class_docstr: Kumaraswamy distribution.
  
  The Kumaraswamy distribution is defined over the `(0, 1)` interval using
  parameters
  `concentration1` (aka 'alpha') and `concentration0` (aka 'beta').  It has a
  shape similar to the Beta distribution, but is easier to reparameterize.
  
  #### Mathematical Details
  
  The probability density function (pdf) is,
  
  ```none
  pdf(x; alpha, beta) = alpha * beta * x**(alpha - 1) * (1 - x**alpha)**(beta -
  1)
  ```
  
  where:
  
  * `concentration1 = alpha`,
  * `concentration0 = beta`,
  
  Distribution parameters are automatically broadcast in all functions; see
  examples for details.
  
  #### Examples
  
  ```python
  # Create a batch of three Kumaraswamy distributions.
  alpha = [1, 2, 3]
  beta = [1, 2, 3]
  dist = Kumaraswamy(alpha, beta)
  
  dist.sample([4, 5])  # Shape [4, 5, 3]
  
  # `x` has three batch entries, each with two samples.
  x = [[.1, .4, .5],
       [.2, .3, .5]]
  # Calculate the probability of each pair of samples under the corresponding
  # distribution in `dist`.
  dist.prob(x)         # Shape [2, 3]
  ```
  
  ```python
  # Create batch_shape=[2, 3] via parameter broadcast:
  alpha = [[1.], [2]]      # Shape [2, 1]
  beta = [3., 4, 5]        # Shape [3]
  dist = Kumaraswamy(alpha, beta)
  
  # alpha broadcast as: [[1., 1, 1,],
  #                      [2, 2, 2]]
  # beta broadcast as:  [[3., 4, 5],
  #                      [3, 4, 5]]
  # batch_Shape [2, 3]
  dist.sample([4, 5])  # Shape [4, 5, 2, 3]
  
  x = [.2, .3, .5]
  # x will be broadcast as [[.2, .3, .5],
  #                         [.2, .3, .5]],
  # thus matching batch_shape [2, 3].
  dist.prob(x)         # Shape [2, 3]
  ```
code: def _default_event_space_bijector(self):
      return sigmoid_bijector.Sigmoid(validate_args=self.validate_args)
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import kumaraswamy_cdf
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 11 for calling the Kumaraswamy obj's underlying member method code with example usage: obj._default_event_space_bijector() and returns: sigmoid_bijector