<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def cudnnGetConvolution2dDescriptor(convDesc):
 """" Get a convolution descriptor. This function queries a previously initialized 2D convolution descriptor object. Parameters convDesc : cudnnConvolutionDescriptor Handle to a previously created convolution descriptor. Returns ------- pad_h : int zero-padding height: number of rows of zeros implicitly concatenated onto the top and onto the bottom of input images. pad_w : int zero-padding width: number of columns of zeros implicitly concatenated onto the left and onto the right of input images. u : int Vertical filter stride. v : int Horizontal filter stride. dilation_h : int Filter height dilation. dilation_w : int Filter width dilation. mode : cudnnConvolutionMode Either CUDNN_CONVOLUTION or CUDNN_CROSS_CORRELATION. computeType : cudnnDataType Compute precision """

    pad_h = ctypes.c_int()
    pad_w = ctypes.c_int()
    u = ctypes.c_int()
    v = ctypes.c_int()
    dilation_h = ctypes.c_int()
    dilation_w = ctypes.c_int()
    mode = ctypes.c_int()
    computeType = ctypes.c_int()

    status = _libcudnn.cudnnGetConvolution2dDescriptor(convDesc, ctypes.byref(pad_h),
                                                    ctypes.byref(pad_w), ctypes.byref(u),
                                                    ctypes.byref(v), ctypes.byref(dilation_h),
                                                    ctypes.byref(dilation_w),
                                                    ctypes.byref(mode), ctypes.byref(computeType))

    cudnnCheckStatus(status)

    return (pad_h.value, pad_w.value, u.value, v.value, upscalex.value, upscaley.value, mode.value,
            computeType.value)