code: def image_linear_solve(self, kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_else, inv_bool=False):
          
          wls_list, error_map_list, cov_param_list, param_list = [], [], [], []
          for i in range(self._num_bands):
              wls_model, error_map, cov_param, param = self._imageModel_list[i].image_linear_solve(kwargs_lens,
                                                                                                   kwargs_source,
                                                                                                   kwargs_lens_light,
                                                                                                   kwargs_else,
                                                                                                   inv_bool=inv_bool)
              wls_list.append(wls_model)
              error_map_list.append(error_map)
              cov_param_list.append(cov_param)
              param_list.append(param)
          return wls_list, error_map_list, cov_param_list, param_list
docs: computes the image (lens and source surface brightness with a given lens model).
          The linear parameters are computed with a weighted linear least square optimization (i.e. flux normalization of the brightness profiles)
          :param kwargs_lens: list of keyword arguments corresponding to the superposition of different lens profiles
          :param kwargs_source: list of keyword arguments corresponding to the superposition of different source light profiles
          :param kwargs_lens_light: list of keyword arguments corresponding to different lens light surface brightness profiles
          :param kwargs_else: keyword arguments corresponding to "other" parameters, such as external shear and point source image positions
          :param inv_bool: if True, invert the full linear solver Matrix Ax = y for the purpose of the covariance matrix.
          :return: 1d array of surface brightness pixels of the optimal solution of the linear parameters to match the data