def word_vec(self, word, use_norm=False):
        """
        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, ...])
        """
        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)
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, ...])