class_docstr: GDumb plugin.
  
  At each experience the model is trained  from scratch using a buffer of
  samples collected from all the previous learning experiences.
  The buffer is updated at the start of each experience to add new classes or
  new examples of already encountered classes.
  In multitask scenarios, mem_size is the memory size for each task.
  This plugin can be combined with a Naive strategy to obtain the
  standard GDumb strategy.
  https://www.robots.ox.ac.uk/~tvg/publications/2020/gdumb.pdf
code: def after_train_dataset_adaptation(
      self, strategy: "SupervisedTemplate", **kwargs
  ):
      self.storage_policy.update(strategy, **kwargs)
      strategy.adapted_dataset = self.storage_policy.buffer
  
file_path: avalanche/avalanche/training/plugins/gdumb.py
imports: import copy
  from typing import TYPE_CHECKING, Optional
  from avalanche.training.plugins.strategy_plugin import SupervisedPlugin
  from avalanche.training.storage_policy import ClassBalancedBuffer
label_desc: Use image node_id 4 for calling the GDumbPlugin obj's underlying member method code with example usage: obj.after_train_dataset_adaptation(strategy) without return types