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","."]
docstr: Given a batch of data `x`, run the optimizer (backpropagate the gradient),
  and return the computed loss.
  
  :param x: batch of data or a single datum (MNIST image).
  :return: loss computed on the data batch.
code: def compute_loss_and_gradient(self, x):
      """
      Given a batch of data `x`, run the optimizer (backpropagate the gradient),
      and return the computed loss.
  
      :param x: batch of data or a single datum (MNIST image).
      :return: loss computed on the data batch.
      """
      return
  
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 3 for calling the VAE obj's underlying member method code with example usage: obj.compute_loss_and_gradient(x) without return types