class_docstr: Implementation of a baseline attribute inference, not using a model.
  
  The idea is to train a simple neural network to learn the attacked feature from the rest of the features, and the
  true label. Should be used to compare with other attribute inference results.
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.classification.classifier import ClassifierMixin
  from art.estimators.regression import RegressorMixin
  from art.attacks.attack import AttributeInferenceAttack
  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 AttributeInferenceBaselineTrueLabel obj's underlying member method code with example usage: obj._get_attack_dataset(feature, label) and returns: AttackDataset, len, self,  self