You could create a separate model then pass the autoencoder into it and load the weights in that.  Then you could create a separate sequential layer with the classification layers and then in the forward pass you could pass the input through those two. Then too freeze the autoencoder weights in the optimizer you could just set the parameters as only the classification layers.