code: def testHuggingFaceVocabularyBuilder():
      from tokenizers import models, trainers
      from tokenizers.pre_tokenizers import Whitespace
  
      corpus = """hello world"""
  
      corpus_file = tempfile.NamedTemporaryFile()
      with open(corpus_file.name, "w") as fp:
          fp.write(corpus)
  
      model = models.BPE(unk_token="[UNK]")
      special_tokens = ["[UNK]"]
      trainer = trainers.BpeTrainer(
          vocab_size=25000, special_tokens=special_tokens
      )
  
      # Build vocabulary by wrapping in huggingface vocabulary builder
      vb = HuggingFaceVocabularyBuilder(model=model, trainer=trainer)
      vb.tokenizer.pre_tokenizer = Whitespace()
      vb.build([corpus_file.name])
  
      vocab_file = tempfile.NamedTemporaryFile()
      vb.save(vocab_file.name)
  
      # Load vocabulary and do a basic sanity check on the vocabulary
      with open(vocab_file.name, "r") as f:
          data = json.loads(f.read())
      assert len(data["added_tokens"]) == 1  # [UNK]
      assert list(data["model"]["vocab"].keys()) == [
          "[UNK]",
          "d",
          "e",
          "h",
          "l",
          "o",
          "r",
          "w",
          "el",
          "hel",
          "ld",
          "lo",
          "or",
          "wor",
          "hello",
          "world",
      ]
      assert data["model"]["merges"] == [
          "e l",
          "h el",
          "l d",
          "l o",
          "o r",
          "w or",
          "hel lo",
          "wor ld",
      ]
  
label_desc: Use image node_id 1 for calling a global function with example usage: testHuggingFaceVocabularyBuilder() without return types