Examples:
  >>> m = Gumbel(torch.tensor([1.0]), torch.tensor([2.0]))
  >>> m.sample()  # sample from Gumbel distribution with loc=1, scale=2
  tensor([ 1.0124])

Parameters:
      * loc (float or Tensor) -- Location parameter of the
        distribution
  * **scale** (*float** or **Tensor*) -- Scale parameter of the
    distribution

arg_constraints: Dict[str, constraints.Constraint] = {'loc': Real(), 'scale': GreaterThan(lower_bound=0.0)}
entropy()
expand(batch_shape, _instance=None)
log_prob(value)
property mean
property mode
property stddev
support = Real()
property variance
HalfCauchy
class torch.distributions.half_cauchy.HalfCauchy(scale, validate_args=None)
Bases: "TransformedDistribution"
Creates a half-Cauchy distribution parameterized by scale where:
  X ~ Cauchy(0, scale)
  Y = |X| ~ HalfCauchy(scale)

Example:
  >>> m = HalfCauchy(torch.tensor([1.0]))
