class_docstr: The GenericStreamForwardTransfer metric, describing the average evaluation
  forward transfer detected over all experiences observed during training.
  
  In particular, the user should override:
  * __init__ by calling `super` and instantiating the `self.current_metric`
  property as a valid avalanche metric
  * `metric_update`, to update `current_metric`
  * `metric_result` to get the result from `current_metric`.
  * `__str__` to define the experience forgetting  name.
  
  This metric is computed during the eval phase only.
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docstr: Creates an instance of the GenericStreamForwardTransfer metric.
docstr_tok: ["Creates","an","instance","of","the","GenericStreamForwardTransfer","metric","."]
code: def __init__(self, current_metric: TMetric):
      """
      Creates an instance of the GenericStreamForwardTransfer metric.
      """
  
      super().__init__(current_metric)
  
      self.stream_forward_transfer = Mean()
      """
          The average forward transfer over all experiences
          """
  
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 1 to create a new GenericStreamForwardTransfer object from inherited base classes: GenericExperienceForwardTransfer with example: obj = GenericStreamForwardTransfer(current_metric)