def METHOD_NAME():
    input_map = dict(
        bias_corrected_affine=dict(
            field="estwrite.output.bias.affine",
            usedefault=True,
        ),
        bias_corrected_native=dict(
            field="estwrite.output.bias.native",
            usedefault=True,
        ),
        bias_corrected_normalized=dict(
            field="estwrite.output.bias.warped",
            usedefault=True,
        ),
        bias_fwhm=dict(
            field="estwrite.opts.biasfwhm",
            usedefault=True,
        ),
        bias_regularization=dict(
            field="estwrite.opts.biasreg",
            usedefault=True,
        ),
        cleanup_partitions=dict(
            field="estwrite.extopts.cleanup",
            usedefault=True,
        ),
        csf_dartel=dict(
            field="estwrite.output.CSF.dartel",
            usedefault=True,
        ),
        csf_modulated_normalized=dict(
            field="estwrite.output.CSF.modulated",
            usedefault=True,
        ),
        csf_native=dict(
            field="estwrite.output.CSF.native",
            usedefault=True,
        ),
        csf_normalized=dict(
            field="estwrite.output.CSF.warped",
            usedefault=True,
        ),
        dartel_template=dict(
            extensions=[".hdr", ".img", ".img.gz", ".nii"],
            field="estwrite.extopts.dartelwarp.normhigh.darteltpm",
        ),
        deformation_field=dict(
            field="estwrite.output.warps",
            usedefault=True,
        ),
        display_results=dict(
            field="estwrite.extopts.print",
            usedefault=True,
        ),
        gaussians_per_class=dict(
            usedefault=True,
        ),
        gm_dartel=dict(
            field="estwrite.output.GM.dartel",
            usedefault=True,
        ),
        gm_modulated_normalized=dict(
            field="estwrite.output.GM.modulated",
            usedefault=True,
        ),
        gm_native=dict(
            field="estwrite.output.GM.native",
            usedefault=True,
        ),
        gm_normalized=dict(
            field="estwrite.output.GM.warped",
            usedefault=True,
        ),
        in_files=dict(
            copyfile=False,
            field="estwrite.data",
            mandatory=True,
        ),
        jacobian_determinant=dict(
            field="estwrite.jacobian.warped",
            usedefault=True,
        ),
        matlab_cmd=dict(),
        mfile=dict(
            usedefault=True,
        ),
        mrf_weighting=dict(
            field="estwrite.extopts.mrf",
            usedefault=True,
        ),
        paths=dict(),
        pve_label_dartel=dict(
            field="estwrite.output.label.dartel",
            usedefault=True,
        ),
        pve_label_native=dict(
            field="estwrite.output.label.native",
            usedefault=True,
        ),
        pve_label_normalized=dict(
            field="estwrite.output.label.warped",
            usedefault=True,
        ),
        sampling_distance=dict(
            field="estwrite.opts.samp",
            usedefault=True,
        ),
        spatial_normalization=dict(
            usedefault=True,
        ),
        tissues=dict(
            extensions=[".hdr", ".img", ".img.gz", ".nii"],
            field="estwrite.tpm",
        ),
        use_mcr=dict(),
        use_sanlm_denoising_filter=dict(
            field="estwrite.extopts.sanlm",
            usedefault=True,
        ),
        use_v8struct=dict(
            min_ver="8",
            usedefault=True,
        ),
        warping_regularization=dict(
            field="estwrite.opts.warpreg",
            usedefault=True,
        ),
        wm_dartel=dict(
            field="estwrite.output.WM.dartel",
            usedefault=True,
        ),
        wm_modulated_normalized=dict(
            field="estwrite.output.WM.modulated",
            usedefault=True,
        ),
        wm_native=dict(
            field="estwrite.output.WM.native",
            usedefault=True,
        ),
        wm_normalized=dict(
            field="estwrite.output.WM.warped",
            usedefault=True,
        ),
    )
    inputs = VBMSegment.input_spec()
    for key, metadata in list(input_map.items()):
        for metakey, value in list(metadata.items()):
            assert getattr(inputs.traits()[key], metakey) == value