code: def test_entity_classification_padding_pytorch_tensors(self):
      tokenizer = LukeTokenizer.from_pretrained(
          "studio-ousia/luke-base",
          task="entity_classification",
          return_token_type_ids=True,
      )
      sentence = (
          "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped"
          " the new world number one avoid a humiliating second- round exit at Wimbledon ."
      )
      # entity information
      span = (39, 42)
  
      encoding = tokenizer(
          sentence,
          entity_spans=[span],
          return_token_type_ids=True,
          padding="max_length",
          return_tensors="pt",
      )
  
      # test words
      self.assertEqual(encoding["input_ids"].shape, (1, 512))
      self.assertEqual(encoding["attention_mask"].shape, (1, 512))
      self.assertEqual(encoding["token_type_ids"].shape, (1, 512))
  
      # test entities
      self.assertEqual(encoding["entity_ids"].shape, (1, 1))
      self.assertEqual(encoding["entity_attention_mask"].shape, (1, 1))
      self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 1))
      self.assertEqual(
          encoding["entity_position_ids"].shape,
          (
              1,
              tokenizer.max_entity_length,
              tokenizer.max_mention_length,
          ),
      )
  
imports: import unittest
  from typing import Tuple
  from transformers import AddedToken, LukeTokenizer
  from transformers.testing_utils import get_tests_dir, require_torch, slow
  from ...test_tokenization_common import TokenizerTesterMixin
label_desc: Use image node_id 9 for calling the LukeTokenizerIntegrationTests obj's underlying member method code with example usage: obj.test_entity_classification_padding_pytorch_tensors() without return types