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: Reset the running count and total.
  
  Args:
      state (dict): The model state.
docstr_tok: ["Reset","the","running","count","and","total",".","Args",":","state","(","dict",")",":","The","model","state","."]
code: def reset(self, state):
      """Reset the running count and total.
  
      Args:
          state (dict): The model state.
      """
      super(Mean, self).reset(state)
      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 4 for calling the Mean obj's underlying member method code with example usage: obj.reset(state) without return types