class_docstr: A :class:`RunningMetric` which outputs the running mean of its input tensors over the course of an epoch.
  
  Args:
      name (str): The name of this running mean.
      batch_size (int): The size of the deque to store of previous results.
      step_size (int): The number of iterations between aggregations.
      dim (int, tuple): The dimension(s) on which to perform the mean. If left as None, this will mean over the whole
          Tensor
class_docstr_tok: ["A",":","class",":","`","RunningMetric","`","which","outputs","the","running","mean","of","its","input","tensors","over","the","course","of","an","epoch",".","Args",":","name","(","str",")",":","The","name","of","this","running","mean",".","batch_size","(","int",")",":","The","size","of","the","deque","to","store","of","previous","results",".","step_size","(","int",")",":","The","number","of","iterations","between","aggregations",".","dim","(","int",",","tuple",")",":","The","dimension","(","s",")","on","which","to","perform","the","mean",".","If","left","as","None",",","this","will","mean","over","the","whole","Tensor"]
code: def _process_train(self, *args):
      data = args[0]
      res = data.mean(**self._kwargs)
      return res
  
imports: from collections import deque
  import torch
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
label_desc: Use image node_id 2 for calling the RunningMean obj's underlying member method code with example usage: obj._process_train() and returns: res