class_docstr: Compute `Y = g(X) = 1 - exp( -(X/scale)**2 / 2 ), X >= 0`.
  
  This bijector maps inputs from `[0, inf]` 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
  [Rayleigh distribution](https://en.wikipedia.org/wiki/Rayleigh_distribution):
  
  ```none
  Y ~ Rayleigh(scale)
  pdf(y; scale, y >= 0) =
      (1 / scale) * (y / scale) *
      exp(-(y / scale)**2 / 2)
  ```
  
  Likwewise, the forward of this bijector is the Rayleigh distribution CDF.
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docstr: Instantiates the `RayleighCDF` bijector.
  
  Args:
    scale: Positive Float-type `Tensor`.
      This is `l` in `Y = g(X) = 1 - exp( -(X/l)**2 / 2 ), X >= 0`.
    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, scale=1.0, validate_args=False, name="rayleigh_cdf"
  ):
      """Instantiates the `RayleighCDF` bijector.
  
      Args:
        scale: Positive Float-type `Tensor`.
          This is `l` in `Y = g(X) = 1 - exp( -(X/l)**2 / 2 ), X >= 0`.
        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(
              [scale], dtype_hint=tf.float32
          )
          self._scale = tensor_util.convert_nonref_to_tensor(
              scale, dtype=dtype, name="scale"
          )
          super(RayleighCDF, self).__init__(
              forward_min_event_ndims=0,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
              dtype=dtype,
          )
  
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 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 RayleighCDF object from inherited base classes: bijector, bijector with example: obj = RayleighCDF(scale, validate_args, name)