class_docstr: Derive a specific sub-class of a VAE for SVHN 
class_docstr_tok: ["Derive","a","specific","sub-class","of","a","VAE","for","SVHN"]
code: def pz_params(self):
      return self._pz_params[0], F.softmax(
          self._pz_params[1], dim=1
      ) * self._pz_params[1].size(-1)
  
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 2 for calling the SVHN obj's underlying member method code with example usage: obj.pz_params() and returns: self,  unknown