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: Sets the state of this LearnerGroup.
  
  Note that all Learners share the same state.
  
  Args:
      state: The state dict mapping str keys to state information.
docstr_tok: ["Sets","the","state","of","this","LearnerGroup",".","Note","that","all","Learners","share","the","same","state",".","Args",":","state",":","The","state","dict","mapping","str","keys","to","state","information","."]
code: def set_state(self, state: Dict[str, Any]) -> None:
      """Sets the state of this LearnerGroup.
  
      Note that all Learners share the same state.
  
      Args:
          state: The state dict mapping str keys to state information.
      """
      learner_state = state.get("learner_state")
      if learner_state is not None:
          if self.is_local:
              self._learner.set_state(learner_state)
          else:
              self._worker_manager.foreach_actor(
                  lambda w: w.set_state(learner_state)
              )
      if state.get("should_module_be_updated_fn"):
          self.set_should_module_be_updated_fn(
              state["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 16 for calling the LearnerGroup obj's underlying member method code with example usage: obj.set_state(state) without return types