class_docstr: Inverse Gaussian distribution.
  
  The [inverse Gaussian distribution]
  (https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution)
  is parameterized by a `loc` and a `concentration` parameter. It's also known
  as the Wald distribution. Some, e.g., the Python scipy package, refer to the
  special case when `loc` is 1 as the Wald distribution.
  
  The "inverse" in the name does not refer to the distribution associated to
  the multiplicative inverse of a random variable. Rather, the cumulant
  generating function of this distribution is the inverse to that of a Gaussian
  random variable.
  
  #### Mathematical Details
  
  The probability density function (pdf) is,
  
  ```none
  pdf(x; mu, lambda) = [lambda / (2 pi x ** 3)] ** 0.5
                       exp{-lambda(x - mu) ** 2 / (2 mu ** 2 x)}
  ```
  
  where
  * `loc = mu`
  * `concentration = lambda`.
  
  The support of the distribution is defined on `(0, infinity)`.
  
  Mapping to R and Python scipy's parameterization:
  * R: statmod::invgauss
     - mean = loc
     - shape = concentration
     - dispersion = 1 / concentration. Used only if shape is NULL.
  * Python: scipy.stats.invgauss
     - mu = loc / concentration
     - scale = concentration
code: def _sample_control_dependencies(self, x):
      assertions = []
      if not self.validate_args:
          return assertions
      assertions.append(
          assert_util.assert_non_negative(
              x, message="Sample must be non-negative."
          )
      )
      return assertions
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import special_math
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math import generic
label_desc: Use image node_id 12 for calling the InverseGaussian obj's underlying member method code with example usage: obj._sample_control_dependencies(x) and returns: assertions, assertions