def METHOD_NAME(
    data, norm, use_idf, smooth_idf, sublinear_tf, client
):
    # Testing across multiple-n_parts
    for n_parts in range(1, data.shape[0]):
        dask_sp_array = create_cp_sparse_dask_array(data, n_parts)
        tfidf = TfidfTransformer(
            norm=norm,
            use_idf=use_idf,
            smooth_idf=smooth_idf,
            sublinear_tf=sublinear_tf,
        )
        sk_tfidf = SkTfidfTransformer(
            norm=norm,
            use_idf=use_idf,
            smooth_idf=smooth_idf,
            sublinear_tf=sublinear_tf,
        )
        res = tfidf.fit_transform(dask_sp_array)
        res = create_scipy_sparse_array_from_dask_cp_sparse_array(
            res
        ).todense()
        ref = sk_tfidf.fit_transform(data).todense()
        cp.testing.assert_array_almost_equal(res, ref)