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: Moves to the next phase.
  
  This will consolidate the class accuracies recorded so far.
docstr_tok: ["Moves","to","the","next","phase",".","This","will","consolidate","the","class","accuracies","recorded","so","far","."]
code: def finish_phase(self):
      """
      Moves to the next phase.
  
      This will consolidate the class accuracies recorded so far.
      """
      for stream_name in self._streams_in_this_phase:
          self._amcas[stream_name].next_experience()
  
      self._streams_in_this_phase.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 MultiStreamAMCA obj's underlying member method code with example usage: obj.finish_phase() without return types