code: def test_sequence_ids(self):
      tokenizers = self.get_tokenizers()
      for tokenizer in tokenizers:
          if not tokenizer.is_fast:
              continue
          with self.subTest(f"{tokenizer.__class__.__name__}"):
              seq_0 = "Test this method."
              seq_1 = ["With", "these", "inputs."]
              xpaths = ["html/body" for _ in range(len(seq_1))]
  
              # We want to have sequence 0 and sequence 1 are tagged
              # respectively with 0 and 1 token_ids
              # (regardless of whether the model use token type ids)
              # We use this assumption in the QA pipeline among other place
              output = tokenizer(seq_0.split(), xpaths=xpaths)
              self.assertIn(0, output.sequence_ids())
  
              output = tokenizer(seq_0, seq_1, xpaths=xpaths)
              self.assertIn(0, output.sequence_ids())
              self.assertIn(1, output.sequence_ids())
  
              if tokenizer.num_special_tokens_to_add(pair=True):
                  self.assertIn(None, output.sequence_ids())
  
imports: import inspect
  import json
  import os
  import re
  import shutil
  import tempfile
  import unittest
  from typing import List
  from transformers import AddedToken, MarkupLMTokenizerFast, SpecialTokensMixin, is_tf_available, is_torch_available, logging
  from transformers.models.markuplm.tokenization_markuplm import VOCAB_FILES_NAMES, MarkupLMTokenizer
  from transformers.testing_utils import is_pt_tf_cross_test, require_tokenizers, require_torch, slow
  from ...test_tokenization_common import SMALL_TRAINING_CORPUS, TokenizerTesterMixin, merge_model_tokenizer_mappings
label_desc: Use image node_id 37 for calling the MarkupLMTokenizationTest obj's underlying member method code with example usage: obj.test_sequence_ids() without return types