code: def training_step(self) -> ResultDict:
      # Trigger asynchronous rollouts on all RolloutWorkers.
      # - Rollout results are sent directly to correct replay buffer
      #   shards, instead of here (to the driver).
      with self._timers[SAMPLE_TIMER]:
          # if there are no remote workers (e.g. num_workers=0)
          if not self.workers.remote_workers():
              worker = self.workers.local_worker()
              statistics = worker.apply(self._sample_and_send_to_buffer)
              sample_results = {worker: [statistics]}
          else:
              self._sampling_actor_manager.call_on_all_available(
                  self._sample_and_send_to_buffer
              )
              sample_results = self._sampling_actor_manager.get_ready()
      # Update sample counters.
      for sample_result in sample_results.values():
          for env_steps, agent_steps in sample_result:
              self._counters[NUM_ENV_STEPS_SAMPLED] += env_steps
              self._counters[NUM_AGENT_STEPS_SAMPLED] += agent_steps
  
      # Trigger asynchronous training update requests on all learning
      # policies.
      with self._timers[LEARN_ON_BATCH_TIMER]:
          for pid, pol_actor, repl_actor in self.distributed_learners:
              if pol_actor not in self._learner_worker_manager.workers:
                  self._learner_worker_manager.add_workers(pol_actor)
              self._learner_worker_manager.call(
                  self._update_policy,
                  actor=pol_actor,
                  fn_args=[repl_actor, pid],
              )
          train_results = self._learner_worker_manager.get_ready()
  
      # Update sample counters.
      for train_result in train_results.values():
          for result in train_result:
              if NUM_AGENT_STEPS_TRAINED in result:
                  self._counters[NUM_AGENT_STEPS_TRAINED] += result[
                      NUM_AGENT_STEPS_TRAINED
                  ]
  
      # For those policies that have been updated in this iteration
      # (not all policies may have undergone an updated as we are
      # requesting updates asynchronously):
      # - Gather train infos.
      # - Update weights to those remote rollout workers that contain
      #   the respective policy.
      with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
          train_infos = {}
          policy_weights = {}
          for pol_actor, policy_results in train_results.items():
              results_have_same_structure = True
              for result1, result2 in zip(
                  policy_results, policy_results[1:]
              ):
                  try:
                      tree.assert_same_structure(result1, result2)
                  except (ValueError, TypeError):
                      results_have_same_structure = False
                      break
              if (
                  len(policy_results) > 1
                  and results_have_same_structure
              ):
                  policy_result = tree.map_structure(
                      lambda *_args: sum(_args) / len(policy_results),
                      *policy_results,
                  )
              else:
                  policy_result = policy_results[-1]
              if policy_result:
                  pid = self.distributed_learners.get_policy_id(
                      pol_actor
                  )
                  train_infos[pid] = policy_result
                  policy_weights[pid] = pol_actor.get_weights.remote()
  
          policy_weights_ref = ray.put(policy_weights)
  
          global_vars = {
              "timestep": self._counters[NUM_ENV_STEPS_SAMPLED],
              "league_builder": self.league_builder.__getstate__(),
          }
  
          for worker in self.workers.remote_workers():
              worker.set_weights.remote(policy_weights_ref, global_vars)
  
      return train_infos
  
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 5 for calling the AlphaStar obj's underlying member method code with example usage: obj.training_step() and returns: train_infos