code: def main(args):
      train_loader, test_loader = setup(args)
      if args.impl == "pyro":
          vae = PyroVAEImpl(args, train_loader, test_loader)
          print("Running Pyro VAE implementation")
      elif args.impl == "pytorch":
          vae = PyTorchVAEImpl(args, train_loader, test_loader)
          print("Running PyTorch VAE implementation")
      else:
          raise ValueError(
              "Incorrect implementation specified: {}".format(args.impl)
          )
      for i in range(args.num_epochs):
          vae.train(i)
          if not args.skip_eval:
              vae.test(i)
  
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 2 for calling a global function with example usage: main(args) without return types