code: def _get_multilabel_accuracy(preds: ArrayLike, labels: ArrayLike):
      """Computes the accuracy over multilabel data if it is missing.
  
      Compute multilabel binary accuracy. AUC is a better measure of model quality
      for multilabel classification than accuracy, in particular when the classes
      are imbalanced. For consistency with the single label classification case,
      we compute and return the binary accuracy over the labels and predictions.
  
      Args:
        preds: Prediction probabilities.
        labels: Ground truth multihot labels.
  
      Returns:
        The binary accuracy averaged across all labels.
      """
      if preds is None or labels is None:
          return None
      return _get_numpy_binary_accuracy(preds, labels)
  
imports: import logging
  from typing import Iterable, List, Optional, Union
  import numpy
  from scipy import special
  from sklearn import metrics
  from sklearn import model_selection
  from tensorflow_privacy.privacy.privacy_tests import epsilon_lower_bound
  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 AttackResults
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import DataSize
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import EPSILON_ALPHA
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import EPSILON_K
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import EPSILON_METHODS
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import EpsilonLowerBoundValue
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import MembershipProbabilityResults
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import PrivacyReportMetadata
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import RocCurve
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SingleAttackResult
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SingleMembershipProbabilityResult
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SingleSliceSpec
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SlicingSpec
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.dataset_slicing import get_single_slice_specs
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.dataset_slicing import get_slice
label_desc: Use image node_id 12 for calling a global function with example usage: _get_multilabel_accuracy(preds, labels) and returns: _get_numpy_binary_accuracy, None