code: def _sample_and_send_to_buffer(worker: RolloutWorker):
      # Generate a sample.
      sample = worker.sample()
      # Send the per-agent SampleBatches to the correct buffer(s),
      # depending on which policies participated in the episode.
      assert isinstance(sample, MultiAgentBatch)
      for pid, batch in sample.policy_batches.items():
          # Don't send data, if policy is not trainable.
          (
              replay_actor,
              _,
          ) = worker._distributed_learners.get_replay_and_policy_actors(
              pid
          )
          if replay_actor is not None:
              ma_batch = MultiAgentBatch({pid: batch}, batch.count)
              replay_actor.add.remote(ma_batch)
      # Return counts (env-steps, agent-steps).
      return sample.count, sample.agent_steps()
  
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 8 for calling the AlphaStar obj's underlying member method code with example usage: obj._sample_and_send_to_buffer(worker) and returns: sample,  sample