class_docstr: Loss wrapped to monitor the average loss throughout training. 
docstr: Computes and accumulates the loss given the prediction labels and the true labels.
  
  Parameters
  ----------
  y_pred
      Prediction labels.
  y_true
      True labels.
  
  Returns
  -------
  Loss value.
docstr_tok: ["Computes","and","accumulates","the","loss","given","the","prediction","labels","and","the","true","labels",".","Parameters","--","--","--","--","--","y_pred","Prediction","labels",".","y_true","True","labels",".","Returns","--","--","--","-","Loss","value","."]
code: def __call__(
      self, y_pred: torch.Tensor, y_true: torch.Tensor
  ) -> torch.Tensor:
      """
      Computes and accumulates the loss given the prediction labels and the true labels.
  
      Parameters
      ----------
      y_pred
          Prediction labels.
      y_true
          True labels.
  
      Returns
      -------
      Loss value.
      """
  
      # compute loss
      loss = self.loss(y_pred, y_true)
  
      # add loss to the total
      self.total += loss.item()
      self.count += 1
      return loss
  
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 2 for calling the LossContainer obj's underlying member method code with example usage: obj.__call__(y_pred, y_true) and returns: loss