entropy()
enumerate_support(expand=True)
expand(batch_shape, _instance=None)
property has_enumerate_support
property has_rsample
log_prob(value)
property mean
property mode
rsample(sample_shape=torch.Size([]))
sample(sample_shape=torch.Size([]))
property support
property variance
Kumaraswamy
class torch.distributions.kumaraswamy.Kumaraswamy(concentration1, concentration0, validate_args=None)
Bases: "TransformedDistribution"
Samples from a Kumaraswamy distribution.
Example:
  >>> m = Kumaraswamy(torch.tensor([1.0]), torch.tensor([1.0]))
  >>> m.sample()  # sample from a Kumaraswamy distribution with concentration alpha=1 and beta=1
  tensor([ 0.1729])

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