code: def test_accuracy_runner():
      # Set up data that should deliver accuracy of 0.20 if all goes right
      class MockAlgo:
          def fit(self, X, y):
              return
  
          def predict(self, X):
              nr = X.shape[0]
              res = np.zeros(nr)
              res[0 : int(nr / 5.0)] = 1.0
              return res
  
      pair = algorithms.AlgorithmPair(
          MockAlgo,
          MockAlgo,
          shared_args={},
          name="Mock",
          bench_func=fit_predict,
          accuracy_function=metrics.accuracy_score,
      )
  
      runner = AccuracyComparisonRunner(
          [20], [5], dataset_name="zeros", test_fraction=0.20
      )
      results = runner.run(pair)[0]
  
      assert results["cuml_acc"] == pytest.approx(0.80)
  
imports: from cuml.benchmark.bench_helper_funcs import fit, fit_predict
  import time
  from sklearn import metrics
  from cuml.internals.safe_imports import gpu_only_import_from
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
  from cuml.benchmark import datagen, algorithms
  from cuml.benchmark.bench_helper_funcs import _training_data_to_numpy
  from cuml.benchmark.runners import AccuracyComparisonRunner, SpeedupComparisonRunner, run_variations
  from cuml.internals.import_utils import has_umap
  from cuml.internals.import_utils import has_xgboost
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 7 for calling a global function with example usage: test_accuracy_runner() and returns: res