class_docstr: Accuracy metric. This is a standalone metric.
  
  The update method computes the accuracy incrementally
  by keeping a running average of the <prediction, target> pairs
  of Tensors provided over time.
  
  The "prediction" and "target" tensors may contain plain labels or
  one-hot/logit vectors.
  
  Each time `result` is called, this metric emits the average accuracy
  across all predictions made since the last `reset`.
  
  The reset method will bring the metric to its initial state. By default
  this metric in its initial state will return an accuracy value of 0.
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docstr: Resets the metric.
  
  :return: None.
docstr_tok: ["Resets","the","metric",".",":","return",":","None","."]
code: def reset(self) -> None:
      """Resets the metric.
  
      :return: None.
      """
      self._mean_accuracy.reset()
  
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 4 for calling the Accuracy obj's underlying member method code with example usage: obj.reset() without return types