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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code: def _is_increasing(cls):
      return True
  
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 5 for calling the KumaraswamyCDF obj's underlying member method code with example usage: obj._is_increasing(cls) and returns: True