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 _maybe_assert_valid_x(self, x):
      if not self.validate_args:
          return []
      return [
          assert_util.assert_non_negative(
              x,
              message="Forward transformation input must be at least 0.",
          )
      ]
  
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 WeibullCDF obj's underlying member method code with example usage: obj._maybe_assert_valid_x(x) and returns: list, list