def METHOD_NAME():
    net = ConvInUpsampleNet(
        [4, 4, 4, 4],
        "LeakyReLU", {"negative_slope": 0.2},
        freq_axis_kernel_size=3,
        aux_context_window=0)
    net2 = upsample.ConvInUpsampleNetwork(
        [4, 4, 4, 4],
        nonlinear_activation="LeakyReLU",
        nonlinear_activation_params={"negative_slope": 0.2},
        freq_axis_kernel_size=3,
        aux_context_window=0).to(device)
    summary(net)
    for k, v in net2.named_parameters():
        print(k, v.shape)
        net.state_dict()[k].set_value(v.data.cpu().numpy())
    c = paddle.randn([4, 80, 180])
    synchronize()
    with timer(unit='s') as t:
        out = net(c)
        synchronize()
        print(f"paddle conv_in_upsample_net forward takes {t.elapse}s.")
    with timer(unit='s') as t:
        out.sum().backward()
        synchronize()
        print(f"paddle conv_in_upsample_net backward takes {t.elapse}s.")
    c_torch = torch.as_tensor(c.numpy()).to(device)
    torch.cuda.synchronize()
    with timer(unit='s') as t:
        out2 = net2(c_torch)
        print(f"torch conv_in_upsample_net forward takes {t.elapse}s.")
    with timer(unit='s') as t:
        out2.sum().backward()
        print(f"torch conv_in_upsample_net backward takes {t.elapse}s.")
    print("forward check")
    print(out.numpy()[0])
    print(out2.data.cpu().numpy()[0])
    print("backward check")
    print(net.conv_in.weight.grad.numpy()[0])
    print(net2.conv_in.weight.grad.data.cpu().numpy()[0])