def METHOD_NAME():
    # test with sample_weight input
    cl = CleanLearning(verbose=1)
    cl.fit(X, y, sample_weight=np.random.random(size=len(y)))
    cl.fit(X, y, label_issues=cl.get_label_issues(), sample_weight=np.random.random(size=len(y)))
    # test with uncertainty input
    cl = CleanLearning()
    cl.find_label_issues(X, y, uncertainty=5)  # constant uncertainty
    cl.find_label_issues(X, y, uncertainty=np.random.random(size=len(y)))  # per-example uncertainty
    # test with not calculating uncertainty
    cl = CleanLearning(n_boot=0, include_aleatoric_uncertainty=False)
    cl.find_label_issues(X, y)
    # test with odd grid search sizes
    cl = CleanLearning()
    cl.find_label_issues(X, y, coarse_search_range=[0.2])
    cl.find_label_issues(X, y, fine_search_size=0)
    cl.fit(
        X, y, find_label_issues_kwargs={"coarse_search_range": [0.2, 0.1], "fine_search_size": 2}
    )