                   "\n\t Pixel_Gradient_Difference - Instead of minimizing the difference between "
                   "the absolute value of each pixel in two reference images, compute the pixel to "
                   "pixel spatial difference in each image and then minimize that difference "
                   "between two images. Allows for large color shifts,but maintains the structure "
                   "of the image.\n")
          self.add_item(
              section=section,
              title="mask_loss_function",
              datatype=str,
              group="loss",
 
</s> remove                  "from float32.\nWhile mixed precision will run on most Nvidia models, it will "
 </s> add                  "from float32.\n\nWhile mixed precision will run on most Nvidia models, it will " </s> remove         self._get_loss_names(model.outputs)
        self._get_loss_functions(model.output_names)
 </s> add         self._set_loss_names(model.outputs)
        self._set_loss_functions(model.output_names) </s> remove     def _get_loss_functions(self, output_names):
        """ Set the loss functions.
 </s> add     def _set_loss_functions(self, output_names):
        """ Set the loss functions and their associated weights. </s> remove                 loss_func = selected_loss
 </s> add                 if (self._config["loss_function"] in self._uses_l2_reg
                        and self._config["l2_reg_term"] != 0):
                    loss_funcs = [selected_loss, self._loss_dict["mse"]]
                    loss_weights = [1.0, self._config["l2_reg_term"] / 100.0]
                else:
                    loss_funcs = [selected_loss]
                    loss_weights = [1.0]

                if self._config["penalized_mask_loss"]:
                    loss_funcs = [losses.PenalizedLoss(loss) for loss in loss_funcs]

                loss_func = losses.LossWrapper(loss_functions=list(zip(loss_funcs, loss_weights)))
 </s> remove             if get_backend() == "amd":
                self._funcs[output_name] = loss_func
            else:
                self._funcs.setdefault(output_name, []).append(loss_func)
        logger.debug(self._funcs)
 </s> add             self._funcs[output_name] = loss_func
        logger.debug("functions: %s", self._funcs)
                 "of the image.")
                   "\n\t Pixel_Gradient_Difference - Instead of minimizing the difference between "
                   "the absolute value of each pixel in two reference images, compute the pixel to "
                   "pixel spatial difference in each image and then minimize that difference "
                   "between two images. Allows for large color shifts,but maintains the structure "
                 "of the image.")
          self.add_item(
              section=section,
              title="mask_loss_function",
              datatype=str,
              group="loss",
keep keep keep keep replace keep keep keep keep keep
 Training - Add L2 Regularization to Structural Losses 
plugins/train/_config.py