code: def _check_registered_optimizer(
      self,
      optimizer: Optimizer,
      params: Sequence[Param],
  ) -> None:
      super()._check_registered_optimizer(optimizer, params)
      if not isinstance(optimizer, torch.optim.Optimizer):
          raise ValueError(
              f"The optimizer ({optimizer}) is not a torch.optim.Optimizer! "
              "Only use torch.optim.Optimizer subclasses for TorchLearner."
          )
      for param in params:
          if not isinstance(param, torch.Tensor):
              raise ValueError(
                  f"One of the parameters ({param}) in the registered optimizer "
                  "is not a torch.Tensor!"
              )
  
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 20 for calling the TorchLearner obj's underlying member method code with example usage: obj._check_registered_optimizer(optimizer, params) without return types