code: def postprocess_trajectory(
      self,
      sample_batch: SampleBatch,
      other_agent_batches: Optional[Dict[Any, SampleBatch]] = None,
      episode: Optional[Episode] = None,
  ) -> SampleBatch:
      return postprocess_nstep_and_prio(
          self, sample_batch, other_agent_batches, episode
      )
  
imports: import logging
  from typing import Any, Dict, List, Optional, Tuple, Type
  import gymnasium
  from rllib_ddpg.ddpg.utils import make_ddpg_models, validate_spaces
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, postprocess_nstep_and_prio
  from ray.rllib.evaluation import Episode
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDeterministic, TorchDirichlet, TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  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.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss, l2_loss
  from ray.rllib.utils.typing import AlgorithmConfigDict, ModelGradients, TensorType
label_desc: Use image node_id 6 for calling the DDPGTorchPolicy obj's underlying member method code with example usage: obj.postprocess_trajectory(sample_batch, other_agent_batches, episode) and returns: postprocess_nstep_and_prio