class_docstr: PyTorch policy class used with PPO.
class_docstr_tok: ["PyTorch","policy","class","used","with","PPO","."]
code: def extra_grad_process(self, local_optimizer, loss):
      return apply_grad_clipping(self, local_optimizer, loss)
  
imports: import logging
  from typing import Dict, List, Type, Union
  import ray
  from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
  from ray.rllib.evaluation.postprocessing import Postprocessing, compute_gae_for_sample_batch
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import EntropyCoeffSchedule, KLCoeffMixin, 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, explained_variance, sequence_mask, warn_if_infinite_kl_divergence
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 3 for calling the PPOTorchPolicy obj's underlying member method code with example usage: obj.extra_grad_process(local_optimizer, loss) and returns: apply_grad_clipping