code: def update_lens_scaling(self, kwargs_cosmo, kwargs_lens, inverse=False):
          
          kwargs_lens_updated = copy.deepcopy(kwargs_lens)
          if self._mass_scaling is False:
              return kwargs_lens_updated
          scale_factor_list = np.array(kwargs_cosmo['scale_factor'])
          if inverse is True:
              scale_factor_list = 1. / np.array(kwargs_cosmo['scale_factor'])
          for i, kwargs in enumerate(kwargs_lens_updated):
              if self._mass_scaling_list[i] is not False:
                  scale_factor = scale_factor_list[self._mass_scaling_list[i] - 1]
                  if 'theta_E' in kwargs:
                      kwargs['theta_E'] *= scale_factor
                  elif 'theta_Rs' in kwargs:
                      kwargs['theta_Rs'] *= scale_factor
                  elif 'sigma0' in kwargs:
                      kwargs['sigma0'] *= scale_factor
                  elif 'k_eff' in kwargs:
                      kwargs['k_eff'] *= scale_factor
          return kwargs_lens_updated
docs: multiplies the scaling parameters of the profiles
  
          :param args:
          :param kwargs_lens:
          :param i:
          :param inverse:
          :return: