class_docstr: Compute words embedding features for mentions
docstr: Embedding for a word in a sentence 
docstr_tok: ["Embedding","for","a","word","in","a","sentence"]
code: def get_word_in_sentence(self, word_idx, sentence):
      """Embedding for a word in a sentence"""
      if word_idx < sentence.start or word_idx >= sentence.end:
          return self.get_word_embedding(None)
      return self.get_word_embedding(sentence.doc[word_idx])
  
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 7 for calling the EmbeddingExtractor obj's underlying member method code with example usage: obj.get_word_in_sentence(word_idx, sentence) and returns: self, self