Variational Autoencoders

I'm making written guides on generative deep learning . In my last guide we went through a simple auto encoder, for each image we generated 16 codes and regenerated the image using only 16 numbers,now let's talk about Variational auto encoders which has a similar architecture but they belong to probabilistic graphical models . In our VAE model our encoder generates two parameters, mean and variance we make a sample from that and pass it to decoder to rebuild the input . We can not write VAEs like simple auto encoders because we have a more complex loss function, so how can we make a VAE model?