code: def _download_and_preprocess_data(data_dir):
      # Conditionally download data
      TED_DATA = "TEDLIUM_release2.tar.gz"
      TED_DATA_URL = (
          "http://www.openslr.org/resources/19/TEDLIUM_release2.tar.gz"
      )
      local_file = maybe_download(TED_DATA, data_dir, TED_DATA_URL)
  
      # Conditionally extract TED data
      TED_DIR = "TEDLIUM_release2"
      _maybe_extract(data_dir, TED_DIR, local_file)
  
      # Conditionally convert TED sph data to wav
      _maybe_convert_wav(data_dir, TED_DIR)
  
      # Conditionally split TED wav and text data into sentences
      train_files, dev_files, test_files = _maybe_split_sentences(
          data_dir, TED_DIR
      )
  
      # Write sets to disk as CSV files
      train_files.to_csv(
          path.join(data_dir, "ted-train.csv"), index=False
      )
      dev_files.to_csv(path.join(data_dir, "ted-dev.csv"), index=False)
      test_files.to_csv(
          path.join(data_dir, "ted-test.csv"), index=False
      )
  
file_path: DeepSpeech/bin/import_ted.py
imports: import sys
  import tarfile
  import unicodedata
  import wave
  from glob import glob
  from os import makedirs, path, remove, rmdir
  import pandas
  from sox import Transformer
  from tensorflow.python.platform import gfile
  from deepspeech_training.util.downloader import maybe_download
  from deepspeech_training.util.stm import parse_stm_file
label_desc: Use image node_id 1 for calling a global function with example usage: _download_and_preprocess_data(data_dir) without return types