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 forward(self, z):
      z = z.unsqueeze(-1).unsqueeze(-1)  # fit deconv layers
      out = self.dec(z.view(-1, *z.size()[-3:]))
      out = out.view(*z.size()[:-3], *out.size()[1:])
      return out, torch.tensor(0.01).to(z.device)
  
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 2 for calling the Dec obj's underlying member method code with example usage: obj.forward(z) and returns: out,  torch