class_docstr: Compute `Y = g(X) = 1 - exp( -( X / scale) ** concentration), X >= 0`.
  
  This bijector maps inputs from `[0, 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
  [Weibull distribution](https://en.wikipedia.org/wiki/Weibull_distribution):
  
  ```none
  Y ~ Weibull(scale, concentration)
  pdf(y; scale, concentration, y >= 0) =
      (concentration / scale) * (y / scale)**(concentration - 1) *
      exp(-(y / scale)**concentration)
  ```
  
  Likwewise, the forward of this bijector is the Weibull distribution CDF.
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 12 for calling the WeibullCDF obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: assertions, list