code: def _load_optimizers(self, path: Union[str, pathlib.Path]) -> None:
      path = pathlib.Path(path)
      for name in self._named_optimizers.keys():
          new_optim = self._load_optimizer_from_hparams(path, name)
          old_optim = self._named_optimizers[name]
  
          # assign replace the old optim with the new optim in the learner's state
          self._named_optimizers[name] = new_optim
          param_seq = self._optimizer_parameters.pop(old_optim)
          self._optimizer_parameters[new_optim] = []
          for param_ref in param_seq:
              self._optimizer_parameters[new_optim].append(param_ref)
  
          # delete the old optimizer / free its memory
          del old_optim
          # these are the variables that the optimizer is supposed to optimize over
          variable_list = [
              self._params[param_ref]
              for param_ref in self._optimizer_parameters[new_optim]
          ]
          # initialize the optimizer with the variables that it is supposed to
          # optimize over
          new_optim.build(variable_list)
  
          # This loads in the actual state of the optimizer.
          self._load_optimizer_state(path, new_optim, name)
  
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 11 for calling the TfLearner obj's underlying member method code with example usage: obj._load_optimizers(path) without return types