code: def test_scores_are_equivalent(estimator_cls):
      try:
          lk, rk = ("with_h2o_frames", "with_numpy_arrays")
          est_scores = scores[estimator_cls]
          if lk in est_scores and rk in est_scores:
              assert (
                  abs(est_scores[lk] - abs(est_scores[rk])) < 1e-6
              ), "expected equivalent scores but got {lk}={lscore} and {rk}={rscore}".format(
                  lk=lk,
                  rk=rk,
                  lscore=est_scores[lk],
                  rscore=est_scores[rk],
              )
          elif lk not in est_scores:
              print(
                  "no scores for {}".format(
                      estimator_cls.__name__ + " " + lk
                  )
              )
          else:
              print(
                  "no scores for {}".format(
                      estimator_cls.__name__ + " " + rk
                  )
              )
      except AssertionError as e:
          if _get_custom_behaviour(estimator_cls).get(
              "scores_may_differ", False
          ):
              print("ERROR !!! " + str(e))
          else:
              raise e
  
file_path: h2o-3/h2o-py/tests/testdir_sklearn/pyunit_sklearn_classification_all_estimators.py
imports: from collections import defaultdict
  from functools import partial
  import gc, inspect, os, sys
  import numpy
  from sklearn.datasets import make_classification
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  import h2o
  from h2o.sklearn.wrapper import H2OConnectionMonitorMixin
  from tests import pyunit_utils, Namespace
label_desc: Use image node_id 6 for calling a global function with example usage: test_scores_are_equivalent(estimator_cls) without return types