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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def benchmark(self, func, c_args, threads, grid, times):
 """runs the kernel repeatedly, returns averaged returned value The C function tuning is a little bit more flexible than direct CUDA or OpenCL kernel tuning. The C function needs to measure time, or some other quality metric you wish to tune on, on its own and should therefore return a single floating-point value. Benchmark runs the C function repeatedly and returns the average of the values returned by the C function. The number of iterations is set during the creation of the CFunctions object. For all measurements the lowest and highest values are discarded and the rest is included in the average. The reason for this is to be robust against initialization artifacts and other exceptional cases. :param func: A C function compiled for this specific configuration :type func: ctypes._FuncPtr :param c_args: A list of arguments to the function, order should match the order in the code. The list should be prepared using ready_argument_list(). :type c_args: list(Argument) :param threads: Ignored, but left as argument for now to have the same interface as CudaFunctions and OpenCLFunctions. :type threads: any :param grid: Ignored, but left as argument for now to have the same interface as CudaFunctions and OpenCLFunctions. :type grid: any :param times: Return the execution time of all iterations. :type times: bool :returns: All execution times, if times=True, or a robust average for the kernel execution time. :rtype: float """

        time = []
        for _ in range(self.iterations):
            value = self.run_kernel(func, c_args, threads, grid)

            #I would like to replace the following with actually capturing
            #stderr and detecting the error directly in Python, it proved
            #however that capturing stderr for non-Python functions from Python
            #is a rather difficult thing to do
            #
            #The current, less than ideal, scheme uses the convention that a
            #negative time indicates a 'too many resources requested for launch'
            if value < 0.0:
                raise Exception("too many resources requested for launch")

            time.append(value)
        time = sorted(time)
        if times:
            return time
        else:
            if self.iterations > 4:
                return numpy.mean(time[1:-1])
            else:
                return numpy.mean(time)