class_docstr: The expert autoencoder that determines which expert classifier to select
  for the incoming data.
class_docstr_tok: ["The","expert","autoencoder","that","determines","which","expert","classifier","to","select","for","the","incoming","data","."]
code: def forward(self, x):
      # Preprocessing step
      x = x.to(self.device)
      x = self.feature_module(x)
      x = sigmoid(x)
  
      # Encode input
      x = self.encoder(x)
  
      # Reconstruction
      x = self.decoder(x)
  
      return x.view(-1, *self.shape)
  
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 2 for calling the ExpertAutoencoder obj's underlying member method code with example usage: obj.forward(x) and returns: x