code: def add_policy(
      self,
      policy_id: PolicyID,
      policy_cls: Type[Policy],
      *,
      observation_space: Optional[gym.spaces.Space] = None,
      action_space: Optional[gym.spaces.Space] = None,
      config: Optional[PartialAlgorithmConfigDict] = None,
      policy_state: Optional[PolicyState] = None,
      **kwargs,
  ) -> Policy:
      # Add the new policy to all our train- and eval RolloutWorkers
      # (including the local worker).
      new_policy = super().add_policy(
          policy_id,
          policy_cls,
          observation_space=observation_space,
          action_space=action_space,
          config=config,
          policy_state=policy_state,
          **kwargs,
      )
  
      # Do we have to create a policy-learner actor from it as well?
      if policy_id in kwargs.get("policies_to_train", []):
          new_policy_actor = self.distributed_learners.add_policy(
              policy_id,
              PolicySpec(
                  policy_cls,
                  new_policy.observation_space,
                  new_policy.action_space,
                  self.config,
              ),
          )
          # Set state of new policy actor, if provided.
          if policy_state is not None:
              ray.get(new_policy_actor.set_state.remote(policy_state))
  
      return new_policy
  
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 6 for calling the AlphaStar obj's underlying member method code with example usage: obj.add_policy(policy_id, policy_cls) and returns: new_policy