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: Add a module to the Learners maintained by this LearnerGroup.
  
  Args:
      module_id: The id of the module to add.
      module_spec:  #TODO (Kourosh) fill in here.
docstr_tok: ["Add","a","module","to","the","Learners","maintained","by","this","LearnerGroup",".","Args",":","module_id",":","The","id","of","the","module","to","add",".","module_spec",":","#","TODO","(","Kourosh",")","fill","in","here","."]
code: def add_module(
      self,
      *,
      module_id: ModuleID,
      module_spec: SingleAgentRLModuleSpec,
  ) -> None:
      """Add a module to the Learners maintained by this LearnerGroup.
  
      Args:
          module_id: The id of the module to add.
          module_spec:  #TODO (Kourosh) fill in here.
      """
      if self.is_local:
          self._learner.add_module(
              module_id=module_id,
              module_spec=module_spec,
          )
      else:
          results = self._worker_manager.foreach_actor(
              lambda w: w.add_module(
                  module_id=module_id,
                  module_spec=module_spec,
              )
          )
          return self._get_results(results)
  
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 11 for calling the LearnerGroup obj's underlying member method code with example usage: obj.add_module() and returns: self