code: def test_gen_adv_neighbor_for_feature_columns_with_int_feature_v2(
      self,
  ):
      (
          x,
          y,
          w,
          expected_neighbor_fc1,
          expected_neighbor_fc2,
      ) = (
          self._test_gen_adv_neighbor_for_feature_columns_with_int_feature()
      )
  
      with tf.GradientTape() as tape:
          tape.watch(x)
          # Simple linear regression.
          x_stacked = tf.concat(
              [
                  x["fc1"],
                  x["fc2"],
                  tf.cast(x["fc_int"], tf.dtypes.float32),
              ],
              axis=1,
          )
          y_hat = tf.matmul(x_stacked, w)
          loss = tf.math.squared_difference(y, y_hat)
  
      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, gradient_tape=tape
      )
  
      actual_neighbor = self.evaluate(adv_neighbor)
      self.assertAllClose(expected_neighbor_fc1, actual_neighbor["fc1"])
      self.assertAllClose(expected_neighbor_fc2, actual_neighbor["fc2"])
      self.assertAllClose([[2]], actual_neighbor["fc_int"])
  
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 19 for calling the GenAdvNeighborTest obj's underlying member method code with example usage: obj.test_gen_adv_neighbor_for_feature_columns_with_int_feature_v2() without return types