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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def __boost(self, grad, hess):
 """Boost Booster for one iteration with customized gradient statistics. Note ---- For multi-class task, the score is group by class_id first, then group by row_id. If you want to get i-th row score in j-th class, the access way is score[j * num_data + i] and you should group grad and hess in this way as well. Parameters grad : 1-D numpy array or 1-D list The first order derivative (gradient). hess : 1-D numpy array or 1-D list The second order derivative (Hessian). Returns ------- is_finished : bool Whether the boost was successfully finished. """

        grad = list_to_1d_numpy(grad, name='gradient')
        hess = list_to_1d_numpy(hess, name='hessian')
        assert grad.flags.c_contiguous
        assert hess.flags.c_contiguous
        if len(grad) != len(hess):
            raise ValueError("Lengths of gradient({}) and hessian({}) don't match"
                             .format(len(grad), len(hess)))
        is_finished = ctypes.c_int(0)
        _safe_call(_LIB.LGBM_BoosterUpdateOneIterCustom(
            self.handle,
            grad.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
            hess.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
            ctypes.byref(is_finished)))
        self.__is_predicted_cur_iter = [False for _ in range_(self.__num_dataset)]
        return is_finished.value == 1