                     "sub-par performance."
                     "\nL|unet-dfl: Mask designed to provide smart segmentation of mostly frontal "
                     "faces. The mask model has been trained by community members and will need "
                     "testing for further description. Profile faces may result in sub-par "
                     "performance.")))
          argument_list.append(dict(
              opts=("-w", "--writer"),
              action=Radio,
              type=str,
              default="opencv",
 
</s> remove "L|unet-dfl: Mask designed to provide smart segmentation of mostly frontal faces. The mask model has been trained by community members and will need testing for further description. Profile faces may result in sub-par performance."
 </s> add "L|unet-dfl: Mask designed to provide smart segmentation of mostly frontal faces. The mask model has been trained by community members and will need testing for further description. Profile faces may result in sub-par performance.\n"
"L|predicted: If the 'Learn Mask' option was enabled during training, this will use the mask that was created by the trained model." </s> remove #: lib/cli/args.py:699
 </s> add #: lib/cli/args.py:701 </s> remove #: lib/cli/args.py:1059
 </s> add #: lib/cli/args.py:1061 </s> remove #: lib/cli/args.py:1051
 </s> add #: lib/cli/args.py:1053 </s> remove #: lib/cli/args.py:1044
 </s> add #: lib/cli/args.py:1046
                   "performance."
                   "\nL|predicted: If the 'Learn Mask' option was enabled during training, this "
                   "will use the mask that was created by the trained model.")))
                     "sub-par performance."
                     "\nL|unet-dfl: Mask designed to provide smart segmentation of mostly frontal "
                     "faces. The mask model has been trained by community members and will need "
                     "testing for further description. Profile faces may result in sub-par "
                   "performance."
                   "\nL|predicted: If the 'Learn Mask' option was enabled during training, this "
                   "will use the mask that was created by the trained model.")))
          argument_list.append(dict(
              opts=("-w", "--writer"),
              action=Radio,
              type=str,
              default="opencv",
keep keep keep keep replace keep keep keep keep keep
 Help text - Add "predicted" help tesk to convert mask option 
lib/cli/args.py