          for name in self._model.output_names:
              new_names.append(f"{name}_{seen[name]}")
              seen[name] += 1
          logger.debug("Output names rewritten: (old: %s, new: %s)",
                       self._model.output_names, new_names)
          self._model.output_names = new_names
  
      def _legacy_mapping(self) -> Optional[dict]:  # pylint:disable=no-self-use
          """ The mapping of separate model files to single model layers for transferring of legacy
          weights.
  
 
</s> remove         for name in self._model.output_names:
 </s> add         for name in self.model.output_names: </s> remove         seen = {name: 0 for name in set(self._model.output_names)}
 </s> add         seen = {name: 0 for name in set(self.model.output_names)} </s> remove         if len(self._model.output_names) == len(set(self._model.output_names)):
            logger.debug("Output names are unique, not rewriting: %s", self._model.output_names)
 </s> add         if len(self.model.output_names) == len(set(self.model.output_names)):
            logger.debug("Output names are unique, not rewriting: %s", self.model.output_names) </s> remove         shapes = [tuple(K.int_shape(output)[-3:]) for output in self._model.outputs]
 </s> add         shapes = [tuple(K.int_shape(output)[-3:]) for output in self.model.outputs] </s> remove         loss_wrapper.add_loss(loss_function,
 </s> add         loss_wrapper.add_loss(self._get_function(loss_function),
                     self.model.output_names, new_names)
        self.model.output_names = new_names
          for name in self._model.output_names:
              new_names.append(f"{name}_{seen[name]}")
              seen[name] += 1
          logger.debug("Output names rewritten: (old: %s, new: %s)",
                     self.model.output_names, new_names)
        self.model.output_names = new_names
                     self.model.output_names, new_names)
        self.model.output_names = new_names
  
      def _legacy_mapping(self) -> Optional[dict]:  # pylint:disable=no-self-use
          """ The mapping of separate model files to single model layers for transferring of legacy
          weights.
  
keep keep keep keep replace replace keep keep keep keep keep
 Core updates
   - Change loss loading mechanism
   - Autosize tooltips based on content size
   - Random linting + code modernisation 
plugins/train/model/_base/model.py