class_docstr: Compute words embedding features for mentions
docstr: Embedding for a single word (tuned if possible, otherwise static) 
docstr_tok: ["Embedding","for","a","single","word","(","tuned","if","possible",",","otherwise","static",")"]
code: def get_word_embedding(self, word, static=False):
      """Embedding for a single word (tuned if possible, otherwise static)"""
      norm_word = self.normalize_word(word)
      if static:
          return self.get_stat_word(norm_word)
      else:
          if norm_word in self.tuned_embeddings:
              return norm_word, self.tuned_embeddings.get(norm_word)
          else:
              return self.get_stat_word(norm_word)
  
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 6 for calling the EmbeddingExtractor obj's underlying member method code with example usage: obj.get_word_embedding(word, static) and returns: self, norm_word,  self, self