def test_inference_no_inputshape():
    torch.manual_seed(0)
    in0 = torch.rand(1, 3, 224, 224, dtype=torch.float)
    #in0 = torch.rand(dtype=null)
    out = []

    with ncnn.Net() as net:
         net.load_param("D:\\myProgram\\ideaJava\\yiZhiXiangMuZu\\RWKV\\faster-rwkv\\models\\mobilenet_v2\\win_Optional\\mobilenet_v2.ncnn.param")
         net.load_model("D:\\myProgram\\ideaJava\\yiZhiXiangMuZu\\RWKV\\faster-rwkv\\models\\mobilenet_v2\\win_Optional\\mobilenet_v2.ncnn.bin")

         with net.create_extractor() as ex:
            ex.input("in0", ncnn.Mat(in0.squeeze(0).numpy()).clone())

            _, out0 = ex.extract("out0")
            out.append(torch.from_numpy(np.array(out0)).unsqueeze(0))
    print(len(out))
    if len(out) == 1:
        return out[0]
    else:
        return tuple(out)
result = torch.eq(test_inference(), test_inference_no_inputshape())
print(result)