docs: def light2mass_interpol(lens_light_model_list, kwargs_lens_light, numPix=100, deltaPix=0.05, subgrid_res=5, center_x=0, center_y=0):
          """
          takes a lens light model and turns it numerically in a lens model
          (with all lensmodel quantities computed on a grid). Then provides an interpolated grid for the quantities.
  
          :param kwargs_lens_light: lens light keyword argument list
          :param numPix: number of pixels per axis for the return interpolation
          :param deltaPix: interpolation/pixel size
          :param center_x: center of the grid
          :param center_y: center of the grid
          :param subgrid: subgrid for the numerical integrals
          :return:
          """
          # make sugrid
          x_grid_sub, y_grid_sub = util.make_grid(numPix=numPix*5, deltapix=deltaPix, subgrid_res=subgrid_res)
          import lenstronomy.Util.mask as mask_util
          mask = mask_util.mask_sphere(x_grid_sub, y_grid_sub, center_x, center_y, r=1)
          x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
          # compute light on the subgrid
          lightModel = LightModel(light_model_list=lens_light_model_list)
          flux = lightModel.surface_brightness(x_grid_sub, y_grid_sub, kwargs_lens_light)
          flux_norm = np.sum(flux[mask == 1]) / np.sum(mask)
          flux /= flux_norm
          from lenstronomy.LensModel.numerical_profile_integrals import ConvergenceIntegrals
          integral = ConvergenceIntegrals()
  
          # compute lensing quantities with subgrid
          convergence_sub = flux
          f_x_sub, f_y_sub = integral.deflection_from_kappa(convergence_sub, x_grid_sub, y_grid_sub,
                                                            deltaPix=deltaPix/float(subgrid_res))
          f_sub = integral.potential_from_kappa(convergence_sub, x_grid_sub, y_grid_sub,
                                                            deltaPix=deltaPix/float(subgrid_res))
          # interpolation function on lensing quantities
          x_axes_sub, y_axes_sub = util.get_axes(x_grid_sub, y_grid_sub)
          from lenstronomy.LensModel.Profiles.interpol import Interpol
          interp_func = Interpol()
          interp_func.do_interp(x_axes_sub, y_axes_sub, f_sub, f_x_sub, f_y_sub)
          # compute lensing quantities on sparser grid
          x_axes, y_axes = util.get_axes(x_grid, y_grid)
          f_ = interp_func.function(x_grid, y_grid)
          f_x, f_y = interp_func.derivatives(x_grid, y_grid)
          # numerical differentials for second order differentials
          from lenstronomy.LensModel.numeric_lens_differentials import NumericLens
          lens_differential = NumericLens(lens_model_list=['INTERPOL'])
          kwargs = [{'grid_interp_x': x_axes_sub, 'grid_interp_y': y_axes_sub, 'f_': f_sub,
                     'f_x': f_x_sub, 'f_y': f_y_sub}]
          f_xx, f_xy, f_yx, f_yy = lens_differential.hessian(x_grid, y_grid, kwargs)
          kwargs_interpol = {'grid_interp_x': x_axes, 'grid_interp_y': y_axes, 'f_': util.array2image(f_),
                     'f_x': util.array2image(f_x), 'f_y': util.array2image(f_y), 'f_xx': util.array2image(f_xx),
                             'f_xy': util.array2image(f_xy), 'f_yy': util.array2image(f_yy)}
          return kwargs_interpol