code: def multi_gaussian_lens(self, kwargs_lens, model_bool_list=None, e1=0, e2=0, n_comp=20):
          
          if 'center_x' in kwargs_lens[0]:
              center_x = kwargs_lens[0]['center_x']
              center_y = kwargs_lens[0]['center_y']
          else:
              raise ValueError('no keyword center_x defined!')
          theta_E = self._lensModelExtensions.effective_einstein_radius(kwargs_lens)
          r_array = np.logspace(-4, 2, 200) * theta_E
          x_coords, y_coords = param_util.transform_e1e2(r_array, np.zeros_like(r_array), e1=-e1, e2=-e2)
          x_coords += center_x
          y_coords += center_y
          #r_array = np.logspace(-2, 1, 50) * theta_E
          if model_bool_list is None:
              model_bool_list = [True] * len(kwargs_lens)
          kappa_s = np.zeros_like(r_array)
          for i in range(len(kwargs_lens)):
              if model_bool_list[i] is True:
                  kappa_s += self.LensModel.kappa(x_coords, y_coords, kwargs_lens, k=i)
          amplitudes, sigmas, norm = mge.mge_1d(r_array, kappa_s, N=n_comp)
          return amplitudes, sigmas, center_x, center_y
docs: multi-gaussian lens model in convergence space
  
          :param kwargs_lens:
          :param n_comp:
          :return: