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 _check_params(self) -> None:
      super()._check_params()
  
      if not isinstance(self._is_continuous, bool):
          raise ValueError("is_continuous must be a boolean.")
  
      if self._attack_model_type not in [
          "nn",
          "rf",
          "gb",
          "lr",
          "dt",
          "knn",
          "svm",
      ]:
          raise ValueError(
              "Illegal value for parameter `attack_model_type`."
          )
  
      if self._non_numerical_features and (
          (not isinstance(self._non_numerical_features, list))
          or (
              not all(
                  isinstance(item, int)
                  for item in self._non_numerical_features
              )
          )
      ):
          raise ValueError(
              "non_numerical_features must be a list of int."
          )
  
      if self._encoder is not None and (
          not isinstance(self._encoder, OrdinalEncoder)
          and not isinstance(self._encoder, OneHotEncoder)
          and not isinstance(self._encoder, ColumnTransformer)
      ):
          raise ValueError(
              "encoder must be a OneHotEncoder, OrdinalEncoder or ColumnTransformer object."
          )
  
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 5 for calling the AttributeInferenceBaselineTrueLabel obj's underlying member method code with example usage: obj._check_params() without return types