code: def testTransform(self):
      num_embedding = 1024
      shape = (1024,)
      transformer = IDTransformer(
          num_embedding,
          transform_config={
              "type": "naive",
          },
      )
      python_transformer = PythonIdTransformer(num_embedding)
      global_ids = torch.empty(shape, dtype=torch.int64)
  
      for timestamp in range(10):
          global_ids.random_(0, 512)
          cache_ids = torch.empty_like(global_ids)
  
          result = transformer.transform(
              TensorList([global_ids]),
              TensorList([cache_ids]),
              timestamp,
          )
          success, ids_to_fetch = result.success, result.ids_to_fetch
          self.assertTrue(success)
  
          python_cache_ids = python_transformer.transform(global_ids)
          self.assertTrue(torch.all(cache_ids == python_cache_ids))
  
          if ids_to_fetch is not None:
              ids_map = {
                  global_id: cache_id
                  for global_id, cache_id in ids_to_fetch.tolist()
              }
              for global_id, cache_id in zip(
                  global_ids.tolist(), cache_ids.tolist()
              ):
                  if global_id in ids_map:
                      self.assertEqual(cache_id, ids_map[global_id])
  
file_path: torchrec/contrib/dynamic_embedding/tests/test_id_transformer.py
imports: import unittest
  import torch
  from torchrec_dynamic_embedding.id_transformer import IDTransformer
  from torchrec_dynamic_embedding.tensor_list import TensorList
label_desc: Use image node_id 2 for calling the TestIDTransformer obj's underlying member method code with example usage: obj.testTransform() without return types