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
docstr: Constructs inverse Gaussian distribution with `loc` and `concentration`.
  
  Args:
    loc: Floating-point `Tensor`, the loc params. Must contain only positive
      values.
    concentration: Floating-point `Tensor`, the concentration params.
      Must contain only positive values.
    validate_args: Python `bool`, default `False`. When `True` distribution
      parameters are checked for validity despite possibly degrading runtime
      performance. When `False` invalid inputs may silently render incorrect
      outputs.
      Default value: `False` (i.e. do not validate args).
    allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
      (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
      result is undefined. When `False`, an exception is raised if one or
      more of the statistic's batch members are undefined.
      Default value: `True`.
    name: Python `str` name prefixed to Ops created by this class.
      Default value: 'InverseGaussian'.
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code: def __init__(
      self,
      loc,
      concentration,
      validate_args=False,
      allow_nan_stats=True,
      name="InverseGaussian",
  ):
      """Constructs inverse Gaussian distribution with `loc` and `concentration`.
  
      Args:
        loc: Floating-point `Tensor`, the loc params. Must contain only positive
          values.
        concentration: Floating-point `Tensor`, the concentration params.
          Must contain only positive values.
        validate_args: Python `bool`, default `False`. When `True` distribution
          parameters are checked for validity despite possibly degrading runtime
          performance. When `False` invalid inputs may silently render incorrect
          outputs.
          Default value: `False` (i.e. do not validate args).
        allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
          (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
          result is undefined. When `False`, an exception is raised if one or
          more of the statistic's batch members are undefined.
          Default value: `True`.
        name: Python `str` name prefixed to Ops created by this class.
          Default value: 'InverseGaussian'.
      """
      parameters = dict(locals())
      with tf.name_scope(name):
          dtype = dtype_util.common_dtype(
              [loc, concentration], dtype_hint=tf.float32
          )
          self._concentration = tensor_util.convert_nonref_to_tensor(
              concentration, dtype=dtype, name="concentration"
          )
          self._loc = tensor_util.convert_nonref_to_tensor(
              loc, dtype=dtype, name="loc"
          )
  
          super(InverseGaussian, self).__init__(
              dtype=self._loc.dtype,
              reparameterization_type=reparameterization.FULLY_REPARAMETERIZED,
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              parameters=parameters,
              name=name,
          )
  
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 1 to create a new InverseGaussian object from inherited base classes: distribution with example: obj = InverseGaussian(loc, concentration, validate_args, allow_nan_stats, name)