def METHOD_NAME():
    edge_index = torch.tensor([[0, 5, 2, 3, 3, 4, 4, 3, 5, 5, 6],
                               [0, 0, 3, 2, 0, 0, 2, 1, 2, 3, 1]])
    edge_attr = torch.tensor(
        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0])
    idx = (torch.tensor([2, 3, 5]), torch.tensor([2, 3]))
    mask = (index_to_mask(idx[0], 7), index_to_mask(idx[1], 4))
    indices = (idx[0].tolist(), idx[1].tolist())
    mixed = (mask[0], idx[1])
    for subset in [idx, mask, indices, mixed]:
        out = bipartite_subgraph(subset, edge_index, edge_attr,
                                 return_edge_mask=True)
        assert out[0].tolist() == [[2, 3, 5, 5], [3, 2, 2, 3]]
        assert out[1].tolist() == [3.0, 4.0, 9.0, 10.0]
        assert out[2].tolist() == [0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0]
        out = bipartite_subgraph(subset, edge_index, edge_attr,
                                 relabel_nodes=True)
        assert out[0].tolist() == [[0, 1, 2, 2], [1, 0, 0, 1]]
        assert out[1].tolist() == [3.0, 4.0, 9.0, 10.0]