class_docstr: The running epoch time metric.
  This plugin metric only works at training time.
  
  For each iteration, this metric logs the average time
  between the start of the
  epoch and the current iteration.
class_docstr_tok: ["The","running","epoch","time","metric",".","This","plugin","metric","only","works","at","training","time",".","For","each","iteration",",","this","metric","logs","the","average","time","between","the","start","of","the","epoch","and","the","current","iteration","."]
code: def before_training_epoch(self, strategy):
      super().before_training_epoch(strategy)
      self._time_mean.reset()
      self._time.update()
  
imports: import time
  from typing import TYPE_CHECKING, List
  from avalanche.evaluation import Metric, PluginMetric, GenericPluginMetric
  from avalanche.evaluation.metric_results import MetricValue, MetricResult
  from avalanche.evaluation.metric_utils import get_metric_name
  from avalanche.evaluation.metrics.mean import Mean
label_desc: Use image node_id 2 for calling the RunningEpochTime obj's underlying member method code with example usage: obj.before_training_epoch(strategy) without return types