code: def test_padding_different_model_input_name(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
              )
              self.assertEqual(
                  tokenizer_p.pad_token_id, tokenizer_r.pad_token_id
              )
              pad_token_id = tokenizer_p.pad_token_id
  
              words, boxes = self.get_words_and_boxes_batch()
  
              input_r = tokenizer_r.batch_encode_plus(
                  words, boxes=boxes
              )
              input_p = tokenizer_r.batch_encode_plus(
                  words, boxes=boxes
              )
  
              # rename encoded batch to "inputs"
              input_r["inputs"] = input_r[
                  tokenizer_r.model_input_names[0]
              ]
              del input_r[tokenizer_r.model_input_names[0]]
  
              input_p["inputs"] = input_p[
                  tokenizer_p.model_input_names[0]
              ]
              del input_p[tokenizer_p.model_input_names[0]]
  
              # Renaming `input_ids` to `inputs`
              tokenizer_r.model_input_names = [
                  "inputs"
              ] + tokenizer_r.model_input_names[1:]
              tokenizer_p.model_input_names = [
                  "inputs"
              ] + tokenizer_p.model_input_names[1:]
  
              input_r = tokenizer_r.pad(input_r, padding="longest")
              input_p = tokenizer_r.pad(input_p, padding="longest")
  
              max_length = len(input_p["inputs"][0])
              self.assert_batch_padded_input_match(
                  input_r,
                  input_p,
                  max_length,
                  pad_token_id,
                  model_main_input_name="inputs",
              )
  
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 46 for calling the LayoutXLMTokenizationTest obj's underlying member method code with example usage: obj.test_padding_different_model_input_name() without return types