class_docstr: Extension of standard TorchModelV2 to provide dueling-Q functionality.
class_docstr_tok: ["Extension","of","standard","TorchModelV2","to","provide","dueling-Q","functionality","."]
docstr: Returns the state value prediction for the given state embedding.
docstr_tok: ["Returns","the","state","value","prediction","for","the","given","state","embedding","."]
code: def get_state_value(self, model_out):
      """Returns the state value prediction for the given state embedding."""
  
      return self.value_module(model_out)
  
file_path: ray/python/ray/rllib/algorithms/dqn/dqn_torch_model.py
imports: from typing import Sequence
  import gymnasium
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.models.torch.modules.noisy_layer import NoisyLayer
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import ModelConfigDict
label_desc: Use image node_id 3 for calling the DQNTorchModel obj's underlying member method code with example usage: obj.get_state_value(model_out) and returns: self