def METHOD_NAME(self) -> None:
    model_path = get_asset_path("spm_example.model")
    input = "SentencePiece is an unsupervised text tokenizer and detokenizer"
    expected = [
        "▁Sent",
        "ence",
        "P",
        "ie",
        "ce",
        "▁is",
        "▁an",
        "▁un",
        "super",
        "vis",
        "ed",
        "▁text",
        "▁to",
        "ken",
        "izer",
        "▁and",
        "▁de",
        "to",
        "ken",
        "izer",
    ]
    with self.subTest("pybind"):
        save_path = os.path.join(self.test_dir, "spm_pybind.pt")
        spm = sentencepiece_tokenizer((model_path))
        torch.save(spm, save_path)
        loaded_spm = torch.load(save_path)
        self.assertEqual(expected, loaded_spm(input))
    with self.subTest("torchscript"):
        save_path = os.path.join(self.test_dir, "spm_torchscript.pt")
        # Call the __prepare_scriptable__() func and convert the building block to the torbhind version
        # Not expect users to use the torchbind version on eager mode but still need a CI test here.
        spm = sentencepiece_tokenizer((model_path)).__prepare_scriptable__()
        torch.save(spm, save_path)
        loaded_spm = torch.load(save_path)
        self.assertEqual(expected, loaded_spm(input))