code: def get_attack_classifier_pt(num_features):
      """
      PyTorch classifier for testing membership inference attacks.
  
      :param num_features: The number of features in the attack model.
      :type num_features: `int`
      :return: Model for attack.
      :rtype: :class:`.PyTorchClassifier`
      """
      import torch.nn as nn
      import torch.optim as optim
  
      from art.estimators.classification.pytorch import (
          PyTorchClassifier,
      )
  
      class AttackModel(nn.Module):
          def __init__(self, num_features):
              super(AttackModel, self).__init__()
              self.layer = nn.Linear(num_features, 1)
              self.output = nn.Sigmoid()
  
          def forward(self, x):
              return self.output(self.layer(x))
  
      # Create model
      model = AttackModel(num_features)
  
      # Define a loss function and optimizer
      loss_fn = nn.BCELoss()
      optimizer = optim.Adam(model.parameters(), lr=0.0001)
      attack_model = PyTorchClassifier(
          model=model,
          loss=loss_fn,
          optimizer=optimizer,
          input_shape=(num_features,),
          nb_classes=2,
      )
  
      return attack_model
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import json
  import logging
  import os
  import pickle
  import time
  import unittest
  import warnings
  import sklearn
  import numpy
  from art.estimators.classification.tensorflow import TensorFlowV2Classifier
  from art.estimators.encoding.tensorflow import TensorFlowEncoder
  from art.estimators.generation.tensorflow import TensorFlowGenerator, TensorFlowV2Generator
  from art.estimators.gan.tensorflow import TensorFlowV2GAN
  from art.utils import load_dataset
label_desc: Use image node_id 34 for calling a global function with example usage: get_attack_classifier_pt(num_features) and returns: attack_model, self