def METHOD_NAME(self):
    num_embedding = 9
    transformer = IDTransformer(
        num_embedding,
        transform_config={
            "type": "naive",
        },
    )
    global_ids = torch.tensor([1, 2, 3, 4], dtype=torch.long)
    cache_ids = torch.empty_like(global_ids)
    result = transformer.transform(
        TensorList([global_ids]), TensorList([cache_ids]), 0
    )
    self.assertTrue(result.success)
    id_pairs = transformer.save().tolist()
    self.assertEqual(len(id_pairs), 4)
    id_dict = {1: 0, 2: 1, 3: 2, 4: 3}
    for global_id, cache_id in id_pairs:
        self.assertTrue(global_id in id_dict)
        self.assertEqual(cache_id, id_dict[global_id])
    global_ids = torch.tensor([1, 3, 5, 7], dtype=torch.long)
    result = transformer.transform(
        TensorList([global_ids]), TensorList([cache_ids]), 1
    )
    self.assertTrue(result.success)
    id_pairs = transformer.save().tolist()
    self.assertEqual(len(id_pairs), 4)
    id_dict = {1: 0, 3: 2, 5: 4, 7: 5}
    for global_id, cache_id in id_pairs:
        self.assertTrue(global_id in id_dict)
        self.assertEqual(cache_id, id_dict[global_id])