SuZr5XUZ3bVnn0It9SEIYlcC70LqVWCZOPOzuCuLtI4cSsJKvkvkOsRRiLu6RGhbWGenkxVGEX6zkazbbNH48f+iVgYQNY2wYxFaFkQxcRhQf/ZpRFUhcj0iz6P65H8RtJMJptC1cRbn8Ro13PNzBI5NHAaEVhLDXXvmCQJz4wG2Z88kL4s4Rh8ZR0qlsWfPEFomoqaRvmEvgijRfOFHl54DL+Tkf5xi9VgVvaARR0mTSd/0iIKIo186Rm48hyAJWMsdWJe6u0BzpsnMkzOkB9Kk+lMs/HAhme0X4Ng/n6CwLY8oC1irSZnihSyCuWfmWTuVZCToRZ04gtVjq3htDwRonW8Tx7B0OHmo43i9kAHw2h7VEzVacy3mv38e4pjAC7vfm4sWS4eWWX5xYwIoMD1yWYFmI6ZYFKi/uMCxWoeDb8/QWumQkgLICHRWOiy9sMLK0erGsnZAJh3TqIU0X5wnrDYJNB1BELDrDq355Lo0Z5uc+dZZlIyS6FbUbHw7onLzMHrJQEmpiKpE5IWY55ukB7Ndr8leMclPlnFqHSRdRtJlvIaDmtfoLJo4taQsN4oDFhtHaNmL3dQu12/jhw5RHDBb/SFprYwgiDh+K5HijDZCS4nBOoEoSgShh+VWu0bqXO15MnoFUZBw/TZRHHWNXrV9hrp1DkGQ0OUsYexjOVWCMDnnttegbs5St2aTDcXgh8moJViPQXfcejcPN4qCbqGD47eoW+dodOa6ywaRu8ng18wzOH4bVTbwQwc/2BgRRVGA7TewvXx30u+nwQX1xcu1Ro/jZPR2tWGt1WrIiWmfT39mIwUuBtx1bznyk/CWmtdJD+dQcjpu1SL0AoyBLK1TVYyBLEpGJ/QCBEmgdaqKeBk9hqvhdSiJxASNOkFjay8jbLfotC8/VHEX5rj4narmy2jlAURERFUncm0iq4OIRNBsoObKEIMsCChGDr9ZJTOxm8hz8RpV9PIASiqPpBlEnkNQq6GN7sJenkFOZ1FSOcJOB2dhc9J/0G4StJsIioIxsYPQ7iShBc9FNFLI2TxKsYQgSVuWKQedgNVjW7fpMJcszKXLz6rHcUz9dAMQUFIKgigw8tZhAjsRi26ea5EZSmOUDHzLR8trZIaztM+3kRSJxpmkX1RxsohR0OnfV8Fbz39tnNk6z9JtuiwdunwTzP1jDrLs0mxGGEYyrHO9mHe+R+fb33IYG5NYWoqQGmv0OR2em3YYHxRZlUXCsMN22WNuLqRcFlFVgTUz4oFHNL79TYfBQWg1m0g0GR6WsNMxxYMipqny8jG/a+gvMHjnOPaqRX5nmcpNKn7HJ+j4yGkVohi/k2SF6KU+7GqHOIzou2UI3/KxdRO9aOC+onowjHxa9taFG47fepXhdUzbWcEPL9VW8AKTNfPS0VCy3o1rYXKpYbPc2palvQB+6FC3Lt8SvuPV6XhXVrdKjnlhi28EFMkgo/VTt+a63u/rQVMhkxEZ6E+ue3+fSL0p0mpHeB5kMwK5rEguK5JJC/T3JQLmjWbycghDmJn1ufugzslTPlYnZn4xwLIS49hohagq3HeXQS7jslILOb8QosiQzYoM9kuoKlTKIn0VkbYZ4zgx//Q1i9//YI73Puxz6mxAqSgiCvDE/9iEgozenyEK1oXw1y2+13QSofUgQu/LJCXlHQ+/7SCpEnolsyls9Xr5qauMSaksoqqjFXWiwEsmvAIf2cjgCqtJaphmQBQhG2nctWUCq0UU+EhGClFWUYv9OCvzIIpEvkfoWES+g6T1EYVy8hq9TLpqHIYE9TXSu/eRuWEvoW2jVfpRimWaL/74mupCdBGSGvbW+TaIAnGU5MBmR7KYCyapvhSSJtGeb1PYlsfv+CwfWcVrefTtraAYMtaKRaovhVE2yAxm8CyP6ss1wte5v5U+kUPP+9x/v8bx4z59/SJHj/g06hHNRszkpEAuJ3D2TMjM2ZD5+ZDhEYl8TmR4ROTY0YAohlxOYHBIYn4+pNGIaDRiRscgXxCpVETOzYYcvEvl5FTiDWZzIq3W5n121myMShp71cRvu/gdn/ZsndF7dzL/1GlyO0ooGRWn2iEzksVesTDnmgR2gNu0iYMYraBjLVybOOW1LCn/aSOJKvnUCMXUKJKosNKaekMTaDftV/ntX88yPirTbEV87CMFlpdDPvs3LY6+7PGr783w7gcMdC3xVj/58SKHXvT40081uuv4y8+1+PCjOf74DwscetHli18xsazknjg+5fMPXzZ56IEUb79b50tfNVlcstm5XeGDv5Xlhp0Kphnz2KM5fuU9IV/4ssl3n3b45pMdPD/mfQ+ned8jImv1kMe/0VnPZQ9Y+t7Z7vYFYT3mvD6Eqr+8nHjW66flwtyZIF6bOdBrL3hzlRhD2xAkMZHU830EWSF0bSQtReS7SHqKOAwIbAvZSBPaFkgSoigjqCqCICLKMl5zbb2ooo3eN4JbX0HSDERFw11bvqJ4iagbpHfegD4y0RU86Zw9hT0/k7yKryFjd42iZlVmnppFVqWk8iyKSVUM4jjGWu6QH88lhQN9qeQmcQOa51pJ59ZbBjCXLOxah9JkiU6tg1HSsWvJkP31ZjIcuElh7lzA7j0Knpes4/TpgP0HFKZPBuzYLuG4cHIqYOekxPTJgJtuVpIJEDsmnxdoNGOK6x7F8eM+N+5VmJ4OGBuTEASw7RhdFxBFWKtFScrYWkS9fo1vs4semDdKObMDTU6z3JraFHL4/4oqpxgp3oIsaaw0p2hu6Qn3eKNcX5WxHj+TdL2FHj16XNHoXhtp/h4/8/QMbo8er42e0e3Ro0eP68gVwws9evTo0ePa0vN0e/To0eM60jO6PXr06HEd6RndHj169LiO9Ixujx49elxHeka3R48ePa4jPaPbo0ePHteR/wODYxNH6DVWFwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generating a wordcloud\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "joined_text = \" \".join(token for token in words)\n",
    "# print(joined_text)\n",
    "wordcloud = WordCloud().generate(joined_text)\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "# plt.savefig('wordcloud.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones. But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do. The most visible advances have been in natural language processing (NLP), the branch of AI focused on how computers can process language like humans do. It has been used to write an article for The Guardian, and AI-authored blog posts have gone viral — feats that were not possible a few years ago. AI even excels at cognitive tasks like programming where it is able to generate programs for simple video games from human instructions.\n",
