code: def start(authkey, queues, mode="local"):
      """Create a new multiprocess.Manager (or return existing one).
  
      Args:
        :authkey: string authorization key
        :queues: *INTERNAL_USE*
        :mode: 'local' indicates that the manager will only be accessible from the same host, otherwise remotely accessible.
  
      Returns:
        A TFManager instance, which is also cached in local memory of the Python worker process.
      """
      global mgr, qdict, kdict
      qdict.clear()
      kdict.clear()
      for q in queues:
          qdict[q] = JoinableQueue()
  
      TFManager.register(
          "get_queue", callable=lambda qname: _get_queue(qname)
      )
      TFManager.register("get", callable=lambda key: _get(key))
      TFManager.register(
          "set", callable=lambda key, value: _set(key, value)
      )
      if mode == "remote":
          mgr = TFManager(address=("", 0), authkey=authkey)
      else:
          mgr = TFManager(authkey=authkey)
      mgr.start()
      return mgr
  
file_path: TensorFlowOnSpark/tensorflowonspark/TFManager.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import nested_scopes
  from __future__ import print_function
  from multiprocessing.managers import BaseManager
  from multiprocessing import JoinableQueue
label_desc: Use image node_id 4 for calling a global function with example usage: start(authkey, queues, mode) and returns: mgr