class_docstr: A simple, hard-coded dueling head model.
code: def get_q_values(self, underlying_output):
      # Calculate q-values following dueling logic:
      v = self.V(underlying_output)  # value
      a = self.A(underlying_output)  # advantages (per action)
      advantages_mean = torch.mean(a, 1)
      advantages_centered = a - torch.unsqueeze(advantages_mean, 1)
      return v + advantages_centered
  
imports: from gymnasium.spaces import Box
  from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
  from ray.rllib.models.tf.tf_modelv2 import TFModelV2
  from ray.rllib.models.torch.fcnet import FullyConnectedNetwork
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
label_desc: Use image node_id 2 for calling the TorchDuelingQModel obj's underlying member method code with example usage: obj.get_q_values(underlying_output) and returns: unknown