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 = Boolean()
property variance
Beta
class torch.distributions.beta.Beta(concentration1, concentration0, validate_args=None)
Bases: "ExponentialFamily"
Beta distribution parameterized by "concentration1" and
   "concentration0".
Example:
  >>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
  >>> m.sample()  # Beta distributed with concentration concentration1 and concentration0
  tensor([ 0.1046])

Parameters:
      * concentration1 (float or Tensor) -- 1st
        concentration parameter of the distribution (often referred to
        as alpha)
  * **concentration0** (*float** or **Tensor*) -- 2nd
