class_docstr: The worker context class.
  
  This context object provides configuration information for each task. One
  context manager with a worker context object will be created per
  invocation to the `worker_fn` where `get_current_worker_context` can be called
  to access the worker context object.
docstr: Waits for other workers to reach the same call to this method.
  
  Raises:
    ValueError: if `worker_barrier` is not passed to the __init__ method.
docstr_tok: ["Waits","for","other","workers","to","reach","the","same","call","to","this","method",".","Raises",":","ValueError",":","if","`","worker_barrier","`","is","not","passed","to","the","__init__","method","."]
code: def wait_for_other_workers(self):
      """Waits for other workers to reach the same call to this method.
  
      Raises:
        ValueError: if `worker_barrier` is not passed to the __init__ method.
      """
      if not self._worker_barrier:
          # TODO(yuefengz): we should throw an error in independent worker mode.
          return
      self._worker_barrier.wait()
  
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 7 for calling the _WorkerContext obj's underlying member method code with example usage: obj.wait_for_other_workers() without return types