                          for output in self.network.outputs]
          output_names = ["{}{}".format(name,
                                        "" if output_types.count(name) == 1 else "_{}".format(idx))
                          for idx, name in enumerate(output_types)]
          logger.debug(output_names)
          return output_names
  
      def load(self, fullpath=None):
          """ Load model """
          fullpath = fullpath if fullpath else self.filename
 
</s> remove         if self.is_output and not any(name == "face_out" for name in output_names):
 </s> add         if self.is_output and not any(name.startswith("face_out") for name in output_names): </s> remove         return tuple(out[0])
 </s> add         return [tuple(shape) for shape in out]

    @property
    def output_shape(self):
        """ The output shape of the model (shape of largest face output) """
        return self.output_shapes[self.largest_face_index]

    @property
    def largest_face_index(self):
        """ Return the index from model.outputs of the largest face
            Required for multi-output model prediction. The largest face
            is assumed to be the final output
        """
        sizes = [shape[1] for shape in self.output_shapes if shape[2] == 3]
        if not sizes:
            return None
        max_face = max(sizes)
        retval = [idx for idx, shape in enumerate(self.output_shapes)
                  if shape[1] == max_face and shape[2] == 3][0]
        logger.debug(retval)
        return retval

    @property
    def largest_mask_index(self):
        """ Return the index from model.outputs of the largest mask
            Required for multi-output model prediction. The largest face
            is assumed to be the final output
        """
        sizes = [shape[1] for shape in self.output_shapes if shape[2] == 1]
        if not sizes:
            return None
        max_mask = max(sizes)
        retval = [idx for idx, shape in enumerate(self.output_shapes)
                  if shape[1] == max_mask and shape[2] == 1][0]
        logger.debug(retval)
        return retval </s> remove         if "mask_out" in output_network.output_names:
            mask_idx = output_network.output_names.index("mask_out")
            mask_shape = output_network.output_shapes[mask_idx]
 </s> add         mask_idx = [idx for idx, name in enumerate(output_network.output_names)
                    if name.startswith("mask")]
        if mask_idx:
            mask_shape = output_network.output_shapes[mask_idx[0]]
        logger.debug("Overridden output_names: %s", output_names)
                          for output in self.network.outputs]
          output_names = ["{}{}".format(name,
                                        "" if output_types.count(name) == 1 else "_{}".format(idx))
                          for idx, name in enumerate(output_types)]
        logger.debug("Overridden output_names: %s", output_names)
          return output_names
  
      def load(self, fullpath=None):
          """ Load model """
          fullpath = fullpath if fullpath else self.filename
keep keep keep keep replace keep keep keep keep keep
 Training: Add Multi-Output Support

- Name output nodes of models
- Add support for multiple outputs in models and in training
- Update loss output format for cli and gui
- Sort graphs tabs on training
- Fix analysis graph for new loss names
- Multi output support in convert 
plugins/train/model/_base.py