code: def predict_magnification(self, Xnew, kern=None, mean=True, covariance=True, dimensions=None):
          
          G = self.predict_wishart_embedding(Xnew, kern, mean, covariance)
          if dimensions is None:
              dimensions = self.get_most_significant_input_dimensions()[:2]
          G = G[:, dimensions][:,:,dimensions]
          from ..util.linalg import jitchol
          mag = np.empty(Xnew.shape[0])
          for n in range(Xnew.shape[0]):
              try:
                  mag[n] = np.sqrt(np.exp(2*np.sum(np.log(np.diag(jitchol(G[n, :, :]))))))
              except:
                  mag[n] = np.sqrt(np.linalg.det(G[n, :, :]))
          return mag
docs: Predict the magnification factor as
  
          sqrt(det(G))
  
          for each point N in Xnew.
  
          :param bool mean: whether to include the mean of the wishart embedding.
          :param bool covariance: whether to include the covariance of the wishart embedding.
          :param array-like dimensions: which dimensions of the input space to use [defaults to self.get_most_significant_input_dimensions()[:2]]