How to use the niworkflows.interfaces.utils.CopyXForm function in niworkflows

outputnode = pe.Node(
        niu.IdentityInterface(
            fields=[
                "out_file",
                "out_mask",
                "bias_corrected",
                "bias_image",
                "out_segm",
                "out_tpms",
            ]
        ),
        name="outputnode",
    )

    copy_xform = pe.Node(
        CopyXForm(fields=["out_file", "out_mask", "bias_corrected", "bias_image"]),
        name="copy_xform",
        run_without_submitting=True,
    )

    trunc = pe.MapNode(
        ImageMath(operation="TruncateImageIntensity", op2="0.01 0.999 256"),
        name="truncate_images",
        iterfield=["op1"],
    )
    inu_n4 = pe.MapNode(
        N4BiasFieldCorrection(
            dimension=3,
            save_bias=False,
            copy_header=True,
            n_iterations=[50] * 4,
            convergence_threshold=1e-7,

n4_correct.inputs.rescale_intensities = True

    # Create a generous BET mask out of the bias-corrected EPI
    skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2, mask=True),
                                    name='skullstrip_first_pass')
    bet_dilate = pe.Node(fsl.DilateImage(
        operation='max', kernel_shape='sphere', kernel_size=6.0,
        internal_datatype='char'), name='skullstrip_first_dilate')
    bet_mask = pe.Node(fsl.ApplyMask(), name='skullstrip_first_mask')

    # Use AFNI's unifize for T2 contrast & fix header
    unifize = pe.Node(afni.Unifize(
        t2=True, outputtype='NIFTI_GZ',
        args='-clfrac 0.2 -rbt 18.3 65.0 90.0',
        out_file='uni.nii.gz'), name='unifize')
    fixhdr_unifize = pe.Node(CopyXForm(), name='fixhdr_unifize', mem_gb=0.1)

    # Run AFNI's 3dAutomask to extract a refined brain mask
    skullstrip_second_pass = pe.Node(afni.Automask(dilate=1,
                                                   outputtype='NIFTI_GZ'),
                                     name='skullstrip_second_pass')
    fixhdr_skullstrip2 = pe.Node(CopyXForm(), name='fixhdr_skullstrip2', mem_gb=0.1)

    # Take intersection of both masks
    combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
                            name='combine_masks')

    normalize = pe.Node(niu.Function(function=_normalize), name="normalize")

    # Compute masked brain
    apply_mask = pe.Node(ApplyMask(), name='apply_mask')

)
    bet_mask = pe.Node(fsl.ApplyMask(), name="skullstrip_first_mask")

    # Use AFNI's unifize for T2 constrast & fix header
    unifize = pe.Node(
        afni.Unifize(
            t2=True,
            outputtype="NIFTI_GZ",
            # Default -clfrac is 0.1, 0.4 was too conservative
            # -rbt because I'm a Jedi AFNI Master (see 3dUnifize's documentation)
            args="-clfrac 0.2 -rbt 18.3 65.0 90.0",
            out_file="uni.nii.gz",
        ),
        name="unifize",
    )
    fixhdr_unifize = pe.Node(CopyXForm(), name="fixhdr_unifize", mem_gb=0.1)

    # Run ANFI's 3dAutomask to extract a refined brain mask
    skullstrip_second_pass = pe.Node(
        afni.Automask(dilate=1, outputtype="NIFTI_GZ"), name="skullstrip_second_pass"
    )
    fixhdr_skullstrip2 = pe.Node(CopyXForm(), name="fixhdr_skullstrip2", mem_gb=0.1)

    # Take intersection of both masks
    combine_masks = pe.Node(fsl.BinaryMaths(operation="mul"), name="combine_masks")

    # Compute masked brain
    apply_mask = pe.Node(fsl.ApplyMask(), name="apply_mask")

    if not pre_mask:
        bold_template = get_template(
            "MNI152NLin2009cAsym", resolution=2, desc="fMRIPrep", suffix="boldref"

def _outputs(self):
        base = super(CopyXForm, self)._outputs()
        if self._fields:
            fields = self._fields.copy()
            if "in_file" in fields:
                idx = fields.index("in_file")
                fields.pop(idx)
                fields.insert(idx, "out_file")

            base = add_traits(base, fields)
        return base

"""
    wf = pe.Workflow(name)

    inputnode = pe.Node(
        niu.IdentityInterface(fields=["in_files", "in_mask", "in_mask_dilated"]),
        name="inputnode",
    )
    outputnode = pe.Node(
        niu.IdentityInterface(fields=["out_mask", "out_segm", "out_tpms"]),
        name="outputnode",
    )

    copy_xform = pe.Node(
        CopyXForm(fields=["out_mask", "out_segm", "out_tpms"]),
        name="copy_xform",
        run_without_submitting=True,
    )

    # Run atropos (core node)
    atropos = pe.Node(
        Atropos(
            dimension=3,
            initialization="KMeans",
            number_of_tissue_classes=in_segmentation_model[0],
            n_iterations=3,
            convergence_threshold=0.0,
            mrf_radius=[1, 1, 1],
            mrf_smoothing_factor=0.1,
            likelihood_model="Gaussian",
            use_random_seed=use_random_seed,

operation='max', kernel_shape='sphere', kernel_size=6.0,
        internal_datatype='char'), name='skullstrip_first_dilate')
    bet_mask = pe.Node(fsl.ApplyMask(), name='skullstrip_first_mask')

    # Use AFNI's unifize for T2 contrast & fix header
    unifize = pe.Node(afni.Unifize(
        t2=True, outputtype='NIFTI_GZ',
        args='-clfrac 0.2 -rbt 18.3 65.0 90.0',
        out_file='uni.nii.gz'), name='unifize')
    fixhdr_unifize = pe.Node(CopyXForm(), name='fixhdr_unifize', mem_gb=0.1)

    # Run AFNI's 3dAutomask to extract a refined brain mask
    skullstrip_second_pass = pe.Node(afni.Automask(dilate=1,
                                                   outputtype='NIFTI_GZ'),
                                     name='skullstrip_second_pass')
    fixhdr_skullstrip2 = pe.Node(CopyXForm(), name='fixhdr_skullstrip2', mem_gb=0.1)

    # Take intersection of both masks
    combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
                            name='combine_masks')

    normalize = pe.Node(niu.Function(function=_normalize), name="normalize")

    # Compute masked brain
    apply_mask = pe.Node(ApplyMask(), name='apply_mask')

    workflow.connect([
        (inputnode, n4_correct, [('in_file', 'input_image'),
                                 ('pre_mask', 'mask_image')]),
        (inputnode, fixhdr_unifize, [('in_file', 'hdr_file')]),
        (inputnode, fixhdr_skullstrip2, [('in_file', 'hdr_file')]),
        (n4_correct, skullstrip_first_pass, [('output_image', 'in_file')]),