class_docstr: The average loss across all minibatches up to the current
  epoch iteration.
  This plugin metric only works at training time.
  
  At each iteration, this metric logs the loss averaged over all patterns
  seen so far in the current epoch.
  The metric resets its state after each training epoch.
class_docstr_tok: ["The","average","loss","across","all","minibatches","up","to","the","current","epoch","iteration",".","This","plugin","metric","only","works","at","training","time",".","At","each","iteration",",","this","metric","logs","the","loss","averaged","over","all","patterns","seen","so","far","in","the","current","epoch",".","The","metric","resets","its","state","after","each","training","epoch","."]
docstr: Creates an instance of the RunningEpochLoss metric.
code: def __init__(self):
      """
      Creates an instance of the RunningEpochLoss metric.
      """
  
      super(RunningEpochLoss, self).__init__(
          reset_at="epoch", emit_at="iteration", mode="train"
      )
  
imports: from typing import List, Dict
  import torch
  from torch import Tensor
  from avalanche.evaluation import PluginMetric, Metric, GenericPluginMetric
  from avalanche.evaluation.metrics.mean import Mean
  from avalanche.evaluation.metric_utils import phase_and_task
  from collections import defaultdict
label_desc: Use image node_id 1 to create a new RunningEpochLoss object from inherited base classes: LossPluginMetric with example: obj = RunningEpochLoss()