How to use the fitlins.interfaces.afni.FirstLevelModel function in fitlins

raise ValueError(f"Invalid smoothing level {smoothing_level}")
        elif smoothing_level.lower() not in (step['Level'].lower()
                                             for step in model_dict['Steps']):
            raise ValueError(f"Invalid smoothing level {smoothing_level}")

    design_matrix = pe.MapNode(
        DesignMatrix(drop_missing=drop_missing),
        iterfield=['session_info', 'bold_file'],
        name='design_matrix')

    if estimator == 'afni':
        from ..interfaces.afni import FirstLevelModel
    else:
        from ..interfaces.nistats import FirstLevelModel
    l1_model = pe.MapNode(
        FirstLevelModel(),
        iterfield=['design_matrix', 'contrast_info', 'bold_file', 'mask_file'],
        mem_gb=3,
        name='l1_model')

    def _deindex(tsv):
        from pathlib import Path
        import pandas as pd
        out_tsv = str(Path.cwd() / Path(tsv).name)
        pd.read_csv(tsv, sep='\t', index_col=0).to_csv(out_tsv, sep='\t', index=False)
        return out_tsv

    deindex_tsv = pe.MapNode(niu.Function(function=_deindex),
                             iterfield=['tsv'], name='deindex_tsv')

    # Set up common patterns
    image_pattern = 'reports/[sub-{subject}/][ses-{session}/]figures/[run-{run}/]' \

(15, "laplace", "Laplace"),
        (16, "uniform", "Uniform"),
        (17, "non central t test", "Ttest_nonc"),
        (18, "weibull", "Weibull"),
        (19, "chi", "Chi"),
        (20, "inverse gaussian", "Invgauss"),
        (21, "extreme value 1", "Extval"),
        (22, "p value", "Pval"),
        (23, "log p value", "LogPval"),
        (24, "log10 p value", "Log10Pval"),
    ),
    fields=("code", "label", "stat_code"),
)


class FirstLevelModel(FirstLevelModel):
    def _run_interface(self, runtime):
        """
        Fit a GLM using AFNI's 3dREMLfit
        """
        from nipype import logging
        import nibabel as nb
        from nipype.interfaces import afni

        logger = logging.getLogger("nipype.interface")

        mat = pd.read_csv(self.inputs.design_matrix, delimiter="\t", index_col=0)
        contrasts = prepare_contrasts(self.inputs.contrast_info, mat.columns.tolist())
        tmpdir = op.join(runtime.cwd)
        t_r = mat.index[1]
        design_fname = op.join(tmpdir, "design.xmat.1D")
        stim_labels = self.get_stim_labels()