code: def apply_gradients(self, gradients_dict: ParamDict) -> None:
      # TODO (Avnishn, kourosh): apply gradients doesn't work in cases where
      #  only some agents have a sample batch that is passed but not others.
      #  This is probably because of the way that we are iterating over the
      #  parameters in the optim_to_param_dictionary.
      for optimizer in self._optimizer_parameters:
          optim_grad_dict = self.filter_param_dict_for_optimizer(
              optimizer=optimizer, param_dict=gradients_dict
          )
          variable_list = []
          gradient_list = []
          for param_ref, grad in optim_grad_dict.items():
              if grad is not None:
                  variable_list.append(self._params[param_ref])
                  gradient_list.append(grad)
          optimizer.apply_gradients(zip(gradient_list, variable_list))
  
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 4 for calling the TfLearner obj's underlying member method code with example usage: obj.apply_gradients(gradients_dict) without return types