1 along the last dimension. "logits" will return this normalized
     value.
See also: "torch.multinomial()"
Example:
  >>> m = Categorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
  >>> m.sample()  # equal probability of 0, 1, 2, 3
  tensor(3)

Parameters:
      * probs (Tensor) -- event probabilities
  * **logits** (*Tensor*) -- event log probabilities
    (unnormalized)

arg_constraints = {'logits': IndependentConstraint(Real(), 1), 'probs': Simplex()}
entropy()
enumerate_support(expand=True)
expand(batch_shape, _instance=None)
has_enumerate_support = True
log_prob(value)
property logits
property mean
property mode
property param_shape
property probs
sample(sample_shape=torch.Size([]))
property support
property variance
Cauchy
class torch.distributions.cauchy.Cauchy(loc, scale, validate_args=None)
Bases: "Distribution"
Samples from a Cauchy (Lorentz) distribution. The distribution of