class_docstr: The Frechet cumulative density function.
  
  Computes `Y = g(X) = exp(-((X - loc) / scale)**(-concentration))`, the Frechet
  CDF.
  
  This bijector maps inputs from `[loc, 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
  [Frechet distribution](https://en.wikipedia.org/wiki/Frechet_distribution):
  
  ```none
  Y ~ Frechet(loc, scale, concentration)
  pdf(y; loc, scale, concentration) = (concentration / scale)
      * exp(- ((x - loc)/scale)**(-concentration))
      * ((y - loc) / scale)**(-(1 + concentration))
  ```
code: def _forward_log_det_jacobian(self, x):
      with tf.control_dependencies(self._maybe_assert_valid_x(x)):
          scale = tf.convert_to_tensor(self.scale)
          concentration = tf.convert_to_tensor(self.concentration)
          z = (x - self.loc) / scale
          return (
              tf.math.log(concentration)
              - tf.math.log(scale)
              - (1.0 + concentration) * tf.math.log(z)
              - z ** (-concentration)
          )
  
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 10 for calling the FrechetCDF obj's underlying member method code with example usage: obj._forward_log_det_jacobian(x) and returns: unknown