def METHOD_NAME(args, img, wego_mod):
    with torch.no_grad():
        img = torch.unsqueeze(img, 0)
        output = wego_mod(img)
        prob = torch.nn.functional.softmax(output[0], dim=0)
        top5_prob, top5_catid = cal_topk(prob, 5)
        categories = get_categories()
        print("=====================TopK Result=====================")
        for i in range(top5_prob.size(0)):
            print(categories[top5_catid[i]], top5_prob[i].item())