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)
    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)
    num_to_evict = 2
    evicted_tensor = transformer.evict(num_to_evict)
    self.assertEqual(num_to_evict, evicted_tensor.shape[0])
    evicted_ids = sorted(evicted_tensor.tolist())
    self.assertEqual(evicted_ids, [[2, 1], [4, 3]])