diagonals and unit Euclidean norm for each row. The transform is
   processed as follows:
  1. First we convert x into a lower triangular matrix in row
     order.

  2. For each row X_i of the lower triangular part, we apply a
     *signed* version of class "StickBreakingTransform" to
     transform X_i into a unit Euclidean length vector using the
     following steps: - Scales into the interval (-1, 1) domain:
     r_i = \tanh(X_i). - Transforms into an unsigned domain: z_i =
     r_i^2. - Applies s_i = StickBreakingTransform(z_i). -
     Transforms back into signed domain: y_i = sign(r_i) *
     \sqrt{s_i}.

class torch.distributions.transforms.CumulativeDistributionTransform(distribution, cache_size=0)
Transform via the cumulative distribution function of a probability
   distribution.
Parameters:
      distribution (Distribution) -- Distribution whose
      cumulative distribution function to use for the transformation.
Example: