class_docstr: Base class for all accuracies plugin metrics
class_docstr_tok: ["Base","class","for","all","accuracies","plugin","metrics"]
docstr: Creates the Accuracy plugin
  
  :param reset_at:
  :param emit_at:
  :param mode:
  :param split_by_task: whether to compute task-aware accuracy or not.
docstr_tok: ["Creates","the","Accuracy","plugin",":","param","reset_at",":",":","param","emit_at",":",":","param","mode",":",":","param","split_by_task",":","whether","to","compute","task-aware","accuracy","or","not","."]
code: def __init__(self, reset_at, emit_at, mode):
      """Creates the Accuracy plugin
  
      :param reset_at:
      :param emit_at:
      :param mode:
      :param split_by_task: whether to compute task-aware accuracy or not.
      """
      super().__init__(
          TaskAwareAccuracy(),
          reset_at=reset_at,
          emit_at=emit_at,
          mode=mode,
      )
  
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 AccuracyPerTaskPluginMetric object from inherited base classes: GenericPluginMetric with example: obj = AccuracyPerTaskPluginMetric(reset_at, emit_at, mode)