code: def VAE_loss(X, forward_output):
      """
      Loss function of a VAE using mean squared error for reconstruction loss.
      This is the criterion for VAE training loop.
  
      :param X: Original input batch.
      :param forward_output: Return value of a VAE.forward() call.
                  Triplet consisting of (X_hat, mean. logvar), ie.
                  (Reconstructed input after subsequent Encoder and Decoder,
                  mean of the VAE output distribution,
                  logvar of the VAE output distribution)
      """
      X_hat, mean, logvar = forward_output
      reconstruction_loss = MSE_loss(X_hat, X)
      KL_divergence = 0.5 * torch.sum(
          -1 - logvar + torch.exp(logvar) + mean**2
      )
      return reconstruction_loss + KL_divergence
  
imports: from abc import abstractmethod
  from typing import Union
  from matplotlib import transforms
  import torch
  import torch.nn
  from torchvision import transforms
  from avalanche.models.utils import MLP, Flatten
  from avalanche.models.base_model import BaseModel
label_desc: Use image node_id 1 for calling a global function with example usage: VAE_loss(X, forward_output) and returns: unknown