However, it relies on a reparametrization trick to propagate reconstruction error's gradient back to the recognition neural network (Kingma and Welling, 2014).
the sampling of latent variables should be drawn from a fixed distribution (without parameters) and then transformed to a desired sample using the distribution's parameters.