code: def forward(self, x, y):
      # put x and y together in the same image for simplification
      xc = x.clone()
      xc[x == -1] = y[x == -1]
      xc = xc.view(-1, 784)
      # then compute the hidden units
      hidden = self.relu(self.fc1(xc))
      hidden = self.relu(self.fc2(hidden))
      # then return a mean vector and a (positive) square root covariance
      # each of size batch_size x z_dim
      z_loc = self.fc31(hidden)
      z_scale = torch.exp(self.fc32(hidden))
      return z_loc, z_scale
  
imports: from pathlib import Path
  import numpy
  import torch
  import torch.nn
  from tqdm import tqdm
  import pyro
  import pyro.distributions
  from pyro.infer import SVI, Trace_ELBO
label_desc: Use image node_id 2 for calling the Encoder obj's underlying member method code with example usage: obj.forward(x, y) and returns: z_loc,  z_scale