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 _inverse(self, y):
      with tf.control_dependencies(self._maybe_assert_valid_y(y)):
          return self.scale * (-tf.math.log1p(-y)) ** (
              1 / self.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 7 for calling the WeibullCDF obj's underlying member method code with example usage: obj._inverse(y) and returns: unknown