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: :param lambda_e: Euclidean loss hyper parameter
docstr_tok: [":","param","lambda_e",":","Euclidean","loss","hyper","parameter"]
code: def __init__(self, lambda_e):
      """
      :param lambda_e: Euclidean loss hyper parameter
      """
      super().__init__()
  
      self.lambda_e = lambda_e
      self.prev_model = None
  
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 1 to create a new LFLPlugin object from inherited base classes: SupervisedPlugin with example: obj = LFLPlugin(lambda_e)