code: def mention_words_idx(embed_extractor, mention, debug=False):
      # index of the word in the tuned embeddings no need for normalizing,
      # it is already performed in set_mentions_features()
      # We take them in the tuned vocabulary which is a smaller voc tailored from conll
      words = []
      for _, w in sorted(mention.words_embeddings_.items()):
          if w not in embed_extractor.tun_idx:
              if debug:
                  print(
                      "No matching tokens in tuned voc for word ",
                      w,
                      "surrounding or inside mention",
                      mention,
                  )
              words.append(MISSING_WORD)
          else:
              words.append(w)
      return [embed_extractor.tun_idx[w] for w in words]
  
imports: import re
  import argparse
  import time
  import os
  import io
  import pickle
  import spacy
  import numpy
  from tqdm import tqdm
  from neuralcoref.train.compat import unicode_
  from neuralcoref.train.document import Mention, Document, Speaker, EmbeddingExtractor, MISSING_WORD, extract_mentions_spans
  from neuralcoref.train.utils import parallel_process
label_desc: Use image node_id 2 for calling a global function with example usage: mention_words_idx(embed_extractor, mention, debug) and returns: unknown