code: def test_gen_adv_neighbor_not_knowing_input_in_advance(self):
      @tf.function
      def _gen_adv_neighbor(x):
          w = tf.constant([[3.0], [4.0]])
          loss = tf.matmul(x, w)
          adv_config = configs.AdvNeighborConfig(
              feature_mask=None, adv_step_size=0.1, adv_grad_norm="l2"
          )
          adv_neighbor, _ = adv_lib.gen_adv_neighbor(
              x, loss, adv_config
          )
          return adv_neighbor
  
      x = tf.constant([[1.0, 1.0], [0.0, 0.0], [-1.0, -1.0]])
      actual_neighbor = self.evaluate(_gen_adv_neighbor(x))
  
      # gradient = w
      # perturbation = adv_step_size * normalize(gradient) = [0.06, 0.08]
      expected_neighbor = [[1.06, 1.08], [0.06, 0.08], [-0.94, -0.92]]
      self.assertAllClose(expected_neighbor, actual_neighbor)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import neural_structured_learning.configs
  from neural_structured_learning.lib import adversarial_neighbor
  import numpy
  import tensorflow
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 38 for calling the GenAdvNeighborTest obj's underlying member method code with example usage: obj.test_gen_adv_neighbor_not_knowing_input_in_advance() and returns: adv_neighbor