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: Save a copy of the model after each experience and
  update self.prev_classes to include the newly learned classes.
docstr_tok: ["Save","a","copy","of","the","model","after","each","experience","and","update","self.prev_classes","to","include","the","newly","learned","classes","."]
code: def after_training_exp(self, strategy, **kwargs):
      """
      Save a copy of the model after each experience and
      update self.prev_classes to include the newly learned classes.
      """
      self.lwf.update(strategy.experience, strategy.model)
  
imports: from avalanche.training import LearningWithoutForgetting
  from avalanche.training.plugins.strategy_plugin import SupervisedPlugin
label_desc: Use image node_id 3 for calling the LwFPlugin obj's underlying member method code with example usage: obj.after_training_exp(strategy) without return types