class_docstr: An extension of the Average Mean Class Accuracy (AMCA) metric
  (class:`AverageMeanClassAccuracy`) able to separate the computation of the
  AMCA based on the current stream.
class_docstr_tok: ["An","extension","of","the","Average","Mean","Class","Accuracy","(","AMCA",")","metric","(","class",":","`","AverageMeanClassAccuracy","`",")","able","to","separate","the","computation","of","the","AMCA","based","on","the","current","stream","."]
docstr: Switches to a specific stream.
  
  :param stream_name: The name of the stream.
docstr_tok: ["Switches","to","a","specific","stream",".",":","param","stream_name",":","The","name","of","the","stream","."]
code: def set_stream(self, stream_name: str):
      """
      Switches to a specific stream.
  
      :param stream_name: The name of the stream.
      """
      self._current_stream = stream_name
      if not self._is_stream_tracked(stream_name):
          return
  
      if self._current_stream not in self._amcas:
          self._amcas[stream_name] = AverageMeanClassAccuracy(
              classes=self._limit_classes
          )
      self._streams_in_this_phase.add(stream_name)
  
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 4 for calling the MultiStreamAMCA obj's underlying member method code with example usage: obj.set_stream(stream_name) without return types