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.
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, 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 5 for calling the AttributeInferenceBaseline obj's underlying member method code with example usage: obj._check_params() without return types