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 train(self, epoch):
      self.set_train(is_train=True)
      train_loss = 0
      for batch_idx, (x, _) in enumerate(self.train_loader):
          loss = self.compute_loss_and_gradient(x)
          train_loss += loss
      print(
          "====> Epoch: {} \nTraining loss: {:.4f}".format(
              epoch, train_loss / len(self.train_loader.dataset)
          )
      )
  
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 5 for calling the VAE obj's underlying member method code with example usage: obj.train(epoch) without return types