  
      @property
      def pre_encode(self):
          """ Return the writer's pre-encoder """
          dummy = np.zeros((20, 20, 3)).astype("uint8")
          test = self.writer.pre_encode(dummy)
          retval = None if test is None else self.writer.pre_encode
          logger.debug("Writer pre_encode function: %s", retval)
          return retval
  
 
</s> remove         mask = np.zeros(self.model.state.mask_shapes[0], dtype="float32")
        retval = np.expand_dims(mask, 0)
        return retval
 </s> add         mask = np.zeros((1, ) + self.model.state.mask_shapes[0], dtype="float32")
        return mask </s> remove         if swap:
            model = self.predictors["a"]
        else:
            model = self.predictors["b"]
 </s> add         side = "a" if swap else "b"
        model = self.predictors[side] </s> remove             self.args.gpus,
 </s> add             gpus=self.args.gpus, </s> remove             ksize = int(ksize * self.multipass_factor)
 </s> add             ksize = round(ksize * self.multipass_factor)
        dummy = np.zeros((20, 20, 3), dtype="uint8")
  
      @property
      def pre_encode(self):
          """ Return the writer's pre-encoder """
        dummy = np.zeros((20, 20, 3), dtype="uint8")
          test = self.writer.pre_encode(dummy)
          retval = None if test is None else self.writer.pre_encode
          logger.debug("Writer pre_encode function: %s", retval)
          return retval
  
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 
scripts/convert.py