code: def _set_coroutine_parameters(jdc_model, raw_parameters):
      """Wraps a coroutine to intercept yielded params, yielding dists only."""
  
      def handled_coroutine():
          gen = jdc_model()
          to_send = None
          parameter_idx = 0
          try:
              while True:
                  val = gen.send(to_send)
                  if isinstance(val, trainable_state_util.Parameter):
                      to_send = raw_parameters[parameter_idx]
                      parameter_idx += 1
                  else:
                      to_send = yield val
          except StopIteration:
              pass
  
      return handled_coroutine
  
imports: import collections
  import copy
  import functools
  import inspect
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import restructure
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import chi2
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import markov_chain
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import truncated_normal
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import trainable_state_util
label_desc: Use image node_id 13 for calling a global function with example usage: _set_coroutine_parameters(jdc_model, raw_parameters) and returns: handled_coroutine