def METHOD_NAME(self, lstm_hidden_size):
    input_spec1 = TensorSpec((3, 20, 20))
    input_spec2 = TensorSpec((100, ))
    conv_layer_params = ((8, 3, 1), (16, 3, 2, 1))
    embedding_dim = 100
    image = input_spec1.zeros(outer_dims=(1, ))
    vector = input_spec2.zeros(outer_dims=(1, ))
    network_ctor, state = self._init(lstm_hidden_size)
    value_net = network_ctor(
        input_tensor_spec=[input_spec1, input_spec2],
        input_preprocessors=[
            EmbeddingPreprocessor(
                input_spec1,
                embedding_dim=embedding_dim,
                conv_layer_params=conv_layer_params), None
        ],
        preprocessing_combiner=NestConcat())
    value, state = value_net([image, vector], state)
    self.assertEqual(value_net.output_spec, TensorSpec(()))
    # (batch_size,)
    self.assertEqual(value.shape, (1, ))