class_docstr: PyTorch policy class used with PPO.
class_docstr_tok: ["PyTorch","policy","class","used","with","PPO","."]
code: def postprocess_trajectory(
      self, sample_batch, other_agent_batches=None, episode=None
  ):
      # Do all post-processing always with no_grad().
      # Not using this here will introduce a memory leak
      # in torch (issue #6962).
      # TODO: no_grad still necessary?
      with torch.no_grad():
          return compute_gae_for_sample_batch(
              self, sample_batch, other_agent_batches, episode
          )
  
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 5 for calling the PPOTorchPolicy obj's underlying member method code with example usage: obj.postprocess_trajectory(sample_batch, other_agent_batches, episode) and returns: compute_gae_for_sample_batch