code: 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
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from cleverhans.serial import NoRefModel
label_desc: Use image node_id 3 for calling a global function with example usage: _residual(x, in_filter, out_filter, stride, activate_before_residual) and returns: x