code: def model(self, x):
      # register PyTorch module `decoder` with Pyro
      pyro.module("decoder", self.decoder)
      with pyro.plate("data", x.shape[0]):
          # setup hyperparameters for prior p(z)
          z_loc = torch.zeros(
              x.shape[0], self.z_dim, dtype=x.dtype, device=x.device
          )
          z_scale = torch.ones(
              x.shape[0], self.z_dim, dtype=x.dtype, device=x.device
          )
          # sample from prior (value will be sampled by guide when computing the ELBO)
          z = pyro.sample(
              "latent", dist.Normal(z_loc, z_scale).to_event(1)
          )
          # decode the latent code z
          loc_img = self.decoder.forward(z)
          # score against actual images (with relaxed Bernoulli values)
          pyro.sample(
              "obs",
              dist.Bernoulli(loc_img, validate_args=False).to_event(1),
              obs=x.reshape(-1, 784),
          )
          # return the loc so we can visualize it later
          return loc_img
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import visdom
  from utils.mnist_cached import MNISTCached
  from utils.mnist_cached import setup_data_loaders
  from utils.vae_plots import mnist_test_tsne, plot_llk, plot_vae_samples
  import pyro
  import pyro.distributions
  from pyro.infer import SVI, JitTrace_ELBO, Trace_ELBO
  from pyro.optim import Adam
label_desc: Use image node_id 2 for calling the VAE obj's underlying member method code with example usage: obj.model(x) and returns: loc_img