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.
docstr: Train the attack model.
  
  :param x: Input to training process. Includes all features used to train the original model.
  :param y: True labels of the features.
docstr_tok: ["Train","the","attack","model",".",":","param","x",":","Input","to","training","process",".","Includes","all","features","used","to","train","the","original","model",".",":","param","y",":","True","labels","of","the","features","."]
code: def fit(self, x: np.ndarray, y: np.ndarray) -> None:
      """
      Train the attack model.
  
      :param x: Input to training process. Includes all features used to train the original model.
      :param y: True labels of the features.
      """
  
      # Checks:
      if (
          isinstance(self.attack_feature, int)
          and self.attack_feature >= x.shape[1]
      ):
          raise ValueError(
              "attack_feature must be a valid index to a feature in x"
          )
  
      # get vector of attacked feature
      attacked_feature = x[:, self.attack_feature]
      y_ready = attacked_feature
      if not self._is_continuous:
          self._values = get_feature_values(
              attacked_feature, isinstance(self.attack_feature, int)
          )
          nb_classes = len(self._values)
          if isinstance(self.attack_feature, int):
              y_one_hot = float_to_categorical(attacked_feature)
          else:
              y_one_hot = floats_to_one_hot(attacked_feature)
          y_ready = check_and_transform_label_format(
              y_one_hot, nb_classes=nb_classes, return_one_hot=True
          )
          if y_ready is None:
              raise ValueError("None value detected.")
          if self._attack_model_type in ("gb", "lr", "svm"):
              y_ready = np.argmax(y_ready, axis=1)
  
      # create training set for attack model
      if self.is_regression:
          if self.scale_range is not None:
              normalized_labels = minmax_scale(
                  y, feature_range=self.scale_range
              )
          else:
              normalized_labels = y * self.prediction_normal_factor
          normalized_labels = normalized_labels.reshape(-1, 1)
      else:
          normalized_labels = check_and_transform_label_format(
              y, nb_classes=None, return_one_hot=True
          )
  
      x_train = np.delete(x, self.attack_feature, 1)
  
      if self._non_numerical_features and self._encoder is None:
          if isinstance(self.attack_feature, int):
              compare_index = self.attack_feature
              size = 1
          else:
              compare_index = self.attack_feature.start
              size = (
                  self.attack_feature.stop - self.attack_feature.start
              ) // self.attack_feature.step
          new_indexes = [
              (f - size) if f > compare_index else f
              for f in self._non_numerical_features
          ]
          categorical_transformer = OrdinalEncoder()
          self._encoder = ColumnTransformer(
              transformers=[
                  ("cat", categorical_transformer, new_indexes),
              ],
              remainder="passthrough",
          )
          self._encoder.fit(x_train)
  
      # train attack model
      if self._encoder is not None:
          x_train = self._encoder.transform(x_train)
      x_train = np.concatenate(
          (x_train, normalized_labels), axis=1
      ).astype(np.float32)
  
      if self._attack_model_type == "nn":
          import torch
          from torch import nn
          from torch import optim
          from torch.utils.data import DataLoader
          from art.utils import to_cuda
  
          self.epochs = 100
          self.batch_size = 100
          self.learning_rate = 0.0001
  
          if self._is_continuous:
  
              class MembershipInferenceAttackModelRegression(nn.Module):
                  """
                  Implementation of a pytorch model for learning a membership inference attack.
  
                  The features used are probabilities/logits or losses for the attack training data along with
                  its true labels.
                  """
  
                  def __init__(self, num_features):
                      self.num_features = num_features
  
                      super().__init__()
  
                      self.features = nn.Sequential(
                          nn.Linear(self.num_features, 100),
                          nn.ReLU(),
                          nn.Linear(100, 64),
                          nn.ReLU(),
                          nn.Linear(64, 1),
                      )
  
                  def forward(self, x):
                      """Forward the model."""
                      return self.features(x)
  
              self.attack_model = (
                  MembershipInferenceAttackModelRegression(
                      x_train.shape[1]
                  )
              )
              loss_fn: Any = nn.MSELoss()
          else:
  
              class MembershipInferenceAttackModel(nn.Module):
                  """
                  Implementation of a pytorch model for learning an attribute inference attack.
  
                  The features used are the remaining n-1 features of the attack training data along with
                  the model's predictions.
                  """
  
                  def __init__(self, num_features, num_classes):
                      self.num_classes = num_classes
                      self.num_features = num_features
  
                      super().__init__()
  
                      self.features = nn.Sequential(
                          nn.Linear(self.num_features, 512),
                          nn.ReLU(),
                          nn.Linear(512, 100),
                          nn.ReLU(),
                          nn.Linear(100, 64),
                          nn.ReLU(),
                          nn.Linear(64, num_classes),
                      )
  
                      self.output = nn.Softmax()
  
                  def forward(self, x):
                      """Forward the model."""
                      out = self.features(x)
                      return self.output(out)
  
              self.attack_model = MembershipInferenceAttackModel(
                  x_train.shape[1], len(self._values)
              )
              loss_fn = nn.CrossEntropyLoss()
  
          optimizer = optim.Adam(self.attack_model.parameters(), lr=self.learning_rate)  # type: ignore
  
          attack_train_set = self._get_attack_dataset(
              feature=x_train, label=y_ready
          )
          train_loader = DataLoader(
              attack_train_set,
              batch_size=self.batch_size,
              shuffle=True,
              num_workers=0,
          )
  
          self.attack_model = to_cuda(self.attack_model)  # type: ignore
          self.attack_model.train()  # type: ignore
  
          for _ in range(self.epochs):
              for input1, targets in train_loader:
                  input1, targets = to_cuda(input1), to_cuda(targets)
                  _, targets = torch.autograd.Variable(
                      input1
                  ), torch.autograd.Variable(targets)
  
                  optimizer.zero_grad()
                  outputs = self.attack_model(input1)  # type: ignore
                  loss = loss_fn(outputs, targets)
                  loss.backward()
                  optimizer.step()
      elif self.attack_model is not None:
          self.attack_model.fit(x_train, y_ready)
  
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 2 for calling the AttributeInferenceBaselineTrueLabel obj's underlying member method code with example usage: obj.fit(x, y) and returns: self, self