class_docstr: PyTorch Policy class used with A3C.
class_docstr_tok: ["PyTorch","Policy","class","used","with","A3C","."]
code: def extra_grad_process(
      self, optimizer: "torch.optim.Optimizer", loss: TensorType
  ) -> Dict[str, TensorType]:
      return apply_grad_clipping(self, optimizer, loss)
  
imports: from typing import Dict, List, Optional, Type, Union
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.postprocessing import Postprocessing, compute_gae_for_sample_batch
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import EntropyCoeffSchedule, LearningRateSchedule, ValueNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import apply_grad_clipping, sequence_mask
  from ray.rllib.utils.typing import AgentID, TensorType
label_desc: Use image node_id 6 for calling the A3CTorchPolicy obj's underlying member method code with example usage: obj.extra_grad_process(optimizer, loss) and returns: apply_grad_clipping