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

est_apply_duration = sample_proc_est / self._SAMPLE_SIZE * self._obj.shape[0]

            # if pandas sample apply takes too long and not performing str processing, use dask
            if (est_apply_duration > self._dask_threshold) and allow_dask_processing:
                return self._dask_applymap(func)
            else:  # use pandas
                if self._progress_bar:
                    tqdm.pandas(desc=self._progress_bar_desc or "Pandas Apply")
                    applymap_func = self._obj.progress_applymap
                else:
                    applymap_func = self._obj.applymap

                return applymap_func(func)


class Transformation(_SwifterObject):
    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)

sample_proc_est = timed / N_REPEATS
            est_apply_duration = sample_proc_est / self._SAMPLE_SIZE * self._obj.shape[0]

            # if pandas sample apply takes too long and not performing str processing, use dask
            if (est_apply_duration > self._dask_threshold) and allow_dask_processing:
                return self._dask_apply(func, convert_dtype, *args, **kwds)
            else:  # use pandas
                if self._progress_bar:
                    tqdm.pandas(desc=self._progress_bar_desc or "Pandas Apply")
                    return self._obj.progress_apply(func, convert_dtype=convert_dtype, args=args, **kwds)
                else:
                    return self._obj.apply(func, convert_dtype=convert_dtype, args=args, **kwds)


@pd.api.extensions.register_dataframe_accessor("swifter")
class DataFrameAccessor(_SwifterObject):
    def _wrapped_apply(self, func, axis=0, raw=None, result_type=None, args=(), **kwds):
        def wrapped():
            with suppress_stdout_stderr():
                self._obj.iloc[: self._SAMPLE_SIZE, :].apply(
                    func, axis=axis, raw=raw, result_type=result_type, args=args, **kwds
                )

        return wrapped

    def _dask_apply(self, func, axis=0, raw=None, result_type=None, *args, **kwds):
        sample = self._obj.iloc[: self._npartitions * 2, :]
        with suppress_stdout_stderr():
            meta = sample.apply(func, axis=axis, raw=raw, result_type=result_type, args=args, **kwds)
        try:
            with suppress_stdout_stderr():
                # check that the dask apply matches the pandas apply

"on": on,
            "level": level,
        }
        return Resampler(
            self._obj,
            self._npartitions,
            self._dask_threshold,
            self._scheduler,
            self._progress_bar,
            self._progress_bar_desc,
            **kwds
        )


@pd.api.extensions.register_series_accessor("swifter")
class SeriesAccessor(_SwifterObject):
    def _wrapped_apply(self, func, convert_dtype=True, args=(), **kwds):
        def wrapped():
            with suppress_stdout_stderr():
                self._obj.iloc[: self._SAMPLE_SIZE].apply(func, convert_dtype=convert_dtype, args=args, **kwds)

        return wrapped

    def _dask_apply(self, func, convert_dtype, *args, **kwds):
        sample = self._obj.iloc[: self._npartitions * 2]
        with suppress_stdout_stderr():
            meta = sample.apply(func, convert_dtype=convert_dtype, args=args, **kwds)
        try:
            # check that the dask map partitions matches the pandas apply
            with suppress_stdout_stderr():
                tmp_df = (
                    dd.from_pandas(sample, npartitions=self._npartitions)