Transforms
class torch.distributions.transforms.AbsTransform(cache_size=0)
Transform via the mapping y = |x|.
class torch.distributions.transforms.AffineTransform(loc, scale, event_dim=0, cache_size=0)
Transform via the pointwise affine mapping y = \text{loc} +
   \text{scale} \times x.
Parameters:
      * loc (Tensor or float) -- Location parameter.
  * **scale** (*Tensor** or **float*) -- Scale parameter.

  * **event_dim** (*int*) -- Optional size of *event_shape*. This
    should be zero for univariate random variables, 1 for
    distributions over vectors, 2 for distributions over matrices,
    etc.

class torch.distributions.transforms.CatTransform(tseq, dim=0, lengths=None, cache_size=0)
Transform functor that applies a sequence of transforms tseq
   component-wise to each submatrix at dim, of length
   lengths[dim], in a way compatible with "torch.cat()".
Example: