code: def generate(self, runPath, epoch):
      N = 64
      samples_list = super(MNIST_SVHN, self).generate(N)
      for i, samples in enumerate(samples_list):
          samples = samples.data.cpu()
          # wrangle things so they come out tiled
          samples = samples.view(N, *samples.size()[1:])
          save_image(
              samples,
              "{}/gen_samples_{}_{:03d}.png".format(runPath, i, epoch),
              nrow=int(sqrt(N)),
          )
  
imports: import os
  import torch
  import torch.distributions
  import torch.nn
  import torch.nn.functional
  from numpy import sqrt, prod
  from torch.utils.data import DataLoader
  from torchnet.dataset import TensorDataset, ResampleDataset
  from torchvision.utils import save_image, make_grid
  from vis import plot_embeddings, plot_kls_df
  from .mmvae import MMVAE
  from .vae_mnist import MNIST
  from .vae_svhn import SVHN
label_desc: Use image node_id 4 for calling the MNIST_SVHN obj's underlying member method code with example usage: obj.generate(runPath, epoch) without return types