class_docstr: Add method to evaluate the central value function from the model.
class_docstr_tok: ["Add","method","to","evaluate","the","central","value","function","from","the","model","."]
code: def __init__(self):
      if self.config["framework"] != "torch":
          self.compute_central_vf = make_tf_callable(
              self.get_session()
          )(self.model.central_value_function)
      else:
          self.compute_central_vf = self.model.central_value_function
  
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 to create a new CentralizedValueMixin object  with example: obj = CentralizedValueMixin()