docstr: Load an optimizer from the hyperparameters saved at path/optim_name_hparams.json.
  
  Args:
      path: The path to the directory to load the hyperparameters from.
      optim_name: The name of the optimizer.
  
  Returns:
      The optimizer loaded from the hyperparameters.
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code: def _load_optimizer_from_hparams(
      self, path: pathlib.Path, optim_name: str
  ) -> "tf.keras.optimizers.Optimizer":
      """Load an optimizer from the hyperparameters saved at path/optim_name_hparams.json.
  
      Args:
          path: The path to the directory to load the hyperparameters from.
          optim_name: The name of the optimizer.
  
      Returns:
          The optimizer loaded from the hyperparameters.
  
      """
      with open(path / f"{optim_name}_hparams.json", "r") as f:
          state = json.load(f)
      return tf.keras.optimizers.deserialize(state)
  
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 9 for calling the TfLearner obj's underlying member method code with example usage: obj._load_optimizer_from_hparams(path, optim_name) and returns: tf