code: def testAddPreprocessingLayers(self):
      batch_size = 3
      num_actions = 2
      states = (
          tf.random.uniform([batch_size, 1]),
          tf.random.uniform([batch_size]),
      )
      preprocessing_layers = (
          tf.keras.layers.Dense(4),
          tf.keras.Sequential(
              [tf.keras.layers.Reshape((1,)), tf.keras.layers.Dense(4)]
          ),
      )
      network = q_network.QNetwork(
          input_tensor_spec=(
              tensor_spec.TensorSpec([1], tf.float32),
              tensor_spec.TensorSpec([], tf.float32),
          ),
          preprocessing_layers=preprocessing_layers,
          preprocessing_combiner=tf.keras.layers.Add(),
          action_spec=tensor_spec.BoundedTensorSpec(
              [1], tf.int32, 0, num_actions - 1
          ),
      )
      q_values, _ = network(states)
      self.assertAllEqual(
          q_values.shape.as_list(), [batch_size, num_actions]
      )
      # At least 2 variables each for the preprocessing layers.
      self.assertGreater(len(network.trainable_variables), 4)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gin
  import tensorflow
  from tf_agents.networks import q_network
  from tf_agents.specs import tensor_spec
label_desc: Use image node_id 5 for calling the SingleObservationSingleActionTest obj's underlying member method code with example usage: obj.testAddPreprocessingLayers() without return types