code: def _random_inverse_gaussian_jvp(shape, primals, tangents):
      """Computes JVP for inverse_gaussian sample (supports JAX custom derivative)."""
      loc, concentration, seed = primals
      dloc, dconcentration, dseed = tangents
      del dseed
      # TODO(https://github.com/google/jax/issues/3768): eliminate broadcast_to?
      dconcentration = tf.broadcast_to(dconcentration, shape)
      dloc = tf.broadcast_to(dloc, shape)
  
      samples = _random_inverse_gaussian_no_gradient(
          shape, loc, concentration, seed
      )
  
      partial_concentration, partial_loc = _compute_partials(
          samples, loc, concentration
      )
  
      dsamples = (
          partial_concentration * dconcentration + partial_loc * dloc
      )
      return samples, dsamples
  
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 5 for calling a global function with example usage: _random_inverse_gaussian_jvp(shape, primals, tangents) and returns: samples,  dsamples