code: def test_predict_by_adv_model_with_labels(self, model_fn):
      model = model_fn(
          input_shape=(2,), weights=np.array([[1.0], [-1.0]])
      )
      inputs = {
          "feature": tf.constant([[5.0, 3.0]]),
          "label": tf.constant([[1.0]]),
      }
  
      adv_model = adversarial_regularization.AdversarialRegularization(
          model
      )
      adv_model.compile(
          optimizer=tf.keras.optimizers.SGD(0.01), loss="MSE"
      )
  
      prediction = adv_model.predict(x=inputs, steps=1, batch_size=1)
      self.assertAllEqual([[2.0]], prediction)
  
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 1 for calling the AdversarialRegularizationTest obj's underlying member method code with example usage: obj.test_predict_by_adv_model_with_labels(model_fn) without return types