class_docstr: Loss wrapped to monitor the average loss throughout training. 
docstr: Computes the average loss obtain by dividing the cumulated loss by the number of steps
  
  Returns
  -------
  Average loss.
docstr_tok: ["Computes","the","average","loss","obtain","by","dividing","the","cumulated","loss","by","the","number","of","steps","Returns","--","--","--","-","Average","loss","."]
code: def result(self) -> Dict[str, float]:
      """
      Computes the average loss obtain by dividing the cumulated loss by the number of steps
  
      Returns
      -------
      Average loss.
      """
      return {self.name: self.total / self.count}
  
imports: import torch
  import numpy
  from enum import Enum
  from abc import ABC, abstractmethod
  from typing import Dict, Union, Callable
label_desc: Use image node_id 3 for calling the LossContainer obj's underlying member method code with example usage: obj.result() and returns: dict