likewise be normalized so that the resulting probabilities sum to
     1 along the last dimension. "logits" will return this normalized
     value.
See also: "torch.distributions.Categorical()" for specifications of
   "probs" and "logits".
Example:
  >>> m = OneHotCategorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
  >>> m.sample()  # equal probability of 0, 1, 2, 3
  tensor([ 0.,  0.,  0.,  1.])

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([]))
support = OneHot()
property variance
Pareto