class_docstr: The Top-k Accuracy metric. This is a standalone metric.
  It is defined using torchmetrics.functional accuracy with top_k
class_docstr_tok: ["The","Top-k","Accuracy","metric",".","This","is","a","standalone","metric",".","It","is","defined","using","torchmetrics.functional","accuracy","with","top_k"]
docstr: Resets the metric.
  :param task_label: if None, reset the entire dictionary.
      Otherwise, reset the value associated to `task_label`.
  
  :return: None.
docstr_tok: ["Resets","the","metric",".",":","param","task_label",":","if","None",",","reset","the","entire","dictionary",".","Otherwise",",","reset","the","value","associated","to","`","task_label","`",".",":","return",":","None","."]
code: def reset(self, task_label=None) -> None:
      """
      Resets the metric.
      :param task_label: if None, reset the entire dictionary.
          Otherwise, reset the value associated to `task_label`.
  
      :return: None.
      """
      assert task_label is None or isinstance(task_label, int)
      if task_label is None:
          self._topk_acc_dict = defaultdict(Mean)
      else:
          self._topk_acc_dict[task_label].reset()
  
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 6 for calling the TopkAccuracy obj's underlying member method code with example usage: obj.reset(task_label) without return types