code: def _run_between_graph_client(
      worker_fn,
      strategy,
      eval_fn,
      eval_strategy,
      cluster_spec,
      session_config,
      rpc_layer,
  ):
      """Runs a standalone client for between-graph replication."""
      coord = coordinator.Coordinator()
      eval_thread = None
      if _TaskType.EVALUATOR in cluster_spec.jobs:
          eval_thread = threading.Thread(
              target=_run_single_worker,
              args=(
                  eval_fn,
                  eval_strategy,
                  cluster_spec,
                  _TaskType.EVALUATOR,
                  0,
                  session_config,
              ),
              kwargs={
                  "rpc_layer": rpc_layer,
                  "coord": coord,
              },
          )
          eval_thread.start()
  
      threads = []
      worker_barrier = _Barrier(_get_num_workers(cluster_spec))
      for task_type in [_TaskType.CHIEF, _TaskType.WORKER]:
          for task_id in range(
              len(cluster_spec.as_dict().get(task_type, []))
          ):
              t = threading.Thread(
                  target=_run_single_worker,
                  args=(
                      worker_fn,
                      strategy,
                      cluster_spec,
                      task_type,
                      task_id,
                      session_config,
                  ),
                  kwargs={
                      "rpc_layer": rpc_layer,
                      "worker_barrier": worker_barrier,
                      "coord": coord,
                  },
              )
              t.start()
              threads.append(t)
  
      if eval_thread:
          # TODO(yuefengz): is it necessary to join eval thread?
          threads_to_join = threads + [eval_thread]
      else:
          threads_to_join = threads
      coord.join(threads_to_join)
  
      # TODO(yuefengz): we probably want to return results from all workers?
      return None
  
imports: import copy
  import json
  import os
  import threading
  import time
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.client import session
  from tensorflow.python.distribute import distribute_coordinator_context
  from tensorflow.python.distribute import multi_worker_util
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.training import coordinator
  from tensorflow.python.training import monitored_session
  from tensorflow.python.training import server_lib
label_desc: Use image node_id 5 for calling a global function with example usage: _run_between_graph_client(worker_fn, strategy, eval_fn, eval_strategy, cluster_spec, session_config, rpc_layer) and returns: None