def METHOD_NAME(self) -> None:
    tensorA = torch.tensor([1, 0], dtype=torch.float)
    tensorB = torch.tensor([0, 1], dtype=torch.float)
    unk_tensor = torch.tensor([0, 0], dtype=torch.float)
    tokens = ["a", "b"]
    vecs = torch.stack((tensorA, tensorB), 0)
    vectors_obj = build_vectors(tokens, vecs, unk_tensor=unk_tensor)
    jit_vectors_obj = torch.jit.script(vectors_obj)
    assert not vectors_obj.is_jitable
    # 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.
    assert vectors_obj.__prepare_scriptable__().is_jitable
    self.assertEqual(vectors_obj["a"], jit_vectors_obj["a"])
    self.assertEqual(vectors_obj["b"], jit_vectors_obj["b"])
    self.assertEqual(vectors_obj["not_in_it"], jit_vectors_obj["not_in_it"])