class_docstr: Abstract class for meters used in metric stats.
  Every subclass has cached values for some metric
  
  Parameters
  ----------
  min_mode: bool or None
      if set to true, the smaller value is considered better and vice versa;
      if set to None, no optimality is defined.
code: def __init__(self, min_mode: Optional[bool] = True):
      self.min_mode = min_mode
      if min_mode is True:
          self.best = float("inf")
      elif min_mode is False:
          self.best = float("-inf")
      else:
          self.best = None
      self._initialize()
  
imports: from __future__ import annotations
  import time
  import warnings
  from abc import ABC, abstractmethod
  from typing import Optional, Union
  from torch import Tensor
  from ..engine.distributed import reduce_value
label_desc: Use image node_id 1 to create a new Meter object from inherited base classes: ABC with example: obj = Meter(min_mode)