class_docstr: Transform inputs by Kumaraswamy bijector.
  
  Uses Kumaraswamy bijector to warp features. The purpose of this is to is turn
  a stationary kernel in to a non-stationary one. Note that this kernel requires
  inputs to be in `[0, 1]` (the domain of the bijector).
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docstr: Construct a KumaraswamtTransformed kernel instance.
  
  This kernel wraps a kernel `k`, pre-transforming inputs with an
  Kumaraswamy bijector's `inverse` function before passign off to `k`.
  
  Args:
    kernel: The `PositiveSemidefiniteKernel` instance to transform.
    concentration1: forwarded to the concentration1 parameter of the
      Kumaraswamy bijector.
    concentration0: forwarded to the concentration0 parameter of the
      Kumaraswamy bijector.
    validate_args: forwarded to the `validate_args` parameter of the
      Kumaraswamy bijector.
    name: Python `str` name given to ops managed by this object.
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code: def __init__(
      self,
      kernel,
      concentration1,
      concentration0,
      validate_args=False,
      name="KumaraswamyTransformedKernel",
  ):
      """Construct a KumaraswamtTransformed kernel instance.
  
      This kernel wraps a kernel `k`, pre-transforming inputs with an
      Kumaraswamy bijector's `inverse` function before passign off to `k`.
  
      Args:
        kernel: The `PositiveSemidefiniteKernel` instance to transform.
        concentration1: forwarded to the concentration1 parameter of the
          Kumaraswamy bijector.
        concentration0: forwarded to the concentration0 parameter of the
          Kumaraswamy bijector.
        validate_args: forwarded to the `validate_args` parameter of the
          Kumaraswamy bijector.
        name: Python `str` name given to ops managed by this object.
      """
      parameters = dict(locals())
  
      # Delayed import to avoid circular dependency between `tfp.bijectors` and
      # `tfp.math`
      from tensorflow_probability.python.bijectors import (
          kumaraswamy_cdf,
      )  # pylint: disable=g-import-not-at-top
  
      with tf.name_scope(name):
          self._concentration1 = tensor_util.convert_nonref_to_tensor(
              concentration1, name="concentration1"
          )
          self._concentration0 = tensor_util.convert_nonref_to_tensor(
              concentration0, name="concentration0"
          )
  
          def transform_by_kumaraswamy(x, feature_ndims, example_ndims):
              """Apply a Kumaraswamy bijector to features."""
              concentration1 = util.pad_shape_with_ones(
                  self.concentration1,
                  example_ndims,
                  start=-(feature_ndims + 1),
              )
              concentration0 = util.pad_shape_with_ones(
                  self.concentration0,
                  example_ndims,
                  start=-(feature_ndims + 1),
              )
              bij = kumaraswamy_cdf.KumaraswamyCDF(
                  concentration1,
                  concentration0,
                  validate_args=validate_args,
              )
              return bij.forward(x)
  
          super(KumaraswamyTransformed, self).__init__(
              kernel,
              transformation_fn=transform_by_kumaraswamy,
              validate_args=validate_args,
              parameters=parameters,
          )
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/kumaraswamy_transformed.py
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_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.psd_kernels import feature_transformed
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 1 to create a new KumaraswamyTransformed object from inherited base classes: feature_transformed with example: obj = KumaraswamyTransformed(kernel, concentration1, concentration0, validate_args, name)