class_docstr: Discrete Q Function with dualing MLP network.
  
  This class implements a Q-value network. It predicts Q-value based on the
  input state and action. It uses an MLP to fit the function Q(s, a).
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      name (str): Name of the q-function, also serves as the variable scope.
      hidden_sizes (list[int]): Output dimension of dense layer(s).
          For example, (32, 32) means the MLP of this q-function consists of
          two hidden layers, each with 32 hidden units.
      hidden_nonlinearity (callable): Activation function for intermediate
          dense layer(s). It should return a tf.Tensor. Set it to
          None to maintain a linear activation.
      hidden_w_init (callable): Initializer function for the weight
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      hidden_b_init (callable): Initializer function for the bias
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      output_nonlinearity (callable): Activation function for output dense
          layer. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      output_w_init (callable): Initializer function for the weight
          of output dense layer(s). The function should return a
          tf.Tensor.
      output_b_init (callable): Initializer function for the bias
          of output dense layer(s). The function should return a
          tf.Tensor.
      layer_normalization (bool): Bool for using layer normalization.
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docstr: Build the symbolic graph for q-network.
  
  Args:
      state_input (tf.Tensor): The state input tf.Tensor to the network.
      name (str): Network variable scope.
  
  Return:
      tf.Tensor: The tf.Tensor output of Discrete MLP QFunction.
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code: def build(self, state_input, name):
      """Build the symbolic graph for q-network.
  
      Args:
          state_input (tf.Tensor): The state input tf.Tensor to the network.
          name (str): Network variable scope.
  
      Return:
          tf.Tensor: The tf.Tensor output of Discrete MLP QFunction.
  
      """
      return super().build(state_input, name=name).outputs
  
file_path: garage/src/garage/tf/q_functions/discrete_mlp_dueling_q_function.py
imports: import tensorflow
  from garage.tf.models import MLPDuelingModel
label_desc: Use image node_id 5 for calling the DiscreteMLPDuelingQFunction obj's underlying member method code with example usage: obj.build(state_input, name) and returns: super