How to use the niworkflows.engine.workflows.LiterateWorkflow function in niworkflows

"""
    from ..interfaces.reports import AboutSummary, SubjectSummary
    if name in ('single_subject_wf', 'single_subject_smripreptest_wf'):
        # for documentation purposes
        subject_data = {
            't1w': ['/completely/made/up/path/sub-01_T1w.nii.gz'],
        }
    else:
        subject_data = collect_data(layout, subject_id, bids_filters=bids_filters)[0]

    if not subject_data['t1w']:
        raise Exception("No T1w images found for participant {}. "
                        "All workflows require T1w images.".format(subject_id))

    workflow = Workflow(name=name)
    workflow.__desc__ = """
Results included in this manuscript come from preprocessing
performed using *sMRIPprep* {smriprep_ver}
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* {nipype_ver}
(@nipype1; @nipype2; RRID:SCR_002502).

""".format(smriprep_ver=__version__, nipype_ver=nipype_ver)
    workflow.__postdesc__ = """

For more details of the pipeline, see [the section corresponding
to workflows in *sMRIPrep*'s documentation]\
(https://smriprep.readthedocs.io/en/latest/workflows.html \
"sMRIPrep's documentation").

in_file
        Anatomical, merged T1w image after INU correction
    subjects_dir
        FreeSurfer SUBJECTS_DIR
    subject_id
        FreeSurfer subject ID
    fsnative2t1w_xfm
        LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w

    Outputs
    -------
    out_file
        The selected segmentation, after resampling in native space

    """
    workflow = Workflow(name='%s_%s' % (name, segmentation))
    inputnode = pe.Node(
        niu.IdentityInterface(['in_file', 'subjects_dir', 'subject_id', 'fsnative2t1w_xfm']),
        name='inputnode')
    outputnode = pe.Node(niu.IdentityInterface(['out_file']), name='outputnode')
    # Extract the aseg and aparc+aseg outputs
    fssource = pe.Node(nio.FreeSurferSource(), name='fs_datasource')
    # Resample from T1.mgz to T1w.nii.gz, applying any offset in fsnative2t1w_xfm,
    # and convert to NIfTI while we're at it
    resample = pe.Node(fs.ApplyVolTransform(transformed_file='seg.nii.gz'), name='resample')

    if segmentation.startswith('aparc'):
        if segmentation == 'aparc_aseg':
            def _sel(x): return [parc for parc in x if 'aparc+' in parc][0]  # noqa
        elif segmentation == 'aparc_a2009s':
            def _sel(x): return [parc for parc in x if 'a2009s+' in parc][0]  # noqa
        elif segmentation == 'aparc_dkt':

------
    subjects_dir
        FreeSurfer SUBJECTS_DIR
    subject_id
        FreeSurfer subject ID
    fsnative2t1w_xfm
        LTA formatted affine transform file (inverse)

    Outputs
    -------
    surfaces
        GIFTI surfaces for gray/white matter boundary, pial surface,
        midthickness (or graymid) surface, and inflated surfaces

    """
    workflow = Workflow(name=name)

    inputnode = pe.Node(niu.IdentityInterface(['subjects_dir', 'subject_id',
                                               'fsnative2t1w_xfm']),
                        name='inputnode')
    outputnode = pe.Node(niu.IdentityInterface(['surfaces']), name='outputnode')

    get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')

    midthickness = pe.MapNode(
        MakeMidthickness(thickness=True, distance=0.5, out_name='midthickness'),
        iterfield='in_file',
        name='midthickness')

    save_midthickness = pe.Node(nio.DataSink(parameterization=False),
                                name='save_midthickness')

def init_pepolar_estimate_wf(debug=False, generate_report=True, name="pepolar_estimate_wf"):
    """Initialize a barebones TOPUP implementation."""
    from nipype.interfaces.afni import Automask
    from nipype.interfaces.fsl.epi import TOPUP
    from niworkflows.interfaces.nibabel import MergeSeries
    from sdcflows.interfaces.fmap import get_trt
    from ...interfaces.images import RescaleB0
    wf = Workflow(name=name)

    inputnode = pe.Node(niu.IdentityInterface(fields=["metadata", "in_data"]),
                        name="inputnode")
    outputnode = pe.Node(niu.IdentityInterface(fields=["fieldmap", "corrected", "corrected_mask"]),
                         name="outputnode")

    concat_blips = pe.Node(MergeSeries(), name="concat_blips")
    readout_time = pe.MapNode(niu.Function(
        input_names=["in_meta", "in_file"], function=get_trt), name="readout_time",
        iterfield=["in_meta", "in_file"], run_without_submitting=True
    )

    topup = pe.Node(TOPUP(config=_pkg_fname(
        "dmriprep", f"data/flirtsch/b02b0{'_quick' * debug}.cnf")), name="topup")

