def test_prop_nodes_bfs(idtype):
g = dgl.graph(nx.path_graph(5), idtype=idtype, device=F.ctx())
g.ndata['x'] = F.ones((5, 2))
dgl.prop_nodes_bfs(g, 0, message_func=mfunc, reduce_func=rfunc, apply_node_func=None)
# pull nodes using bfs order will result in a cumsum[i] + data[i] + data[i+1]
assert F.allclose(g.ndata['x'],
F.tensor([[2., 2.], [4., 4.], [6., 6.], [8., 8.], [9., 9.]]))
def test_prop_nodes_bfs():
g = dgl.DGLGraph(nx.path_graph(5))
g.ndata['x'] = th.ones((5, 2))
g.register_message_func(mfunc)
g.register_reduce_func(rfunc)
dgl.prop_nodes_bfs(g, 0)
# pull nodes using bfs order will result in a cumsum[i] + data[i] + data[i+1]
assert U.allclose(g.ndata['x'],
th.tensor([[2., 2.], [4., 4.], [6., 6.], [8., 8.], [9., 9.]]))
def test_prop_nodes_bfs():
g = dgl.DGLGraph(nx.path_graph(5))
g.ndata['x'] = mx.nd.ones(shape=(5, 2))
g.register_message_func(mfunc)
g.register_reduce_func(rfunc)
dgl.prop_nodes_bfs(g, 0)
# pull nodes using bfs order will result in a cumsum[i] + data[i] + data[i+1]
assert np.allclose(
g.ndata['x'].asnumpy(),
np.array([[2., 2.], [4., 4.], [6., 6.], [8., 8.], [9., 9.]]))