code: def _check_registered_optimizer(
      self,
      optimizer: Optimizer,
      params: Sequence[Param],
  ) -> None:
      super()._check_registered_optimizer(optimizer, params)
      if not isinstance(optimizer, tf.keras.optimizers.Optimizer):
          raise ValueError(
              f"The optimizer ({optimizer}) is not a tf keras optimizer! "
              "Only use tf.keras.optimizers.Optimizer subclasses for TfLearner."
          )
      for param in params:
          if not isinstance(param, tf.Variable):
              raise ValueError(
                  f"One of the parameters ({param}) in the registered optimizer "
                  "is not a tf.Variable!"
              )
  
imports: import json
  import logging
  import pathlib
  from typing import Any, Callable, Dict, Hashable, Sequence, Tuple, TYPE_CHECKING, Union
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.rl_module.rl_module import RLModule, SingleAgentRLModuleSpec
  from ray.rllib.core.rl_module.tf.tf_rl_module import TfRLModule
  from ray.rllib.policy.eager_tf_policy import _convert_to_tf
  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_tf
  from ray.rllib.utils.metrics import ALL_MODULES
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.serialization import convert_numpy_to_python_primitives
  from ray.rllib.utils.typing import ModuleID, Optimizer, Param, ParamDict, TensorType
label_desc: Use image node_id 18 for calling the TfLearner obj's underlying member method code with example usage: obj._check_registered_optimizer(optimizer, params) without return types