concentration parameter of the distribution (often referred to
        as beta)
arg_constraints = {'concentration0': GreaterThan(lower_bound=0.0), 'concentration1': GreaterThan(lower_bound=0.0)}
property concentration0
property concentration1
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
log_prob(value)
property mean
property mode
rsample(sample_shape=())
support = Interval(lower_bound=0.0, upper_bound=1.0)
property variance
Binomial
class torch.distributions.binomial.Binomial(total_count=1, probs=None, logits=None, validate_args=None)
Bases: "Distribution"
Creates a Binomial distribution parameterized by "total_count" and
   either "probs" or "logits" (but not both). "total_count" must be
   broadcastable with "probs"/"logits".
Example:
  >>> m = Binomial(100, torch.tensor([0 , .2, .8, 1]))
  >>> x = m.sample()
  tensor([   0.,   22.,   71.,  100.])
