code: def AE_loss(target, reconstruction):
      """Calculates the MSE loss for the autoencoder by comparing the
      reconstruction to the pre-processed input.
  
      :param target: the target for the autoencoder
      :param reconstruction: output of the autoencoder
      :return: mean squared error loss between the target and reconstruction
      """
      reconstruction_loss = mse_loss(
          input=reconstruction, target=target, reduction="sum"
      )
      return reconstruction_loss
  
imports: from collections import OrderedDict
  from copy import deepcopy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import sigmoid
  from torch.nn.functional import mse_loss, softmax
  from torchvision import transforms
  import torchvision.models
  from .utils import FeatureExtractorBackbone
  from avalanche.models import MultiTaskModule
  from avalanche.models.utils import Flatten
  from avalanche.benchmarks.scenarios.generic_scenario import CLExperience
label_desc: Use image node_id 1 for calling a global function with example usage: AE_loss(target, reconstruction) and returns: reconstruction_loss