class_docstr: Metric used by the CumulativeAccuracyPluginMetric,
  holds a dictionnary of per-task cumulative accuracies
  and updates the cumulative accuracy based on the classes splits
  provided for the growing incremental task.
  The update is performed as described in the paper
  "On the importance of cross-task
  features for class-incremental learning"
  Soutif et. al, https://arxiv.org/abs/2106.11930
class_docstr_tok: ["Metric","used","by","the","CumulativeAccuracyPluginMetric",",","holds","a","dictionnary","of","per-task","cumulative","accuracies","and","updates","the","cumulative","accuracy","based","on","the","classes","splits","provided","for","the","growing","incremental","task",".","The","update","is","performed","as","described","in","the","paper","''","On","the","importance","of","cross-task","features","for","class-incremental","learning","''","Soutif","et",".","al",",","https",":","\/\/arxiv.org\/abs\/2106.11930"]
docstr: Resets the metric.
  
  :return: None.
docstr_tok: ["Resets","the","metric",".",":","return",":","None","."]
code: def reset(self) -> None:
      """Resets the metric.
  
      :return: None.
      """
      for t in self._mean_accuracy:
          self._mean_accuracy[t].reset()
  
imports: from collections import defaultdict
  from typing import Dict, List, Union, TYPE_CHECKING
  import numpy
  import torch
  from torch import Tensor
  from avalanche.benchmarks import OnlineCLExperience
  from avalanche.evaluation import GenericPluginMetric, Metric, PluginMetric
  from avalanche.evaluation.metrics.mean import Mean
  from avalanche.evaluation.metric_utils import phase_and_task, stream_type, generic_get_metric_name, default_metric_name_template
  from avalanche.evaluation.metric_results import MetricValue
label_desc: Use image node_id 4 for calling the CumulativeAccuracy obj's underlying member method code with example usage: obj.reset() without return types