tensor([   0.,   22.,   71.,  100.])
  >>> m = Binomial(torch.tensor([[5.], [10.]]), torch.tensor([0.5, 0.8]))
  >>> x = m.sample()
  tensor([[ 4.,  5.],
          [ 7.,  6.]])

Parameters:
      * total_count (int or Tensor) -- number of Bernoulli
        trials
  * **probs** (*Tensor*) -- Event probabilities

  * **logits** (*Tensor*) -- Event log-odds

arg_constraints = {'logits': Real(), 'probs': Interval(lower_bound=0.0, upper_bound=1.0), 'total_count': IntegerGreaterThan(lower_bound=0)}
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
Categorical
class torch.distributions.categorical.Categorical(probs=None, logits=None, validate_args=None)
Bases: "Distribution"