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. 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.
docstr_tok: ["Train","the","attack","model",".",":","param","x",":","Input","to","training","process",".","Includes","all","features","used","to","train","the","original","model","."]
code: def fit(self, x: np.ndarray) -> None:
      """
      Train the attack model.
  
      :param x: Input to training process. Includes all features used to train the original model.
      """
  
      # 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
      y = x[:, self.attack_feature]
      y_ready = y
      if not self._is_continuous:
          self._values = get_feature_values(
              y, isinstance(self.attack_feature, int)
          )
          # number of values in case of single column, number of columns in case of multi-column feature
          nb_classes = len(self._values)
          if isinstance(self.attack_feature, int):
              y_one_hot = float_to_categorical(y)
          else:
              y_one_hot = floats_to_one_hot(y)
          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
      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 = x_train.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
  
          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, 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 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 AttributeInferenceBaseline obj's underlying member method code with example usage: obj.fit(x) and returns: self, self