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
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docstr: Compute values required for the variance from the input. The input should be a torch Tensor. The sum and sum
  of squares will be computed over the provided dimension.
  
  Args:
      args (torch.Tensor):  The output of some previous call to :meth:`.Metric.process`.
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code: def process(self, *args):
      """Compute values required for the variance from the input. The input should be a torch Tensor. The sum and sum
      of squares will be computed over the provided dimension.
  
      Args:
          args (torch.Tensor):  The output of some previous call to :meth:`.Metric.process`.
      """
      data = args[0]
      tot = data.sum(**self._kwargs)
      self._sum += tot
      self._sum_sq += data.pow(2).sum(**self._kwargs)
      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 Var obj's underlying member method code with example usage: obj.process() without return types