code: def postprocess_nstep_and_prio(
      policy: Policy, batch: SampleBatch, other_agent=None, episode=None
  ) -> SampleBatch:
      # N-step Q adjustments.
      if policy.config["n_step"] > 1:
          adjust_nstep(
              policy.config["n_step"], policy.config["gamma"], batch
          )
  
      # Create dummy prio-weights (1.0) in case we don't have any in
      # the batch.
      if PRIO_WEIGHTS not in batch:
          batch[PRIO_WEIGHTS] = np.ones_like(batch[SampleBatch.REWARDS])
  
      # Prioritize on the worker side.
      if batch.count > 0 and policy.config["replay_buffer_config"].get(
          "worker_side_prioritization", False
      ):
          td_errors = policy.compute_td_error(
              batch[SampleBatch.OBS],
              batch[SampleBatch.ACTIONS],
              batch[SampleBatch.REWARDS],
              batch[SampleBatch.NEXT_OBS],
              batch[SampleBatch.TERMINATEDS],
              batch[PRIO_WEIGHTS],
          )
          # Retain compatibility with old-style Replay args
          epsilon = policy.config.get("replay_buffer_config", {}).get(
              "prioritized_replay_eps"
          ) or policy.config.get("prioritized_replay_eps")
          if epsilon is None:
              raise ValueError(
                  "prioritized_replay_eps not defined in config."
              )
  
          new_priorities = np.abs(convert_to_numpy(td_errors)) + epsilon
          batch[PRIO_WEIGHTS] = new_priorities
  
      return batch
  
imports: from typing import Dict
  import gymnasium
  import numpy
  import ray
  from ray.rllib.algorithms.dqn.distributional_q_tf_model import DistributionalQTFModel
  from ray.rllib.algorithms.simple_q.utils import Q_SCOPE, Q_TARGET_SCOPE
  from ray.rllib.evaluation.postprocessing import adjust_nstep
  from ray.rllib.models import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import get_categorical_class_with_temperature
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.exploration import ParameterNoise
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.tf_utils import huber_loss, l2_loss, make_tf_callable, minimize_and_clip, reduce_mean_ignore_inf
  from ray.rllib.utils.typing import AlgorithmConfigDict, ModelGradients, TensorType
label_desc: Use image node_id 10 for calling a global function with example usage: postprocess_nstep_and_prio(policy, batch, other_agent, episode) and returns: batch