class_docstr: Metric aggregator which calculates the mean of process outputs between calls to reset.
  
  Args:
      name (str): The name of this metric.
      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: ["Metric","aggregator","which","calculates","the","mean","of","process","outputs","between","calls","to","reset",".","Args",":","name","(","str",")",":","The","name","of","this","metric",".","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 __init__(self, name, dim=None):
      super(Mean, self).__init__(name)
      self._kwargs = {"dim": dim} if dim is not None else {}
      self._sum = 0.0
      self._count = 0
  
imports: from collections import deque
  import torch
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
label_desc: Use image node_id 1 to create a new Mean object from inherited base classes: Metric with example: obj = Mean(name, dim)