code: def compute_samples(self):
          
          mu, std, h, w = self.params
          shape = tf.shape(mu)
          eps = tf.random_normal(shape)
          z = tf.reshape(eps * std + mu, tf.stack([-1, h, w, self.num_latent]))
          return z
docs: Sample from a Normal distribution with inferred mu and std