class_docstr: Main data class: encapsulate list of utterances, mentions and speakers
  Process utterances to extract mentions and pre-compute mentions features
docstr: Return the number of mentions (not utterances) since it is what we really care about 
docstr_tok: ["Return","the","number","of","mentions","(","not","utterances",")","since","it","is","what","we","really","care","about"]
code: def __len__(self):
      """Return the number of mentions (not utterances) since it is what we really care about"""
      return len(self.mentions)
  
imports: import re
  import io
  from six import string_types, integer_types
  from spacy.tokens import Span, Token
  from neuralcoref.train.compat import unicode_
  from neuralcoref.train.utils import encode_distance, parallel_process
  import spacy
  import numpy
label_desc: Use image node_id 4 for calling the Document obj's underlying member method code with example usage: obj.__len__() and returns: len