class_docstr: Learning without Forgetting plugin.
  
  LwF uses distillation to regularize the current loss with soft targets
  taken from a previous version of the model.
  When used with multi-headed models, all heads are distilled.
class_docstr_tok: ["Learning","without","Forgetting","plugin",".","LwF","uses","distillation","to","regularize","the","current","loss","with","soft","targets","taken","from","a","previous","version","of","the","model",".","When","used","with","multi-headed","models",",","all","heads","are","distilled","."]
docstr: Add distillation loss
docstr_tok: ["Add","distillation","loss"]
code: def before_backward(self, strategy, **kwargs):
      """
      Add distillation loss
      """
  
      strategy.loss += self.lwf(
          strategy.mb_x, strategy.mb_output, strategy.model
      )
  
imports: from avalanche.training import LearningWithoutForgetting
  from avalanche.training.plugins.strategy_plugin import SupervisedPlugin
label_desc: Use image node_id 2 for calling the LwFPlugin obj's underlying member method code with example usage: obj.before_backward(strategy) without return types