code: def effective_einstein_radius(self, kwargs_lens_list, k=None, spacing=1000):
          
          if 'center_x' in kwargs_lens_list[0]:
              center_x = kwargs_lens_list[0]['center_x']
              center_y = kwargs_lens_list[0]['center_y']
          elif self._lensModel.lens_model_list[0] in ['INTERPOL', 'INTERPOL_SCALED']:
              center_x, center_y = 0, 0
          else:
              center_x, center_y = 0, 0
          numPix = 200
          deltaPix = 0.05
          x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
          x_grid += center_x
          y_grid += center_y
          kappa = self._lensModel.kappa(x_grid, y_grid, kwargs_lens_list, k=k)
          if self._lensModel.lens_model_list[0] in ['INTERPOL', 'INTERPOL_SCALED']:
              center_x = x_grid[kappa == np.max(kappa)]
              center_y = y_grid[kappa == np.max(kappa)]
          kappa = util.array2image(kappa)
          r_array = np.linspace(0.0001, numPix*deltaPix/2., spacing)
          for r in r_array:
              mask = np.array(1 - mask_util.mask_center_2d(center_x, center_y, r, x_grid, y_grid))
              sum_mask = np.sum(mask)
              if sum_mask > 0:
                  kappa_mean = np.sum(kappa*mask)/np.sum(mask)
                  if kappa_mean < 1:
                      return r
          print(kwargs_lens_list, "Warning, no Einstein radius computed!")
          return r_array[-1]
docs: computes the radius with mean convergence=1
  
          :param kwargs_lens:
          :param spacing: number of annular bins to compute the convergence (resolution of the Einstein radius estimate)
          :return: