class_docstr_tok: []
code: def __init__(
      self,
      num_filters=50,
      latent_dim=10,
      num_channels=3,
      image_size=128,
      activation_type="relu",
      args=None,
      cuda_available=None,
      init_networks=True,
      mode=None,
      output_activation_type="tanh",
      likelihood_type=None,
      beta=1.0,
  ):
      super(ConvDIPVAE, self).__init__(
          latent_dim=latent_dim,
          activation_type=activation_type,
          args=args,
          cuda_available=cuda_available,
          init_networks=False,
          mode=mode,
          output_activation_type=output_activation_type,
          likelihood_type=likelihood_type,
          beta=beta,
      )
  
      if init_networks:
          self.generative_net = ConvDecNet(
              latent_dim=latent_dim,
              num_filters=num_filters,
              num_channels=num_channels,
              image_size=image_size,
              activation_type=activation_type,
              args=args,
          )
          self.inference_net = ConvEncNet(
              latent_dim=latent_dim,
              num_filters=num_filters,
              num_channels=num_channels,
              image_size=image_size,
              activation_type=activation_type,
              args=args,
          )
  
      self.name = "ConvDIPVAE"
  
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 ConvDIPVAE object from inherited base classes: DIPVAE with example: obj = ConvDIPVAE(num_filters, latent_dim, num_channels, image_size, activation_type, args, cuda_available, init_networks, mode, output_activation_type, likelihood_type, beta)