class_docstr: Compute `Y = g(X) = exp(-exp(-(X - loc) / scale))`, the Gumbel CDF.
  
  This bijector maps inputs from `[-inf, 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
  [Gumbel distribution](https://en.wikipedia.org/wiki/Gumbel_distribution):
  
  ```none
  Y ~ GumbelCDF(loc, scale)
  pdf(y; loc, scale) = exp(
    -( (y - loc) / scale + exp(- (y - loc) / scale) ) ) / scale
  ```
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code: def _forward_log_det_jacobian(self, x):
      scale = tf.convert_to_tensor(self.scale)
      z = (x - self.loc) / scale
      return -z - tf.exp(-z) - tf.math.log(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 9 for calling the GumbelCDF obj's underlying member method code with example usage: obj._forward_log_det_jacobian(x) and returns: unknown