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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict(self, quadruplets):
 """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. """

    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))