code: def var(self, *args, **kwargs):
      """Computes variance of tensor along specified dimensions."""
      # preprocess inputs:
      if len(args) == 0:
          dim = None
          unbiased = kwargs.get("unbiased", False)
          mean = self.mean()
      elif len(args) == 1:
          dim = args[0]
          unbiased = kwargs.get("unbiased", False)
          keepdim = kwargs.get("keepdim", False)
      elif len(args) == 2:
          dim, unbiased = args[0], args[1]
          keepdim = kwargs.get("keepdim", False)
      else:
          dim, unbiased, keepdim = args[0], args[1], args[2]
  
      if dim is not None:  # dimension is specified
          mean = self.mean(dim, keepdim=True)
  
      # Compute square error
      result = (self - mean).square()
      if dim is None:
          result = result.sum()
      else:
          result = result.sum(dim, keepdim=keepdim)
  
      # Determine divisor
      divisor = self.nelement() // result.nelement()
      if not unbiased:
          divisor -= 1
  
      # Compute mean square error
      if divisor in [0, 1]:
          return result
      return result.div(divisor)
  
imports: import torch
  from ..tensor_types import is_tensor
  from ..util import torch_cat, torch_stack
label_desc: Use image node_id 10 for calling a global function with example usage: var() and returns: result, result