class_docstr: Compute `Y = g(X) = (1 - X**a)**b, X in [0, 1]`.
  
  This bijector maps inputs from `[0, 1]` to `[0, 1]`. The inverse of the
  bijector applied to a uniform random variable `X ~ U(0, 1)` gives back a
  random variable with the [Kumaraswamy distribution](
  https://en.wikipedia.org/wiki/Kumaraswamy_distribution):
  
  ```none
  Y ~ Kumaraswamy(a, b)
  pdf(y; a, b, 0 <= y <= 1) = a * b * y ** (a - 1) * (1 - y**a) ** (b - 1)
  ```
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docstr: Instantiates the `KumaraswamyCDF` bijector.
  
  Args:
    concentration1: Python `float` scalar indicating the transform power,
      i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `a` is
      `concentration1`.
    concentration0: Python `float` scalar indicating the transform power,
      i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `b` is
      `concentration0`.
    validate_args: Python `bool` indicating whether arguments should be
      checked for correctness.
    name: Python `str` name given to ops managed by this object.
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code: def __init__(
      self,
      concentration1=1.0,
      concentration0=1.0,
      validate_args=False,
      name="kumaraswamy_cdf",
  ):
      """Instantiates the `KumaraswamyCDF` bijector.
  
      Args:
        concentration1: Python `float` scalar indicating the transform power,
          i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `a` is
          `concentration1`.
        concentration0: Python `float` scalar indicating the transform power,
          i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `b` is
          `concentration0`.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness.
        name: Python `str` name given to ops managed by this object.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          dtype = dtype_util.common_dtype(
              [concentration0, concentration1], dtype_hint=tf.float32
          )
          self._concentration0 = tensor_util.convert_nonref_to_tensor(
              concentration0, dtype=dtype, name="concentration0"
          )
          self._concentration1 = tensor_util.convert_nonref_to_tensor(
              concentration1, dtype=dtype, name="concentration1"
          )
          super(KumaraswamyCDF, self).__init__(
              forward_min_event_ndims=0,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import softplus
  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
label_desc: Use image node_id 1 to create a new KumaraswamyCDF object from inherited base classes: bijector, bijector with example: obj = KumaraswamyCDF(concentration1, concentration0, validate_args, name)