class_docstr: Plugin metric for the Average Mean Class Accuracy (AMCA).
  
  The AMCA is tracked for the classes and streams defined in the constructor.
  
  In addition, by default, the results obtained through the periodic
  evaluation (mid-training validation) mechanism are ignored.
class_docstr_tok: ["Plugin","metric","for","the","Average","Mean","Class","Accuracy","(","AMCA",")",".","The","AMCA","is","tracked","for","the","classes","and","streams","defined","in","the","constructor",".","In","addition",",","by","default",",","the","results","obtained","through","the","periodic","evaluation","(","mid-training","validation",")","mechanism","are","ignored","."]
code: def update(self, strategy: "SupervisedTemplate"):
      if self._is_training and self._ignore_validation:
          # Running a validation (eval phase inside a train phase), ignore it
          return
  
      self._ms_amca.update(
          strategy.mb_output, strategy.mb_y, strategy.mb_task_id
      )
  
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 2 for calling the AMCAPluginMetric obj's underlying member method code with example usage: obj.update(strategy) without return types