code: def predict(self, pairs):
      
      check_is_fitted(self, ['threshold_', 'transformer_'])
      return 2 * (- self.decision_function(pairs) <= self.threshold_) - 1
docs: Predicts the learned metric between input pairs. (For now it just
      calls decision function).
  
      Returns the learned metric value between samples in every pair. It should
      ideally be low for similar samples and high for dissimilar samples.
  
      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 learned metric value between samples in every pair.