How to use the swifter.tqdm_dask_progressbar.TQDMDaskProgressBar function in swifter

with TQDMDaskProgressBar(desc=self._progress_bar_desc or "Dask Apply"):
                    return (
                        dd.from_pandas(self._obj, npartitions=self._npartitions)
                        .map_partitions(func, *args, meta=meta, **kwds)
                        .compute(scheduler=self._scheduler)
                    )
            else:
                return (
                    dd.from_pandas(self._obj, npartitions=self._npartitions)
                    .map_partitions(func, *args, meta=meta, **kwds)
                    .compute(scheduler=self._scheduler)
                )
        except ERRORS_TO_HANDLE:
            # if map partitions doesn't match pandas apply, we can use dask apply, but it will be a bit slower
            if self._progress_bar:
                with TQDMDaskProgressBar(desc=self._progress_bar_desc or "Dask Apply"):
                    return (
                        dd.from_pandas(self._obj, npartitions=self._npartitions)
                        .apply(lambda x: func(x, *args, **kwds), convert_dtype=convert_dtype, meta=meta)
                        .compute(scheduler=self._scheduler)
                    )
            else:
                return (
                    dd.from_pandas(self._obj, npartitions=self._npartitions)
                    .apply(lambda x: func(x, *args, **kwds), convert_dtype=convert_dtype, meta=meta)
                    .compute(scheduler=self._scheduler)
                )

sample = self._obj.iloc[: self._npartitions * 2, :]
        with suppress_stdout_stderr():
            meta = sample.applymap(func)
        try:
            with suppress_stdout_stderr():
                # check that the dask apply matches the pandas apply
                tmp_df = (
                    dd.from_pandas(sample, npartitions=self._npartitions)
                    .applymap(func, meta=meta)
                    .compute(scheduler=self._scheduler)
                )
                self._validate_apply(
                    tmp_df.equals(meta), error_message="Dask applymap sample does not match pandas applymap sample."
                )
            if self._progress_bar:
                with TQDMDaskProgressBar(desc=self._progress_bar_desc or "Dask Applymap"):
                    return (
                        dd.from_pandas(self._obj, npartitions=self._npartitions)
                        .applymap(func, meta=meta)
                        .compute(scheduler=self._scheduler)
                    )
            else:
                return (
                    dd.from_pandas(self._obj, npartitions=self._npartitions)
                    .applymap(func, meta=meta)
                    .compute(scheduler=self._scheduler)
                )
        except ERRORS_TO_HANDLE:
            # if dask apply doesn't match pandas apply, fallback to pandas
            if self._progress_bar:
                tqdm.pandas(desc=self._progress_bar_desc or "Pandas Apply")
                applymap_func = self._obj.progress_applymap

def __init__(self, start=None, start_state=None, pretask=None, posttask=None, finish=None, **kwargs):
        super(TQDMDaskProgressBar, self).__init__(
            start=start, start_state=start_state, pretask=pretask, posttask=posttask, finish=finish
        )
        self.tqdm_args = kwargs
        self.states = ["ready", "waiting", "running", "finished"]

def _dask_apply(self, func, *args, **kwds):
        try:
            # check that the dask rolling apply matches the pandas apply
            with suppress_stdout_stderr():
                tmp_df = (
                    dd.from_pandas(self._comparison_pd, npartitions=self._npartitions)
                    .rolling(**{k: v for k, v in self._rolling_kwds.items() if k not in ["on", "closed"]})
                    .apply(func, *args, **kwds)
                    .compute(scheduler=self._scheduler)
                )
                self._validate_apply(
                    tmp_df.equals(self._comparison_pd.rolling(**self._rolling_kwds).apply(func, *args, **kwds)),
                    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)