code: def _build_sklearn_operator_list():
      """
      Put all supported Sklearn operators on a list.
      """
      if sklearn_installed():
          # Tree-based models
          from sklearn.ensemble import (
              ExtraTreesClassifier,
              ExtraTreesRegressor,
              GradientBoostingClassifier,
              GradientBoostingRegressor,
              HistGradientBoostingClassifier,
              HistGradientBoostingRegressor,
              IsolationForest,
              RandomForestClassifier,
              RandomForestRegressor,
          )
  
          from sklearn.tree import (
              DecisionTreeClassifier,
              DecisionTreeRegressor,
          )
  
          # Linear-based models
          from sklearn.linear_model import (
              LinearRegression,
              LogisticRegression,
              LogisticRegressionCV,
              SGDClassifier,
              RidgeCV,
              ElasticNet,
              Ridge,
              Lasso,
              TweedieRegressor,
              PoissonRegressor,
              GammaRegressor,
          )
  
          # SVM-based models
          from sklearn.svm import LinearSVC, SVC, NuSVC, LinearSVR
  
          # Imputers
          from sklearn.impute import MissingIndicator, SimpleImputer
  
          # MLP Models
          from sklearn.neural_network import MLPClassifier, MLPRegressor
  
          # Naive Bayes Models
          from sklearn.naive_bayes import (
              BernoulliNB,
              GaussianNB,
              MultinomialNB,
          )
  
          # Matrix decomposition transformers
          from sklearn.decomposition import (
              PCA,
              KernelPCA,
              FastICA,
              TruncatedSVD,
          )
  
          # Cross decomposition
          from sklearn.cross_decomposition import PLSRegression
  
          # KNeighbors models
          from sklearn.neighbors import KNeighborsClassifier
          from sklearn.neighbors import KNeighborsRegressor
  
          # Clustering models
          from sklearn.cluster import KMeans, MeanShift
  
          # Model selection
          from sklearn.model_selection import (
              GridSearchCV,
              RandomizedSearchCV,
          )
  
          # Preprocessing
          from sklearn.preprocessing import (
              Binarizer,
              KBinsDiscretizer,
              LabelEncoder,
              MaxAbsScaler,
              MinMaxScaler,
              Normalizer,
              OneHotEncoder,
              PolynomialFeatures,
              RobustScaler,
              StandardScaler,
          )
  
          try:
              from sklearn.preprocessing import Imputer
          except ImportError:
              # Imputer was deprecate in sklearn >= 0.22
              Imputer = None
  
          # Features
          from sklearn.feature_selection import (
              SelectKBest,
              SelectPercentile,
              VarianceThreshold,
          )
  
          # Mixture models
          from sklearn.mixture import BayesianGaussianMixture
  
          supported_ops = [
              # Trees
              DecisionTreeClassifier,
              DecisionTreeRegressor,
              ExtraTreesClassifier,
              ExtraTreesRegressor,
              GradientBoostingClassifier,
              GradientBoostingRegressor,
              HistGradientBoostingClassifier,
              HistGradientBoostingRegressor,
              IsolationForest,
              OneHotEncoder,
              RandomForestClassifier,
              RandomForestRegressor,
              # Linear-methods
              LinearRegression,
              LinearSVC,
              LinearSVR,
              LogisticRegression,
              LogisticRegressionCV,
              SGDClassifier,
              RidgeCV,
              Lasso,
              ElasticNet,
              Ridge,
              TweedieRegressor,
              PoissonRegressor,
              GammaRegressor,
              # Clustering
              KMeans,
              MeanShift,
              # Other models
              BernoulliNB,
              GaussianNB,
              KNeighborsClassifier,
              KNeighborsRegressor,
              MLPClassifier,
              MLPRegressor,
              MultinomialNB,
              # SVM
              NuSVC,
              SVC,
              # Imputers
              Imputer,
              MissingIndicator,
              SimpleImputer,
              # Preprocessing
              Binarizer,
              KBinsDiscretizer,
              LabelEncoder,
              MaxAbsScaler,
              MinMaxScaler,
              Normalizer,
              PolynomialFeatures,
              RobustScaler,
              StandardScaler,
              # Matrix Decomposition
              FastICA,
              KernelPCA,
              PCA,
              TruncatedSVD,
              # Cross Decomposition
              PLSRegression,
              # Feature selection
              SelectKBest,
              SelectPercentile,
              VarianceThreshold,
              # Mixture models
              BayesianGaussianMixture,
          ]
  
          # Remove all deprecated operators given the sklearn version. E.g., Imputer for sklearn > 0.21.3.
          return [x for x in supported_ops if x is not None]
  
      return []
  
file_path: hummingbird/hummingbird/ml/supported.py
imports: from collections import defaultdict
  from .exceptions import MissingConverter
  from ._utils import torch_installed, sklearn_installed, lightgbm_installed, xgboost_installed, onnx_runtime_installed, tvm_installed, sparkml_installed, prophet_installed
label_desc: Use image node_id 1 for calling a global function with example usage: _build_sklearn_operator_list() and returns: list, unknown