class_docstr: Compute `Y = g(X) = (1 - exp(-rate * X)) * exp(-c * exp(-rate * X))`.
  
  This bijector maps inputs from `[-inf, inf]` to `[0, inf]`. The inverse of the
  bijector applied to a uniform random variable `X ~ U(0, 1)` gives back a
  random variable with the
  [Shifted Gompertz distribution](
    https://en.wikipedia.org/wiki/Shifted_Gompertz_distribution):
  
  ```none
  Y ~ ShiftedGompertzCDF(concentration, rate)
  pdf(y; c, r) = r * exp(-r * y - exp(-r * y) / c) * (1 + (1 - exp(-r * y)) / c)
  ```
  
  Note: Even though this is called `ShiftedGompertzCDF`, when applied to the
  `Uniform` distribution, this is not the same as applying a `GompertzCDF` with
  a `Shift` bijector (i.e. the Shifted Gompertz distribution is not the same as
  a Gompertz distribution with a location parameter).
  
  Note: Because the Shifted Gompertz distribution concentrates its mass close
  to zero, for larger rates or larger concentrations, `bijector.forward` will
  quickly saturate to 1.
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code: def _is_increasing(cls):
      return True
  
imports: import numpy
  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
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 5 for calling the ShiftedGompertzCDF obj's underlying member method code with example usage: obj._is_increasing(cls) and returns: True