code: def test_bert_tokenizer_unit():
      vocab_tokens = [
          "[PAD]",
          "further",
          "##more",
          ",",
          "under",
          "the",
          "micro",
          "##scope",
          "neither",
          "entity",
          "contains",
          "glands",
          ".",
          "此",
          "外",
          "在",
          "显",
          "微",
          "镜",
          "下",
      ]
      raw_text = "furthermore, \r under the microscope \t neither entity  \n contains sebaceous glands. 此外, 在显微镜下"
  
      golden_tokens = [
          "further",
          "##more",
          ",",
          "under",
          "the",
          "micro",
          "##scope",
          "neither",
          "entity",
          "contains",
          "[UNK]",
          "glands",
          ".",
          "此",
          "外",
          ",",
          "在",
          "显",
          "微",
          "镜",
          "下",
      ]
  
      vocab_dict = {}
      for idx, token in enumerate(vocab_tokens):
          vocab_dict[token] = idx
  
      clean_unit = units.BertClean()
      cleaned_text = clean_unit.transform(raw_text)
      chinese_tokenize_unit = units.ChineseTokenize()
      chinese_tokenized_text = chinese_tokenize_unit.transform(
          cleaned_text
      )
      basic_tokenize_unit = units.BasicTokenize()
      basic_tokens = basic_tokenize_unit.transform(
          chinese_tokenized_text
      )
      wordpiece_unit = units.WordPieceTokenize(vocab_dict)
      wordpiece_tokens = wordpiece_unit.transform(basic_tokens)
  
      assert wordpiece_tokens == golden_tokens
  
imports: import pytest
  import numpy
  from matchzoo.preprocessors import units
  import matchzoo
  import tempfile
  import os
label_desc: Use image node_id 16 for calling a global function with example usage: test_bert_tokenizer_unit() without return types