class_docstr: Synchronizes policy updates and other information across workers on local machine.
  
  Used for dummy and sub-process distribution scenarios.
  
  The BroadcastingContainer object can be read by all workers in order to update their policy, and it can be
  accessed by the main thread to write the updated policy from the learner into it.
class_docstr_tok: ["Synchronizes","policy","updates","and","other","information","across","workers","on","local","machine",".","Used","for","dummy","and","sub-process","distribution","scenarios",".","The","BroadcastingContainer","object","can","be","read","by","all","workers","in","order","to","update","their","policy",",","and","it","can","be","accessed","by","the","main","thread","to","write","the","updated","policy","from","the","learner","into","it","."]
code: def __init__(self):
      self._policy_version_counter = 0
      self._pickled_policy_state_dict = None
      self._stop_flag = False
      self._aux_data = None
  
file_path: maze/maze/train/parallelization/broadcasting_container.py
imports: from multiprocessing.managers import BaseManager
  from typing import Dict, NoReturn, Optional
  import cloudpickle
label_desc: Use image node_id 1 to create a new BroadcastingContainer object  with example: obj = BroadcastingContainer()