def METHOD_NAME():
    input_map = dict(
        args=dict(argstr="%s"),
        autocorr_estimate_only=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="-ac",
        ),
        autocorr_noestimate=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="-noest",
        ),
        brightness_threshold=dict(argstr="-epith %d"),
        design_file=dict(argstr="%s"),
        environ=dict(usedefault=True),
        fit_armodel=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="-ar",
        ),
        full_data=dict(argstr="-v"),
        in_file=dict(mandatory=True, argstr="%s"),
        mask_size=dict(argstr="-ms %d"),
        multitaper_product=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="-mt %d",
        ),
        output_pwdata=dict(argstr="-output_pwdata"),
        output_type=dict(),
        results_dir=dict(usedefault=True, argstr="-rn %s"),
        smooth_autocorr=dict(argstr="-sa"),
        threshold=dict(argstr="%f"),
        tukey_window=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="-tukey %d",
        ),
        use_pava=dict(argstr="-pava"),
    )
    input_map2 = dict(
        args=dict(argstr="%s"),
        autocorr_estimate_only=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="--ac",
        ),
        autocorr_noestimate=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="--noest",
        ),
        brightness_threshold=dict(argstr="--epith=%d"),
        design_file=dict(argstr="--pd=%s"),
        environ=dict(usedefault=True),
        fit_armodel=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="--ar",
        ),
        full_data=dict(argstr="-v"),
        in_file=dict(mandatory=True, argstr="--in=%s"),
        mask_size=dict(argstr="--ms=%d"),
        multitaper_product=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="--mt=%d",
        ),
        output_pwdata=dict(argstr="--outputPWdata"),
        output_type=dict(),
        results_dir=dict(argstr="--rn=%s", usedefault=True),
        smooth_autocorr=dict(argstr="--sa"),
        threshold=dict(usedefault=True, argstr="--thr=%f"),
        tukey_window=dict(
            xor=[
                "autocorr_estimate_only",
                "fit_armodel",
                "tukey_window",
                "multitaper_product",
                "use_pava",
                "autocorr_noestimate",
            ],
            argstr="--tukey=%d",
        ),
        use_pava=dict(argstr="--pava"),
    )
    instance = FILMGLS()
    if isinstance(instance.inputs, FILMGLSInputSpec):
        for key, metadata in list(input_map.items()):
            for metakey, value in list(metadata.items()):
                assert getattr(instance.inputs.traits()[key], metakey) == value
    else:
        for key, metadata in list(input_map2.items()):
            for metakey, value in list(metadata.items()):
                assert getattr(instance.inputs.traits()[key], metakey) == value