import tensorflow as tf

                               strides=(2, 2), padding="SAME")
          x = common_layers.layer_norm(x)
    else:
      x = common_layers.double_discriminator(x)
      x = tf.expand_dims(tf.expand_dims(x, axis=1), axis=1)
    x = tf.tanh(tf.layers.dense(x, hparams.bottleneck_bits, name="bottleneck"))
    d = x + tf.stop_gradient(2.0 * tf.to_float(tf.less(0.0, x)) - 1.0 - x)
    if hparams.mode == tf.estimator.ModeKeys.TRAIN:
