def METHOD_NAME():
    net = ResidualBlock(dilation=9)
    net2 = residual_block.ResidualBlock(dilation=9)
    summary(net)
    summary(net2)
    for k, v in net2.named_parameters():
        net.state_dict()[k].set_value(v.data.cpu().numpy())
    x = paddle.randn([4, 64, 180])
    c = paddle.randn([4, 80, 180])
    res, skip = net(x, c)
    res2, skip2 = net2(torch.as_tensor(x.numpy()), torch.as_tensor(c.numpy()))
    print("forward:")
    print(res.numpy()[0])
    print(res2.data.cpu().numpy()[0])
    print(skip.numpy()[0])
    print(skip2.data.cpu().numpy()[0])
    (res.sum() + skip.sum()).backward()
    (res2.sum() + skip2.sum()).backward()
    print("backward:")
    print(net.conv.weight.grad.numpy().squeeze()[0])
    print(net2.conv.weight.grad.data.cpu().numpy().squeeze()[0])