d27b4859127f4a45afd52fea70b1b5bc60d1affd,python/dgl/transform.py,,remove_edges,#Any#Any#,900

Before Change

    new_graph = DGLHeteroGraph(new_graph_index, g.ntypes, g.etypes)
    for i, canonical_etype in enumerate(g.canonical_etypes):
        new_graph.edges[canonical_etype].data[EID] = F.zerocopy_from_dgl_ndarray(
            induced_eids_nd[i].data)

    return new_graph

def in_subgraph(g, nodes):

After Change

    new_graph = DGLHeteroGraph(new_graph_index, g.ntypes, g.etypes)
    for i, canonical_etype in enumerate(g.canonical_etypes):
        data = induced_eids_nd[i].data
        if len(data) == 0:
            // Empty means that no edges are removed and edges are not shuffled.
            new_graph.edges[canonical_etype].data[EID] = F.arange(
                0, g.number_of_edges(canonical_etype))
        else:
            new_graph.edges[canonical_etype].data[EID] = F.zerocopy_from_dgl_ndarray(data)

    return new_graph

def in_subgraph(g, nodes):
    Extract the subgraph containing only the in edges of the given nodes.

Instances