class_docstr: Compute `Y = g(X) = 1 - exp( -( X / scale) ** concentration), 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
  [Weibull distribution](https://en.wikipedia.org/wiki/Weibull_distribution):
  
  ```none
  Y ~ Weibull(scale, concentration)
  pdf(y; scale, concentration, y >= 0) =
      (concentration / scale) * (y / scale)**(concentration - 1) *
      exp(-(y / scale)**concentration)
  ```
  
  Likwewise, the forward of this bijector is the Weibull distribution CDF.
docstr: Instantiates the `WeibullCDF` bijector.
  
  Args:
    scale: Positive Float-type `Tensor` that is the same dtype and is
      broadcastable with `concentration`.
      This is `l` in `Y = g(X) = 1 - exp( -( x / l) ** k)`.
    concentration: Positive Float-type `Tensor` that is the same dtype and is
      broadcastable with `scale`.
      This is `k` in `Y = g(X) = 1 - exp( -( x / l) ** k)`.
    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,
      concentration=1.0,
      validate_args=False,
      name="weibull_cdf",
  ):
      """Instantiates the `WeibullCDF` bijector.
  
      Args:
        scale: Positive Float-type `Tensor` that is the same dtype and is
          broadcastable with `concentration`.
          This is `l` in `Y = g(X) = 1 - exp( -( x / l) ** k)`.
        concentration: Positive Float-type `Tensor` that is the same dtype and is
          broadcastable with `scale`.
          This is `k` in `Y = g(X) = 1 - exp( -( x / l) ** k)`.
        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, concentration], dtype_hint=tf.float32
          )
          self._scale = tensor_util.convert_nonref_to_tensor(
              scale, dtype=dtype, name="scale"
          )
          self._concentration = tensor_util.convert_nonref_to_tensor(
              concentration, dtype=dtype, name="concentration"
          )
          super(WeibullCDF, 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 WeibullCDF object from inherited base classes: bijector, bijector with example: obj = WeibullCDF(scale, concentration, validate_args, name)