def task1_tta_predict(model, img_arr):
    img_arr_tta = cyclic_stacking(img_arr)
    mask_arr_tta = []
    for _img_crops in img_arr_tta:
        _mask_crops = model.predict(_img_crops)
        mask_arr_tta.append(_mask_crops)
    mask_crops_pred = cyclic_pooling(*mask_arr_tta)
    return mask_crops_pred
    def task3_tta_predict(model, img_arr):
        img_arr_tta = cyclic_stacking(img_arr)
        pred_logits = np.zeros(shape=(img_arr.shape[0], 7))

        for _img_crops in img_arr_tta:
            pred_logits += model.predict(_img_crops)

        pred_logits = pred_logits/len(img_arr_tta)

        return pred_logits