code: def _run_in_graph_client(
      worker_fn,
      strategy,
      eval_fn,
      eval_strategy,
      cluster_spec,
      session_config,
      rpc_layer,
  ):
      """Runs a standalone client for in-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()
  
      worker_result = _run_single_worker(
          worker_fn,
          strategy,
          cluster_spec,
          None,
          None,
          session_config,
          rpc_layer=rpc_layer,
          coord=coord,
      )
  
      if eval_thread:
          coord.join([eval_thread])
  
      return worker_result
  
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 6 for calling a global function with example usage: _run_in_graph_client(worker_fn, strategy, eval_fn, eval_strategy, cluster_spec, session_config, rpc_layer) and returns: worker_result