code: def _get_coroutine_parameters(jdc_model, seed):
      """Runs a coroutine and intercepts yielded dists, yielding params only."""
      gen = jdc_model()
      to_send = None
      raw_parameters = []
      try:
          while True:
              val = gen.send(to_send)
              if isinstance(val, trainable_state_util.Parameter):
                  to_send = yield val
                  raw_parameters.append(to_send)
              else:  # Random variable.
                  seed, local_seed = samplers.split_seed(seed, n=2)
                  to_send = (
                      val.distribution.sample(seed=local_seed)
                      if isinstance(val, Root)
                      else val.sample(seed=local_seed)
                  )
      except StopIteration:
          pass
      return raw_parameters
  
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 12 for calling a global function with example usage: _get_coroutine_parameters(jdc_model, seed) and returns: raw_parameters