def _residual(self, x, in_filter, out_filter, stride,
                activate_before_residual=False):
    """Residual unit with 2 sub layers."""
    if activate_before_residual:
      with tf.variable_scope('shared_activation'):
        x = self._layer_norm('init_bn', x)
        x = self._relu(x, self.hps.relu_leakiness)
        orig_x = x
    else:
      with tf.variable_scope('residual_only_activation'):
        orig_x = x
        x = self._layer_norm('init_bn', x)
        x = self._relu(x, self.hps.relu_leakiness)

    with tf.variable_scope('sub1'):
      x = self._conv('conv1', x, 3, in_filter, out_filter, stride)

    with tf.variable_scope('sub2'):
      x = self._layer_norm('bn2', x)
      x = self._relu(x, self.hps.relu_leakiness)
      x = self._conv('conv2', x, 3, out_filter, out_filter, [1, 1, 1, 1])

    with tf.variable_scope('sub_add'):
      if in_filter != out_filter:
        orig_x = tf.nn.avg_pool(orig_x, stride, stride, 'VALID')
        orig_x = tf.pad(
            orig_x, [[0, 0], [0, 0], [0, 0],
                     [(out_filter - in_filter) // 2,
                      (out_filter - in_filter) // 2]])
      x += orig_x

    return x
Residual unit with 2 sub layers.