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 _parameter_control_dependencies(self, is_init):
      if not self.validate_args:
          return []
      assertions = []
      if is_init != tensor_util.is_ref(self.scale):
          assertions.append(
              assert_util.assert_positive(
                  self.scale,
                  message="Argument `scale` must be positive.",
              )
          )
      if is_init != tensor_util.is_ref(self.concentration):
          assertions.append(
              assert_util.assert_positive(
                  self.concentration,
                  message="Argument `concentration` must be positive.",
              )
          )
      return assertions
  
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 13 for calling the FrechetCDF obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: assertions, list