code: def test_multilabel_create_attacker_data_logits_labels_sample_weights(
      self,
  ):
      attack_input = AttackInputData(
          logits_train=np.array([[1, 2], [5, 6], [8, 9]]),
          logits_test=np.array([[10, 11], [14, 15]]),
          labels_train=np.array([[0, 1], [1, 1], [1, 0]]),
          labels_test=np.array([[1, 0], [1, 1]]),
          sample_weight_train=np.array([1.0, 0.5, 0.0]),
          sample_weight_test=np.array([1.0, 0.5]),
      )
      attacker_data = models.create_attacker_data(
          attack_input, balance=False
      )
      self.assertLen(attacker_data.features_all, 5)
      self.assertLen(attacker_data.fold_indices, 5)
      self.assertEmpty(attacker_data.left_out_indices)
      self.assertTrue(
          attack_input.is_multilabel_data(),
          msg="Expected multilabel check to pass.",
      )
      self.assertTrue(
          attack_input.has_nonnull_sample_weights(),
          msg="Expected to have sample weights.",
      )
  
file_path: privacy/tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/models_test.py
imports: from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import models
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackInputData
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType
label_desc: Use image node_id 6 for calling the TrainedAttackerTest obj's underlying member method code with example usage: obj.test_multilabel_create_attacker_data_logits_labels_sample_weights() without return types