code: def covariance(self):
          
  
          mu = self.moments_central
          if mu[0, 0] != 0:
              m = mu / mu[0, 0]
              covariance = self._check_covariance(
                  np.array([[m[0, 2], m[1, 1]], [m[1, 1], m[2, 0]]]))
              return covariance * u.pix**2
          else:
              return np.empty((2, 2)) * np.nan * u.pix**2
docs: The covariance matrix of the 2D Gaussian function that has the
          same second-order moments as the source.