then normalizing.
This is not bijective and cannot be used for HMC. However this acts
   mostly coordinate-wise (except for the final normalization), and
   thus is appropriate for coordinate-wise optimization algorithms.
class torch.distributions.transforms.StackTransform(tseq, dim=0, cache_size=0)
Transform functor that applies a sequence of transforms tseq
   component-wise to each submatrix at dim in a way compatible with
   "torch.stack()".
Example:
  x = torch.stack([torch.range(1, 10), torch.range(1, 10)], dim=1)
  t = StackTransform([ExpTransform(), identity_transform], dim=1)
  y = t(x)

class torch.distributions.transforms.StickBreakingTransform(cache_size=0)
Transform from unconstrained space to the simplex of one additional
   dimension via a stick-breaking process.
This transform arises as an iterated sigmoid transform in a stick-
   breaking construction of the Dirichlet distribution: the first