def METHOD_NAME(self, graph_list, feature, m2v_feature, label_y, label_idx, batch_nodes):
    m2v_fc = self.input_drop(self.m2v_fc(m2v_feature))
    feature = feature + m2v_fc
    label_embed = self.label_embed(label_y)
    label_embed = self.input_drop(label_embed)
    feature_label = paddle.gather(feature, label_idx)
    label_embed = paddle.concat([label_embed, feature_label], axis=1)
    label_embed = self.label_mlp(label_embed)
    feature = paddle.scatter(feature, label_idx, label_embed, overwrite=True)
    for idx, (edge_src, edge_dst, edge_split, next_num_nodes, num_nodes) in enumerate(graph_list):
        temp_feat = []
        skip_feat = feature[:next_num_nodes]  # 当前采样图中心点对应的特征
        skip_feat = self.skips[idx](skip_feat)
        skip_feat = self.norms[idx][0](skip_feat)
        skip_feat = F.elu(skip_feat)
        temp_feat.append(skip_feat)
        for i in range(self.edge_type):
            m_sg = self.get_subgraph_by_edge_split(i, edge_src, edge_dst, edge_split, num_nodes)
            if m_sg is not None:
                feature_temp = self.gats[idx][i](m_sg, feature)
                feature_temp = self.norms[idx][i + 1](feature_temp[:next_num_nodes])
                feature_temp = F.elu(feature_temp)
                temp_feat.append(feature_temp)
        temp_feat = paddle.stack(temp_feat, axis=1)
        temp_feat_attn = self.path_attns[idx](temp_feat)
        temp_feat_attn = F.softmax(temp_feat_attn, axis=1)
        temp_feat_attn = paddle.transpose(temp_feat_attn, perm=[0, 2, 1])
        skip_feat = paddle.bmm(temp_feat_attn, temp_feat)[:, 0]
        skip_feat = self.path_norms[idx](skip_feat)
        feature = self.dropout(skip_feat)
    output = self.mlp(feature)
    return output