code: def testHandlePreprocessingLayers(self):
      observation_spec = (
          tensor_spec.TensorSpec([1], tf.float32),
          tensor_spec.TensorSpec([], tf.float32),
      )
      time_step_spec = ts.time_step_spec(observation_spec)
      time_step = tensor_spec.sample_spec_nest(
          time_step_spec, outer_dims=(2, 3)
      )
  
      preprocessing_layers = (
          tf.keras.layers.Dense(4),
          tf.keras.Sequential(
              [tf.keras.layers.Reshape((1,)), tf.keras.layers.Dense(4)]
          ),
      )
  
      net = value_rnn_network.ValueRnnNetwork(
          observation_spec,
          preprocessing_layers=preprocessing_layers,
          preprocessing_combiner=tf.keras.layers.Add(),
      )
  
      value, _ = net(
          time_step.observation,
          step_type=time_step.step_type,
          network_state=net.get_initial_state(batch_size=2),
      )
      self.assertEqual([2, 3], value.shape.as_list())
      self.assertGreater(len(net.trainable_variables), 4)
  
file_path: agents/tf_agents/networks/value_rnn_network_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.networks import value_rnn_network
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
label_desc: Use image node_id 4 for calling the ValueRnnNetworkTest obj's underlying member method code with example usage: obj.testHandlePreprocessingLayers() without return types