Update README.rst

README.rst

@@ -149,7 +149,7 @@ If one wants to use a parametrized tensor in different places in their model, or
 
 Of course, this ``with`` statement may be used simply inside the forward function where the parametrized layer is used several times.
 
-These ideas fall in the context of parametrized optimization, where one wraps a tensor ``X`` with a function ``f``, and rather than using ``X``, uses ``f(X)``. Particular examples of this idea are pruning, weight normalization, and spectral normalization among others. This repository implements a framework to approach this kind of problems. This framework was accepted to core PyTorch 1.8. It can be found under `torch.nn.utils.parametrize`_ and `torch.nn.utils.parametrizations`_. When using PyTorch 1.10 or higher, these functions are used, and the user can interact with the parametrizations in GeoTorch using the functions in PyTorch.
+These ideas fall in the context of parametrized optimization, where one wraps a tensor ``X`` with a function ``f``, and rather than using ``X``, uses ``f(X)``. Particular examples of this idea are pruning, weight normalization, and spectral normalization among others. This repository implements a framework to approach this kind of problems. This framework was accepted to core PyTorch 1.8. It can be found under `torch.nn.utils.parametrize`_ and `torch.nn.utils.parametrizations`_. When using PyTorch 1.10 or higher, the native PyTorch functions are used within GeoTorch. In this case, the user can interact with the parametrizations in GeoTorch using the PyTorch functions.
 
 As every space in GeoTorch is, at its core, a map from a flat space into a manifold, the tools implemented here also serve as a building block in normalizing flows. Using a factorized space such as |low|_ it is direct to compute the determinant of the transformation it defines, as we have direct access to the singular values of the layer.