code: def _run_attack(
      attack_input: AttackInputData,
      attack_type: AttackType,
      balance_attacker_training: bool = True,
      min_num_samples: int = 1,
      backend: Optional[str] = None,
  ):
      """Runs membership inference attacks for specified input and type.
  
      Args:
        attack_input: input data for running an attack
        attack_type: the attack to run
        balance_attacker_training: Whether the training and test sets for the
          membership inference attacker should have a balanced (roughly equal)
          number of samples from the training and test sets used to develop the
          model under attack.
        min_num_samples: minimum number of examples in either training or test data.
        backend: The Scikit-Learn/Joblib backend to use for model training, defaults
          to `None`, which will use single-threaded training. Note that some systems
          may not support multiprocessing and in those cases the `threading` backend
          should be used. See https://joblib.readthedocs.io/en/latest/parallel.html
          for more details.
  
      Returns:
        the attack result.
      """
      attack_input.validate()
      if (
          min(
              attack_input.get_train_size(),
              attack_input.get_test_size(),
          )
          < min_num_samples
      ):
          return None
  
      if attack_type.is_trained_attack:
          return _run_trained_attack(
              attack_input,
              attack_type,
              balance_attacker_training,
              backend=backend,
          )
      if attack_type == AttackType.THRESHOLD_ENTROPY_ATTACK:
          return _run_threshold_entropy_attack(attack_input)
      return _run_threshold_attack(attack_input)
  
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 5 for calling a global function with example usage: _run_attack(attack_input, attack_type, balance_attacker_training, min_num_samples, backend) and returns: _run_threshold_attack, None, _run_trained_attack, _run_threshold_entropy_attack