class_docstr: Loss wrapped to monitor the average loss throughout training. 
docstr: Constructor.
  
  Parameters
  ----------
  loss
      Loss function.
  name
      Name of the loss function
docstr_tok: ["Constructor",".","Parameters","--","--","--","--","--","loss","Loss","function",".","name","Name","of","the","loss","function"]
code: def __init__(
      self,
      loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
      name: str,
  ):
      """
      Constructor.
  
      Parameters
      ----------
      loss
          Loss function.
      name
          Name of the loss function
      """
      self.name = name
      self.loss = loss
      self.total, self.count = 0.0, 0
  
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 1 to create a new LossContainer object  with example: obj = LossContainer(loss, name)