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: Set the weights of the MultiAgentRLModule maintained by each Learner.
  
  The weights don't have to include all the modules in the MARLModule.
  This way the weights of only some of the Agents can be set.
  
  Args:
      weights: The weights to set each RLModule in the MARLModule to.
docstr_tok: ["Set","the","weights","of","the","MultiAgentRLModule","maintained","by","each","Learner",".","The","weights","do","n't","have","to","include","all","the","modules","in","the","MARLModule",".","This","way","the","weights","of","only","some","of","the","Agents","can","be","set",".","Args",":","weights",":","The","weights","to","set","each","RLModule","in","the","MARLModule","to","."]
code: def set_weights(self, weights: Dict[str, Any]) -> None:
      """Set the weights of the MultiAgentRLModule maintained by each Learner.
  
      The weights don't have to include all the modules in the MARLModule.
      This way the weights of only some of the Agents can be set.
  
      Args:
          weights: The weights to set each RLModule in the MARLModule to.
  
      """
      if self.is_local:
          self._learner.set_module_state(weights)
      else:
          results_or_errors = self._worker_manager.foreach_actor(
              lambda w: w.set_module_state(weights)
          )
          # raise errors if any
          self._get_results(results_or_errors)
  
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 14 for calling the LearnerGroup obj's underlying member method code with example usage: obj.set_weights(weights) without return types