class_docstr: Main data class: encapsulate list of utterances, mentions and speakers
  Process utterances to extract mentions and pre-compute mentions features
docstr: Features for anaphoricity test (single mention features + genre if conll)
docstr_tok: ["Features","for","anaphoricity","test","(","single","mention","features","+","genre","if","conll",")"]
code: def get_single_mention_features(self, mention):
      """Features for anaphoricity test (single mention features + genre if conll)"""
      features_ = mention.features_
      features_["DocGenre"] = self.genre_
      return (
          features_,
          np.concatenate([mention.features, self.genre], axis=0),
      )
  
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 11 for calling the Document obj's underlying member method code with example usage: obj.get_single_mention_features(mention) and returns: features_,  np