import tensorflow as tf

        encoder_output_label_, _ = encoder(x_input_l, reuse=True, supervised=True)

    # Generate output images
    with tf.variable_scope(tf.get_variable_scope()):
        decoder_image = decoder(manual_decoder_input, reuse=True)

    # Classification accuracy of encoder
    correct_pred = tf.equal(tf.argmax(encoder_output_label_, 1), tf.argmax(y_input, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

    # Autoencoder loss
    autoencoder_loss = tf.reduce_mean(tf.square(x_target - decoder_output))

    # Gaussian Discriminator Loss
