class_docstr: The Forward Transfer averaged over all the evaluation experiences.
  
  This plugin metric, computed over all observed experiences during training,
  is the average over the difference between the accuracy result obtained
  after the previous experience and the accuracy result obtained
  on random initialization.
class_docstr_tok: ["The","Forward","Transfer","averaged","over","all","the","evaluation","experiences",".","This","plugin","metric",",","computed","over","all","observed","experiences","during","training",",","is","the","average","over","the","difference","between","the","accuracy","result","obtained","after","the","previous","experience","and","the","accuracy","result","obtained","on","random","initialization","."]
code: def metric_update(self, strategy):
      self._current_metric.update(strategy.mb_y, strategy.mb_output, 0)
  
imports: from abc import ABC, abstractmethod
  from typing import Dict, TYPE_CHECKING, Generic, List, Optional, TypeVar, Union
  from avalanche.evaluation.metric_definitions import Metric, PluginMetric
  from avalanche.evaluation.metric_results import MetricValue, MetricResult
  from avalanche.evaluation.metrics import TaskAwareAccuracy, Mean
  from avalanche.evaluation.metric_utils import get_metric_name, phase_and_task, stream_type
label_desc: Use image node_id 2 for calling the StreamForwardTransfer obj's underlying member method code with example usage: obj.metric_update(strategy) without return types