code: def forward(self, x):
      x = self.conv1(x)
      x = self.activation(x)
      x = self.features(x)
      mu_z = self.conv_mu(x).view(x.size(0), -1)
      var_z = self.conv_var(x).view(x.size(0), -1)
      return torch.cat([mu_z, var_z], dim=1)
  
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 ConvEncNet obj's underlying member method code with example usage: obj.forward(x) and returns: torch