class_docstr: Overall parent module that holds the dictionary of expert autoencoders
  and expert classifiers.
class_docstr_tok: ["Overall","parent","module","that","holds","the","dictionary","of","expert","autoencoders","and","expert","classifiers","."]
code: def forward(self, x):
      # If not in training mode, select the best expert for the input data
      if not self.training:
          # Build an error tensor to hold errors for all autoencoders
          all_errors = [None] * len(self.autoencoder_dict)
  
          # Iterate through all autoencoders to populate error tensor
          for autoencoder_id in self.autoencoder_dict:
              error = self._get_average_reconstruction_error(
                  autoencoder_id, x
              )
              error = -error / self.temp
              all_errors[int(autoencoder_id)] = torch.tensor(error)
  
          # Softmax to get probabilites
          probabilities = softmax(torch.Tensor(all_errors), dim=-1)
  
          # Select an expert for this input using the most likely autoencoder
          most_relevant_expert_key = torch.argmax(probabilities)
          self.expert = self.expert_dict[
              str(most_relevant_expert_key.item())
          ]
  
      x = x.to(self.device)
      self.expert = self.expert.to(self.device)
  
      return self.expert(x)
  
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 3 for calling the ExpertGate obj's underlying member method code with example usage: obj.forward(x) and returns: self