code: def gen_whole_word_mask(args, dictionary):
      def is_beginning_of_word(i):
          if i < dictionary.nspecial:
              # special elements are always considered beginnings
              return True
          tok = dictionary[i]
          if tok.startswith("madeupword"):
              return True
  
          if tok in ["<unk>", "<s>", "</s>", "<pad>"]:
              return True
          return tok.startswith("\u2581")
  
      if args.use_mask_whole_words:
          mask_whole_words = torch.ByteTensor(
              list(map(is_beginning_of_word, range(len(dictionary))))
          )
      else:
          # it will mask every token as word leading token, since no bpe model is loaded for phoneme tokens
          return get_whole_word_mask(args, dictionary)
      return mask_whole_words
  
imports: import itertools
  import logging
  import os
  import re
  import numpy
  import torch
  from examples.speech_text_joint_to_text.data.pair_denoising_dataset import LanguagePairDenoisingDataset
  from fairseq import utils
  from fairseq.data import ConcatDataset, Dictionary, LanguagePairDataset, ResamplingDataset, TransformEosConcatLangPairDataset, TransformEosLangPairDataset, data_utils, indexed_dataset
  from fairseq.data.encoders.utils import get_whole_word_mask
  from fairseq.tasks import register_task
  from fairseq.tasks.translation import TranslationTask
label_desc: Use image node_id 1 for calling a global function with example usage: gen_whole_word_mask(args, dictionary) and returns: mask_whole_words, tok, get_whole_word_mask, True, True, True