class_docstr: Generate an image given a sample from the latent space. 
class_docstr_tok: ["Generate","an","image","given","a","sample","from","the","latent","space","."]
code: def __init__(self, latentDim):
      super(Dec, self).__init__()
      modules = [
          nn.ConvTranspose2d(latentDim, fBase * 8, 4, 1, 0, bias=True),
          nn.ReLU(True),
      ]
  
      modules.extend(
          [
              nn.ConvTranspose2d(
                  fBase * 8, fBase * 4, 4, 2, 1, bias=True
              ),
              nn.ReLU(True),
              # size: (fBase * 4) x 16 x 16
              nn.ConvTranspose2d(
                  fBase * 4, fBase * 2, 4, 2, 1, bias=True
              ),
              nn.ReLU(True),
              # size: (fBase * 2) x 32 x 32
              nn.ConvTranspose2d(fBase * 2, fBase, 4, 2, 1, bias=True),
              nn.ReLU(True),
              # size: (fBase) x 64 x 64
              nn.ConvTranspose2d(fBase, imgChans, 4, 2, 1, bias=True),
              nn.Sigmoid()
              # Output size: 3 x 128 x 128
          ]
      )
      self.dec = nn.Sequential(*modules)
  
imports: import torch
  import torch.distributions
  import torch.nn
  import torch.nn.functional
  import torch.utils.data
  from numpy import sqrt
  from torchvision import datasets, transforms
  from torchvision.utils import make_grid, save_image
  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(latentDim)