code: def centralized_critic_postprocessing(
      policy, sample_batch, other_agent_batches=None, episode=None
  ):
      pytorch = policy.config["framework"] == "torch"
      if (pytorch and hasattr(policy, "compute_central_vf")) or (
          not pytorch and policy.loss_initialized()
      ):
          assert other_agent_batches is not None
          if policy.config["enable_connectors"]:
              [(_, _, opponent_batch)] = list(
                  other_agent_batches.values()
              )
          else:
              [(_, opponent_batch)] = list(other_agent_batches.values())
  
          # also record the opponent obs and actions in the trajectory
          sample_batch[OPPONENT_OBS] = opponent_batch[
              SampleBatch.CUR_OBS
          ]
          sample_batch[OPPONENT_ACTION] = opponent_batch[
              SampleBatch.ACTIONS
          ]
  
          # overwrite default VF prediction with the central VF
          if args.framework == "torch":
              sample_batch[SampleBatch.VF_PREDS] = (
                  policy.compute_central_vf(
                      convert_to_torch_tensor(
                          sample_batch[SampleBatch.CUR_OBS],
                          policy.device,
                      ),
                      convert_to_torch_tensor(
                          sample_batch[OPPONENT_OBS], policy.device
                      ),
                      convert_to_torch_tensor(
                          sample_batch[OPPONENT_ACTION], policy.device
                      ),
                  )
                  .cpu()
                  .detach()
                  .numpy()
              )
          else:
              sample_batch[SampleBatch.VF_PREDS] = convert_to_numpy(
                  policy.compute_central_vf(
                      sample_batch[SampleBatch.CUR_OBS],
                      sample_batch[OPPONENT_OBS],
                      sample_batch[OPPONENT_ACTION],
                  )
              )
      else:
          # Policy hasn't been initialized yet, use zeros.
          sample_batch[OPPONENT_OBS] = np.zeros_like(
              sample_batch[SampleBatch.CUR_OBS]
          )
          sample_batch[OPPONENT_ACTION] = np.zeros_like(
              sample_batch[SampleBatch.ACTIONS]
          )
          sample_batch[SampleBatch.VF_PREDS] = np.zeros_like(
              sample_batch[SampleBatch.REWARDS], dtype=np.float32
          )
  
      completed = sample_batch[SampleBatch.TERMINATEDS][-1]
      if completed:
          last_r = 0.0
      else:
          last_r = sample_batch[SampleBatch.VF_PREDS][-1]
  
      train_batch = compute_advantages(
          sample_batch,
          last_r,
          policy.config["gamma"],
          policy.config["lambda"],
          use_gae=policy.config["use_gae"],
      )
      return train_batch
  
imports: import argparse
  import numpy
  from gymnasium.spaces import Discrete
  import os
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.ppo.ppo import PPO, PPOConfig
  from ray.rllib.algorithms.ppo.ppo_tf_policy import PPOTF1Policy, PPOTF2Policy
  from ray.rllib.algorithms.ppo.ppo_torch_policy import PPOTorchPolicy
  from ray.rllib.evaluation.postprocessing import compute_advantages, Postprocessing
  from ray.rllib.examples.env.two_step_game import TwoStepGame
  from ray.rllib.examples.models.centralized_critic_models import CentralizedCriticModel, TorchCentralizedCriticModel
  from ray.rllib.models import ModelCatalog
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.test_utils import check_learning_achieved
  from ray.rllib.utils.tf_utils import explained_variance, make_tf_callable
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: centralized_critic_postprocessing(policy, sample_batch, other_agent_batches, episode) and returns: train_batch