class_docstr: Generate a CUB image feature given a sample from the latent space. 
class_docstr_tok: ["Generate","a","CUB","image","feature","given","a","sample","from","the","latent","space","."]
code: def forward(self, z):
      p = self.dec(z.view(-1, z.size(-1)))
      mean = self.fc31(p).view(*z.size()[:-1], -1)
      return mean, torch.tensor([0.01]).to(mean.device)
  
imports: import torch
  import torch.distributions
  import torch.nn
  import torch.nn.functional
  import torch.utils.data
  from numpy import sqrt
  from torchvision.utils import make_grid, save_image
  from datasets import CUBImageFt
  from utils import Constants, NN_lookup
  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: mean,  torch