class_docstr: Generate latent parameters for CUB image data. 
class_docstr_tok: ["Generate","latent","parameters","for","CUB","image","data","."]
code: def forward(self, x):
      e = self.enc(x)
      return (
          self.c1(e).squeeze(),
          F.softplus(self.c2(e)).squeeze() + Constants.eta,
      )
  
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 Enc obj's underlying member method code with example usage: obj.forward(x) and returns: self,  unknown