docs: def _add_new_labels(self, sentences):
          '''
          Adds new sentences to the internal indexing of the model.
  
          Args: 
              sentences (list): LabeledSentences for each doc to be added
  
          Returns:
              int: number of sentences added to the model
  
          '''
          sentence_no = -1
          total_words = 0
          vocab = self.model.vocab
          model_sentence_n = len([l for l in vocab if l.startswith("DOC_")])
          n_sentences = 0
          for sentence_no, sentence in enumerate(sentences):
              sentence_length = len(sentence.words)
              for label in sentence.labels:
                  total_words += 1
                  if label in vocab:
                      vocab[label].count += sentence_length
                  else:
                      vocab[label] = gensim.models.word2vec.Vocab(
                          count=sentence_length)
  
                      vocab[label].index = len(self.model.vocab) - 1
                      vocab[label].code = [0]
                      vocab[label].sample_probability = 1.
                      self.model.index2word.append(label)
                      n_sentences += 1
                      
          return n_sentences