def METHOD_NAME():
    s = pd.Series(list("abca"))
    with warns_that_defaulting_to_pandas():
        pd.get_dummies(s)
    s1 = ["a", "b", np.nan]
    with warns_that_defaulting_to_pandas():
        pd.get_dummies(s1)
    with warns_that_defaulting_to_pandas():
        pd.get_dummies(s1, dummy_na=True)
    data = {"A": ["a", "b", "a"], "B": ["b", "a", "c"], "C": [1, 2, 3]}
    modin_df = pd.DataFrame(data)
    pandas_df = pandas.DataFrame(data)
    modin_result = pd.get_dummies(modin_df, prefix=["col1", "col2"])
    pandas_result = pandas.get_dummies(pandas_df, prefix=["col1", "col2"])
    df_equals(modin_result, pandas_result)
    assert modin_result._to_pandas().columns.equals(pandas_result.columns)
    assert modin_result.shape == pandas_result.shape
    modin_result = pd.get_dummies(pd.DataFrame(pd.Series(list("abcdeabac"))))
    pandas_result = pandas.get_dummies(
        pandas.DataFrame(pandas.Series(list("abcdeabac")))
    )
    df_equals(modin_result, pandas_result)
    assert modin_result._to_pandas().columns.equals(pandas_result.columns)
    assert modin_result.shape == pandas_result.shape
    with pytest.raises(NotImplementedError):
        pd.get_dummies(modin_df, prefix=["col1", "col2"], sparse=True)
    with warns_that_defaulting_to_pandas():
        pd.get_dummies(pd.Series(list("abcaa")))
    with warns_that_defaulting_to_pandas():
        pd.get_dummies(pd.Series(list("abcaa")), drop_first=True)
    with warns_that_defaulting_to_pandas():
        pd.get_dummies(pd.Series(list("abc")), dtype=float)
    with warns_that_defaulting_to_pandas():
        pd.get_dummies(1)
    # test from #5184
    pandas_df = pandas.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": ["7", "8", "9"]})
    modin_df = pd.DataFrame(pandas_df)
    pandas_result = pandas.get_dummies(pandas_df, columns=["a", "b"])
    modin_result = pd.get_dummies(modin_df, columns=["a", "b"])
    df_equals(modin_result, pandas_result)