code: def reparameterize(self, mu, logvar):
      if self.training:
          std = dy.exp(logvar * 0.5)
          eps = dy.random_normal(dim=std.dim()[0], mean=0.0, stddev=1.0)
          return dy.cmult(eps, std) + mu
      else:
          return mu
  
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 the VAE obj's underlying member method code with example usage: obj.reparameterize(mu, logvar) and returns: unknown, mu