class_docstr: Coordinator of n (possibly remote) Learner workers.
  
  Each Learner worker has a copy of the RLModule, the loss function(s), and
  one or more optimizers.
docstr: Returns the current stats for the input queue for this learner group.
docstr_tok: ["Returns","the","current","stats","for","the","input","queue","for","this","learner","group","."]
code: def get_stats(self) -> Dict[str, Any]:
      """Returns the current stats for the input queue for this learner group."""
      return {
          "learner_group_ts_dropped": self._ts_dropped,
          "actor_manager_num_outstanding_async_reqs": (
              0
              if self.is_local
              else self._worker_manager.num_outstanding_async_reqs()
          ),
      }
  
imports: from collections import defaultdict, Counter
  from functools import partial
  import pathlib
  from typing import Any, Callable, Dict, List, Optional, Set, Type, Union
  import uuid
  import ray
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.learner.reduce_result_dict_fn import _reduce_mean_results
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec, RLMODULE_STATE_DIR_NAME
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils.actor_manager import FaultTolerantActorManager
  from ray.rllib.utils.deprecation import Deprecated, deprecation_warning
  from ray.rllib.utils.minibatch_utils import ShardBatchIterator, ShardEpisodesIterator
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.typing import EpisodeType, ModuleID, ResultDict, RLModuleSpec, ShouldModuleBeUpdatedFn
  from ray.train._internal.backend_executor import BackendExecutor
  from ray.tune.utils.file_transfer import sync_dir_between_nodes
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 2 for calling the LearnerGroup obj's underlying member method code with example usage: obj.get_stats() and returns: dict