code: def create_attacker(
      attack_type: data_structures.AttackType,
      backend: Optional[str] = None,
  ) -> TrainedAttacker:
      """Returns the corresponding attacker for the provided attack_type."""
      # Compare by name instead of the variable itself to support module reload.
      if (
          attack_type.name
          == data_structures.AttackType.LOGISTIC_REGRESSION.name
      ):
          return LogisticRegressionAttacker(backend=backend)
      if (
          attack_type.name
          == data_structures.AttackType.MULTI_LAYERED_PERCEPTRON.name
      ):
          return MultilayerPerceptronAttacker(backend=backend)
      if (
          attack_type.name
          == data_structures.AttackType.RANDOM_FOREST.name
      ):
          return RandomForestAttacker(backend=backend)
      if (
          attack_type.name
          == data_structures.AttackType.K_NEAREST_NEIGHBORS.name
      ):
          return KNearestNeighborsAttacker(backend=backend)
      raise NotImplementedError(
          "Attack type %s not implemented yet." % attack_type
      )
  
imports: import contextlib
  import dataclasses
  import logging
  from typing import Optional
  import numpy
  from sklearn import ensemble
  from sklearn import linear_model
  from sklearn import model_selection
  from sklearn import neighbors
  from sklearn import neural_network
  from sklearn.utils import parallel_backend
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import data_structures
label_desc: Use image node_id 4 for calling a global function with example usage: create_attacker(attack_type, backend) and returns: LogisticRegressionAttacker, MultilayerPerceptronAttacker, RandomForestAttacker, KNearestNeighborsAttacker