code: def compute(self, x, yerr):
          
          # Compute the kernel matrix.
          K = self.kernel.get_value(x)
          K[np.diag_indices_from(K)] += yerr ** 2
  
          # Factor the matrix and compute the log-determinant.
          self._factor = (cholesky(K, overwrite_a=True, lower=False), False)
          self.log_determinant = 2 * np.sum(np.log(np.diag(self._factor[0])))
          self.computed = True
docs: Compute and factorize the covariance matrix.
  
          Args:
              x (ndarray[nsamples, ndim]): The independent coordinates of the
                  data points.
              yerr (ndarray[nsamples] or float): The Gaussian uncertainties on
                  the data points at coordinates ``x``. These values will be
                  added in quadrature to the diagonal of the covariance matrix.