class_docstr: The Average Mean Class Accuracy (AMCA) 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.
  
  Beware that this class does not provide mechanisms to separate scores based
  on the originating data stream. For this, please refer to
  :class:`MultiStreamAMCA`.
  
  The "prediction" and "target" tensors may contain plain labels or
  one-hot/logit vectors.
  
  Each time `result` is called, this metric emits the average mean accuracy
  as the average accuracy of all previous experiences (also considering the
  accuracy in the current experience).
  The metric expects that the :meth:`next_experience` method will be called
  after each experience. This is needed to consolidate the current mean
  accuracy. After calling :meth:`next_experience`, a new experience with
  accuracy 0.0 is immediately started. If you need to obtain the AMCA up to
  experience `t-1`, obtain the :meth:`result` before calling
  :meth:`next_experience`.
  
  
  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
  (tracked classes will be kept). By default, this metric in its initial state
  will return a `{task_id -> amca}` dictionary in which all AMCAs are set to 0
  (that is, the `reset` method will hardly be useful when using this metric).
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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._updated_once = False
      self._class_accuracies.reset()
      self._prev_exps_accuracies.clear()
  
imports: from typing import Callable, Dict, Iterable, List, Union, TYPE_CHECKING, Optional, Sequence, Set
  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 generic_get_metric_name
  from avalanche.evaluation.metrics.class_accuracy import ClassAccuracy, TrackedClassesType
label_desc: Use image node_id 5 for calling the AverageMeanClassAccuracy obj's underlying member method code with example usage: obj.reset() without return types