code: def get_samples_and_store_to_replay_buffers(self):
      # in the case the num_workers = 0
      if self.workers.num_remote_workers() <= 0:
          with self._timers[SAMPLE_TIMER]:
              local_sampling_worker = self.workers.local_worker()
              batch = local_sampling_worker.sample()
              actor_id = random.choice(
                  self._replay_actor_manager.healthy_actor_ids()
              )
              self._replay_actor_manager.foreach_actor(
                  lambda actor: actor.add(batch),
                  remote_actor_ids=[actor_id],
                  timeout_seconds=0,
              )
              batch_statistics = [
                  (
                      0,
                      {
                          "agent_steps": batch.agent_steps(),
                          "env_steps": batch.env_steps(),
                      },
                  )
              ]
              return batch_statistics
  
      replay_actor_manager = self._replay_actor_manager
  
      def remote_worker_sample_and_store(worker: RolloutWorker):
          # This function is run as a remote function on sampling workers,
          # and should only be used with the RolloutWorker's apply function ever.
          # It is used to gather samples, and trigger the operation to store them to
          # replay actors from the rollout worker instead of returning the obj
          # refs for the samples to the driver process and doing the sampling
          # operation on there.
          _batch = worker.sample()
          _actor = random.choice(
              replay_actor_manager.healthy_actor_ids()
          )
          replay_actor_manager.foreach_actor(
              lambda actor: actor.add(_batch),
              remote_actor_ids=[_actor],
              timeout_seconds=0,
          )
          _batch_statistics = {
              "agent_steps": _batch.agent_steps(),
              "env_steps": _batch.env_steps(),
          }
          return _batch_statistics
  
      # Sample and Store in the Replay Actors on the sampling workers.
      with self._timers[SAMPLE_TIMER]:
          self.workers.foreach_worker_async(
              func=remote_worker_sample_and_store,
              healthy_only=True,
          )
          num_samples_ready = self.workers.fetch_ready_async_reqs(
              timeout_seconds=self._sample_req_tiemeout_s
          )
      return num_samples_ready
  
imports: import copy
  import platform
  import random
  from collections import defaultdict
  from typing import Any, Dict, List, Optional, Tuple, Union
  import ray
  from ray._private.dict import merge_dicts
  from ray.actor import ActorHandle
  from ray.rllib.algorithms import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.dqn.dqn import DQN, DQNConfig
  from ray.rllib.algorithms.dqn.learner_thread import LearnerThread
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.evaluation.worker_set import handle_remote_call_result_errors
  from ray.rllib.utils.actor_manager import FaultTolerantActorManager
  from ray.rllib.utils.actors import create_colocated_actors
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
  from ray.rllib.utils.metrics import LAST_TARGET_UPDATE_TS, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, NUM_TARGET_UPDATES, SAMPLE_TIMER, SYNCH_WORKER_WEIGHTS_TIMER, TARGET_NET_UPDATE_TIMER
  from ray.rllib.utils.typing import PartialAlgorithmConfigDict, ResultDict, SampleBatchType
  from ray.tune.execution.placement_groups import PlacementGroupFactory
  from ray.tune.trainable import Trainable
label_desc: Use image node_id 5 for calling the ApexDQN obj's underlying member method code with example usage: obj.get_samples_and_store_to_replay_buffers() and returns: num_samples_ready, _batch_statistics, batch_statistics