code: def __init__(
      self,
      latent_dim=20,
      num_filters=64,
      num_channels=3,
      image_size=128,
      activation_type="relu",
      args=None,
  ):
      super(ConvDecNet, self).__init__()
      self.args = args
      if activation_type == "relu":
          self.activation = nn.ReLU(inplace=True)
      elif activation_type == "tanh":
          self.activation = nn.Tanh(inplace=True)
      else:
          print("Activation Type not supported")
          return
  
      self.deconv_hidden = []
  
      num_layers = math.log2(image_size)
      assert num_layers == round(
          num_layers
      ), "Image size that are power of 2 are supported."
      num_layers = int(num_layers)
  
      self.deconv1 = nn.ConvTranspose2d(
          latent_dim,
          num_filters * 2 ** (num_layers - 3),
          4,
          1,
          0,
          bias=True,
      )
      self.bn1 = nn.BatchNorm2d(num_filters * 2 ** (num_layers - 3))
  
      for i in np.arange(num_layers - 3, 0, -1):
          self.deconv_hidden.append(
              nn.ConvTranspose2d(
                  num_filters * 2**i,
                  num_filters * 2 ** (i - 1),
                  4,
                  2,
                  1,
                  bias=True,
              )
          )
          self.deconv_hidden.append(
              nn.BatchNorm2d(num_filters * 2 ** (i - 1))
          )
          self.deconv_hidden.append(self.activation)
  
      self.features = nn.Sequential(*self.deconv_hidden)
  
      self.deconv_out = nn.ConvTranspose2d(
          num_filters, num_channels, 4, 2, 1, bias=True
      )
  
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
          elif isinstance(m, nn.BatchNorm2d):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
          elif isinstance(m, nn.Linear):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
  
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 1 to create a new ConvDecNet object from inherited base classes: nn with example: obj = ConvDecNet(latent_dim, num_filters, num_channels, image_size, activation_type, args)