              group="network",
              info="R|[Nvidia Only], NVIDIA GPUs can run operations in float16 faster than in "
                   "float32. Mixed precision allows you to use a mix of float16 with float32, to "
                   "get the performance benefits from float16 and the numeric stability benefits "
                   "from float32.\nWhile mixed precision will run on most Nvidia models, it will "
                   "only speed up training on more recent GPUs. Those with compute capability 7.0 "
                   "or higher will see the greatest performance benefit from mixed precision "
                   "because they have Tensor Cores. Older GPUs offer no math performance benefit "
                   "for using mixed precision, however memory and bandwidth savings can enable some "
                   "speedups. Generally RTX GPUs and later will offer the most benefit.")
 
</s> remove                  "of the image.\n")
 </s> add                  "of the image.") </s> remove from keras import backend as K

 </s> add  </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)
                 "from float32.\n\nWhile mixed precision will run on most Nvidia models, it will "
              group="network",
              info="R|[Nvidia Only], NVIDIA GPUs can run operations in float16 faster than in "
                   "float32. Mixed precision allows you to use a mix of float16 with float32, to "
                   "get the performance benefits from float16 and the numeric stability benefits "
                 "from float32.\n\nWhile mixed precision will run on most Nvidia models, it will "
                   "only speed up training on more recent GPUs. Those with compute capability 7.0 "
                   "or higher will see the greatest performance benefit from mixed precision "
                   "because they have Tensor Cores. Older GPUs offer no math performance benefit "
                   "for using mixed precision, however memory and bandwidth savings can enable some "
                   "speedups. Generally RTX GPUs and later will offer the most benefit.")
keep keep keep keep replace keep keep keep keep keep
 Training - Add L2 Regularization to Structural Losses 
plugins/train/_config.py