class_docstr: Compute `Y = g(X) = 1 - exp(-c * (exp(rate * X) - 1)`, the Gompertz CDF.
  
  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
  [Gompertz distribution](https://en.wikipedia.org/wiki/Gompertz_distribution):
  
  ```none
  Y ~ GompertzCDF(concentration, rate)
  pdf(y; c, r) = r * c * exp(r * y + c - c * exp(-c * exp(r * y)))
  ```
  
  Note: Because the 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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docstr: The `rate` in `Y = g(X) = 1 - exp(-c * (exp(rate * X) - 1)`.
docstr_tok: ["The","`","rate","`","in","`","Y","=","g","(","X",")","=","1","-","exp","(","-c","*","(","exp","(","rate","*","X",")","-","1",")","`","."]
code: def rate(self):
      """The `rate` in `Y = g(X) = 1 - exp(-c * (exp(rate * X) - 1)`."""
      return self._rate
  
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 4 for calling the GompertzCDF obj's underlying member method code with example usage: obj.rate() and returns: self