class_docstr: Samples from a Kumaraswamy distribution.
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> 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])
  
  Args:
      concentration1 (float or Tensor): 1st concentration parameter of the distribution
          (often referred to as alpha)
      concentration0 (float or Tensor): 2nd concentration parameter of the distribution
          (often referred to as beta)
code: def entropy(self):
      t1 = 1 - self.concentration1.reciprocal()
      t0 = 1 - self.concentration0.reciprocal()
      H0 = torch.digamma(self.concentration0 + 1) + euler_constant
      return (
          t0
          + t1 * H0
          - torch.log(self.concentration1)
          - torch.log(self.concentration0)
      )
  
file_path: pytorch/torch/distributions/kumaraswamy.py
imports: import torch
  from torch import nan
  from torch.distributions import constraints
  from torch.distributions.transformed_distribution import TransformedDistribution
  from torch.distributions.transforms import AffineTransform, PowerTransform
  from torch.distributions.uniform import Uniform
  from torch.distributions.utils import broadcast_all, euler_constant
label_desc: Use image node_id 6 for calling the Kumaraswamy obj's underlying member method code with example usage: obj.entropy() and returns: unknown