class_docstr: The base class for all distributed actors.
  
  Distributed actors run rollouts independently. Rollouts are recorded and made available in batches
  to be used during training. When a new policy version is made available, it is distributed to all actors.
  
  :param env_factory: Factory function for envs to run rollouts on.
  :param policy: Structured policy to sample actions from.
  :param n_rollout_steps: Number of rollouts steps to record in one rollout.
  :param n_actors: Number of distributed actors to run simultaneously.
  :param batch_size: Size of the batch the rollouts are collected in.
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docstr: Stop all distributed actors
docstr_tok: ["Stop","all","distributed","actors"]
code: def stop(self) -> None:
      """Stop all distributed actors"""
      raise NotImplementedError
  
file_path: maze/maze/train/parallelization/distributed_actors/distributed_actors.py
imports: from abc import abstractmethod
  from typing import Tuple, Callable, Optional, Union, Dict
  from maze.core.agent.torch_policy import TorchPolicy
  from maze.core.env.structured_env import StructuredEnv
  from maze.core.env.structured_env_spaces_mixin import StructuredEnvSpacesMixin
  from maze.core.log_stats.log_stats import LogStatsAggregator, LogStatsLevel, get_stats_logger, LogStatsValue
  from maze.core.log_stats.log_stats_env import LogStatsEnv
  from maze.core.trajectory_recording.records.structured_spaces_record import StructuredSpacesRecord
label_desc: Use image node_id 3 for calling the DistributedActors obj's underlying member method code with example usage: obj.stop() without return types