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: Saves the state of the LearnerGroup.
  
  Args:
      path: The path to save the state to.
docstr_tok: ["Saves","the","state","of","the","LearnerGroup",".","Args",":","path",":","The","path","to","save","the","state","to","."]
code: def save_state(self, path: str) -> None:
      """Saves the state of the LearnerGroup.
  
      Args:
          path: The path to save the state to.
      """
      if self.is_local:
          self._learner.save_state(path)
      else:
          worker = self._worker_manager.healthy_actor_ids()[0]
          worker_ip_addr = self._worker_manager.foreach_actor(
              self._get_ip_address, remote_actor_ids=[worker]
          )
          worker_ip_addr = self._get_results(worker_ip_addr)[0]
          self_ip_addr = self._get_ip_address()
  
          if worker_ip_addr == self_ip_addr:
              self._worker_manager.foreach_actor(
                  lambda w: w.save_state(path),
                  remote_actor_ids=[worker],
              )
          else:
              # save the checkpoint to a temporary location on the worker
  
              # create a temporary directory on the worker
              worker_temp_dir = self._worker_manager.foreach_actor(
                  self._create_temporary_dir, remote_actor_ids=[worker]
              )
              worker_temp_dir = self._get_results(worker_temp_dir)[0]
  
              # save the checkpoint to the temporary directory on the worker
              self._worker_manager.foreach_actor(
                  lambda w: w.save_state(worker_temp_dir),
                  remote_actor_ids=[worker],
              )
  
              # sync the temporary directory on the worker to the local directory
              sync_dir_between_nodes(
                  worker_ip_addr, worker_temp_dir, self_ip_addr, path
              )
  
              # creating this function here instead of making it a member funciton
              # becasue it uses the worker_temp_dir variable, and this can't
              # be passed in as an argument to foreach_actor
              def remove_dir(w):
                  import shutil
  
                  shutil.rmtree(worker_temp_dir)
  
              # remove the temporary directory on the worker
              self._worker_manager.foreach_actor(
                  remove_dir, remote_actor_ids=[worker]
              )
  
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 19 for calling the LearnerGroup obj's underlying member method code with example usage: obj.save_state(path) without return types