code: def default_resource_request(
      cls,
      config: Union[AlgorithmConfig, PartialAlgorithmConfigDict],
  ):
      if isinstance(config, AlgorithmConfig):
          cf: AlphaStarConfig = config
      else:
          cf: AlphaStarConfig = (
              cls.get_default_config().update_from_dict(config)
          )
      # Construct a dummy LeagueBuilder, such that it gets the opportunity to
      # adjust the multiagent config, according to its setup, and we can then
      # properly infer the resources to allocate.
      from_config(cf.league_builder_config, algo=None, algo_config=cf)
  
      max_num_policies_to_train = cf.max_num_policies_to_train or len(
          cf.policies_to_train or cf.policies
      )
      num_learner_shards = min(
          cf.num_gpus or max_num_policies_to_train,
          max_num_policies_to_train,
      )
      num_gpus_per_shard = cf.num_gpus / num_learner_shards
      num_policies_per_shard = (
          max_num_policies_to_train / num_learner_shards
      )
  
      fake_gpus = cf._fake_gpus
  
      eval_config = cf.get_evaluation_config_object()
  
      # Return PlacementGroupFactory containing all needed resources
      # (already properly defined as device bundles).
      return PlacementGroupFactory(
          bundles=[
              {
                  # Driver (no GPUs).
                  "CPU": cf.num_cpus_for_local_worker,
              }
          ]
          + [
              {
                  # RolloutWorkers (no GPUs).
                  "CPU": cf.num_cpus_per_worker,
              }
              for _ in range(cf.num_workers)
          ]
          + [
              {
                  # Policy learners (and Replay buffer shards).
                  # 1 CPU for the replay buffer.
                  # 1 CPU (or fractional GPU) for each learning policy.
                  "CPU": 1
                  + (num_policies_per_shard if fake_gpus else 0),
                  "GPU": 0 if fake_gpus else num_gpus_per_shard,
              }
              for _ in range(num_learner_shards)
          ]
          + (
              [
                  {
                      # Evaluation (remote) workers.
                      # Note: The local eval worker is located on the driver
                      # CPU or not even created iff >0 eval workers.
                      "CPU": eval_config.get(
                          "num_cpus_per_worker", cf.num_cpus_per_worker
                      ),
                  }
                  for _ in range(cf.evaluation_num_workers)
              ]
              if cf.evaluation_interval
              else []
          ),
          strategy=cf.placement_strategy,
      )
  
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 1 for calling the AlphaStar obj's underlying member method code with example usage: obj.default_resource_request(cls, config) and returns: PlacementGroupFactory