How to use the jwst.superbias.SuperBiasStep function in jwst

The full path to the calibrated file
        """

        output_filename = filename.replace('_uncal', '').replace('.fits', '_superbias_refpix.fits')

        if not os.path.isfile(output_filename):
            # Run the group_scale and dq_init steps on the input file
            if group_scale:
                model = GroupScaleStep.call(filename)
                model = DQInitStep.call(model)
            else:
                model = DQInitStep.call(filename)

            # Run the saturation and superbias steps
            model = SaturationStep.call(model)
            model = SuperBiasStep.call(model)

            # Run the refpix step and save the output
            model = RefPixStep.call(model, odd_even_rows=odd_even_rows, odd_even_columns=odd_even_columns, use_side_ref_pixels=use_side_ref_pixels)
            model.save(output_filename)
            set_permissions(output_filename)
        else:
            logging.info('\t{} already exists'.format(output_filename))

        return output_filename

from jwql.utils.constants import JWST_INSTRUMENT_NAMES_UPPERCASE

# Define the fits header keyword that accompanies each step
PIPE_KEYWORDS = {'S_GRPSCL': 'group_scale', 'S_DQINIT': 'dq_init', 'S_SATURA': 'saturation',
                 'S_IPC': 'ipc', 'S_REFPIX': 'refpix', 'S_SUPERB': 'superbias',
                 'S_PERSIS': 'persistence', 'S_DARK': 'dark_current', 'S_LINEAR': 'linearity',
                 'S_FRSTFR': 'firstframe', 'S_LASTFR': 'lastframe', 'S_RSCD': 'rscd',
                 'S_JUMP': 'jump', 'S_RAMP': 'rate'}

PIPELINE_STEP_MAPPING = {'dq_init': DQInitStep, 'dark_current': DarkCurrentStep,
                         'firstframe': FirstFrameStep, 'group_scale': GroupScaleStep,
                         'ipc': IPCStep, 'jump': JumpStep, 'lastframe': LastFrameStep,
                         'linearity': LinearityStep, 'persistence': PersistenceStep,
                         'rate': RampFitStep, 'refpix': RefPixStep, 'rscd': RSCD_Step,
                         'saturation': SaturationStep, 'superbias': SuperBiasStep}

# Readout patterns that have nframes != a power of 2. These readout patterns
# require the group_scale pipeline step to be run.
GROUPSCALE_READOUT_PATTERNS = ['NRSIRS2']


def completed_pipeline_steps(filename):
    """Return a list of the completed pipeline steps for a given file.

    Parameters
    ----------
    filename : str
        File to examine

    Returns
    -------