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: The scale parameter in the Generalized Extreme Value CDF.
docstr_tok: ["The","scale","parameter","in","the","Generalized","Extreme","Value","CDF","."]
code: def scale(self):
      """The scale parameter in the Generalized Extreme Value CDF."""
      return self._scale
  
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 4 for calling the GeneralizedExtremeValueCDF obj's underlying member method code with example usage: obj.scale() and returns: self