code: def tokenize_to_file(tokenizer, fin, fout, chunk_size=500):
      raw_text = fin.read()
      documents = NEWLINE_SPLIT_RE.split(raw_text)
      for chunk_start in tqdm(
          range(0, len(documents), chunk_size), leave=False
      ):
          chunk_end = min(chunk_start + chunk_size, len(documents))
          chunk = documents[chunk_start:chunk_end]
          in_docs = [stanza.Document([], text=d) for d in chunk]
          out_docs = tokenizer.bulk_process(in_docs)
          for document in out_docs:
              for sent_idx, sentence in enumerate(document.sentences):
                  if sent_idx > 0:
                      fout.write(" ")
                  fout.write(" ".join(x.text for x in sentence.tokens))
              fout.write("\n")
  
file_path: stanza/stanza/models/tokenization/tokenize_files.py
imports: import argparse
  import io
  import os
  import time
  import re
  import zipfile
  import torch
  import stanza
  from stanza.models.common.utils import open_read_text, default_device
  from stanza.models.tokenization.data import TokenizationDataset
  from stanza.models.tokenization.utils import output_predictions
  from stanza.pipeline.tokenize_processor import TokenizeProcessor
  from stanza.utils.get_tqdm import get_tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: tokenize_to_file(tokenizer, fin, fout, chunk_size) without return types