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 mean(self):
      return _moments(self.concentration1, self.concentration0, 1)
  
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 3 for calling the Kumaraswamy obj's underlying member method code with example usage: obj.mean() and returns: _moments