class_docstr: Main data class: encapsulate list of utterances, mentions and speakers
  Process utterances to extract mentions and pre-compute mentions features
code: def __str__(self):
      formatted = "\n ".join(
          unicode_(i) + " " + unicode_(s)
          for i, s in zip(self.utterances, self.utterances_speaker)
      )
      mentions = "\n ".join(
          unicode_(i) + " " + unicode_(i.speaker) for i in self.mentions
      )
      return f"<utterances, speakers> \n {formatted}\n<mentions> \n {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 3 for calling the Document obj's underlying member method code with example usage: obj.__str__() and returns: str, formatted, str, mentions