code: def generate(self, z_mu=None):
          
          if z_mu is None:
              z_mu = np.random.normal(size=self.network_architecture["n_z"])
          # Note: This maps to mean of distribution, we could alternatively
          # sample from Gaussian distribution
          return self.sess.run(self.x_reconstr_mean, 
                               feed_dict={self.z: z_mu})
docs: Generate data by sampling from latent space.
          
          If z_mu is not None, data for this point in latent space is
          generated. Otherwise, z_mu is drawn from prior in latent 
          space.