code: def word_vec(self, word, use_norm=False):
          
          if word in self.vocab:
              if use_norm:
                  result = self.syn0norm[self.vocab[word].index]
              else:
                  result = self.syn0[self.vocab[word].index]
  
              result.setflags(write=False)
              return result
          else:
              raise KeyError("word '%s' not in vocabulary" % word)
docs: Accept a single word as input.
          Returns the word's representations in vector space, as a 1D numpy array.
          If `use_norm` is True, returns the normalized word vector.
          Example::
            >>> trained_model['office']
            array([ -1.40128313e-02, ...])