class_docstr: Implementation of a simple black-box attribute inference attack.
  
  The idea is to train a simple neural network to learn the attacked feature from the rest of the features and the
  model's predictions. Assumes the availability of the attacked model's predictions for the samples under attack,
  in addition to the rest of the feature values. If this is not available, the true class label of the samples may be
  used as a proxy.
class_docstr_tok: ["Implementation","of","a","simple","black-box","attribute","inference","attack",".","The","idea","is","to","train","a","simple","neural","network","to","learn","the","attacked","feature","from","the","rest","of","the","features","and","the","model","'s","predictions",".","Assumes","the","availability","of","the","attacked","model","'s","predictions","for","the","samples","under","attack",",","in","addition","to","the","rest","of","the","feature","values",".","If","this","is","not","available",",","the","true","class","label","of","the","samples","may","be","used","as","a","proxy","."]
code: def _get_attack_dataset(self, feature, label=None):
      from torch.utils.data.dataset import Dataset
  
      class AttackDataset(Dataset):
          """
          Implementation of a pytorch dataset for membership inference attack.
  
          The features are probabilities/logits or losses for the attack training data (`x_1`) along with
          its true labels (`x_2`). The labels (`y`) are a boolean representing whether this is a member.
          """
  
          def __init__(self, x, y=None):
              import torch
  
              self.x = torch.from_numpy(x.astype(np.float64)).type(
                  torch.FloatTensor
              )
  
              if y is not None:
                  self.y = torch.from_numpy(y.astype(np.float32)).type(
                      torch.FloatTensor
                  )
              else:
                  self.y = torch.zeros(x.shape[0])
  
          def __len__(self):
              return len(self.x)
  
          def __getitem__(self, idx):
              if idx >= len(self.x):  # pragma: no cover
                  raise IndexError("Invalid Index")
  
              return self.x[idx], self.y[idx]
  
      return AttackDataset(x=feature, y=label)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Union, Tuple, List, Any, TYPE_CHECKING
  import numpy
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor
  from sklearn.linear_model import LogisticRegression, LinearRegression
  from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.svm import SVC, SVR
  from sklearn.preprocessing import minmax_scale, OneHotEncoder, OrdinalEncoder
  from sklearn.compose import ColumnTransformer
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.attacks.attack import AttributeInferenceAttack
  from art.estimators.regression import RegressorMixin
  from art.utils import check_and_transform_label_format, float_to_categorical, floats_to_one_hot, get_feature_values, remove_attacked_feature
label_desc: Use image node_id 4 for calling the AttributeInferenceBlackBox obj's underlying member method code with example usage: obj._get_attack_dataset(feature, label) and returns: AttackDataset, len, self,  self