code: def apply_gradients(self, gradients_dict: ParamDict) -> None:
      # Make sure the parameters do not carry gradients on their own.
      for optim in self._optimizer_parameters:
          optim.zero_grad(set_to_none=True)
  
      # Set the gradient of the parameters.
      for pid, grad in gradients_dict.items():
          self._params[pid].grad = grad
  
      # For each optimizer call its step function.
      for optim in self._optimizer_parameters:
          optim.step()
  
imports: import logging
  import pathlib
  from typing import Any, Callable, Dict, Hashable, Sequence, Union, Tuple
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, TorchCompileWhatToCompile
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModule
  from ray.rllib.core.rl_module.rl_module import RLModule, SingleAgentRLModuleSpec
  from ray.rllib.core.rl_module.torch.torch_rl_module import TorchCompileConfig, TorchDDPRLModule, TorchRLModule
  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_torch
  from ray.rllib.utils.metrics import ALL_MODULES
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor, copy_torch_tensors
  from ray.rllib.utils.typing import ModuleID, Optimizer, Param, ParamDict, TensorType
label_desc: Use image node_id 5 for calling the TorchLearner obj's underlying member method code with example usage: obj.apply_gradients(gradients_dict) without return types