def METHOD_NAME(g, k):
    device = g.device
    out_graph_list = []
    in_graph_list = []
    wadj, ind, outd = DiffConv.get_weight_matrix(g)
    adj = sparse.coo_matrix(wadj / outd.cpu().numpy())
    outg = dgl.from_scipy(adj, eweight_name="weight").to(device)
    outg.edata["weight"] = outg.edata["weight"].float().to(device)
    out_graph_list.append(outg)
    for i in range(k - 1):
        out_graph_list.append(
            DiffConv.diffuse(out_graph_list[-1], wadj, outd)
        )
    adj = sparse.coo_matrix(wadj.T / ind.cpu().numpy())
    ing = dgl.from_scipy(adj, eweight_name="weight").to(device)
    ing.edata["weight"] = ing.edata["weight"].float().to(device)
    in_graph_list.append(ing)
    for i in range(k - 1):
        in_graph_list.append(
            DiffConv.diffuse(in_graph_list[-1], wadj.T, ind)
        )
    return out_graph_list, in_graph_list