code: def predict(self, Xnew):
          
          Xnew_warped = self.transform_data(Xnew, test_data=True)
          mean, var = super(InputWarpedGP, self).predict(Xnew_warped, kern=self.kernel, full_cov=False)
          return mean, var
docs: Prediction on the new data
  
          Parameters
          ----------
          Xnew : array_like, shape = (n_samples, n_features)
              The test data.
  
          Returns
          -------
          mean : array_like, shape = (n_samples, output.dim)
              Posterior mean at the location of Xnew
  
          var : array_like, shape = (n_samples, 1)
              Posterior variance at the location of Xnew