class_docstr: The task-aware Accuracy metric.
  
  The metric computes a dictionary of <task_label, accuracy value> pairs.
  update/result/reset methods are all task-aware.
  
  See :class:`avalanche.evaluation.Accuracy` for the documentation about
  the `Accuracy` metric.
class_docstr_tok: ["The","task-aware","Accuracy","metric",".","The","metric","computes","a","dictionary","of","<","task_label",",","accuracy","value",">","pairs",".","update\/result\/reset","methods","are","all","task-aware",".","See",":","class",":","`","avalanche.evaluation.Accuracy","`","for","the","documentation","about","the","`","Accuracy","`","metric","."]
docstr: Creates an instance of the task-aware Accuracy metric.
docstr_tok: ["Creates","an","instance","of","the","task-aware","Accuracy","metric","."]
code: def __init__(self):
      """Creates an instance of the task-aware Accuracy metric."""
      self._mean_accuracy = defaultdict(Accuracy)
      """
          The mean utility that will be used to store the running accuracy
          for each task label.
          """
  
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 1 to create a new TaskAwareAccuracy object from inherited base classes: Metric with example: obj = TaskAwareAccuracy()