class_docstr: The Class Accuracy metric. This is a standalone metric
  used to compute more specific ones.
  
  Instances of this metric keeps the running average accuracy
  over multiple <prediction, target> pairs of Tensors,
  provided incrementally.
  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
  for all classes seen and across all predictions made since the last `reset`.
  The set of classes to be tracked can be reduced (please refer to the
  constructor parameters).
  
  The reset method will bring the metric to its initial state. By default,
  this metric in its initial state will return a
  `{task_id -> {class_id -> accuracy}}` dictionary in which all accuracies are
  set to 0.
code: def __init_accs_for_known_classes(self):
      for task_id, task_classes in self.classes.items():
          for c in task_classes:
              self._class_accuracies[task_id][c].reset()
  
imports: from typing import Dict, List, Set, Union, TYPE_CHECKING, Iterable, Optional
  from collections import defaultdict, OrderedDict
  import torch
  from torch import Tensor
  from avalanche.evaluation import Metric, PluginMetric, _ExtendedGenericPluginMetric, _ExtendedPluginMetricValue
  from avalanche.evaluation.metric_utils import default_metric_name_template, generic_get_metric_name
  from avalanche.evaluation.metrics import Mean
label_desc: Use image node_id 5 for calling the ClassAccuracy obj's underlying member method code with example usage: obj.__init_accs_for_known_classes() without return types