class_docstr: The standalone Forward Transfer metric.
  This metric returns the forward transfer relative to a specific key.
  Alternatively, this metric returns a dict in which each key is
  associated to the forward transfer.
  Forward transfer is computed as the difference between the value
  recorded for a specific key after the previous experience has
  been trained on, and random initialization before training.
  The value associated to a key can be updated with the `update` method.
  
  At initialization, this metric returns an empty dictionary.
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docstr: Creates an instance of the standalone Forward Transfer metric
docstr_tok: ["Creates","an","instance","of","the","standalone","Forward","Transfer","metric"]
code: def __init__(self):
      """
      Creates an instance of the standalone Forward Transfer metric
      """
  
      self.initial: Dict[int, float] = dict()
      """
          The initial value for each key. This is the accuracy at 
          random initialization.
          """
  
      self.previous: Dict[int, float] = dict()
      """
          The previous experience value detected for each key
          """
  
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 ForwardTransfer object from inherited base classes: Metric with example: obj = ForwardTransfer()