class_docstr: Calculates the variance of a tensor, with an order. e.g.
  if `order = 1` then it will calculate sample variance.
  
  This is equal to mse / (n - order)
class_docstr_tok: ["Calculates","the","variance","of","a","tensor",",","with","an","order",".","e.g",".","if","`","order","=","1","`","then","it","will","calculate","sample","variance",".","This","is","equal","to","mse","\/","(","n","-","order",")"]
code: def __init__(
      self, name: Optional[str] = None, order: int = 0
  ) -> None:
      if name is None:
          if order == 0:
              name = "variance"
          elif order == 1:
              name = "sample_variance"
          else:
              name = f"variance({order})"
  
      super().__init__(name=name, order=order)
      self.order = order
  
imports: from typing import Any, Callable, List, Optional, TYPE_CHECKING
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 to create a new Var object from inherited base classes: Stat with example: obj = Var(name, order)