code: def setup(self, config: AlphaStarConfig):
      # Create the LeagueBuilder object, allowing it to build the multiagent
      # config as well.
      self.league_builder = from_config(
          self.config.league_builder_config,
          algo=self,
          algo_config=self.config,
      )
  
      # Call super's setup to validate config, create RolloutWorkers
      # (train and eval), etc..
      super().setup(config)
  
      local_worker = self.workers.local_worker()
  
      # - Create n policy learner actors (@ray.remote-converted Policies) on
      #   one or more GPU nodes.
      # - On each such node, also locate one replay buffer shard.
  
      # Single CPU replay shard (co-located with GPUs so we can place the
      # policies on the same machine(s)).
      num_gpus = (
          0.01
          if (self.config.num_gpus and not self.config._fake_gpus)
          else 0
      )
      ReplayActor = ray.remote(
          num_cpus=1,
          num_gpus=num_gpus,
      )(MixInMultiAgentReplayBuffer)
  
      # Setup remote replay buffer shards and policy learner actors
      # (located on any GPU machine in the cluster):
      replay_actor_args = [
          self.config["replay_buffer_capacity"],
          self.config["replay_buffer_replay_ratio"],
      ]
  
      # Create a DistributedLearners utility object and set it up with
      # the initial first n learnable policies (found in the config).
      distributed_learners = DistributedLearners(
          config=self.config,
          # By default, set max_num_policies_to_train to the number of policy IDs
          # provided in the multiagent config.
          max_num_policies_to_train=(
              self.config.max_num_policies_to_train
              or len(
                  self.workers.local_worker().get_policies_to_train()
              )
          ),
          replay_actor_class=ReplayActor,
          replay_actor_args=replay_actor_args,
      )
      policies, _ = self.config.get_multi_agent_setup(
          spaces=local_worker.spaces,
          default_policy_class=local_worker.default_policy_class,
      )
      for pid, policy_spec in policies.items():
          if (
              local_worker.is_policy_to_train is None
              or local_worker.is_policy_to_train(pid)
          ):
              distributed_learners.add_policy(pid, policy_spec)
  
      # Store distributed_learners on all RolloutWorkers
      # so they know, to which replay shard to send samples to.
  
      def _set_policy_learners(worker):
          worker._distributed_learners = distributed_learners
  
      ray.get(
          [
              w.apply.remote(_set_policy_learners)
              for w in self.workers.remote_workers()
          ]
      )
  
      self.distributed_learners = distributed_learners
      self._sampling_actor_manager = AsyncRequestsManager(
          self.workers.remote_workers(),
          max_remote_requests_in_flight_per_worker=self.config[
              "max_requests_in_flight_per_sampler_worker"
          ],
          ray_wait_timeout_s=self.config.timeout_s_sampler_manager,
      )
      policy_actors = [
          policy_actor for _, policy_actor, _ in distributed_learners
      ]
      self._learner_worker_manager = AsyncRequestsManager(
          workers=policy_actors,
          max_remote_requests_in_flight_per_worker=self.config[
              "max_requests_in_flight_per_learner_worker"
          ],
          ray_wait_timeout_s=self.config.timeout_s_learner_manager,
      )
  
imports: from typing import Any, Dict, Optional, Type, Union
  import gymnasium
  import tree
  from rllib_alpha_star.alpha_star.distributed_learners import DistributedLearners
  from rllib_alpha_star.alpha_star.league_builder import AlphaStarLeagueBuilder
  import ray
  import ray.rllib.algorithms.appo.appo
  from ray.actor import ActorHandle
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.execution.buffers.mixin_replay_buffer import MixInMultiAgentReplayBuffer
  from ray.rllib.execution.parallel_requests import AsyncRequestsManager
  from ray.rllib.policy.policy import Policy, PolicySpec
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils import deep_update
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.from_config import from_config
  from ray.rllib.utils.metrics import LAST_TARGET_UPDATE_TS, LEARN_ON_BATCH_TIMER, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_TARGET_UPDATES, SAMPLE_TIMER, SYNCH_WORKER_WEIGHTS_TIMER, TARGET_NET_UPDATE_TIMER
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.typing import PartialAlgorithmConfigDict, PolicyID, PolicyState, ResultDict
  from ray.tune.execution.placement_groups import PlacementGroupFactory
  from ray.util.timer import _Timer
label_desc: Use image node_id 3 for calling the AlphaStar obj's underlying member method code with example usage: obj.setup(config) without return types