code: def process_folder(
      data_folder,
      folder_name,
      output_folder,
      detokenizer,
      remove_newlines,
  ):
      if not os.path.isdir(os.path.join(data_folder, folder_name)):
          return
      print(f"Processing {folder_name}")
      train_fname = os.path.join(
          data_folder, folder_name, "train.tfrecord-00000-of-00001"
      )
      valid_fname = os.path.join(
          data_folder, folder_name, "validation.tfrecord-00000-of-00001"
      )
      test_fname = os.path.join(
          data_folder, folder_name, "test.tfrecord-00000-of-00001"
      )
      if not os.path.exists(train_fname):
          print(f"Could not find {train_fname}")
          return
      write_train_val_test_dataset_to_file(
          train_fname,
          folder_name,
          output_folder,
          detokenizer,
          "train",
          remove_newlines,
      )
      if os.path.exists(valid_fname):
          write_train_val_test_dataset_to_file(
              valid_fname,
              folder_name,
              output_folder,
              detokenizer,
              "val",
              remove_newlines,
          )
      if os.path.exists(test_fname):
          write_train_val_test_dataset_to_file(
              test_fname,
              folder_name,
              output_folder,
              detokenizer,
              "test",
              remove_newlines,
          )
  
file_path: NeMo/scripts/nlp_language_modeling/t0/t0_dataset_preproc.py
imports: import json
  import os
  import re
  from argparse import ArgumentParser
  from multiprocessing import Pool
  import tensorflow
  from sacremoses import MosesDetokenizer
  from tasks_splits_and_features import _TASK_SPLITS_AND_FEATURES_DICT
label_desc: Use image node_id 5 for calling a global function with example usage: process_folder(data_folder, folder_name, output_folder, detokenizer, remove_newlines) without return types