class_docstr: Main data class: encapsulate list of utterances, mentions and speakers
  Process utterances to extract mentions and pre-compute mentions features
code: def set_utterances(
      self, utterances, utterances_speaker=None, speakers_names=None
  ):
      self.utterances = []
      self.utterances_speaker = []
      self.mentions = []
      self.speakers = {}
      self.n_sents = 0
      if utterances:
          self.add_utterances(
              utterances, utterances_speaker, speakers_names
          )
  
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 7 for calling the Document obj's underlying member method code with example usage: obj.set_utterances(utterances, utterances_speaker, speakers_names) without return types