class_docstr: Main data class: encapsulate list of utterances, mentions and speakers
  Process utterances to extract mentions and pre-compute mentions features
docstr: Process mentions in a spacy doc (an utterance)
docstr_tok: ["Process","mentions","in","a","spacy","doc","(","an","utterance",")"]
code: def _process_mentions(
      self, mentions_spans, utterance_index, n_sents, speaker
  ):
      """
      Process mentions in a spacy doc (an utterance)
      """
      processed_spans = sorted(
          (m for m in mentions_spans), key=lambda m: (m.root.i, m.start)
      )
      n_mentions = len(self.mentions)
      for mention_index, span in enumerate(processed_spans):
          self.mentions.append(
              Mention(
                  span,
                  mention_index + n_mentions,
                  utterance_index,
                  n_sents,
                  speaker,
              )
          )
  
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 9 for calling the Document obj's underlying member method code with example usage: obj._process_mentions(mentions_spans, utterance_index, n_sents, speaker) without return types