code: def process_assistant(infold, outfold, modes=["train", "test"]):
      """
      https://github.com/xliuhw/NLU-Evaluation-Data - this dataset includes
      about 25 thousand examples with 66 various multi-domain intents and 57 entity types.
      """
      if if_exist(outfold, [f"{mode}_slots.tsv" for mode in modes]):
          logging.info(DATABASE_EXISTS_TMP.format("robot", outfold))
          return outfold
  
      logging.info(
          f"Processing assistant commands dataset and store at {outfold}"
      )
      os.makedirs(outfold, exist_ok=True)
  
      # copy train/test files to the convenient directory to work with
      copy_input_files(infold)
      infold += "/dataset"
  
      # get list of intents from train folder (test folder supposed to be the same)
      intent_names = get_intents(infold + "/trainset")
      write_files(intent_names, f"{outfold}/dict.intents.csv")
  
      # get all train and test queries with their intent
      for mode in modes:
          intent_queries = get_intent_queries(
              infold, intent_names, mode
          )
          write_files(intent_queries, f"{outfold}/{mode}.tsv")
  
      # get list of all unique slots in training and testing files
      slot_types = get_slots(infold, modes)
      write_files(slot_types, f"{outfold}/dict.slots.csv")
  
      # create files of slot queries
      slot_dict = {k: v for v, k in enumerate(slot_types)}
      for mode in modes:
          slot_queries = get_slot_queries(
              infold, slot_dict, mode, intent_names
          )
          write_files(slot_queries, f"{outfold}/{mode}_slots.tsv")
  
file_path: NeMo/scripts/dataset_processing/nlp/intent_and_slot/assistant_utils.py
imports: import os
  import re
  import shutil
  from nemo.collections.nlp.data.data_utils.data_preprocessing import DATABASE_EXISTS_TMP, if_exist, write_files
  from nemo.utils import logging
label_desc: Use image node_id 6 for calling a global function with example usage: process_assistant(infold, outfold, modes) and returns: outfold