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 _maybe_assert_valid_y(self, y):
      if not self.validate_args:
          return []
      is_positive = assert_util.assert_non_negative(
          y,
          message="Inverse transformation input must be greater than 0.",
      )
      less_than_one = assert_util.assert_less_equal(
          y,
          tf.ones([], dtype=y.dtype),
          message="Inverse transformation input must be less than or equal to 1.",
      )
      return [is_positive, less_than_one]
  
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 12 for calling the FrechetCDF obj's underlying member method code with example usage: obj._maybe_assert_valid_y(y) and returns: list, list