def METHOD_NAME():
    data = pd.DataFrame(
        {
            "most_sparse": [float(x) for x in range(10)],  # [0,1,2,3,4,5,6,7,8,9]
            "more_sparse": [x % 5 for x in range(10)],  # [0,1,2,3,4,0,1,2,3,4]
            "sparse": [x % 3 for x in range(10)],  # [0,1,2,0,1,2,0,1,2,0]
            "less_sparse": [x % 2 for x in range(10)],  # [0,1,0,1,0,1,0,1,0,1]
            "not_sparse": [float(1) for x in range(10)],
        },
    )  # [1,1,1,1,1,1,1,1,1,1]
    sparsity_check = SparsityDataCheck(
        problem_type="multiclass",
        threshold=0.4,
        unique_count_threshold=3,
    )
    assert sparsity_check.validate(data) == [
        DataCheckWarning(
            message="Input columns ('most_sparse', 'more_sparse', 'sparse') for multiclass problem type are too sparse.",
            data_check_name=sparsity_data_check_name,
            message_code=DataCheckMessageCode.TOO_SPARSE,
            details={
                "columns": ["most_sparse", "more_sparse", "sparse"],
                "sparsity_score": {
                    "most_sparse": 0,
                    "more_sparse": 0,
                    "sparse": 0.3333333333333333,
                },
            },
            action_options=[
                DataCheckActionOption(
                    DataCheckActionCode.DROP_COL,
                    data_check_name=sparsity_data_check_name,
                    metadata={"columns": ["most_sparse", "more_sparse", "sparse"]},
                ),
            ],
        ).to_dict(),
    ]