code: def training_step(self) -> ResultDict:
      num_samples_ready = self.get_samples_and_store_to_replay_buffers()
      num_worker_samples_collected = defaultdict(int)
  
      for worker_id, samples_info in num_samples_ready:
          self._counters[NUM_AGENT_STEPS_SAMPLED] += samples_info[
              "agent_steps"
          ]
          self._counters[NUM_ENV_STEPS_SAMPLED] += samples_info[
              "env_steps"
          ]
          num_worker_samples_collected[worker_id] += samples_info[
              "agent_steps"
          ]
  
      # Update the weights of the workers that returned samples.
      # Only do this if there are remote workers (config["num_workers"] > 1).
      # Also, only update those policies that were actually trained.
      if self.workers.num_remote_workers() > 0:
          self.update_workers(num_worker_samples_collected)
  
      # Update target network every `target_network_update_freq` sample steps.
      cur_ts = self._counters[
          NUM_AGENT_STEPS_SAMPLED
          if self.config.count_steps_by == "agent_steps"
          else NUM_ENV_STEPS_SAMPLED
      ]
  
      if cur_ts > self.config.num_steps_sampled_before_learning_starts:
          # trigger a sample from the replay actors and enqueue operation to the
          # learner thread.
          self.sample_from_replay_buffer_place_on_learner_queue_non_blocking(
              num_worker_samples_collected
          )
          self.update_replay_sample_priority()
  
      # Training step done. Try to bring replay actors back to life if necessary.
      # Replay actors can start fresh, so we do not need to restore any state.
      self._replay_actor_manager.probe_unhealthy_actors(
          timeout_seconds=self.config.worker_health_probe_timeout_s,
          mark_healthy=True,
      )
  
      return copy.deepcopy(self.learner_thread.learner_info)
  
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 4 for calling the ApexDQN obj's underlying member method code with example usage: obj.training_step() and returns: copy