code: def forward(self, z):
      # define the forward computation on the latent z
      # first compute the hidden units
      hidden = self.softplus(self.fc1(z))
      # return the parameter for the output Bernoulli
      # each is of size batch_size x 784
      loc_img = torch.sigmoid(self.fc21(hidden))
      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 Decoder obj's underlying member method code with example usage: obj.forward(z) and returns: loc_img