How to use the niworkflows.interfaces.fixes.FixHeaderRegistration function in niworkflows

AI(
                fixed_image=str(bold_template),
                fixed_image_mask=str(brain_mask),
                metric=("Mattes", 32, "Regular", 0.2),
                transform=("Affine", 0.1),
                search_factor=(20, 0.12),
                principal_axes=False,
                convergence=(10, 1e-6, 10),
                verbose=True,
            ),
            name="init_aff",
            n_procs=omp_nthreads,
        )

        # Registration().version may be None
        if parseversion(Registration().version or "0.0.0") > Version("2.2.0"):
            init_aff.inputs.search_grid = (40, (0, 40, 40))

        # Set up spatial normalization
        norm = pe.Node(
            Registration(
                from_file=pkgr_fn("niworkflows.data", "epi_atlasbased_brainmask.json")
            ),
            name="norm",
            n_procs=omp_nthreads,
        )
        norm.inputs.fixed_image = str(bold_template)
        map_brainmask = pe.Node(
            ApplyTransforms(
                interpolation="MultiLabel", input_image=str(brain_mask)
            ),
            name="map_brainmask",

# Save outputs (if available)
            init_out = _write_outputs(init_result.runtime, ".nipype-init")
            if init_out:
                NIWORKFLOWS_LOG.info(
                    "Terminal outputs of initialization saved (%s).",
                    ", ".join(init_out),
                )

            ants_args["initial_moving_transform"] = init_result.outputs.out_file

        # For each settings file...
        for ants_settings in settings_files:

            NIWORKFLOWS_LOG.info("Loading settings from file %s.", ants_settings)
            # Configure an ANTs run based on these settings.
            self.norm = Registration(from_file=ants_settings, **ants_args)
            self.norm.resource_monitor = False
            self.norm.terminal_output = self.terminal_output

            cmd = self.norm.cmdline
            # Print the retry number and command line call to the log.
            NIWORKFLOWS_LOG.info("Retry #%d, commandline: \n%s", self.retry, cmd)
            self.norm.ignore_exception = True
            with open("command.txt", "w") as cmdfile:
                print(cmd + "\n", file=cmdfile)

            # Try running registration.
            interface_result = self.norm.run()

            if interface_result.runtime.returncode != 0:
                NIWORKFLOWS_LOG.warning("Retry #%d failed.", self.retry)
                # Save outputs (if available)

# Initialize transforms with antsAI
    init_aff = pe.Node(
        AI(
            metric=("Mattes", 32, "Regular", 0.25),
            transform=("Affine", 0.1),
            search_factor=(15, 0.1),
            principal_axes=False,
            convergence=(10, 1e-6, 10),
            verbose=True,
        ),
        name="init_aff",
        n_procs=omp_nthreads,
    )

    # Tolerate missing ANTs at construction time
    _ants_version = Registration().version
    if _ants_version and parseversion(_ants_version) >= Version("2.3.0"):
        init_aff.inputs.search_grid = (40, (0, 40, 40))

    # Set up spatial normalization
    settings_file = (
        "antsBrainExtraction_%s.json"
        if use_laplacian
        else "antsBrainExtractionNoLaplacian_%s.json"
    )
    norm = pe.Node(
        Registration(
            from_file=pkgr_fn("niworkflows.data", settings_file % normalization_quality)
        ),
        name="norm",
        n_procs=omp_nthreads,
        mem_gb=mem_gb,

def _run_interface(self, runtime, correct_return_codes=(0,)):
        # Run normally
        runtime = super(FixHeaderRegistration, self)._run_interface(
            runtime, correct_return_codes
        )

        # Forward transform
        out_file = self._get_outputfilenames(inverse=False)
        if out_file is not None and out_file:
            _copyxform(
                self.inputs.fixed_image[0],
                os.path.abspath(out_file),
                message="%s (niworkflows v%s)" % (self.__class__.__name__, __version__),
            )

        # Inverse transform
        out_file = self._get_outputfilenames(inverse=True)
        if out_file is not None and out_file:
            _copyxform(

search_factor=(20, 0.12),
                principal_axes=False,
                convergence=(10, 1e-6, 10),
                verbose=True,
            ),
            name="init_aff",
            n_procs=omp_nthreads,
        )

        # Registration().version may be None
        if parseversion(Registration().version or "0.0.0") > Version("2.2.0"):
            init_aff.inputs.search_grid = (40, (0, 40, 40))

        # Set up spatial normalization
        norm = pe.Node(
            Registration(
                from_file=pkgr_fn("niworkflows.data", "epi_atlasbased_brainmask.json")
            ),
            name="norm",
            n_procs=omp_nthreads,
        )
        norm.inputs.fixed_image = str(bold_template)
        map_brainmask = pe.Node(
            ApplyTransforms(
                interpolation="MultiLabel", input_image=str(brain_mask)
            ),
            name="map_brainmask",
        )
        # fmt: off
        workflow.connect([
            (inputnode, init_aff, [("in_file", "moving_image")]),
            (inputnode, map_brainmask, [("in_file", "reference_image")]),

n_procs=omp_nthreads,
    )

    # Tolerate missing ANTs at construction time
    _ants_version = Registration().version
    if _ants_version and parseversion(_ants_version) >= Version("2.3.0"):
        init_aff.inputs.search_grid = (40, (0, 40, 40))

    # Set up spatial normalization
    settings_file = (
        "antsBrainExtraction_%s.json"
        if use_laplacian
        else "antsBrainExtractionNoLaplacian_%s.json"
    )
    norm = pe.Node(
        Registration(
            from_file=pkgr_fn("niworkflows.data", settings_file % normalization_quality)
        ),
        name="norm",
        n_procs=omp_nthreads,
        mem_gb=mem_gb,
    )
    norm.inputs.float = use_float
    fixed_mask_trait = "fixed_image_mask"
    if _ants_version and parseversion(_ants_version) >= Version("2.2.0"):
        fixed_mask_trait += "s"

    map_brainmask = pe.Node(
        ApplyTransforms(interpolation="Gaussian", float=True),
        name="map_brainmask",
        mem_gb=1,
    )