code: def conv_dqn(
      shape_of_inputs,
      number_of_outputs,
      loss_function=None,
      use_placeholder_for_input=False,
  ):
      """Example convolutional neural network for approximating the Q value function.
  
      This is the model used in the original DQN paper
      https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf.
  
      Args:
          shape_of_inputs: tuple of array (input) dimensions.
          number_of_outputs: dimension of output, equals the number of
              possible actions.
          loss_function: if not specified, use squared loss by default.
          use_placeholder_for_input: if true, inputs have to be replaced
              later with actual input_variable.
  
      Returns: a Python dictionary with string-valued keys including
          'inputs', 'outputs', 'loss' and 'f'.
      """
      # input/output
      inputs = (
          C.ops.placeholder(shape=shape_of_inputs)
          if use_placeholder_for_input
          else C.ops.input_variable(
              shape=shape_of_inputs, dtype=np.float32
          )
      )
      outputs = C.ops.input_variable(
          shape=(number_of_outputs,), dtype=np.float32
      )
  
      # network structure
      centered_inputs = inputs - 128
      scaled_inputs = centered_inputs / 256
  
      with C.layers.default_options(activation=C.ops.relu):
          q = C.layers.Sequential(
              [
                  C.layers.Convolution((8, 8), 32, strides=4),
                  C.layers.Convolution((4, 4), 64, strides=2),
                  C.layers.Convolution((3, 3), 64, strides=2),
                  C.layers.Dense((512,)),
                  C.layers.Dense(number_of_outputs, activation=None),
              ]
          )(scaled_inputs)
  
      if loss_function is None:
          loss = C.losses.squared_error(q, outputs)
      else:
          loss = loss_function(q, outputs)
  
      return {
          "inputs": inputs,
          "outputs": outputs,
          "f": q,
          "loss": loss,
      }
  
label_desc: Use image node_id 1 for calling a global function with example usage: conv_dqn(shape_of_inputs, number_of_outputs, loss_function, use_placeholder_for_input) and returns: dict