class_docstr: Metric aggregator which calculates the **sample** variance of process outputs between calls to reset.
  Optionally calculate the population variance if ``unbiased = False``.
  
  Args:
      name (str): The name of this metric.
      unbiased (bool): If True (default), calculates the sample variance, else, the population variance
      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","*","*","variance","of","process","outputs","between","calls","to","reset",".","Optionally","calculate","the","population","variance","if","``","unbiased","=","False","``",".","Args",":","name","(","str",")",":","The","name","of","this","metric",".","unbiased","(","bool",")",":","If","True","(","default",")",",","calculates","the","sample","variance",",","else",",","the","population","variance","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(Var, self).__init__(name)
      self._unbiased = unbiased
      self._kwargs = {"dim": dim} if dim is not None else {}
      self._sum = 0.0
      self._sum_sq = 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 Var object from inherited base classes: Metric with example: obj = Var(name, unbiased, dim)