code: def _update_policy(
      policy: Policy, replay_actor: ActorHandle, pid: PolicyID
  ):
      if not hasattr(policy, "_target_and_kl_stats"):
          policy._target_and_kl_stats = {
              LAST_TARGET_UPDATE_TS: 0,
              NUM_TARGET_UPDATES: 0,
              NUM_AGENT_STEPS_TRAINED: 0,
              TARGET_NET_UPDATE_TIMER: _Timer(),
          }
  
      train_results = policy.learn_on_batch_from_replay_buffer(
          replay_actor=replay_actor, policy_id=pid
      )
  
      if not train_results:
          return train_results
  
      # Update target net and KL.
      with policy._target_and_kl_stats[TARGET_NET_UPDATE_TIMER]:
          policy._target_and_kl_stats[
              NUM_AGENT_STEPS_TRAINED
          ] += train_results[NUM_AGENT_STEPS_TRAINED]
          target_update_freq = (
              policy.config["num_sgd_iter"]
              * policy.config["replay_buffer_capacity"]
              * policy.config["train_batch_size"]
          )
          cur_ts = policy._target_and_kl_stats[NUM_AGENT_STEPS_TRAINED]
          last_update = policy._target_and_kl_stats[
              LAST_TARGET_UPDATE_TS
          ]
  
          # Update target networks on all policy learners.
          if cur_ts - last_update > target_update_freq:
              policy._target_and_kl_stats[NUM_TARGET_UPDATES] += 1
              policy._target_and_kl_stats[
                  LAST_TARGET_UPDATE_TS
              ] = cur_ts
              policy.update_target()
              # Also update Policy's current KL coeff.
              if policy.config["use_kl_loss"]:
                  kl = train_results[LEARNER_STATS_KEY].get("kl")
                  assert kl is not None, train_results
                  # Make the actual `Policy.update_kl()` call.
                  policy.update_kl(kl)
  
      return train_results
  
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 9 for calling the AlphaStar obj's underlying member method code with example usage: obj._update_policy(policy, replay_actor, pid) and returns: train_results, train_results