code: def _compile_iteration_results(self, *args, **kwargs):
      result = super()._compile_iteration_results(*args, **kwargs)
      replay_stats = self._get_shard0_replay_stats()
      exploration_infos_list = self.workers.foreach_policy_to_train(
          lambda p, pid: {pid: p.get_exploration_state()}
      )
      exploration_infos = {}
      for info in exploration_infos_list:
          # we're guaranteed that each info has policy ids that are unique
          exploration_infos.update(info)
      other_results = {
          "exploration_infos": exploration_infos,
          "learner_queue": self.learner_thread.learner_queue_size.stats(),
          "replay_shard_0": replay_stats,
      }
  
      result["info"].update(other_results)
      return result
  
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 11 for calling the ApexDQN obj's underlying member method code with example usage: obj._compile_iteration_results() and returns: result