code: def test_padding_entity_inputs(self):
      tokenizer = self.get_tokenizer()
  
      sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
      span = (15, 34)
      pad_id = tokenizer.entity_vocab["[PAD]"]
      mask_id = tokenizer.entity_vocab["[MASK]"]
  
      encoding = tokenizer(
          [sentence, sentence],
          entity_spans=[[span], [span, span]],
          padding=True,
      )
      self.assertEqual(
          encoding["entity_ids"],
          [[mask_id, pad_id], [mask_id, mask_id]],
      )
  
      # test with a sentence with no entity
      encoding = tokenizer(
          [sentence, sentence],
          entity_spans=[[], [span, span]],
          padding=True,
      )
      self.assertEqual(
          encoding["entity_ids"], [[pad_id, pad_id], [mask_id, mask_id]]
      )
  
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 10 for calling the LukeTokenizerTest obj's underlying member method code with example usage: obj.test_padding_entity_inputs() without return types