def METHOD_NAME(infer_config, predictor, img_list):
    # load preprocess transforms
    transforms = Compose(infer_config.preprocess_infos)
    # predict image
    for img_path in img_list:
        inputs = transforms(img_path)
        inputs_name = [var.name for var in predictor.get_inputs()]
        inputs = {k: inputs[k][None, ] for k in inputs_name}
        outputs = predictor.run(output_names=None, input_feed=inputs)
        print("ONNXRuntime predict: ")
        if infer_config.arch in ["HRNet"]:
            print(np.array(outputs[0]))
        else:
            bboxes = np.array(outputs[0])
            for bbox in bboxes:
                if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
                    print(f"{int(bbox[0])} {bbox[1]} "
                          f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")