def METHOD_NAME(tmpdir):
    base_layer = tf.keras.layers.Dense(1)
    input_shape = [1]
    inputs = tf.keras.layers.Input(shape=input_shape)
    sn_layer = spectral_normalization.SpectralNormalization(base_layer)
    model = tf.keras.models.Sequential(layers=[inputs, sn_layer])
    # initialize model
    model.predict(np.random.uniform(size=(2, 1)))
    model_path = str(tmpdir / "test.h5")
    model.save(model_path)
    new_model = tf.keras.models.load_model(model_path)
    assert model.layers[0].get_config() == new_model.layers[0].get_config()