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","."]
docstr: Store new policy version.
  
  :param state_dict: New state dict to store
  :param aux_data: Dictionary with any auxiliary data to share
docstr_tok: ["Store","new","policy","version",".",":","param","state_dict",":","New","state","dict","to","store",":","param","aux_data",":","Dictionary","with","any","auxiliary","data","to","share"]
code: def set_policy_state_dict(
      self, state_dict: Dict, aux_data: Dict = None
  ) -> NoReturn:
      """Store new policy version.
  
      :param state_dict: New state dict to store
      :param aux_data: Dictionary with any auxiliary data to share
      """
      self._pickled_policy_state_dict = cloudpickle.dumps(state_dict)
      self._policy_version_counter += 1
      self._aux_data = aux_data
  
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 6 for calling the BroadcastingContainer obj's underlying member method code with example usage: obj.set_policy_state_dict(state_dict, aux_data) without return types