class_docstr: TrainGeneratorAfterExpPlugin makes sure that after each experience of
  training the solver of a scholar model, we also train the generator on the
  data of the current experience.
docstr: The training method expects an Experience object
  with a 'dataset' parameter.
docstr_tok: ["The","training","method","expects","an","Experience","object","with","a","'dataset","'","parameter","."]
code: def after_training_exp(self, strategy, **kwargs):
      """
      The training method expects an Experience object
      with a 'dataset' parameter.
      """
      for plugin in strategy.plugins:
          if type(plugin) is GenerativeReplayPlugin:
              plugin.generator_strategy.train(strategy.experience)
  
file_path: avalanche/avalanche/training/plugins/generative_replay.py
imports: from copy import deepcopy
  from typing import Optional
  from avalanche.core import SupervisedPlugin
  import torch
label_desc: Use image node_id 1 for calling the TrainGeneratorAfterExpPlugin obj's underlying member method code with example usage: obj.after_training_exp(strategy) without return types