code: def method_processing(X, clf, methods):
      res = []
      name = []
      for i in methods:
          if i == "predict":
              res.append(clf.predict(X))
              name.append(get_class_name(clf) + ".predict(X)")
          elif i == "predict_proba":
              res.append(clf.predict_proba(X))
              name.append(get_class_name(clf) + ".predict_proba(X)")
          elif i == "decision_function":
              res.append(clf.decision_function(X))
              name.append(get_class_name(clf) + ".decision_function(X)")
          elif i == "kneighbors":
              dist, idx = clf.kneighbors(X)
              res.append(dist)
              name.append("dist")
              res.append(idx)
              name.append("idx")
          elif i == "fit_predict":
              predict = clf.fit_predict(X)
              res.append(predict)
              name.append(get_class_name(clf) + ".fit_predict")
          elif i == "fit_transform":
              res.append(clf.fit_transform(X))
              name.append(get_class_name(clf) + ".fit_transform")
          elif i == "transform":
              res.append(clf.transform(X))
              name.append(get_class_name(clf) + ".transform(X)")
          elif i == "get_covariance":
              res.append(clf.get_covariance())
              name.append(get_class_name(clf) + ".get_covariance()")
          elif i == "get_precision":
              res.append(clf.get_precision())
              name.append(get_class_name(clf) + ".get_precision()")
          elif i == "score_samples":
              res.append(clf.score_samples(X))
              name.append(get_class_name(clf) + ".score_samples(X)")
      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 2 for calling a global function with example usage: method_processing(X, clf, methods) and returns: res,  name