              mask = self._erode(mask)
          raw_mask = np.expand_dims(raw_mask, axis=-1) if raw_mask.ndim != 3 else raw_mask
          mask = np.expand_dims(mask, axis=-1) if mask.ndim != 3 else mask
          logger.trace("mask shape: %s, raw_mask shape: %s", mask.shape, raw_mask.shape)
          return mask.astype("float32") / 255.0, raw_mask.astype("float32") / 255.0
  
      def _get_mask(self, detected_face, predicted_mask):
          """ Return the requested mask with any requested blurring applied.
  
          Parameters
 
</s> remove         raw_mask = np.expand_dims(raw_mask, axis=-1) if raw_mask.ndim != 3 else raw_mask
        mask = np.expand_dims(mask, axis=-1) if mask.ndim != 3 else mask
 </s> add  </s> remove             mask = np.ones_like(self.dummy[:, :, 1], dtype="uint8") * 255
 </s> add             mask = np.ones_like(self.dummy[:, :, 1], dtype="float32")[..., None] </s> remove             mask = predicted_mask
 </s> add             mask = predicted_mask[..., None] </s> remove     def __init__(self, arguments, samples, display, lock, trigger, config_tools, tk_vars):
        logger.debug("Initializing %s: (arguments: '%s', samples: %s: display: %s, lock: %s,"
                     " trigger: %s, config_tools: %s, tk_vars %s)", self.__class__.__name__,
                     arguments, samples, display, lock, trigger, config_tools, tk_vars)
 </s> add     def __init__(self, arguments, available_masks, samples,
                 display, lock, trigger, config_tools, tk_vars):
        logger.debug("Initializing %s: (arguments: '%s', available_masks: %s, samples: %s, "
                     "display: %s, lock: %s, trigger: %s, config_tools: %s, tk_vars %s)",
                     self.__class__.__name__, arguments, available_masks, samples, display, lock,
                     trigger, config_tools, tk_vars)
        return mask, raw_mask
              mask = self._erode(mask)
          raw_mask = np.expand_dims(raw_mask, axis=-1) if raw_mask.ndim != 3 else raw_mask
          mask = np.expand_dims(mask, axis=-1) if mask.ndim != 3 else mask
          logger.trace("mask shape: %s, raw_mask shape: %s", mask.shape, raw_mask.shape)
        return mask, raw_mask
  
      def _get_mask(self, detected_face, predicted_mask):
          """ Return the requested mask with any requested blurring applied.
  
          Parameters
keep keep keep keep replace keep keep keep keep keep
 Convert - bugfixes
- Default mask to an available mask in Preview tool
- Correctly output predicted mask 
plugins/convert/mask/mask_blend.py