code: def clean_token(token):
      cleaned_token = token
      if cleaned_token in NORMALIZE_DICT:
          cleaned_token = NORMALIZE_DICT[cleaned_token]
      if cleaned_token not in REMOVED_CHAR:
          for char in REMOVED_CHAR:
              cleaned_token = cleaned_token.replace(char, "")
      if len(cleaned_token) == 0:
          cleaned_token = ","
      return cleaned_token
  
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 1 for calling a global function with example usage: clean_token(token) and returns: cleaned_token