are interchangeable), you can:
  >>> from torch.distributions.multivariate_normal import MultivariateNormal
  >>> from torch.distributions.normal import Normal
  >>> loc = torch.zeros(3)
  >>> scale = torch.ones(3)
  >>> mvn = MultivariateNormal(loc, scale_tril=torch.diag(scale))
  >>> [mvn.batch_shape, mvn.event_shape]
  [torch.Size([]), torch.Size([3])]
  >>> normal = Normal(loc, scale)
  >>> [normal.batch_shape, normal.event_shape]
  [torch.Size([3]), torch.Size([])]
  >>> diagn = Independent(normal, 1)
  >>> [diagn.batch_shape, diagn.event_shape]
  [torch.Size([]), torch.Size([3])]

Parameters:
      * base_distribution
        (torch.distributions.distribution.Distribution) -- a base
        distribution
  * **reinterpreted_batch_ndims** (*int*) -- the number of batch
    dims to reinterpret as event dims

arg_constraints: Dict[str, Constraint] = {}
entropy()
enumerate_support(expand=True)