class_docstr: Multi-agent model that implements a centralized value function.
  
  It assumes the observation is a dict with 'own_obs' and 'opponent_obs', the
  former of which can be used for computing actions (i.e., decentralized
  execution), and the latter for optimization (i.e., centralized learning).
  
  This model has two parts:
  - An action model that looks at just 'own_obs' to compute actions
  - A value model that also looks at the 'opponent_obs' / 'opponent_action'
    to compute the value (it does this by using the 'obs_flat' tensor).
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code: def value_function(self):
      value_out, _ = self.value_model(
          {"obs": self._model_in[0]},
          self._model_in[1],
          self._model_in[2],
      )
      return torch.reshape(value_out, [-1])
  
imports: from gymnasium.spaces import Box
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_modelv2 import TFModelV2
  from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.models.torch.fcnet import FullyConnectedNetwork
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
label_desc: Use image node_id 3 for calling the YetAnotherTorchCentralizedCriticModel obj's underlying member method code with example usage: obj.value_function() and returns: torch