class_docstr: A proposed fully Convolutional decoder for VQVAE.
class_docstr_tok: ["A","proposed","fully","Convolutional","decoder","for","VQVAE","."]
code: def __init__(self, args: dict):
      BaseDecoder.__init__(self)
      self.input_dim = (1, 28, 28)
      self.n_channels = 1
  
      layers = nn.ModuleList()
  
      layers.append(
          nn.Sequential(
              nn.ConvTranspose2d(1, 1, 3, 2, padding=1),
              # nn.BatchNorm2d(512),
              # nn.ReLU(),
          )
      )
  
      layers.append(
          nn.Sequential(
              nn.ConvTranspose2d(
                  1, 1, 3, 2, padding=1, output_padding=1
              ),
              # nn.BatchNorm2d(256),
              # nn.ReLU(),
          )
      )
  
      layers.append(
          nn.Sequential(
              nn.ConvTranspose2d(
                  1, self.n_channels, 3, 2, padding=1, output_padding=1
              ),
              nn.Sigmoid(),
          )
      )
  
      self.layers = layers
      self.depth = len(layers)
  
file_path: benchmark_VAE/examples/scripts/custom_nn.py
imports: from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.models import BaseAEConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseDiscriminator, BaseEncoder
  from pythae.models.nn.base_architectures import BaseDecoder, BaseEncoder
label_desc: Use image node_id 1 to create a new Fully_Conv_Decoder_Conv_AE_MNIST object from inherited base classes: BaseDecoder with example: obj = Fully_Conv_Decoder_Conv_AE_MNIST(args)