class_docstr: Generate a SVHN image given a sample from the latent space. 
class_docstr_tok: ["Generate","a","SVHN","image","given","a","sample","from","the","latent","space","."]
code: def __init__(self, latent_dim):
      super(Dec, self).__init__()
      self.dec = nn.Sequential(
          nn.ConvTranspose2d(latent_dim, fBase * 4, 4, 1, 0, bias=True),
          nn.ReLU(True),
          # size: (fBase * 4) x 4 x 4
          nn.ConvTranspose2d(fBase * 4, fBase * 2, 4, 2, 1, bias=True),
          nn.ReLU(True),
          # size: (fBase * 2) x 8 x 8
          nn.ConvTranspose2d(fBase * 2, fBase, 4, 2, 1, bias=True),
          nn.ReLU(True),
          # size: (fBase) x 16 x 16
          nn.ConvTranspose2d(fBase, imgChans, 4, 2, 1, bias=True),
          nn.Sigmoid()
          # Output size: 3 x 32 x 32
      )
  
imports: import torch
  import torch.distributions
  import torch.nn
  import torch.nn.functional
  from numpy import sqrt
  from torch.utils.data import DataLoader
  from torchvision import transforms, datasets
  from torchvision.utils import save_image, make_grid
  from utils import Constants
  from vis import plot_embeddings, plot_kls_df
  from .vae import VAE
label_desc: Use image node_id 1 to create a new Dec object from inherited base classes: nn with example: obj = Dec(latent_dim)