def METHOD_NAME(args, img, batch, wego_mod):
    # create batch images based on batch size;
    batch_images = torch.cat([torch.unsqueeze(img, 0) for i in range(batch)], 0)
    # run images with repeated batch images;
    repeat_batch = 400
    all_images = [batch_images] * repeat_batch
    n_images = repeat_batch * len(batch_images)
    print("[Info] begin to run inference with %d images." % (n_images))
    r_n = 20
    t = 0.0
    for i in tqdm(range(r_n)):
        t_ = run(wego_mod, all_images, args.threads)
        t += t_
    print("===================== Perf Result =====================")
    print("[Total Images] %d" % (r_n * n_images))
    print("[Total Time]   %0.6fs" % float(t))
    print("[FPS]          %0.2f" % (float(n_images) / (t / r_n)))