code: def test_multi_iter_gen_adv_neighbor_for_feature_columns_with_unused_v2(
      self,
  ):
      (
          x,
          y,
          w,
          expected_neighbor_fc1,
          expected_neighbor_fc2,
          adv_config,
      ) = (
          self._test_multi_iter_gen_adv_neighbor_for_feature_columns_with_unused_setup()
      )
  
      loss_fn = tf.math.squared_difference
      model_fn = lambda inp: tf.matmul(
          tf.concat([inp["fc1"], inp["fc2"]], axis=1), w
      )
      with tf.GradientTape() as tape:
          tape.watch(x)
          # Simple linear regression
          x_stacked = tf.concat([x["fc1"], x["fc2"]], axis=1)
          y_hat = tf.matmul(x_stacked, w)
          loss = tf.math.squared_difference(y, y_hat)
  
      adv_neighbor, _ = adv_lib.gen_adv_neighbor(
          x,
          loss,
          adv_config,
          gradient_tape=tape,
          pgd_model_fn=model_fn,
          pgd_loss_fn=loss_fn,
          pgd_labels=y,
      )
  
      actual_neighbor = self.evaluate(adv_neighbor)
      self.assertAllClose(expected_neighbor_fc1, actual_neighbor["fc1"])
      self.assertAllClose(expected_neighbor_fc2, actual_neighbor["fc2"])
      self.assertAllClose([[0.0]], actual_neighbor["fc_unused"])
  
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 16 for calling the GenAdvNeighborTest obj's underlying member method code with example usage: obj.test_multi_iter_gen_adv_neighbor_for_feature_columns_with_unused_v2() without return types