  
          # Get the returned image from predictors that emit multiple items
          if not isinstance(preds["a_a"], np.ndarray):
              for key, val in preds.items():
                  preds[key] = val[0]
          logger.debug("Returning predictions: %s", {key: val.shape for key, val in preds.items()})
          return preds
  
      def to_full_frame(self, side, samples, predictions):
          """ Patch the images into the full frame """
 
</s> remove 
        # Get the returned image from predictors that emit multiple items
 </s> add         # Get the returned largest image from predictors that emit multiple items </s> remove                      self.__class__.__name__, model_input_size, model_output_size,
 </s> add                      self.__class__.__name__, model_input_size, model_output_shapes, </s> remove     def __init__(self, model_input_size, model_output_size, training_opts, config):
        logger.debug("Initializing %s: (model_input_size: %s, model_output_shape: %s, "
 </s> add     def __init__(self, model_input_size, model_output_shapes, training_opts, config):
        logger.debug("Initializing %s: (model_input_size: %s, model_output_shapes: %s, " </s> remove         self.model_output_size = model_output_size
 </s> add         self.model_output_shapes = model_output_shapes </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
                preds[key] = val[self.model.largest_face_index]
  
          # Get the returned image from predictors that emit multiple items
          if not isinstance(preds["a_a"], np.ndarray):
              for key, val in preds.items():
                preds[key] = val[self.model.largest_face_index]
          logger.debug("Returning predictions: %s", {key: val.shape for key, val in preds.items()})
          return preds
  
      def to_full_frame(self, side, samples, predictions):
          """ Patch the images into the full frame """
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/trainer/_base.py