code: def process_atis(
      infold, outfold, modes=["train", "test"], do_lower_case=False
  ):
      """
      Process ATIS dataset found at https://www.kaggle.com/siddhadev/atis-dataset-from-ms-cntk
      Args:
          infold: location for input fold of data
          outfold: location for output fold of data
          modes: dataset splits to process
          do_lowercase: whether to lowercase the input utterances
      """
      vocab = get_vocab(f"{infold}/atis.dict.vocab.csv")
  
      if if_exist(outfold, [f"{mode}.tsv" for mode in modes]):
          logging.info(DATABASE_EXISTS_TMP.format("ATIS", outfold))
          return outfold
      logging.info(f"Processing ATIS dataset and storing at {outfold}.")
  
      os.makedirs(outfold, exist_ok=True)
  
      outfiles = {}
      for mode in modes:
          outfiles[mode] = open(
              os.path.join(outfold, mode + ".tsv"),
              "w",
              encoding="utf-8",
          )
          outfiles[mode].write("sentence\tlabel\n")
          outfiles[mode + "_slots"] = open(
              f"{outfold}/{mode}_slots.tsv", "w", encoding="utf-8"
          )
  
          queries = open(
              f"{infold}/atis.{mode}.query.csv", "r", encoding="utf-8"
          ).readlines()
          intents = open(
              f"{infold}/atis.{mode}.intent.csv", "r", encoding="utf-8"
          ).readlines()
          slots = open(
              f"{infold}/atis.{mode}.slots.csv", "r", encoding="utf-8"
          ).readlines()
  
          for i, query in enumerate(queries):
              sentence = ids2text(query.strip().split()[1:-1], vocab)
              if do_lower_case:
                  sentence = sentence.lower()
              outfiles[mode].write(
                  f"{sentence}\t{intents[i].strip()}\n"
              )
              slot = " ".join(slots[i].strip().split()[1:-1])
              outfiles[mode + "_slots"].write(slot + "\n")
  
      shutil.copyfile(
          f"{infold}/atis.dict.intent.csv",
          f"{outfold}/dict.intents.csv",
      )
      shutil.copyfile(
          f"{infold}/atis.dict.slots.csv", f"{outfold}/dict.slots.csv"
      )
      for mode in modes:
          outfiles[mode].close()
  
file_path: NeMo/scripts/dataset_processing/nlp/intent_and_slot/import_datasets.py
imports: import argparse
  import os
  import shutil
  from os.path import exists
  from assistant_utils import process_assistant
  from nemo.collections.nlp.data.data_utils.data_preprocessing import DATABASE_EXISTS_TMP, MODE_EXISTS_TMP, create_dataset, get_dataset, get_vocab, if_exist
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: process_atis(infold, outfold, modes, do_lower_case) and returns: outfold