class_docstr: The WeightCheckpoint Metric.
  
  Instances of this metric keeps the weight checkpoint tensor of the
  model at each experience.
  
  Each time `result` is called, this metric emits the latest experience's
  weight checkpoint tensor since the last `reset`.
  
  The reset method will bring the metric to its initial state. By default
  this metric in its initial state will return None.
class_docstr_tok: ["The","WeightCheckpoint","Metric",".","Instances","of","this","metric","keeps","the","weight","checkpoint","tensor","of","the","model","at","each","experience",".","Each","time","`","result","`","is","called",",","this","metric","emits","the","latest","experience's","weight","checkpoint","tensor","since","the","last","`","reset","`",".","The","reset","method","will","bring","the","metric","to","its","initial","state",".","By","default","this","metric","in","its","initial","state","will","return","None","."]
docstr: Resets the metric.
  
  :return: None.
docstr_tok: ["Resets","the","metric",".",":","return",":","None","."]
code: def reset(self) -> None:
      """
      Resets the metric.
  
      :return: None.
      """
      self.weights = None
  
file_path: avalanche/avalanche/evaluation/metrics/checkpoint.py
imports: import copy
  import io
  from typing import TYPE_CHECKING, Optional
  from torch import Tensor
  import torch
  from avalanche.evaluation import PluginMetric
  from avalanche.evaluation.metric_results import MetricValue, MetricResult
  from avalanche.evaluation.metric_utils import get_metric_name
label_desc: Use image node_id 4 for calling the WeightCheckpoint obj's underlying member method code with example usage: obj.reset() without return types