class_docstr: Less-Forgetful Learning (LFL) Plugin.
  
  LFL satisfies two properties to mitigate catastrophic forgetting.
  1) To keep the decision boundaries unchanged
  2) The feature space should not change much on target(new) data
  LFL uses euclidean loss between features from current and previous version
  of model as regularization to maintain the feature space and avoid
  catastrophic forgetting.
  Refer paper https://arxiv.org/pdf/1607.00122.pdf for more details
  This plugin does not use task identities.
docstr: Save a copy of the model after each experience
  and freeze the prev model and freeze the last layer of current model
docstr_tok: ["Save","a","copy","of","the","model","after","each","experience","and","freeze","the","prev","model","and","freeze","the","last","layer","of","current","model"]
code: def after_training_exp(self, strategy, **kwargs):
      """
      Save a copy of the model after each experience
      and freeze the prev model and freeze the last layer of current model
      """
  
      self.prev_model = copy.deepcopy(strategy.model)
  
      freeze_everything(self.prev_model)
  
      last_fc_name, last_fc = get_last_fc_layer(strategy.model)
  
      for param in last_fc.parameters():
          param.requires_grad = False
  
file_path: avalanche/avalanche/training/plugins/lfl.py
imports: import copy
  import torch
  from avalanche.training.plugins.strategy_plugin import SupervisedPlugin
  from avalanche.training.utils import get_last_fc_layer, freeze_everything
  from avalanche.models.base_model import BaseModel
label_desc: Use image node_id 6 for calling the LFLPlugin obj's underlying member method code with example usage: obj.after_training_exp(strategy) without return types