code: def forward(self, x):
      # we could return the latent representation here after the encoder
      # as the latent representation
      x = self.encoder(x)
      x = self.decoder(x)
      return x
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import torch
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from torch import nn
  from .base import BaseDetector
  from ..utils.stat_models import pairwise_distances_no_broadcast
  from ..utils.torch_utility import get_activation_by_name
label_desc: Use image node_id 2 for calling the InnerAutoencoder obj's underlying member method code with example usage: obj.forward(x) and returns: x