class_docstr: Compute words embedding features for mentions
docstr: Embedding for a list of words 
docstr_tok: ["Embedding","for","a","list","of","words"]
code: def get_average_embedding(self, token_list):
      """Embedding for a list of words"""
      embed_vector = np.zeros(
          self.shape
      )  # We could also use np.copy(self.average_mean)
      word_list = []
      for tok in token_list:
          if tok.lower_ not in [".", "!", "?"]:
              word, embed = self.get_word_embedding(tok, static=True)
              embed_vector += embed
              word_list.append(word)
      return word_list, (embed_vector / max(len(word_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 8 for calling the EmbeddingExtractor obj's underlying member method code with example usage: obj.get_average_embedding(token_list) and returns: word_list,  unknown