class_docstr: The average accuracy across all minibatches up to the current
  epoch iteration.
  This plugin metric only works at training time.
  
  At each iteration, this metric logs the accuracy averaged over all patterns
  seen so far in the current epoch.
  The metric resets its state after each training epoch.
class_docstr_tok: ["The","average","accuracy","across","all","minibatches","up","to","the","current","epoch","iteration",".","This","plugin","metric","only","works","at","training","time",".","At","each","iteration",",","this","metric","logs","the","accuracy","averaged","over","all","patterns","seen","so","far","in","the","current","epoch",".","The","metric","resets","its","state","after","each","training","epoch","."]
code: def __str__(self):
      return "Top1_RunningAcc_Epoch"
  
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 RunningEpochAccuracy obj's underlying member method code with example usage: obj.__str__() and returns: str