      "This mindset can create new opportunities for businesses as well — but shifting needs and priorities can also bring new risks. What can retailers do to attract consumers in this critical time of the year, as people rethink their goals and chart a new course for the months ahead? To better understand how today’s consumers are thinking about their New Year’s Resolutions, we surveyed a diverse group of 500 adults from across the U.S. in the last week of 2022. We found that two out of three planned to make at least one resolution for 2023, and the majority indicated that they would be making multiple resolutions.\n",
      "Elon Musk has finally unveiled a prototype of the much-hyped Optimus robot — a bipedal machine that the Tesla CEO imagines will one day be sold as a general purpose bot that is cheaper than a car and equally capable of working in factories and doing chores at home.\n",
      "While the sort of new HomePod grabbed all the attention this week, the HomePod Mini is also getting some new features. With the launch of iOS 16.3 (likely later this month), the Mini will get all the same smart home capabilities as the new, bigger HomePod second-gen. The differences between the two Apple speaker siblings are now mainly in size and sound capability. From new temperature and humidity sensing tricks to the ability to find your family for you and even set up automations with just your voice, these new features should make the HomePod Mini a little more useful around the house. These new features bring some much-needed functionality to the HomePod line Both HomePods are also getting a Sound Recognition feature later this year, allowing them to listen for smoke and carbon monoxide alarms. I saw a preview of this, and it looks like a useful integration, allowing you to talk directly to anyone in your home through the HomePod from a notification on your lock screen. But my favorite new capability is something Siri now won’t do — which is to talk back to you every time you ask it to turn off the lights. I like my voice assistants to say as little as possible.The new capabilities will show up automatically once you update your devices to iOS 16.3 and HomePod software version 16.3, which should be available later this month. However, the Release Candidate of the iOS 16.3 beta landed on Wednesday, so I downloaded it to get an early look at some of these new features. Here are some first impressions.\n"
     ]
    }
   ],
   "source": [
    "#Multiprocessing with spacy\n",
    "texts=df.para.tolist() #converting dataframe to list\n",
    "docs = nlp.pipe(texts, n_process=4) #using 4 processes to process texts\n",
    "\n",
    "for doc in docs:\n",
    "    print(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "new\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.9', 'sentics': {'introspection': '0.9', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['good', 'cool', 'good_feeling', 'awesome', 'great']}\n",
      "\n",
      "new\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.9', 'sentics': {'introspection': '0.9', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['good', 'cool', 'good_feeling', 'awesome', 'great']}\n",
      "\n",
      "same\n",
      "\n",
      "smart\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.921', 'sentics': {'introspection': '0.995', 'temper': '0.801', 'attitude': '0', 'sensitivity': '0.968'}, 'moodtags': ['#joy', '#eagerness'], 'semantics': ['acquire_knowledge', 'comprehend', 'great', 'accumulate_knowledge', 'increase_knowledge']}\n",
      "\n",
      "new\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.9', 'sentics': {'introspection': '0.9', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['good', 'cool', 'good_feeling', 'awesome', 'great']}\n",
      "\n",
