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","."]
docstr: Creates an instance of the running epoch time metric..
code: def __init__(self):
      """
      Creates an instance of the running epoch time metric..
      """
      self._time_mean = Mean()
  
      super(RunningEpochTime, self).__init__(
          reset_at="epoch", emit_at="iteration", mode="train"
      )
  
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 1 to create a new RunningEpochTime object from inherited base classes: TimePluginMetric with example: obj = RunningEpochTime()