code: def _residual(self, x, in_filter, out_filter, stride,
                  activate_before_residual=False):
      
      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
docs: Residual unit with 2 sub layers.