code: def test_text_pair_padding_pytorch_tensors(self):
      tokenizer = self.tokenizer
  
      sentence = "ISO 639-3 uses the code fas"
      sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
      entities = ["en:ISO 639-3"]
      entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
      spans = [(0, 9)]
      spans_pair = [(31, 35), (40, 47), (49, 60)]
  
      encoding = tokenizer(
          sentence,
          sentence_pair,
          entities=entities,
          entities_pair=entities_pair,
          entity_spans=spans,
          entity_spans_pair=spans_pair,
          return_token_type_ids=True,
          padding="max_length",
          max_length=40,
          max_entity_length=16,
          return_tensors="pt",
      )
  
      # test words
      self.assertEqual(encoding["input_ids"].shape, (1, 40))
      self.assertEqual(encoding["attention_mask"].shape, (1, 40))
      self.assertEqual(encoding["token_type_ids"].shape, (1, 40))
  
      # test entities
      self.assertEqual(encoding["entity_ids"].shape, (1, 16))
      self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16))
      self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16))
      self.assertEqual(
          encoding["entity_position_ids"].shape,
          (1, 16, tokenizer.max_mention_length),
      )
  
imports: import unittest
  from typing import Tuple
  from transformers.models.mluke.tokenization_mluke import MLukeTokenizer
  from transformers.testing_utils import get_tests_dir, require_torch, slow
  from ...test_tokenization_common import TokenizerTesterMixin
label_desc: Use image node_id 7 for calling the MLukeTokenizerIntegrationTests obj's underlying member method code with example usage: obj.test_text_pair_padding_pytorch_tensors() without return types