code: def _get_bpe(in_path: str, model_prefix: str, vocab_size: int):
      arguments = [
          f"--input={in_path}",
          f"--model_prefix={model_prefix}",
          f"--model_type=bpe",
          f"--vocab_size={vocab_size}",
          "--character_coverage=1.0",
          "--normalization_rule_name=identity",
          f"--num_threads={cpu_count()}",
      ]
      sp.SentencePieceTrainer.Train(" ".join(arguments))
  
imports: import argparse
  import os
  import os.path
  from collections import namedtuple
  from multiprocessing import cpu_count
  from typing import List, Optional
  import sentencepiece
  from fairseq.data.encoders.byte_bpe import ByteBPE
  from fairseq.data.encoders.byte_utils import byte_encode
  from fairseq.data.encoders.bytes import Bytes
  from fairseq.data.encoders.characters import Characters
  from fairseq.data.encoders.moses_tokenizer import MosesTokenizer
  from fairseq.data.encoders.sentencepiece_bpe import SentencepieceBPE
label_desc: Use image node_id 7 for calling a global function with example usage: _get_bpe(in_path, model_prefix, vocab_size) without return types