class_docstr: The minibatch plugin top-k accuracy metric.
  This metric only works at training time.
  
  This metric computes the average top-k accuracy over patterns
  from a single minibatch.
  It reports the result after each iteration.
class_docstr_tok: ["The","minibatch","plugin","top-k","accuracy","metric",".","This","metric","only","works","at","training","time",".","This","metric","computes","the","average","top-k","accuracy","over","patterns","from","a","single","minibatch",".","It","reports","the","result","after","each","iteration","."]
code: def __str__(self):
      return "Topk_" + str(self.top_k) + "_Acc_MB"
  
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 MinibatchTopkAccuracy obj's underlying member method code with example usage: obj.__str__() and returns: unknown