class_docstr: Compute words embedding features for mentions
docstr: Embedding for the document 
docstr_tok: ["Embedding","for","the","document"]
code: def get_document_embedding(self, utterances_list):
      """Embedding for the document"""
      #    We could also use this: embed_vector = np.copy(self.average_mean)#np.zeros(self.shape)
      #    return embed_vector
      embed_vector = np.zeros(self.shape)
      for utt in utterances_list:
          _, utt_embed = self.get_average_embedding(utt)
          embed_vector += utt_embed
      return embed_vector / max(len(utterances_list), 1)
  
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 4 for calling the EmbeddingExtractor obj's underlying member method code with example usage: obj.get_document_embedding(utterances_list) and returns: unknown