code: def configure_optimizers_for_module(
      self,
      module_id: ModuleID,
      config: "AlgorithmConfig" = None,
      hps=None,
  ) -> None:
      if hps is not None:
          deprecation_warning(
              old="Learner.configure_optimizers_for_module(.., hps=..)",
              help="Deprecated argument. Use `config` (AlgorithmConfig) instead.",
              error=True,
          )
      module = self._module[module_id]
  
      # For this default implementation, the learning rate is handled by the
      # attached lr Scheduler (controlled by self.config.lr, which can be a
      # fixed value of a schedule setting).
      optimizer = tf.keras.optimizers.Adam()
      params = self.get_parameters(module)
  
      # This isn't strictly necessary, but makes it so that if a checkpoint is
      # computed before training actually starts, then it will be the same in
      # shape / size as a checkpoint after training starts.
      optimizer.build(module.trainable_variables)
  
      # Register the created optimizer (under the default optimizer name).
      self.register_optimizer(
          module_id=module_id,
          optimizer=optimizer,
          params=params,
          lr_or_lr_schedule=config.lr,
      )
  
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 2 for calling the TfLearner obj's underlying member method code with example usage: obj.configure_optimizers_for_module(module_id, config, hps) without return types