code: def _test_multi_iter_gen_adv_neighbor_for_feature_columns_with_int_feature(
      self,
  ):
      # For linear regression
      x = {
          "fc1": tf.constant([[-1.0]]),
          "fc2": tf.constant([[1.0]]),
          "fc_int": tf.constant([[2]], dtype=tf.dtypes.int32),
      }
      y = tf.constant([0.0])
      w = tf.constant([[3.0], [4.0], [1.0]])
      # Two iterations of 0.6 * 0.1, clipped.
      expected_neighbor_fc1 = [[-1.0 + 0.13 * 0.6]]
      # Two iterations of 0.8 * 0.1, clipped.
      expected_neighbor_fc2 = [[1.0 + 0.13 * 0.8]]
      return x, y, w, expected_neighbor_fc1, expected_neighbor_fc2
  
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 20 for calling the GenAdvNeighborTest obj's underlying member method code with example usage: obj._test_multi_iter_gen_adv_neighbor_for_feature_columns_with_int_feature() and returns: x,  y,  w,  expected_neighbor_fc1,  expected_neighbor_fc2