code: def test_embeded_special_tokens(self):
      if not self.test_slow_tokenizer:
          # as we don't have a slow version, we can't compare the outputs between slow and fast versions
          return
  
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(
              f"{tokenizer.__class__.__name__} ({pretrained_name})"
          ):
              tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                  pretrained_name, **kwargs
              )
              tokenizer_p = self.tokenizer_class.from_pretrained(
                  pretrained_name, **kwargs
              )
              words, boxes = self.get_words_and_boxes()
              tokens_r = tokenizer_r.encode_plus(
                  words,
                  boxes=boxes,
                  add_special_tokens=True,
              )
              tokens_p = tokenizer_p.encode_plus(
                  words,
                  boxes=boxes,
                  add_special_tokens=True,
              )
  
              for key in tokens_p.keys():
                  self.assertEqual(tokens_r[key], tokens_p[key])
  
              if "token_type_ids" in tokens_r:
                  self.assertEqual(
                      sum(tokens_r["token_type_ids"]),
                      sum(tokens_p["token_type_ids"]),
                  )
  
              tokens_r = tokenizer_r.convert_ids_to_tokens(
                  tokens_r["input_ids"]
              )
              tokens_p = tokenizer_p.convert_ids_to_tokens(
                  tokens_p["input_ids"]
              )
              self.assertSequenceEqual(tokens_r, tokens_p)
  
imports: import inspect
  import shutil
  import tempfile
  import unittest
  from typing import List
  from transformers import AddedToken, LayoutXLMTokenizerFast, SpecialTokensMixin, is_tf_available, is_torch_available, logging
  from transformers.models.layoutxlm.tokenization_layoutxlm import LayoutXLMTokenizer
  from transformers.testing_utils import get_tests_dir, is_pt_tf_cross_test, require_pandas, require_scatter, require_sentencepiece, require_tokenizers, require_torch, slow
  from ...test_tokenization_common import SMALL_TRAINING_CORPUS, TokenizerTesterMixin, filter_non_english, merge_model_tokenizer_mappings
label_desc: Use image node_id 37 for calling the LayoutXLMTokenizationTest obj's underlying member method code with example usage: obj.test_embeded_special_tokens() without return types