          for i in range(self.passes):
              ksize = int(kwargs["ksize"][0])
              logger.trace("Pass: %s, kernel_size: %s", i + 1, (ksize, ksize))
              blurred = func(blurred, **kwargs)
              ksize = int(ksize * self.multipass_factor)
              kwargs["ksize"] = self.get_kernel_tuple(ksize)
          logger.trace("Returning blurred mask. Shape: %s", blurred.shape)
          return blurred
  
      @property
 
</s> remove             closest = (np.mean(np.square(src_points - dst_points),
                               axis=(1, 2))).argsort()[:10]
 </s> add             closest = (np.mean(np.square(src_points - dst_points), axis=(1, 2))).argsort()[:10] </s> remove         dummy = np.zeros((20, 20, 3)).astype("uint8")
 </s> add         dummy = np.zeros((20, 20, 3), dtype="uint8") </s> remove         radius = max(1, round(mask_diameter * radius_ratio / 100))
        kernel_size = int((radius * 2) + 1)
 </s> add         radius = round(max(1., mask_diameter * radius_ratio / 100.))
        kernel_size = radius * 2 + 1 </s> remove                 mask_func = losses.mean_squared_error
                loss_funcs.append(mask_func)
                logger.debug("mask loss: %s", mask_func)
 </s> add                 loss_funcs.append(loss_dict[mask_loss_config])
                logger.debug("mask loss: %s", mask_loss_config) </s> remove                 face_func = PenalizedLoss(mask[0], loss_func)
                logger.debug("final face loss: %s", face_func)
                loss_funcs.append(face_func)
                if not predict and side.lower() == "a":
                    logger.verbose("Penalizing mask for Loss")
 </s> add                 loss_funcs.append(PenalizedLoss(mask[0], loss_dict[img_loss_config]))
                logger.debug("final face loss: %s", img_loss_config)
            ksize = round(ksize * self.multipass_factor)
          for i in range(self.passes):
              ksize = int(kwargs["ksize"][0])
              logger.trace("Pass: %s, kernel_size: %s", i + 1, (ksize, ksize))
              blurred = func(blurred, **kwargs)
            ksize = round(ksize * self.multipass_factor)
              kwargs["ksize"] = self.get_kernel_tuple(ksize)
          logger.trace("Returning blurred mask. Shape: %s", blurred.shape)
          return blurred
  
      @property
keep keep keep keep replace keep keep keep keep keep
 documentation, pep8, style, clarity updates  - Prep for Segmentation (#812)

* documentation, pep8, style, clarity updates

* Update cli.py

* Update _config.py

remove extra mask and coverage

mask type as dropdown

* Update training_data.py

move coverage / LR to global
cut down on loss description

style change
losses working in PR

* simpler logging

* legacy update 
plugins/convert/mask/_base.py