code: def _set_optimizer_lr(optimizer: "tf.Optimizer", lr: float) -> None:
      # When tf creates the optimizer, it seems to detach the optimizer's lr value
      # from the given tf variable.
      # Thus, updating this variable is NOT sufficient to update the actual
      # optimizer's learning rate, so we have to explicitly set it here inside the
      # optimizer object.
      optimizer.lr = 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 28 for calling the TfLearner obj's underlying member method code with example usage: obj._set_optimizer_lr(optimizer, lr) without return types