code: def train(epoch):
      model.training = True
      train_loss = 0
      train_loader = generate_batch_loader(
          train_data, batch_size=batch_size
      )
      for batch_idx, data in enumerate(train_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()
          train_loss += loss_value
          # Backward
          loss.backward()
          optimizer.update()
  
          if batch_idx % args.log_interval == 0:
              print(
                  "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                      epoch,
                      batch_idx * len(data),
                      len(train_data),
                      100.0
                      * batch_idx
                      / (len(train_data) / batch_size),
                      loss_value / len(data),
                  )
              )
  
      print(
          "====> Epoch: {} Average loss: {:.4f}".format(
              epoch, train_loss / len(train_data)
          )
      )
  
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 3 for calling a global function with example usage: train(epoch) without return types