Residual unit with 2 sub layers.

def _residual(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 = _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