def METHOD_NAME(dpu: "Runner", img, cnt):
    """get tensor"""
    inputTensors = dpu.get_input_tensors()
    outputTensors = dpu.get_output_tensors()
    shapeIn = tuple(inputTensors[0].dims)
    shapeOut = tuple(outputTensors[0].dims)
    pre_output_size = int(outputTensors[0].get_data_size() / shapeIn[0])
    output_fixpos = outputTensors[0].get_attr("fix_point")
    output_scale = 1 / (2**output_fixpos)
    count = 0
    n_of_images = len(img)
    while count < cnt:
        runSize = shapeIn[0]
        """prepare batch input/output """
        outputData = [np.empty(shapeOut, dtype=np.int8, order="C")]
        inputData = [np.empty(shapeIn, dtype=np.int8, order="C")]
        """init input image to input buffer """
        for j in range(runSize):
            imageRun = inputData[0]
            imageRun[j, ...] = img[(count + j) % n_of_images].reshape(shapeIn[1:])
        """run with batch """
        job_id = dpu.execute_async(inputData, outputData)
        dpu.wait(job_id)
        """softmax calculate with batch """
        """Benchmark DPU FPS performance over Vitis AI APIs execute_async() and wait() """
        """Uncomment the following code snippet to include softmax calculation for model’s end-to-end FPS evaluation """
        #for j in range(runSize):
        #    softmax = CPUCalcSoftmax(outputData[0][j], pre_output_size, output_scale)
        #    TopK(softmax, pre_output_size, "./words.txt")
        count = count + runSize