How to use the swifter.swifter.Transformation function in swifter

timed = timeit.timeit(wrapped, number=N_REPEATS)
        sample_proc_est = timed / N_REPEATS
        est_apply_duration = sample_proc_est / self._SAMPLE_SIZE * self._nrows

        # No `allow_dask_processing` variable here, because we don't know the dtypes of the transformation
        if est_apply_duration > self._dask_threshold:
            return self._dask_apply(func, *args, **kwds)
        else:  # use pandas
            if self._progress_bar and hasattr(self._obj_pd, "progress_apply"):
                tqdm.pandas(desc=self._progress_bar_desc or "Pandas Apply")
                return self._obj_pd.progress_apply(func, *args, **kwds)
            else:
                return self._obj_pd.apply(func, *args, **kwds)


class Rolling(Transformation):
    def __init__(
        self,
        pandas_obj,
        npartitions=None,
        dask_threshold=1,
        scheduler="processes",
        progress_bar=True,
        progress_bar_desc=None,
        allow_dask_on_strings=False,
        **kwds
    ):
        super(Rolling, self).__init__(
            pandas_obj, npartitions, dask_threshold, scheduler, progress_bar, progress_bar_desc, allow_dask_on_strings
        )
        self._rolling_kwds = kwds.copy()
        self._comparison_pd = self._obj_pd.iloc[: self._npartitions * 2]

def __init__(
        self,
        pandas_obj,
        npartitions=None,
        dask_threshold=1,
        scheduler="processes",
        progress_bar=True,
        progress_bar_desc=None,
        allow_dask_on_strings=False,
    ):
        super(Transformation, self).__init__(
            pandas_obj, npartitions, dask_threshold, scheduler, progress_bar, progress_bar_desc, allow_dask_on_strings
        )
        self._sample_pd = pandas_obj.iloc[: self._SAMPLE_SIZE]
        self._obj_pd = pandas_obj
        self._obj_dd = dd.from_pandas(pandas_obj, npartitions=npartitions)
        self._nrows = pandas_obj.shape[0]

error_message="Dask rolling apply sample does not match pandas rolling apply sample.",
                )
            if self._progress_bar:
                with TQDMDaskProgressBar(desc=self._progress_bar_desc or "Dask Apply"):
                    return self._obj_dd.apply(func, *args, **kwds).compute(scheduler=self._scheduler)
            else:
                return self._obj_dd.apply(func, *args, **kwds).compute(scheduler=self._scheduler)
        except ERRORS_TO_HANDLE:
            if self._progress_bar:
                tqdm.pandas(desc=self._progress_bar_desc or "Pandas Apply")
                return self._obj_pd.progress_apply(func, *args, **kwds)
            else:
                return self._obj_pd.apply(func, *args, **kwds)


class Resampler(Transformation):
    def __init__(
        self,
        pandas_obj,
        npartitions=None,
        dask_threshold=1,
        scheduler="processes",
        progress_bar=True,
        progress_bar_desc=None,
        allow_dask_on_strings=False,
        **kwds
    ):
        super(Resampler, self).__init__(
            pandas_obj, npartitions, dask_threshold, scheduler, progress_bar, progress_bar_desc, allow_dask_on_strings
        )
        self._resampler_kwds = kwds.copy()
        self._comparison_pd = self._obj_pd.iloc[: self._npartitions * 2]