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","."]
docstr: Creates an instance of TrainedExperienceAccuracy metric by first
  constructing AccuracyPluginMetric
docstr_tok: ["Creates","an","instance","of","TrainedExperienceAccuracy","metric","by","first","constructing","AccuracyPluginMetric"]
code: def __init__(self):
      """
      Creates an instance of TrainedExperienceAccuracy metric by first
      constructing AccuracyPluginMetric
      """
      super(TrainedExperienceAccuracy, self).__init__(
          reset_at="stream", emit_at="stream", mode="eval"
      )
      self._current_experience = 0
  
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 1 to create a new TrainedExperienceAccuracy object from inherited base classes: AccuracyPluginMetric with example: obj = TrainedExperienceAccuracy()