value of df or adjust max_try_correction value for
        argument in .rsample accordingly.
property scale_tril
support = PositiveDefinite()
property variance
KL Divergence
torch.distributions.kl.kl_divergence(p, q)
Compute Kullback-Leibler divergence KL(p | q) between two
   distributions.
  KL(p \| q) = \int p(x) \log\frac {p(x)} {q(x)} \,dx

Parameters:
      * p (Distribution) -- A "Distribution" object.
  * **q** (*Distribution*) -- A "Distribution" object.

Returns:
      A batch of KL divergences of shape batch_shape.
Return type:
      Tensor
Raises:
      NotImplementedError -- If the distribution types have not
      been registered via     "register_kl()".
KL divergence is currently implemented for the following
   distribution pairs:
      * "Bernoulli" and "Bernoulli"
  * "Bernoulli" and "Poisson"

  * "Beta" and "Beta"

  * "Beta" and "ContinuousBernoulli"

  * "Beta" and "Exponential"
