code: def forward(self, x):
      x = x.unsqueeze(-1).unsqueeze(-1)
      x = self.deconv1(x)
      x = self.bn1(x)
      x = self.activation(x)
      x = self.features(x)
      return self.deconv_out(x)
  
imports: import torch
  from torchvision import transforms
  import torch.nn
  import pandas
  from PIL import Image
  import numpy
  import math
  import os
  import matplotlib.pyplot
  from aix360.algorithms.dipvae.dipvae_utils import VAE, DIPVAE
  from aix360.algorithms.dipvae import DIPVAEExplainer
  from aix360.algorithms.dipvae.dipvae_utils import plot_reconstructions, plot_latent_traversal
label_desc: Use image node_id 2 for calling the ConvDecNet obj's underlying member method code with example usage: obj.forward(x) and returns: self