code: def setup(self, config: AlgorithmConfig):
      super().setup(config)
  
      num_replay_buffer_shards = self.config.optimizer[
          "num_replay_buffer_shards"
      ]
  
      # Create copy here so that we can modify without breaking other logic
      replay_actor_config = copy.deepcopy(
          self.config.replay_buffer_config
      )
  
      replay_actor_config["capacity"] = (
          self.config.replay_buffer_config["capacity"]
          // num_replay_buffer_shards
      )
  
      ReplayActor = ray.remote(num_cpus=0, max_restarts=-1)(
          replay_actor_config["type"]
      )
  
      # Place all replay buffer shards on the same node as the learner
      # (driver process that runs this execution plan).
      if replay_actor_config[
          "replay_buffer_shards_colocated_with_driver"
      ]:
          _replay_actors = create_colocated_actors(
              actor_specs=[  # (class, args, kwargs={}, count)
                  (
                      ReplayActor,
                      None,
                      replay_actor_config,
                      num_replay_buffer_shards,
                  )
              ],
              node=platform.node(),  # localhost
          )[
              0
          ]  # [0]=only one item in `actor_specs`.
      # Place replay buffer shards on any node(s).
      else:
          _replay_actors = [
              ReplayActor.remote(*replay_actor_config)
              for _ in range(num_replay_buffer_shards)
          ]
      self._replay_actor_manager = FaultTolerantActorManager(
          _replay_actors,
          max_remote_requests_in_flight_per_actor=(
              self.config.max_requests_in_flight_per_replay_worker
          ),
      )
      self._replay_req_timeout_s = self.config.timeout_s_replay_manager
      self._sample_req_tiemeout_s = (
          self.config.timeout_s_sampler_manager
      )
      self.learner_thread = LearnerThread(self.workers.local_worker())
      self.learner_thread.start()
      self.steps_since_update = defaultdict(int)
      weights = self.workers.local_worker().get_weights()
      self.curr_learner_weights = ray.put(weights)
      self.curr_num_samples_collected = 0
      self._num_ts_trained_since_last_target_update = 0
  
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 1 for calling the ApexDQN obj's underlying member method code with example usage: obj.setup(config) without return types