code: def build_bio_dataset(
      paths,
      udbase_dir,
      tokenizer_dir,
      handparsed_dir,
      short_name,
      model_type,
      augment,
  ):
      """
      Process the en bio datasets
  
      Creates a dataset by combining the en_combined data with one of the bio sets
      """
      random.seed(1234)
      name, bio_dataset = short_name.split("_")
      assert name == "en"
      for dataset in ("train", "dev", "test"):
          output_conllu = common.tokenizer_conllu_name(
              tokenizer_dir, short_name, dataset
          )
          if dataset == "train":
              sents = build_combined_english_dataset(
                  paths, model_type, dataset
              )
              if dataset == "train" and augment:
                  sents = augment_punct(sents)
          else:
              sents = []
          bio_file = os.path.join(
              paths["BIO_UD_DIR"],
              "UD_English-%s" % bio_dataset.upper(),
              "en_%s-ud-%s.conllu" % (bio_dataset.lower(), dataset),
          )
          sents.extend(read_sentences_from_conllu(bio_file))
          write_sentences_to_conllu(output_conllu, sents)
  
imports: import argparse
  import glob
  import os
  import random
  import re
  import tempfile
  from collections import Counter
  from stanza.models.common.constant import treebank_to_short_name
  import stanza.utils.datasets.common
  from stanza.utils.datasets.common import read_sentences_from_conllu, write_sentences_to_conllu, INT_RE, MWT_RE, MWT_OR_COPY_RE
  import stanza.utils.datasets.tokenization.convert_ml_cochin
  import stanza.utils.datasets.tokenization.convert_my_alt
  import stanza.utils.datasets.tokenization.convert_vi_vlsp
  import stanza.utils.datasets.tokenization.convert_th_best
  import stanza.utils.datasets.tokenization.convert_th_lst20
  import stanza.utils.datasets.tokenization.convert_th_orchid
label_desc: Use image node_id 34 for calling a global function with example usage: build_bio_dataset(paths, udbase_dir, tokenizer_dir, handparsed_dir, short_name, model_type, augment) without return types