code: def decision_function(self, X):
          
          predictions = self.predict_proba(X)
          out = np.zeros((predictions.shape[0], 1))
          out[:, 0] = 1 - predictions[:, -1]
          return out
docs: Generate an inlier score for each test data example.
  
          Parameters
          ----------
          X : array
              Test data, of dimension N times d (rows are examples, columns
              are data dimensions)
  
          Returns:
          -------
          scores : array
              A vector of length N, where each element contains an inlier
              score in the range 0-1 (outliers have values close to zero,
              inliers have values close to one).