              opts=("-d", "--distributed"),
              action="store_true",
              default=False,
              backend="nvidia",
              group="training",
              help="Use the Tensorflow Mirrored Distrubution Strategy to train on multiple GPUs."))
          argument_list.append(dict(
              opts=("-s", "--save-interval"),
              action=Slider,
              min_max=(10, 1000),
              rounding=10,
 
</s> remove             group="training",
            help="Length of training in iterations. This is only really used for automation. "
                 "There is no 'correct' number of iterations a model should be trained for. "
                 "You should stop training when you are happy with the previews. However, if "
                 "you want the model to stop automatically at a set number of iterations, you "
                 "can set that value here."))
 </s> add             group=_("training"),
            help=_("Length of training in iterations. This is only really used for automation. "
                   "There is no 'correct' number of iterations a model should be trained for. "
                   "You should stop training when you are happy with the previews. However, if "
                   "you want the model to stop automatically at a set number of iterations, you "
                   "can set that value here."))) </s> remove             group="output",
            help="Draw landmarks on the ouput faces for debugging purposes."))
 </s> add             group=_("output"),
            help=_("Draw landmarks on the ouput faces for debugging purposes."))) </s> remove             group="output",
            help="Extract every 'nth' frame. This option will skip frames when extracting "
                 "faces. For example a value of 1 will extract faces from every frame, a value "
                 "of 10 will extract faces from every 10th frame."))
 </s> add             group=_("output"),
            help=_("Extract every 'nth' frame. This option will skip frames when extracting "
                   "faces. For example a value of 1 will extract faces from every frame, a value "
                   "of 10 will extract faces from every 10th frame."))) </s> remove             group="settings",
            help="[LEGACY] This only needs to be selected if a legacy model is being loaded or "
                 "if there are multiple models in the model folder"))
 </s> add             group=_("settings"),
            help=_("[LEGACY] This only needs to be selected if a legacy model is being loaded or "
                   "if there are multiple models in the model folder"))) </s> remove             group="settings",
            help="Don't run extraction in parallel. Will run each part of the extraction "
                 "process separately (one after the other) rather than all at the smae time. "
                 "Useful if VRAM is at a premium."))
 </s> add             group=_("settings"),
            help=_("Don't run extraction in parallel. Will run each part of the extraction "
                   "process separately (one after the other) rather than all at the smae time. "
                   "Useful if VRAM is at a premium.")))
            group=_("training"),
            help=_("Use the Tensorflow Mirrored Distrubution Strategy to train on multiple "
                   "GPUs.")))
              opts=("-d", "--distributed"),
              action="store_true",
              default=False,
              backend="nvidia",
            group=_("training"),
            help=_("Use the Tensorflow Mirrored Distrubution Strategy to train on multiple "
                   "GPUs.")))
            group=_("training"),
            help=_("Use the Tensorflow Mirrored Distrubution Strategy to train on multiple "
                   "GPUs.")))
          argument_list.append(dict(
              opts=("-s", "--save-interval"),
              action=Slider,
              min_max=(10, 1000),
              rounding=10,
keep keep keep keep replace replace keep keep keep keep keep
 Add Locale support for cli arguments 
lib/cli/args.py