code: def run_patch(model_info, dtype):
      print(get_class_name(model_info["model"]), dtype.__name__)
      X, y = generate_dataset(
          model_info["dataset"],
          dtype,
          get_class_name(model_info["model"]),
      )
      model = model_info["model"]
      model.fit(X, y)
      logging.info("fit")
      for i in model_info["methods"]:
          if i == "predict":
              model.predict(X)
          elif i == "predict_proba":
              model.predict_proba(X)
          elif i == "predict_log_proba":
              model.predict_log_proba(X)
          elif i == "decision_function":
              model.decision_function(X)
          elif i == "fit_predict":
              model.fit_predict(X)
          elif i == "transform":
              model.transform(X)
          elif i == "fit_transform":
              model.fit_transform(X)
          elif i == "kneighbors":
              model.kneighbors(X)
          elif i == "score":
              model.score(X, y)
          else:
              raise ValueError(i + " is wrong method")
          logging.info(i)
  
file_path: scikit-learn-intelex/sklearnex/tests/utils/_launch_algorithms.py
imports: import logging
  import random
  import numpy
  from sklearnex import patch_sklearn
  import pathlib
  import sys
  from sklearn.datasets import load_diabetes, load_iris, make_regression
  from sklearn.metrics import pairwise_distances, roc_auc_score
  from _models_info import MODELS_INFO, TYPES
label_desc: Use image node_id 3 for calling a global function with example usage: run_patch(model_info, dtype) without return types