code: def __init__(self, args: dict):
      BaseDecoder.__init__(self)
      self.input_dim = (3, 64, 64)
      self.latent_dim = args.latent_dim
      self.n_channels = 3
  
      layers = nn.ModuleList()
  
      layers.append(
          nn.Sequential(nn.Linear(args.latent_dim, 1024 * 8 * 8))
      )
  
      layers.append(
          nn.Sequential(
              nn.ConvTranspose2d(1024, 512, 5, 2, padding=2),
              nn.BatchNorm2d(512),
              nn.ReLU(),
          )
      )
  
      layers.append(
          nn.Sequential(
              nn.ConvTranspose2d(
                  512, 256, 5, 2, padding=1, output_padding=0
              ),
              nn.BatchNorm2d(256),
              nn.ReLU(),
          )
      )
  
      layers.append(
          nn.Sequential(
              nn.ConvTranspose2d(
                  256, 128, 5, 2, padding=2, output_padding=1
              ),
              nn.BatchNorm2d(128),
              nn.ReLU(),
          )
      )
  
      layers.append(
          nn.Sequential(
              nn.ConvTranspose2d(128, self.n_channels, 5, 1, padding=1),
              nn.Sigmoid(),
          )
      )
  
      self.layers = layers
      self.depth = len(layers)
  
imports: import logging
  import os
  from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.data.preprocessors import DataProcessor
  from pythae.models import Adversarial_AE, Adversarial_AE_Config
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseDiscriminator, BaseEncoder
  from pythae.trainers import AdversarialTrainer, AdversarialTrainerConfig
label_desc: Use image node_id 1 to create a new Decoder object from inherited base classes: BaseDecoder with example: obj = Decoder(args)