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.
code: def _init_workers(self):
      self.broadcasting_container = BroadcastingContainer()
      self.current_worker_idx = 0
  
      self.workers = []
      self.policy_version_counter = 0
  
      for env_seed in self.env_instance_seeds:
          env = self.env_factory()
          env.seed(env_seed)
          actor = RolloutGenerator(
              env=env,
              record_next_observations=True,
              record_episode_stats=True,
          )
          self.workers.append(actor)
  
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 1 for calling the DummyDistributedWorkersWithBuffer obj's underlying member method code with example usage: obj._init_workers() without return types