code: def _residual(x, in_filter, out_filter, stride,
                activate_before_residual=False):
    
    if activate_before_residual:
      with tf.variable_scope('shared_activation'):
        x = _batch_norm('init_bn', x)
        x = _relu(x, 0.1)
        orig_x = x
    else:
      with tf.variable_scope('residual_only_activation'):
        orig_x = x
        x = _batch_norm('init_bn', x)
        x = _relu(x, 0.1)
  
    with tf.variable_scope('sub1'):
      x = _conv('conv1', x, 3, in_filter, out_filter, stride)
  
    with tf.variable_scope('sub2'):
      x = _batch_norm('bn2', x)
      x = _relu(x, 0.1)
      x = _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
  
    tf.logging.debug('image after unit %s', x.get_shape())
    return x
docs: Residual unit with 2 sub layers.