How to use the mriqc.utils.misc.BIDS_COMP.keys function in mriqc

def get_bids_cols(dataframe):
    """ Returns columns corresponding to BIDS bits """
    bids_comps = list(BIDS_COMP.keys())
    bids_comps_present = list(set(dataframe.columns.ravel().tolist()) & set(bids_comps))
    return [bit for bit in bids_comps if bit in bids_comps_present]

return in_prov, wf_details, sett_dict

    in_iqms = Path(in_iqms)
    with in_iqms.open() as jsonfile:
        iqms_dict = load(jsonfile)

    # Now, the in_iqms file should be correctly named
    out_file = str(Path(in_iqms.with_suffix(".html").name).resolve())

    # Extract and prune metadata
    metadata = iqms_dict.pop("bids_meta", None)
    mod = metadata.pop("modality", None)
    prov, wf_details, _ = _get_details(iqms_dict, mod)

    file_id = [metadata.pop(k, None) for k in list(BIDS_COMP.keys())]
    file_id = [comp for comp in file_id if comp is not None]

    if in_plots is None:
        in_plots = []
    else:
        if any(("melodic_reportlet" in k for k in in_plots)):
            REPORT_TITLES["bold"].insert(3, ("ICA components", "ica-comps"))
        if any(("plot_spikes" in k for k in in_plots)):
            REPORT_TITLES["bold"].insert(3, ("Spikes", "spikes"))

        in_plots = [
            (REPORT_TITLES[mod][i] + (read_report_snippet(v),))
            for i, v in enumerate(in_plots)
        ]

    pred_qa = None  # metadata.pop('mriqc_pred', None)

for root_key in root_adds:
            val = self.inputs._outputs.get(root_key, None)
            if isinstance(val, dict):
                self._out_dict.update(val)
            else:
                config.loggers.interface.warning(
                    'Output "%s" is not a dictionary (value="%s"), '
                    "discarding output.",
                    root_key,
                    str(val),
                )

        # Fill in the "bids_meta" key
        id_dict = {}
        for comp in list(BIDS_COMP.keys()):
            comp_val = getattr(self.inputs, comp, None)
            if isdefined(comp_val) and comp_val is not None:
                id_dict[comp] = comp_val
        id_dict["modality"] = self.inputs.modality

        if isdefined(self.inputs.metadata) and self.inputs.metadata:
            id_dict.update(self.inputs.metadata)

        if self._out_dict.get("bids_meta") is None:
            self._out_dict["bids_meta"] = {}
        self._out_dict["bids_meta"].update(id_dict)

        if isdefined(self.inputs.dataset):
            self._out_dict["bids_meta"]["dataset"] = self.inputs.dataset

        # Fill in the "provenance" key

rate_label="rater_1",
    binarize=True,
    site_name=None,
    rate_selection="random",
    collapse=True,
):
    """
    Reads in the labels. Massage labels table to have the
    appropriate format
    """

    if isinstance(rate_label, str):
        rate_label = [rate_label]
    output_labels = rate_label

    bids_comps = list(BIDS_COMP.keys())
    y_df = pd.read_csv(
        label_file, index_col=False, dtype={col: str for col in bids_comps}
    )

    # Find present bids bits and sort by them
    bids_comps_present = get_bids_cols(y_df)
    y_df = y_df.sort_values(by=bids_comps_present)
    y_df.subject_id = y_df.subject_id.str.lstrip("sub-")
    y_df[rate_label] = y_df[rate_label].apply(pd.to_numeric, errors="raise")

    if len(rate_label) == 2:
        np.random.seed(42)
        ratermask_1 = ~np.isnan(y_df[[rate_label[0]]].values.ravel())
        ratermask_2 = ~np.isnan(y_df[[rate_label[1]]].values.ravel())

        all_rated = ratermask_1 & ratermask_2

),
            (
                [
                    "summary_fg_mean",
                    "summary_fg_stdv",
                    "summary_fg_k",
                    "summary_fg_p05",
                    "summary_fg_p95",
                ],
                None,
            ),
        ],
    }

    if csv_file.suffix == ".csv":
        def_comps = list(BIDS_COMP.keys())
        dataframe = pd.read_csv(
            csv_file, index_col=False, dtype={comp: object for comp in def_comps}
        )

        id_labels = list(set(def_comps) & set(dataframe.columns.ravel().tolist()))
        dataframe["label"] = dataframe[id_labels].apply(
            _format_labels, args=(id_labels,), axis=1
        )
    else:
        dataframe = pd.read_csv(
            csv_file, index_col=False, sep="\t", dtype={"bids_name": object}
        )
        dataframe = dataframe.rename(index=str, columns={"bids_name": "label"})

    nPart = len(dataframe)

def read_iqms(feat_file):
    """ Reads in the features """
    feat_file = Path(feat_file)

    if feat_file.suffix == ".csv":
        bids_comps = list(BIDS_COMP.keys())
        x_df = pd.read_csv(
            feat_file, index_col=False, dtype={col: str for col in bids_comps}
        )
        # Find present bids bits and sort by them
        bids_comps_present = list(set(x_df.columns.ravel().tolist()) & set(bids_comps))
        bids_comps_present = [bit for bit in bids_comps if bit in bids_comps_present]
        x_df = x_df.sort_values(by=bids_comps_present)
        # Remove sub- prefix in subject_id
        x_df.subject_id = x_df.subject_id.str.lstrip("sub-")

        # Remove columns that are not IQMs
        feat_names = list(x_df._get_numeric_data().columns.ravel())
        for col in bids_comps:
            try:
                feat_names.remove(col)
            except ValueError: