code: def default_resource_request(
      cls,
      config: Union[AlgorithmConfig, PartialAlgorithmConfigDict],
  ):
      if isinstance(config, AlgorithmConfig):
          cf: ApexDQNConfig = config
      else:
          cf: ApexDQNConfig = cls.get_default_config().update_from_dict(
              config
          )
  
      eval_config = cf.get_evaluation_config_object()
  
      # Return PlacementGroupFactory containing all needed resources
      # (already properly defined as device bundles).
      return PlacementGroupFactory(
          bundles=[
              {
                  # Local worker + replay buffer actors.
                  # Force replay buffers to be on same node to maximize
                  # data bandwidth between buffers and the learner (driver).
                  # Replay buffer actors each contain one shard of the total
                  # replay buffer and use 1 CPU each.
                  "CPU": cf.num_cpus_for_local_worker
                  + cf.optimizer["num_replay_buffer_shards"],
                  "GPU": 0 if cf._fake_gpus else cf.num_gpus,
              }
          ]
          + [
              {
                  # RolloutWorkers.
                  "CPU": cf.num_cpus_per_worker,
                  "GPU": cf.num_gpus_per_worker,
                  **cf.custom_resources_per_worker,
              }
              for _ in range(cf.num_rollout_workers)
          ]
          + (
              [
                  {
                      # Evaluation workers.
                      # Note: The local eval worker is located on the driver
                      # CPU.
                      "CPU": eval_config.num_cpus_per_worker,
                      "GPU": eval_config.num_gpus_per_worker,
                      **eval_config.custom_resources_per_worker,
                  }
                  for _ in range(cf.evaluation_num_workers)
              ]
              if cf.evaluation_interval
              else []
          ),
          strategy=cf.placement_strategy,
      )
  
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 12 for calling the ApexDQN obj's underlying member method code with example usage: obj.default_resource_request(cls, config) and returns: PlacementGroupFactory