code: def get_clean_sequence(
      self,
      tokenizer,
      with_prefix_space=False,
      max_length=20,
      min_length=5,
  ):
      toks = [
          (i, tokenizer.decode([i], clean_up_tokenization_spaces=False))
          for i in range(len(tokenizer))
      ]
      toks = list(
          filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks)
      )
      toks = list(
          filter(
              lambda t: [t[0]]
              == tokenizer.encode(
                  t[1].split(" "),
                  xpaths=len(t[1]) * ["html/body"],
                  add_special_tokens=False,
              ),
              toks,
          )
      )
      if max_length is not None and len(toks) > max_length:
          toks = toks[:max_length]
      if (
          min_length is not None
          and len(toks) < min_length
          and len(toks) > 0
      ):
          while len(toks) < min_length:
              toks = toks + toks
      # toks_str = [t[1] for t in toks]
      toks_ids = [t[0] for t in toks]
  
      # Ensure consistency
      output_txt = tokenizer.decode(
          toks_ids, clean_up_tokenization_spaces=False
      )
      # an extra blank will cause inconsistency: ["a","b",] & "a b"
      """
          if " " not in output_txt and len(toks_ids) > 1:
              output_txt = (
                  tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
                  + " "
                  + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
              )
          """
      if with_prefix_space:
          output_txt = " " + output_txt
      nodes = output_txt.split(" ")
      xpaths = ["html/body" for i in range(len(nodes))]
      output_ids = tokenizer.encode(
          nodes, xpaths=xpaths, add_special_tokens=False
      )
      return nodes, xpaths, output_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 45 for calling the MarkupLMTokenizationTest obj's underlying member method code with example usage: obj.get_clean_sequence(tokenizer, with_prefix_space, max_length, min_length) and returns: nodes,  xpaths,  output_ids