code: def remove_module(self, module_id: ModuleID) -> None:
      super().remove_module(module_id)
  
      if 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,
          )
  
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 15 for calling the TorchLearner obj's underlying member method code with example usage: obj.remove_module(module_id) without return types