code: def compute_gradients(
      self, loss_per_module: Dict[str, TensorType], **kwargs
  ) -> ParamDict:
      for optim in self._optimizer_parameters:
          # set_to_none is a faster way to zero out the gradients
          optim.zero_grad(set_to_none=True)
      loss_per_module[ALL_MODULES].backward()
      grads = {pid: p.grad for pid, p in self._params.items()}
  
      return grads
  
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 4 for calling the TorchLearner obj's underlying member method code with example usage: obj.compute_gradients(loss_per_module) and returns: grads