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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 __inner_predict(self, data_idx):
 """Predict for training and validation dataset."""

        if data_idx >= self.__num_dataset:
            raise ValueError("Data_idx should be smaller than number of dataset")
        if self.__inner_predict_buffer[data_idx] is None:
            if data_idx == 0:
                n_preds = self.train_set.num_data() * self.__num_class
            else:
                n_preds = self.valid_sets[data_idx - 1].num_data() * self.__num_class
            self.__inner_predict_buffer[data_idx] = np.zeros(n_preds, dtype=np.float64)
        # avoid to predict many time in one iteration
        if not self.__is_predicted_cur_iter[data_idx]:
            tmp_out_len = ctypes.c_int64(0)
            data_ptr = self.__inner_predict_buffer[data_idx].ctypes.data_as(ctypes.POINTER(ctypes.c_double))
            _safe_call(_LIB.LGBM_BoosterGetPredict(
                self.handle,
                ctypes.c_int(data_idx),
                ctypes.byref(tmp_out_len),
                data_ptr))
            if tmp_out_len.value != len(self.__inner_predict_buffer[data_idx]):
                raise ValueError("Wrong length of predict results for data %d" % (data_idx))
            self.__is_predicted_cur_iter[data_idx] = True
        return self.__inner_predict_buffer[data_idx]