
out_shape (torch.Size) -- The output event shape.

class torch.distributions.transforms.SigmoidTransform(cache_size=0)
Transform via the mapping y = \frac{1}{1 + \exp(-x)} and x =
   \text{logit}(y).
class torch.distributions.transforms.SoftplusTransform(cache_size=0)
Transform via the mapping \text{Softplus}(x) = \log(1 + \exp(x)).
   The implementation reverts to the linear function when x > 20.
class torch.distributions.transforms.TanhTransform(cache_size=0)
Transform via the mapping y = \tanh(x).
It is equivalent to "ComposeTransform([AffineTransform(0., 2.),
   SigmoidTransform(), AffineTransform(-1., 2.)])" However this
   might not be numerically stable, thus it is recommended to use
   TanhTransform instead.
Note that one should use cache_size=1 when it comes to NaN/Inf
   values.
class torch.distributions.transforms.SoftmaxTransform(cache_size=0)
Transform from unconstrained space to the simplex via y = \exp(x)
   then normalizing.