arg_constraints = {'df': GreaterThan(lower_bound=0.0), 'loc': Real(), 'scale': GreaterThan(lower_bound=0.0)}
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
log_prob(value)
property mean
property mode
rsample(sample_shape=torch.Size([]))
support = Real()
property variance
TransformedDistribution
class torch.distributions.transformed_distribution.TransformedDistribution(base_distribution, transforms, validate_args=None)
Bases: "Distribution"
Extension of the Distribution class, which applies a sequence of
   Transforms to a base distribution.  Let f be the composition of
   transforms applied:
  X ~ BaseDistribution
  Y = f(X) ~ TransformedDistribution(BaseDistribution, f)
  log p(Y) = log p(X) + log |det (dX/dY)|

Note that the ".event_shape" of a "TransformedDistribution" is the
   maximum shape of its base distribution and its transforms, since
   transforms can introduce correlations among events.