code: def _run_single_worker(
      worker_fn,
      strategy,
      cluster_spec,
      task_type,
      task_id,
      session_config,
      rpc_layer="",
      worker_barrier=None,
      coord=None,
  ):
      """Runs a single worker by calling `worker_fn` under context."""
      session_config = copy.deepcopy(session_config)
      strategy = copy.deepcopy(strategy)
      # If there is an EVALUATOR task, we run single-machine eval on that task.
      if task_type == _TaskType.EVALUATOR:
          # It is possible to not have a strategy object for EVALUATOR task.
          if strategy:
              strategy.configure(session_config)
      else:
          assert strategy
          strategy.configure(
              session_config, cluster_spec, task_type, task_id
          )
  
      context = _WorkerContext(
          strategy,
          cluster_spec,
          task_type,
          task_id,
          session_config=session_config,
          rpc_layer=rpc_layer,
          worker_barrier=worker_barrier,
      )
      with context:
          if coord:
              with coord.stop_on_exception():
                  return worker_fn(strategy)
          else:
              return worker_fn(strategy)
  
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 2 for calling a global function with example usage: _run_single_worker(worker_fn, strategy, cluster_spec, task_type, task_id, session_config, rpc_layer, worker_barrier, coord) and returns: worker_fn, worker_fn