class_docstr: Metric aggregator which calculates the **sample** standard deviation of process outputs between calls to reset.
  Optionally calculate the population std if ``unbiased = False``.
  
  Args:
      name (str): The name of this metric.
      unbiased (bool): If True (default), calculates the sample standard deviation, else, the population standard
          deviation
      dim (int, tuple): The dimension(s) on which to compute the std. If left as None, this will operate over the
          whole Tensor
class_docstr_tok: ["Metric","aggregator","which","calculates","the","*","*","sample","*","*","standard","deviation","of","process","outputs","between","calls","to","reset",".","Optionally","calculate","the","population","std","if","``","unbiased","=","False","``",".","Args",":","name","(","str",")",":","The","name","of","this","metric",".","unbiased","(","bool",")",":","If","True","(","default",")",",","calculates","the","sample","standard","deviation",",","else",",","the","population","standard","deviation","dim","(","int",",","tuple",")",":","The","dimension","(","s",")","on","which","to","compute","the","std",".","If","left","as","None",",","this","will","operate","over","the","whole","Tensor"]
code: def __init__(self, name, unbiased=True, dim=None):
      super(Std, self).__init__(name, unbiased=unbiased, dim=dim)
  
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 Std object from inherited base classes: Var with example: obj = Std(name, unbiased, dim)