hugegraph-ml/src/hugegraph_ml/utils/dgl2hugegraph_utils.py [322:358]:
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            vdata = [vertex_label, properties]
            vdatas.append(vdata)
            idxs.append(idx)
            if len(vdatas) == MAX_BATCH_NUM:
                idx_to_vertex_id.update(_add_batch_vertices(client_graph, vdatas, idxs))
                vdatas.clear()
                idxs.clear()
        if len(vdatas) > 0:
            idx_to_vertex_id.update(_add_batch_vertices(client_graph, vdatas, idxs))
        ntype_idx_to_vertex_id[ntype] = idx_to_vertex_id

    # add edges
    edatas = []
    for canonical_etype in hetero_graph.canonical_etypes:
        # create edge schema
        src_type, etype, dst_type = canonical_etype
        edge_label = f"{dataset_name}_{etype}_e"
        client_schema.edgeLabel(edge_label).sourceLabel(
            ntype_to_vertex_label[src_type]
        ).targetLabel(ntype_to_vertex_label[dst_type]).ifNotExist().create()
        # add edges for batch of canonical_etype
        srcs, dsts = hetero_graph.edges(etype=canonical_etype)
        for src, dst in zip(srcs.numpy(), dsts.numpy()):
            edata = [
                edge_label,
                ntype_idx_to_vertex_id[src_type][src],
                ntype_idx_to_vertex_id[dst_type][dst],
                ntype_to_vertex_label[src_type],
                ntype_to_vertex_label[dst_type],
                {},
            ]
            edatas.append(edata)
            if len(edatas) == MAX_BATCH_NUM:
                _add_batch_edges(client_graph, edatas)
                edatas.clear()
    if len(edatas) > 0:
        _add_batch_edges(client_graph, edatas)
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hugegraph-ml/src/hugegraph_ml/utils/dgl2hugegraph_utils.py [833:869]:
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            vdata = [vertex_label, properties]
            vdatas.append(vdata)
            idxs.append(idx)
            if len(vdatas) == MAX_BATCH_NUM:
                idx_to_vertex_id.update(_add_batch_vertices(client_graph, vdatas, idxs))
                vdatas.clear()
                idxs.clear()
        if len(vdatas) > 0:
            idx_to_vertex_id.update(_add_batch_vertices(client_graph, vdatas, idxs))
        ntype_idx_to_vertex_id[ntype] = idx_to_vertex_id

    # add edges
    edatas = []
    for canonical_etype in hetero_graph.canonical_etypes:
        # create edge schema
        src_type, etype, dst_type = canonical_etype
        edge_label = f"{dataset_name}_{etype}_e"
        client_schema.edgeLabel(edge_label).sourceLabel(
            ntype_to_vertex_label[src_type]
        ).targetLabel(ntype_to_vertex_label[dst_type]).ifNotExist().create()
        # add edges for batch of canonical_etype
        srcs, dsts = hetero_graph.edges(etype=canonical_etype)
        for src, dst in zip(srcs.numpy(), dsts.numpy()):
            edata = [
                edge_label,
                ntype_idx_to_vertex_id[src_type][src],
                ntype_idx_to_vertex_id[dst_type][dst],
                ntype_to_vertex_label[src_type],
                ntype_to_vertex_label[dst_type],
                {},
            ]
            edatas.append(edata)
            if len(edatas) == MAX_BATCH_NUM:
                _add_batch_edges(client_graph, edatas)
                edatas.clear()
    if len(edatas) > 0:
        _add_batch_edges(client_graph, edatas)
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