code: def run_utils():
      # pairwise_distances
      for metric in ["cosine", "correlation"]:
          for t in TYPES:
              X = np.random.rand(1000)
              X = np.array(X, dtype=t)
              print("pairwise_distances", t.__name__)
              _ = pairwise_distances(X.reshape(1, -1), metric=metric)
              logging.info("pairwise_distances")
      # roc_auc_score
      for t in [np.float32, np.float64]:
          a = [random.randint(0, 1) for i in range(1000)]
          b = [random.randint(0, 1) for i in range(1000)]
          a = np.array(a, dtype=t)
          b = np.array(b, dtype=t)
          print("roc_auc_score", t.__name__)
          _ = roc_auc_score(a, b)
          logging.info("roc_auc_score")
  
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 5 for calling a global function with example usage: run_utils() without return types