code: def central_vf_stats(policy, train_batch):
      # Report the explained variance of the central value function.
      return {
          "vf_explained_var": explained_variance(
              train_batch[Postprocessing.VALUE_TARGETS],
              policy._central_value_out,
          )
      }
  
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 3 for calling a global function with example usage: central_vf_stats(policy, train_batch) and returns: dict