class_docstr: At the end of each experience, this plugin metric reports the average
  accuracy for only the experiences that the model has been trained on so far.
  
  This metric only works at eval time.
class_docstr_tok: ["At","the","end","of","each","experience",",","this","plugin","metric","reports","the","average","accuracy","for","only","the","experiences","that","the","model","has","been","trained","on","so","far",".","This","metric","only","works","at","eval","time","."]
code: def after_training_exp(self, strategy):
      self._current_experience = strategy.experience.current_experience
      # Reset average after learning from a new experience
      self.reset()
      return super().after_training_exp(strategy)
  
imports: from typing import List, Optional, Union, Dict
  import torch
  from torch import Tensor
  from avalanche.evaluation import Metric, GenericPluginMetric
  from avalanche.evaluation.metrics.mean import Mean
  from collections import defaultdict
label_desc: Use image node_id 2 for calling the TrainedExperienceAccuracy obj's underlying member method code with example usage: obj.after_training_exp(strategy) and returns: super