code: def test(epoch):
      model.training = False
      test_loss = 0
      test_loader = generate_batch_loader(
          test_data, batch_size=batch_size
      )
      for i, data in enumerate(test_loader):
          # Dymanic Construction of Graph
          dy.renew_cg()
          x = dy.inputTensor(data.reshape(-1, 784).T)
          recon_x, mu, logvar = model.forward(x)
          loss = loss_function(recon_x, x, mu, logvar)
  
          # Forward
          loss_value = loss.value()
          test_loss += loss_value
  
          if i == 0:
              n = min(data.shape[0], 8)
              comparison = np.concatenate(
                  [
                      data[:n],
                      recon_x.npvalue().T.reshape(
                          batch_size, 1, 28, 28
                      )[:n],
                  ]
              )
              save_image(
                  comparison,
                  "results/reconstruction_" + str(epoch) + ".png",
                  nrow=n,
              )
  
      test_loss /= len(test_data)
      print("====> Test set loss: {:.4f}".format(test_loss))
  
file_path: dynet/examples/variational-autoencoder/basic-image-recon/vae.py
imports: from __future__ import print_function
  from utils import load_mnist, make_grid, pre_pillow_float_img_process, save_image
  import numpy
  import argparse
  import dynet
  import os
  import time
label_desc: Use image node_id 4 for calling a global function with example usage: test(epoch) without return types