code: def postprocess_trajectory(
      policy: Policy,
      sample_batch: SampleBatch,
      other_agent_batches: Optional[Dict[AgentID, SampleBatch]] = None,
      episode: Optional[Episode] = None,
  ) -> SampleBatch:
      """Postprocesses a trajectory and returns the processed trajectory.
  
      The trajectory contains only data from one episode and from one agent.
      - If  `config.batch_mode=truncate_episodes` (default), sample_batch may
      contain a truncated (at-the-end) episode, in case the
      `config.rollout_fragment_length` was reached by the sampler.
      - If `config.batch_mode=complete_episodes`, sample_batch will contain
      exactly one episode (no matter how long).
      New columns can be added to sample_batch and existing ones may be altered.
  
      Args:
          policy: The Policy used to generate the trajectory
              (`sample_batch`)
          sample_batch: The SampleBatch to postprocess.
          other_agent_batches (Optional[Dict[AgentID, SampleBatch]]): Optional
              dict of AgentIDs mapping to other agents' trajectory data (from the
              same episode). NOTE: The other agents use the same policy.
          episode (Optional[Episode]): Optional multi-agent episode
              object in which the agents operated.
  
      Returns:
          SampleBatch: The postprocessed, modified SampleBatch (or a new one).
      """
      return postprocess_nstep_and_prio(policy, sample_batch)
  
imports: import copy
  import gymnasium
  from gymnasium.spaces import Box, Discrete
  from functools import partial
  import logging
  from typing import Dict, List, Optional, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.dqn.dqn_tf_policy import postprocess_nstep_and_prio, PRIO_WEIGHTS
  from ray.rllib.algorithms.sac.sac_tf_model import SACTFModel
  from ray.rllib.algorithms.sac.sac_torch_model import SACTorchModel
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.models import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import Beta, Categorical, DiagGaussian, Dirichlet, SquashedGaussian, TFActionDistribution
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.framework import get_variable, try_import_tf
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.utils.tf_utils import huber_loss, make_tf_callable
  from ray.rllib.utils.typing import AgentID, LocalOptimizer, ModelGradients, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 2 for calling a global function with example usage: postprocess_trajectory(policy, sample_batch, other_agent_batches, episode) and returns: postprocess_nstep_and_prio