class_docstr: The average top-k accuracy over a single training epoch.
  This plugin metric only works at training time.
  
  The top-k accuracy will be logged after each training epoch by computing
  the number of correctly predicted patterns during the epoch divided by
  the overall number of patterns encountered in that epoch.
class_docstr_tok: ["The","average","top-k","accuracy","over","a","single","training","epoch",".","This","plugin","metric","only","works","at","training","time",".","The","top-k","accuracy","will","be","logged","after","each","training","epoch","by","computing","the","number","of","correctly","predicted","patterns","during","the","epoch","divided","by","the","overall","number","of","patterns","encountered","in","that","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 EpochTopkAccuracy obj's underlying member method code with example usage: obj.__str__() and returns: unknown