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: Infer the attacked feature.
  
  :param x: Input to attack. Includes all features except the attacked feature.
  :param y: Not used in this attack.
  :param values: Possible values for attacked feature. For a single column feature this should be a simple list
                 containing all possible values, in increasing order (the smallest value in the 0 index and so
                 on). For a multi-column feature (for example 1-hot encoded and then scaled), this should be a
                 list of lists, where each internal list represents a column (in increasing order) and the values
                 represent the possible values for that column (in increasing order).
  :type values: list
  :return: The inferred feature values.
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code: def infer(
      self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs
  ) -> np.ndarray:
      """
      Infer the attacked feature.
  
      :param x: Input to attack. Includes all features except the attacked feature.
      :param y: Not used in this attack.
      :param values: Possible values for attacked feature. For a single column feature this should be a simple list
                     containing all possible values, in increasing order (the smallest value in the 0 index and so
                     on). For a multi-column feature (for example 1-hot encoded and then scaled), this should be a
                     list of lists, where each internal list represents a column (in increasing order) and the values
                     represent the possible values for that column (in increasing order).
      :type values: list
      :return: The inferred feature values.
      """
      values = kwargs.get("values")
  
      # if provided, override the values computed in fit()
      if values is not None:
          self._values = values
  
      x_test = x
      if self._encoder is not None:
          x_test = self._encoder.transform(x)
      x_test = x_test.astype(np.float32)
  
      if self._attack_model_type == "nn":
          from torch.utils.data import DataLoader
          from art.utils import to_cuda, from_cuda
  
          self.attack_model.eval()  # type: ignore
          predictions: np.ndarray = np.array([])
          test_set = self._get_attack_dataset(feature=x_test)
          test_loader = DataLoader(
              test_set,
              batch_size=self.batch_size,
              shuffle=False,
              num_workers=0,
          )
          for input1, _ in test_loader:
              input1 = to_cuda(input1)
              outputs = self.attack_model(input1)  # type: ignore
              predicted = from_cuda(outputs)
  
              if np.size(predictions) == 0:
                  predictions = predicted.detach().numpy()
              else:
                  predictions = np.vstack(
                      (predictions, predicted.detach().numpy())
                  )
              if not self._is_continuous:
                  idx = np.argmax(predictions, axis=-1)
                  predictions = np.zeros(predictions.shape)
                  predictions[np.arange(predictions.shape[0]), idx] = 1
      elif self.attack_model is not None:
          predictions = self.attack_model.predict(x_test)
      if predictions is not None:
          predictions = predictions.astype(np.float32)
  
      if not self._is_continuous and self._values:
          if isinstance(self.attack_feature, int):
              # replace 1-hot encoded prediction with correct single feature value
              if self._attack_model_type in ("gb", "lr", "svm"):
                  indexes = predictions
              else:
                  indexes = np.argmax(predictions, axis=1)
              predictions = np.array(
                  [self._values[int(index)] for index in indexes]
              )
          else:
              if self._attack_model_type in ("gb", "lr", "svm"):
                  predictions = check_and_transform_label_format(
                      predictions,
                      nb_classes=len(self._values),
                      return_one_hot=True,
                  )
              i = 0
              # replace 1-hot encoded prediction with multi-column feature value
              for column in predictions.T:
                  # possible values for ith column
                  for index in range(len(self._values[i])):
                      np.place(
                          column,
                          [column == index],
                          self._values[i][index],
                      )
                  i += 1
      return np.array(predictions)
  
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 3 for calling the AttributeInferenceBaseline obj's underlying member method code with example usage: obj.infer(x, y) and returns: np