code: def add_module(
      self,
      *,
      module_id: ModuleID,
      module_spec: SingleAgentRLModuleSpec,
  ) -> None:
      super().add_module(
          module_id=module_id,
          module_spec=module_spec,
      )
  
      # we need to ddpify the module that was just added to the pool
      module = self._module[module_id]
  
      if self._torch_compile_forward_train:
          module.compile(self._torch_compile_cfg)
      elif self._torch_compile_complete_update:
          # When compiling the update, we need to reset and recompile
          # _uncompiled_update every time we add/remove a module anew.
          torch._dynamo.reset()
          self._compiled_update_initialized = False
          self._possibly_compiled_update = torch.compile(
              self._uncompiled_update,
              backend=self._torch_compile_cfg.torch_dynamo_backend,
              mode=self._torch_compile_cfg.torch_dynamo_mode,
              **self._torch_compile_cfg.kwargs,
          )
  
      if isinstance(module, TorchRLModule):
          self._module[module_id].to(self._device)
          if self.distributed:
              if (
                  self._torch_compile_complete_update
                  or self._torch_compile_forward_train
              ):
                  raise ValueError(
                      "Using torch distributed and torch compile "
                      "together tested for now. Please disable "
                      "torch compile."
                  )
              self._module.add_module(
                  module_id, TorchDDPRLModule(module), override=True
              )
  
imports: import logging
  import pathlib
  from typing import Any, Callable, Dict, Hashable, Sequence, Union, Tuple
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, TorchCompileWhatToCompile
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModule
  from ray.rllib.core.rl_module.rl_module import RLModule, SingleAgentRLModuleSpec
  from ray.rllib.core.rl_module.torch.torch_rl_module import TorchCompileConfig, TorchDDPRLModule, TorchRLModule
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils.annotations import override, OverrideToImplementCustomLogic
  from ray.rllib.utils.deprecation import deprecation_warning
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics import ALL_MODULES
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor, copy_torch_tensors
  from ray.rllib.utils.typing import ModuleID, Optimizer, Param, ParamDict, TensorType
label_desc: Use image node_id 14 for calling the TorchLearner obj's underlying member method code with example usage: obj.add_module() without return types