code: def get_ccppo_policy(base):
      class CCPPOTFPolicy(CentralizedValueMixin, base):
          def __init__(self, observation_space, action_space, config):
              base.__init__(
                  self, observation_space, action_space, config
              )
              CentralizedValueMixin.__init__(self)
  
          @override(base)
          def loss(self, model, dist_class, train_batch):
              # Use super() to get to the base PPO policy.
              # This special loss function utilizes a shared
              # value function defined on self, and the loss function
              # defined on PPO policies.
              return loss_with_central_critic(
                  self, super(), model, dist_class, train_batch
              )
  
          @override(base)
          def postprocess_trajectory(
              self, sample_batch, other_agent_batches=None, episode=None
          ):
              return centralized_critic_postprocessing(
                  self, sample_batch, other_agent_batches, episode
              )
  
          @override(base)
          def stats_fn(self, train_batch: SampleBatch):
              stats = super().stats_fn(train_batch)
              stats.update(central_vf_stats(self, train_batch))
              return stats
  
      return CCPPOTFPolicy
  
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 4 for calling a global function with example usage: get_ccppo_policy(base) and returns: CCPPOTFPolicy, loss_with_central_critic, centralized_critic_postprocessing, stats