code: def setUp(self):
      super().setUp()
  
      spm_model = sp.SentencePieceProcessor()
      spm_model.Load(SAMPLE_SP)
      vocab = ["<s>", "<pad>", "</s>", "<unk>"]
  
      vocab += [
          spm_model.IdToPiece(id_) for id_ in range(len(spm_model))
      ]
      vocab_tokens = dict(zip(vocab, range(len(vocab))))
  
      save_dir = Path(self.tmpdirname)
      save_json(
          vocab_tokens, save_dir / VOCAB_FILES_NAMES["vocab_file"]
      )
      if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
          copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["spm_file"])
  
      tokenizer = Speech2TextTokenizer.from_pretrained(self.tmpdirname)
      tokenizer.save_pretrained(self.tmpdirname)
  
imports: import unittest
  from pathlib import Path
  from shutil import copyfile
  from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
  from transformers.models.speech_to_text import Speech2TextTokenizer
  from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
  from ...test_tokenization_common import TokenizerTesterMixin
label_desc: Use image node_id 1 for calling the SpeechToTextTokenizerTest obj's underlying member method code with example usage: obj.setUp() without return types