class_docstr: Compute the GeneralizedExtremeValue CDF.
  
  Compute `Y = g(X) = exp(-t(X))`,
  
  where `t(x)` is defined to be:
    *`(1 + conc * (x - loc) / scale) ) ** (-1 / conc)` when `conc != 0`;
    *`exp(-(x - loc) / scale)` when `conc = 0`.
  
  This bijector maps inputs from the domain to `[0, 1]`, where the domain is
    * [loc - scale/conc, inf) when conc > 0;
    * (-inf, loc - scale/conc] when conc < 0;
    * (-inf, inf) when conc = 0;
  
  
  
  The inverse of the bijector applied to a uniform random variable
  `X ~ U(0, 1)` gives back a random variable with the
  [Generalized extreme value distribution](
  https://https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution):
  
  When `concentration -> +-inf`, the probability mass concentrates near `loc`.
  
  ```none
  Y ~ GeneralizedExtremeValueCDF(loc, scale, conc)
  pdf(y; loc, scale, conc) = t(y; loc, scale, conc) ** (1 + conc) * exp(
  - t(y; loc, scale, conc) ) / scale
  where t(x) =
    * (1 + conc * (x - loc) / scale) ) ** (-1 / conc) when conc != 0;
    * exp(-(x - loc) / scale) when conc = 0.
  ```
docstr: Instantiates the `GeneralizedExtremeValueCDF` bijector.
  
  Args:
    loc: Float-like `Tensor` that is the same dtype and is broadcastable with
      `scale` and `concentration`.
    scale: Positive Float-like `Tensor` that is the same dtype and is
      broadcastable with `loc` and `concentration`.
    concentration: Nonzero float-like `Tensor` that is the same dtype and is
      broadcastable with `loc` and `scale`.
    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,
      loc=0.0,
      scale=1.0,
      concentration=0,
      validate_args=False,
      name="generalizedextremevalue_cdf",
  ):
      """Instantiates the `GeneralizedExtremeValueCDF` bijector.
  
      Args:
        loc: Float-like `Tensor` that is the same dtype and is broadcastable with
          `scale` and `concentration`.
        scale: Positive Float-like `Tensor` that is the same dtype and is
          broadcastable with `loc` and `concentration`.
        concentration: Nonzero float-like `Tensor` that is the same dtype and is
          broadcastable with `loc` and `scale`.
        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(
              [loc, scale, concentration], dtype_hint=tf.float32
          )
          self._loc = tensor_util.convert_nonref_to_tensor(
              loc, dtype=dtype, name="loc"
          )
          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(GeneralizedExtremeValueCDF, self).__init__(
              validate_args=validate_args,
              forward_min_event_ndims=0,
              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 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 GeneralizedExtremeValueCDF object from inherited base classes: bijector, bijector with example: obj = GeneralizedExtremeValueCDF(loc, scale, concentration, validate_args, name)