def METHOD_NAME():
    net = Model()
    net.eval()
    torch.manual_seed(0)
    x = torch.rand(1, 3, 32)
    y = torch.rand(1, 3, 32, 32)
    z = torch.rand(1, 3, 32, 32, 32)
    w = torch.rand(1, 8, 86, 86)
    a = net(x, y, z, w)
    # export torchscript
    mod = torch.jit.trace(net, (x, y, z, w))
    mod.save("test_F_interpolate.pt")
    # torchscript to pnnx
    import os
    os.system("../src/pnnx test_F_interpolate.pt inputshape=[1,3,32],[1,3,32,32],[1,3,32,32,32],[1,8,86,86]")
    # pnnx inference
    import test_F_interpolate_pnnx
    b = test_F_interpolate_pnnx.test_inference()
    for a0, b0 in zip(a, b):
        if not torch.equal(a0, b0):
            return False
    return True