How to use the fitlins.interfaces.nistats.NistatsBaseInterface function in fitlins

mat = dm.make_first_level_design_matrix(
            frame_times=np.arange(vols) * info['repetition_time'],
            events=sparse,
            add_regs=dense,
            add_reg_names=column_names,
            drift_model=drift_model,
        )

        mat.to_csv('design.tsv', sep='\t')
        self._results['design_matrix'] = os.path.join(runtime.cwd,
                                                      'design.tsv')
        return runtime


class FirstLevelModel(NistatsBaseInterface, FirstLevelEstimatorInterface, SimpleInterface):
    def _run_interface(self, runtime):
        import nibabel as nb
        from nistats import first_level_model as level1
        mat = pd.read_csv(self.inputs.design_matrix, delimiter='\t', index_col=0)
        img = nb.load(self.inputs.bold_file)

        mask_file = self.inputs.mask_file
        if not isdefined(mask_file):
            mask_file = None
        smoothing_fwhm = self.inputs.smoothing_fwhm
        if not isdefined(smoothing_fwhm):
            smoothing_fwhm = None
        flm = level1.FirstLevelModel(
            mask_img=mask_file, smoothing_fwhm=smoothing_fwhm)
        flm.fit(img, design_matrices=mat)

return runtime


def _flatten(x):
    return [elem for sublist in x for elem in sublist]


def _match(query, metadata):
    for key, val in query.items():
        if metadata.get(key) != val:
            return False
    return True


class SecondLevelModel(NistatsBaseInterface, SecondLevelEstimatorInterface, SimpleInterface):
    def _run_interface(self, runtime):
        from nistats import second_level_model as level2
        smoothing_fwhm = self.inputs.smoothing_fwhm
        if not isdefined(smoothing_fwhm):
            smoothing_fwhm = None

        model = level2.SecondLevelModel(smoothing_fwhm=smoothing_fwhm)

        effect_maps = []
        variance_maps = []
        stat_maps = []
        zscore_maps = []
        pvalue_maps = []
        contrast_metadata = []
        out_ents = self.inputs.contrast_info[0]['entities']  # Same for all
        fname_fmt = os.path.join(runtime.cwd, '{}_{}.nii.gz').format

if v != 0 and n not in all_regressors]
                       for row in contrast['weights']])
        if not missing:
            # Fill in zeros
            weights = np.array([
                [row[col] if col in row else 0 for col in all_regressors]
                for row in contrast['weights']
                ])

            out_contrasts.append(
                (contrast['name'], weights, contrast['type']))

    return out_contrasts


class DesignMatrix(NistatsBaseInterface, DesignMatrixInterface, SimpleInterface):

    def _run_interface(self, runtime):
        import nibabel as nb
        from nistats import design_matrix as dm
        info = self.inputs.session_info
        img = nb.load(self.inputs.bold_file)
        vols = img.shape[3]

        drop_missing = bool(self.inputs.drop_missing)

        if info['sparse'] not in (None, 'None'):
            sparse = pd.read_hdf(info['sparse'], key='sparse').rename(
                columns={'condition': 'trial_type',
                         'amplitude': 'modulation'})
            sparse = sparse.dropna(subset=['modulation'])  # Drop NAs
        else: