code: def test_input_errors_randomized(params, expected_error_message):
      # tests specific to HalvingRandomSearchCV
  
      base_estimator = FastClassifier()
      param_grid = {"a": [1]}
      X, y = make_classification(100)
  
      sh = HalvingRandomSearchCV(base_estimator, param_grid, **params)
  
      with pytest.raises(ValueError, match=expected_error_message):
          sh.fit(X, y)
  
imports: from math import ceil
  import numpy
  import pytest
  from scipy.stats import expon, norm, randint
  from sklearn.datasets import make_classification
  from sklearn.dummy import DummyClassifier
  from sklearn.experimental import enable_halving_search_cv
  from sklearn.model_selection import GroupKFold, GroupShuffleSplit, HalvingGridSearchCV, HalvingRandomSearchCV, KFold, LeaveOneGroupOut, LeavePGroupsOut, ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit
  from sklearn.model_selection._search_successive_halving import _SubsampleMetaSplitter, _top_k
  from sklearn.model_selection.tests.test_search import check_cv_results_array_types, check_cv_results_keys
  from sklearn.svm import SVC, LinearSVC
label_desc: Use image node_id 9 for calling a global function with example usage: test_input_errors_randomized(params, expected_error_message) without return types