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: Get the states of this LearnerGroup.
  
  Contains the Learners' state (which should be the same across Learners) and
  some other information.
  
  Returns:
      The state dict mapping str keys to state information.
docstr_tok: ["Get","the","states","of","this","LearnerGroup",".","Contains","the","Learners","'","state","(","which","should","be","the","same","across","Learners",")","and","some","other","information",".","Returns",":","The","state","dict","mapping","str","keys","to","state","information","."]
code: def get_state(self) -> Dict[str, Any]:
      """Get the states of this LearnerGroup.
  
      Contains the Learners' state (which should be the same across Learners) and
      some other information.
  
      Returns:
          The state dict mapping str keys to state information.
      """
      if self.is_local:
          learner_state = self._learner.get_state()
      else:
          worker = self._worker_manager.healthy_actor_ids()[0]
          assert (
              len(self._workers)
              == self._worker_manager.num_healthy_actors()
          )
          results = self._worker_manager.foreach_actor(
              lambda w: w.get_state(), remote_actor_ids=[worker]
          )
          learner_state = self._get_results(results)[0]
  
      return {
          "learner_state": learner_state,
          "should_module_be_updated_fn": self.should_module_be_updated_fn,
      }
  
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 15 for calling the LearnerGroup obj's underlying member method code with example usage: obj.get_state() and returns: dict