code: def test_with_model_kwargs(self):
      w = np.array([[4.0], [-3.0]])
      x0 = np.array([[2.0, 3.0]])
      y0 = np.array([[0.0]])
      model = build_linear_keras_sequential_model(
          input_shape=(2,), weights=w
      )
      model.add(tf.keras.layers.BatchNormalization())
  
      adv_loss = adversarial_regularization.adversarial_loss(
          features={"feature": tf.constant(x0)},
          labels=tf.constant(y0),
          model=model,
          loss_fn=tf.keras.losses.MeanSquaredError(),
          adv_config=self.adv_config,
          model_kwargs={"training": True},
      )
      # BatchNormalization returns 0 for signle-example batch when training=True.
      self.assertAllClose(0.0, self.evaluate(adv_loss))
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  from absl.testing import parameterized
  import neural_structured_learning.configs
  from neural_structured_learning.keras import adversarial_regularization
  import numpy
  import tensorflow
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 7 for calling the AdversarialLossTest obj's underlying member method code with example usage: obj.test_with_model_kwargs() without return types