code: def compute(self, t, yerr=1.123e-12, check_sorted=True,
                  A=None, U=None, V=None):
          
          t = np.atleast_1d(t)
          if check_sorted and np.any(np.diff(t) < 0.0):
              raise ValueError("the input coordinates must be sorted")
          if check_sorted and len(t.shape) > 1:
              raise ValueError("dimension mismatch")
          self._t = t
          self._yerr = np.empty_like(self._t)
          self._yerr[:] = yerr
          (alpha_real, beta_real, alpha_complex_real, alpha_complex_imag,
           beta_complex_real, beta_complex_imag) = self.kernel.coefficients
          self._A = np.empty(0) if A is None else A
          self._U = np.empty((0, 0)) if U is None else U
          self._V = np.empty((0, 0)) if V is None else V
          self.solver.compute(
              self.kernel.jitter,
              alpha_real, beta_real,
              alpha_complex_real, alpha_complex_imag,
              beta_complex_real, beta_complex_imag,
              self._A, self._U, self._V,
              t, self._yerr**2
          )
          self.dirty = False
docs: Compute the extended form of the covariance matrix and factorize
  
          Args:
              x (array[n]): The independent coordinates of the data points.
                  This array must be _sorted_ in ascending order.
              yerr (Optional[float or array[n]]): The measurement uncertainties
                  for the data points at coordinates ``x``. These values will be
                  added in quadrature to the diagonal of the covariance matrix.
                  (default: ``1.123e-12``)
              check_sorted (bool): If ``True``, ``x`` will be checked to make
                  sure that it is properly sorted. If ``False``, the coordinates
                  will be assumed to be in the correct order.
  
          Raises:
              ValueError: For un-sorted data or mismatched dimensions.
              solver.LinAlgError: For non-positive definite matrices.