code: def test_train_with_duplicated_metrics(self):
      w, x0, y0, lr, adv_config, _ = self._set_up_linear_regression()
  
      inputs = {"feature": tf.constant(x0), "label": tf.constant(y0)}
      model = build_linear_keras_functional_model(
          input_shape=(2,), weights=w
      )
      adv_model = adversarial_regularization.AdversarialRegularization(
          model, label_keys=["label"], adv_config=adv_config
      )
      adv_model.compile(
          optimizer=tf.keras.optimizers.SGD(lr),
          loss=["MSE"],
          metrics=[["MSE"]],
      )
      history = adv_model.fit(x=inputs, batch_size=1, steps_per_epoch=1)
  
      self.assertIn("mean_squared_error", history.history)
      self.assertIn("mean_squared_error_2", history.history)
      self.assertEqual(
          history.history["mean_squared_error"],
          history.history["mean_squared_error_2"],
      )
  
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 10 for calling the AdversarialRegularizationTest obj's underlying member method code with example usage: obj.test_train_with_duplicated_metrics() without return types