code: def func(X, Y, clf, methods):
      clf.fit(X, Y)
      res, name = method_processing(X, clf, methods)
  
      for i in clf.__dict__.keys():
          ans = getattr(clf, i)
          if isinstance(
              ans, (bool, float, int, np.ndarray, np.float64)
          ):
              if isinstance(ans, np.ndarray) and None in ans:
                  continue
              res.append(ans)
              name.append(get_class_name(clf) + "." + i)
      return res, name
  
imports: import random
  import numpy
  import pytest
  import daal4py
  from sklearnex import patch_sklearn
  from scipy import sparse
  from sklearn.cluster import DBSCAN, KMeans
  from sklearn.datasets import load_breast_cancer, load_diabetes, load_iris, make_classification, make_regression
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from sklearn.linear_model import ElasticNet, Lasso, LinearRegression, LogisticRegression, LogisticRegressionCV, Ridge
  from sklearn.manifold import TSNE
  from sklearn.metrics import pairwise_distances, roc_auc_score
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor, LocalOutlierFactor, NearestNeighbors
  from sklearn.svm import SVC, SVR, NuSVC, NuSVR
  from daal4py.sklearn._utils import daal_check_version
label_desc: Use image node_id 3 for calling a global function with example usage: func(X, Y, clf, methods) and returns: res,  name