code: def predict(self, quadruplets):
      
      check_is_fitted(self, 'transformer_')
      quadruplets = check_input(quadruplets, type_of_inputs='tuples',
                                preprocessor=self.preprocessor_,
                                estimator=self, tuple_size=self._tuple_size)
      return np.sign(self.decision_function(quadruplets))
docs: Predicts the ordering between sample distances in input quadruplets.
  
      For each quadruplet, returns 1 if the quadruplet is in the right order (
      first pair is more similar than second pair), and -1 if not.
  
      Parameters
      ----------
      quadruplets : array-like, shape=(n_quadruplets, 4, n_features) or
                    (n_quadruplets, 4)
        3D Array of quadruplets to predict, with each row corresponding to four
        points, or 2D array of indices of quadruplets if the metric learner
        uses a preprocessor.
  
      Returns
      -------
      prediction : `numpy.ndarray` of floats, shape=(n_constraints,)
        Predictions of the ordering of pairs, for each quadruplet.