<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def decision_function(self, pairs):
 """Returns the decision function used to classify the pairs. Returns the opposite of the learned metric value between samples in every pair, to be consistent with scikit-learn conventions. Hence it should ideally be low for dissimilar samples and high for similar samples. This is the decision function that is used to classify pairs as similar (+1), or dissimilar (-1). Parameters pairs : array-like, shape=(n_pairs, 2, n_features) or (n_pairs, 2) 3D Array of pairs to predict, with each row corresponding to two points, or 2D array of indices of pairs if the metric learner uses a preprocessor. Returns ------- y_predicted : `numpy.ndarray` of floats, shape=(n_constraints,) The predicted decision function value for each pair. """

    pairs = check_input(pairs, type_of_inputs='tuples',
                        preprocessor=self.preprocessor_,
                        estimator=self, tuple_size=self._tuple_size)
    return - self.score_pairs(pairs)