code: def spm_srcs(tmp_path: Path):
      input_text = tmp_path / "text"
      vocabsize = len(string.ascii_letters) + 4
      model_prefix = tmp_path / "model"
      model = str(model_prefix) + ".model"
      input_sentence_size = 100000
  
      with input_text.open("w") as f:
          f.write(string.ascii_letters + "\n")
  
      spm.SentencePieceTrainer.Train(
          f"--input={input_text} "
          f"--vocab_size={vocabsize} "
          f"--model_prefix={model_prefix} "
          f"--input_sentence_size={input_sentence_size}"
      )
      sp = spm.SentencePieceProcessor()
      sp.load(model)
  
      with input_text.open("r") as f:
          vocabs = {"<unk>", "▁"}
          for line in f:
              tokens = sp.DecodePieces(list(line.strip()))
          vocabs |= set(tokens)
      return model, vocabs
  
file_path: espnet/test/espnet2/text/test_sentencepiece_tokenizer.py
imports: import string
  from pathlib import Path
  import pytest
  import sentencepiece
  from espnet2.text.sentencepiece_tokenizer import SentencepiecesTokenizer
label_desc: Use image node_id 1 for calling a global function with example usage: spm_srcs(tmp_path) and returns: model,  vocabs