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 generate(self, runPath, epoch):
      N, K = 64, 9
      samples = super(SVHN, self).generate(N, K).cpu()
      # wrangle things so they come out tiled
      samples = samples.view(K, N, *samples.size()[1:]).transpose(0, 1)
      s = [make_grid(t, nrow=int(sqrt(K)), padding=0) for t in samples]
      save_image(
          torch.stack(s),
          "{}/gen_samples_{:03d}.png".format(runPath, epoch),
          nrow=int(sqrt(N)),
      )
  
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 4 for calling the SVHN obj's underlying member method code with example usage: obj.generate(runPath, epoch) without return types