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"]
docstr: Add the input to the rolling sum. Input must be a torch tensor.
  
  Args:
      args:  The output of some previous call to :meth:`.Metric.process`.
docstr_tok: ["Add","the","input","to","the","rolling","sum",".","Input","must","be","a","torch","tensor",".","Args",":","args",":","The","output","of","some","previous","call","to",":","meth",":","`",".Metric.process","`","."]
code: def process(self, *args):
      """Add the input to the rolling sum. Input must be a torch tensor.
  
      Args:
          args:  The output of some previous call to :meth:`.Metric.process`.
      """
      data = args[0]
      tot = data.sum(**self._kwargs)
      self._sum += tot
      self._count += data.numel() / tot.numel()
  
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 Mean obj's underlying member method code with example usage: obj.process() without return types