class_docstr: PyTorch policy class used with PG.
class_docstr_tok: ["PyTorch","policy","class","used","with","PG","."]
code: def postprocess_trajectory(
      self,
      sample_batch: SampleBatch,
      other_agent_batches: Optional[
          Dict[AgentID, Tuple["Policy", SampleBatch]]
      ] = None,
      episode: Optional["Episode"] = None,
  ) -> SampleBatch:
      sample_batch = super().postprocess_trajectory(
          sample_batch, other_agent_batches, episode
      )
      return post_process_advantages(
          self, sample_batch, other_agent_batches, episode
      )
  
imports: import logging
  from typing import Dict, List, Type, Union, Optional, Tuple
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.utils.typing import AgentID
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.algorithms.pg.pg import PGConfig
  from ray.rllib.algorithms.pg.utils import post_process_advantages
  from ray.rllib.evaluation.postprocessing import Postprocessing
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import LearningRateSchedule
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 4 for calling the PGTorchPolicy obj's underlying member method code with example usage: obj.postprocess_trajectory(sample_batch, other_agent_batches, episode) and returns: post_process_advantages