class_docstr: Abstract class for the variational auto-encoder. The abstract method
  for training the network is implemented by subclasses.
class_docstr_tok: ["Abstract","class","for","the","variational","auto-encoder",".","The","abstract","method","for","training","the","network","is","implemented","by","subclasses","."]
code: def test(self, epoch):
      self.set_train(is_train=False)
      test_loss = 0
      for i, (x, _) in enumerate(self.test_loader):
          with torch.no_grad():
              recon_x = self.model_eval(x)[0]
              test_loss += self.compute_loss_and_gradient(x)
          if i == 0:
              n = min(x.size(0), 8)
              comparison = torch.cat(
                  [
                      x[:n],
                      recon_x.reshape(self.args.batch_size, 1, 28, 28)[
                          :n
                      ],
                  ]
              )
              save_image(
                  comparison.detach().cpu(),
                  os.path.join(
                      OUTPUT_DIR,
                      "reconstruction_" + str(epoch) + ".png",
                  ),
                  nrow=n,
              )
  
      test_loss /= len(self.test_loader.dataset)
      print("Test set loss: {:.4f}".format(test_loss))
  
imports: import argparse
  import itertools
  import os
  from abc import ABCMeta, abstractmethod
  import torch
  import torch.nn
  from torch.nn import functional
  from torchvision.utils import save_image
  from utils.mnist_cached import DATA_DIR, RESULTS_DIR
  import pyro
  from pyro.contrib.examples import util
  from pyro.distributions import Bernoulli, Normal
  from pyro.infer import SVI, JitTrace_ELBO, Trace_ELBO
  from pyro.optim import Adam
label_desc: Use image node_id 6 for calling the VAE obj's underlying member method code with example usage: obj.test(epoch) without return types