class_docstr: Dummy implementation of distributed workers with buffer creates the workers as a list. Once the outputs are to
  be collected, it simply rolls them out in a loop until is has enough to be added to the buffer.
docstr: Store the newest policy in the shared network object
docstr_tok: ["Store","the","newest","policy","in","the","shared","network","object"]
code: def broadcast_updated_policy(self, state_dict: Dict) -> None:
      """Store the newest policy in the shared network object"""
      converted_state_dict = convert_to_torch(
          state_dict,
          in_place=False,
          cast=None,
          device=self._worker_policy.device,
      )
      self.broadcasting_container.set_policy_state_dict(
          converted_state_dict
      )
  
file_path: maze/maze/train/parallelization/distributed_actors/dummy_distributed_workers_with_buffer.py
imports: import time
  from typing import Tuple, Dict
  from maze.core.annotations import override
  from maze.core.rollout.rollout_generator import RolloutGenerator
  from maze.perception.perception_utils import convert_to_torch
  from maze.train.parallelization.broadcasting_container import BroadcastingContainer
  from maze.train.parallelization.distributed_actors.base_distributed_workers_with_buffer import BaseDistributedWorkersWithBuffer
label_desc: Use image node_id 4 for calling the DummyDistributedWorkersWithBuffer obj's underlying member method code with example usage: obj.broadcast_updated_policy(state_dict) without return types