code: def loss_with_central_critic(
      policy, base_policy, model, dist_class, train_batch
  ):
      # Save original value function.
      vf_saved = model.value_function
  
      # Calculate loss with a custom value function.
      model.value_function = (
          lambda: policy.model.central_value_function(
              train_batch[SampleBatch.CUR_OBS],
              train_batch[OPPONENT_OBS],
              train_batch[OPPONENT_ACTION],
          )
      )
      policy._central_value_out = model.value_function()
      loss = base_policy.loss(model, dist_class, train_batch)
  
      # Restore original value function.
      model.value_function = vf_saved
  
      return loss
  
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 2 for calling a global function with example usage: loss_with_central_critic(policy, base_policy, model, dist_class, train_batch) and returns: loss