class_docstr: The average top-k 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 top-k 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","top-k","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","top-k","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 "Topk_" + str(self.top_k) + "_Acc_Epoch"
  
imports: from typing import TYPE_CHECKING, List, Union, Dict
  import torch
  from torch import Tensor
  import torchmetrics
  from torchmetrics.functional import accuracy
  from avalanche.evaluation import Metric, GenericPluginMetric
  from avalanche.evaluation.metrics.mean import Mean
  from avalanche.evaluation.metric_utils import phase_and_task
  from collections import defaultdict
  from packaging import version
label_desc: Use image node_id 2 for calling the RunningEpochTopkAccuracy obj's underlying member method code with example usage: obj.__str__() and returns: unknown