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: Compute and return the mean of all metric values since the last call to reset.
  
  Returns:
      The mean of the metric values since the last call to reset.
docstr_tok: ["Compute","and","return","the","mean","of","all","metric","values","since","the","last","call","to","reset",".","Returns",":","The","mean","of","the","metric","values","since","the","last","call","to","reset","."]
code: def process_final(self, *args):
      """Compute and return the mean of all metric values since the last call to reset.
  
      Returns:
          The mean of the metric values since the last call to reset.
      """
      res = self._sum / self._count
      return res.item() if res.numel() <= 1 else res.tolist()
  
imports: from collections import deque
  import torch
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
label_desc: Use image node_id 3 for calling the Mean obj's underlying member method code with example usage: obj.process_final() and returns: unknown