code: def _random_inverse_gaussian_fwd(shape, loc, concentration, seed):
      """Compute output, aux (collaborates with _random_inverse_gaussian_bwd)."""
      samples = _random_inverse_gaussian_no_gradient(
          shape, loc, concentration, seed
      )
      return samples, (samples, loc, concentration)
  
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 2 for calling a global function with example usage: _random_inverse_gaussian_fwd(shape, loc, concentration, seed) and returns: samples,  samples,  loc,  concentration