code: def predict(self, X):
          
          kernel_mat = self._get_kernel(X, self.fit_X_)
  
          val = numpy.dot(kernel_mat, self.coef_)
          if hasattr(self, "intercept_"):
              val += self.intercept_
  
          # Order by increasing survival time if objective is pure ranking
          if self.rank_ratio == 1:
              val *= -1
          else:
              # model was fitted on log(time), transform to original scale
              val = numpy.exp(val)
  
          return val
docs: Rank samples according to survival times
  
          Lower ranks indicate shorter survival, higher ranks longer survival.
  
          Parameters
          ----------
          X : array-like, shape = (n_samples, n_features)
              The input samples.
  
          Returns
          -------
          y : ndarray, shape = (n_samples,)
              Predicted ranks.