class_docstr: Main data class: encapsulate list of utterances, mentions and speakers
  Process utterances to extract mentions and pre-compute mentions features
docstr: Return iterator over indexes of mentions in a list of utterances if specified
docstr_tok: ["Return","iterator","over","indexes","of","mentions","in","a","list","of","utterances","if","specified"]
code: def get_candidate_mentions(self, last_utterances_added=False):
      """
      Return iterator over indexes of mentions in a list of utterances if specified
      """
      if last_utterances_added:
          for i, mention in enumerate(self.mentions):
              if self.debug:
                  print(
                      "🤣",
                      i,
                      mention,
                      "utterance index",
                      mention.utterance_index,
                  )
              if mention.utterance_index in self.last_utterances_loaded:
                  yield i
      else:
          iterator = range(len(self.mentions))
          for i in iterator:
              yield i
  
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 13 for calling the Document obj's underlying member method code with example usage: obj.get_candidate_mentions(last_utterances_added) without return types