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.
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docstr: Retrieves the running AMCA for each stream.
  
  Calling this method will not change the internal state of the metric.
  
  :return: A dictionary `{stream_name -> {task_id -> amca}}`. The
      running AMCA of each task is a float value between 0 and 1.
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code: def result(self) -> Dict[str, Dict[int, float]]:
      """
      Retrieves the running AMCA for each stream.
  
      Calling this method will not change the internal state of the metric.
  
      :return: A dictionary `{stream_name -> {task_id -> amca}}`. The
          running AMCA of each task is a float value between 0 and 1.
      """
      all_streams_dict = OrderedDict()
      for stream_name in sorted(self._amcas.keys()):
          stream_metric = self._amcas[stream_name]
          stream_result = stream_metric.result()
          all_streams_dict[stream_name] = stream_result
      return all_streams_dict
  
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 3 for calling the MultiStreamAMCA obj's underlying member method code with example usage: obj.result() and returns: all_streams_dict