      "bigger\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.943', 'sentics': {'introspection': '0.936', 'temper': '0', 'attitude': '0', 'sensitivity': '0.95'}, 'moodtags': ['#joy', '#eagerness'], 'semantics': ['handsome', 'well-favored', 'good-looking', 'better-looking', 'bighearted']}\n",
      "\n",
      "new\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.9', 'sentics': {'introspection': '0.9', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['good', 'cool', 'good_feeling', 'awesome', 'great']}\n",
      "\n",
      "new\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.9', 'sentics': {'introspection': '0.9', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['good', 'cool', 'good_feeling', 'awesome', 'great']}\n",
      "\n",
      "useful\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.13', 'sentics': {'introspection': '0.08', 'temper': '0.21', 'attitude': '-0.07', 'sensitivity': '0.304'}, 'moodtags': ['#calmness', '#eagerness'], 'semantics': ['delicate', 'versatile', 'good', 'light_colored', 'beautiful']}\n",
      "\n",
      "new\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.9', 'sentics': {'introspection': '0.9', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['good', 'cool', 'good_feeling', 'awesome', 'great']}\n",
      "\n",
      "useful\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.13', 'sentics': {'introspection': '0.08', 'temper': '0.21', 'attitude': '-0.07', 'sensitivity': '0.304'}, 'moodtags': ['#calmness', '#eagerness'], 'semantics': ['delicate', 'versatile', 'good', 'light_colored', 'beautiful']}\n",
      "\n",
      "favorite\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.879', 'sentics': {'introspection': '0.799', 'temper': '0', 'attitude': '0.959', 'sensitivity': '0'}, 'moodtags': ['#joy', '#pleasantness'], 'semantics': ['acclamation', 'salutation', 'encore', 'ovation', 'honor']}\n",
      "\n",
      "new\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.9', 'sentics': {'introspection': '0.9', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['good', 'cool', 'good_feeling', 'awesome', 'great']}\n",
      "\n",
      "little\n",
      "\n",
      "possible\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.164', 'sentics': {'introspection': '0', 'temper': '0.164', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#calmness', '#calmness'], 'semantics': ['manageable', 'attainable', 'within_reach', 'viable', 'coordination']}\n",
      "\n",
      "new\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.9', 'sentics': {'introspection': '0.9', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['good', 'cool', 'good_feeling', 'awesome', 'great']}\n",
      "\n",
      "available\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.806', 'sentics': {'introspection': '0.806', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['serene', 'togetherness', 'easily', 'tranquility', 'dreaminess']}\n",
      "\n",
      "early\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.17', 'sentics': {'introspection': '0', 'temper': '0', 'attitude': '0', 'sensitivity': '0.17'}, 'moodtags': ['#eagerness', '#eagerness'], 'semantics': ['chrysalism', 'guarded', 'secure', 'reliable', 'innocuous']}\n",
      "\n",
      "new\n",
      "{'polarity_label': 'positive', 'polarity_value': '0.9', 'sentics': {'introspection': '0.9', 'temper': '0', 'attitude': '0', 'sensitivity': '0'}, 'moodtags': ['#joy', '#joy'], 'semantics': ['good', 'cool', 'good_feeling', 'awesome', 'great']}\n",
      "\n",
      "first\n"
     ]
    }
   ],
   "source": [
    "#Bonus\n",
    "#Concept analysis library (Senticnet)\n",
    "from senticnet.senticnet import SenticNet\n",
    "sn = SenticNet()\n",
    "\n",
    "for token in doc:\n",
    "    try:\n",
    "        if token.pos_ == 'ADJ':\n",
    "            print(\"\\n{0}\".format(token.text))\n",
    "            print(sn.concept(token.text))\n",
    "    except:\n",
    "        pass\n"
   ]
  }
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