def METHOD_NAME(self):
    base_layer = keras.layers.Dense(1)
    input_shape = [1]
    inputs = keras.layers.Input(shape=input_shape)
    sn_layer = keras.layers.SpectralNormalization(base_layer)
    model = keras.models.Sequential(layers=[inputs, sn_layer])
    # initialize model
    model.predict(tf.random.uniform((2, 1)))
    with self.subTest("h5"):
        model.save("test.h5")
        new_model = keras.models.load_model("test.h5")
        self.assertEqual(
            model.layers[0].get_config(), new_model.layers[0].get_config()
        )
    with self.subTest("savedmodel"):
        model.save("test")
        new_model = keras.models.load_model("test")
        self.assertEqual(
            model.layers[0].get_config(), new_model.layers[0].get_config()
        )
    with self.subTest("keras_v3"):
        model.save("test.keras")
        new_model = keras.models.load_model("test.keras")
        self.assertEqual(
            model.layers[0].get_config(), new_model.layers[0].get_config()
        )