code: def _remote_worker_ids_for_metrics(self) -> List[int]:
      # Tag those workers (top 1/3rd indices) that we should collect episodes from
      # for metrics due to `PerWorkerEpsilonGreedy` exploration strategy.
      num_remote_workers_for_metrics = self.config["num_workers"] // 3
      return self.workers.healthy_worker_ids()[
          -num_remote_workers_for_metrics:
      ]
  
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 3 for calling the ApexDQN obj's underlying member method code with example usage: obj._remote_worker_ids_for_metrics() and returns: self