    pre_mask = pe.Node(Automask(dilate=1, outputtype="NIFTI_GZ"),

def init_reportlets_wf(output_dir, name='reportlets_wf'):
    """Set up a battery of datasinks to store reports in the right location."""
    from niworkflows.interfaces.masks import SimpleShowMaskRPT
    workflow = Workflow(name=name)

    inputnode = pe.Node(niu.IdentityInterface(
        fields=['source_file', 'dwi_ref', 'dwi_mask',
                'validation_report']),
        name='inputnode')
    mask_reportlet = pe.Node(SimpleShowMaskRPT(), name='mask_reportlet')

    ds_report_mask = pe.Node(
        DerivativesDataSink(base_directory=output_dir, desc='brain', suffix='mask',
                            datatype="figures"),
        name='ds_report_mask', run_without_submitting=True)
    ds_report_validation = pe.Node(
        DerivativesDataSink(base_directory=output_dir, desc='validation', datatype="figures"),
        name='ds_report_validation', run_without_submitting=True)

    workflow.connect([

The ``moving_mask`` in template space (matches ``standardized`` output).
    std_dseg
        The ``moving_segmentation`` in template space (matches ``standardized``
        output).
    std_tpms
        The ``moving_tpms`` in template space (matches ``standardized`` output).
    template
        Template name extracted from the input parameter ``template``, for further
        use in downstream nodes.
    template_spec
        Template specifications extracted from the input parameter ``template``, for
        further use in downstream nodes.

    """
    ntpls = len(templates)
    workflow = Workflow('anat_norm_wf')

    if templates:
        workflow.__desc__ = """\
Volume-based spatial normalization to {targets} ({targets_id}) was performed through
nonlinear registration with `antsRegistration` (ANTs {ants_ver}),
using brain-extracted versions of both T1w reference and the T1w template.
The following template{tpls} selected for spatial normalization:
""".format(
            ants_ver=ANTsInfo.version() or '(version unknown)',
            targets='%s standard space%s' % (defaultdict(
                'several'.format, {1: 'one', 2: 'two', 3: 'three', 4: 'four'})[ntpls],
                's' * (ntpls != 1)),
            targets_id=', '.join(templates),
            tpls=(' was', 's were')[ntpls != 1]
        )

Inputs
    ------
    t1w
        List of T1-weighted structural images

    Outputs
    -------
    t1w_ref
        Structural reference averaging input T1w images, defining the T1w space.
    t1w_realign_xfm
        List of affine transforms to realign input T1w images
    out_report
        Conformation report

    """
    workflow = Workflow(name=name)

    if num_t1w > 1:
        workflow.__desc__ = """\
A T1w-reference map was computed after registration of
{num_t1w} T1w images (after INU-correction) using
`mri_robust_template` [FreeSurfer {fs_ver}, @fs_template].
""".format(num_t1w=num_t1w, fs_ver=fs.Info().looseversion() or '')

    inputnode = pe.Node(niu.IdentityInterface(fields=['t1w']), name='inputnode')
    outputnode = pe.Node(niu.IdentityInterface(
        fields=['t1w_ref', 't1w_valid_list', 't1w_realign_xfm', 'out_report']),
        name='outputnode')

    # 0. Reorient T1w image(s) to RAS and resample to common voxel space
    t1w_ref_dimensions = pe.Node(TemplateDimensions(), name='t1w_ref_dimensions')
    t1w_conform = pe.MapNode(Conform(), iterfield='in_file', name='t1w_conform')

Reference image
    ref_image
        Contrast-enhanced reference image
    ref_image_brain
        Skull-stripped reference image
    dwi_mask
        Skull-stripping mask of reference image
    validation_report
        HTML reportlet indicating whether ``dwi_file`` had a valid affine

    See Also
    --------
    * :py:func:`~dmriprep.workflows.dwi.util.init_enhance_and_skullstrip_wf`

    """
    workflow = Workflow(name=name)

    inputnode = pe.Node(niu.IdentityInterface(fields=['dwi_file', 'b0_ixs']),
                        name='inputnode')
    outputnode = pe.Node(
        niu.IdentityInterface(fields=['dwi_file', 'raw_ref_image', 'ref_image',
                                      'ref_image_brain',
                                      'dwi_mask', 'validation_report']),
        name='outputnode')

    validate = pe.Node(ValidateImage(), name='validate', mem_gb=mem_gb)

    extract_b0 = pe.Node(ExtractB0(), name='extract_b0')

    reg_b0 = pe.Node(fsl.MCFLIRT(ref_vol=0, interpolation='sinc'), name='reg_b0')

    pre_mask = pe.Node(afni.Automask(dilate=1, outputtype='NIFTI_GZ'),

't1w': ['/completely/made/up/path/sub-01_T1w.nii.gz'],
            'dwi': ['/completely/made/up/path/sub-01_dwi.nii.gz']
        }
    else:
        subject_data = collect_data(layout, subject_id)[0]

    # Make sure we always go through these two checks
    if not anat_only and subject_data['dwi'] == []:
        raise Exception("No DWI data found for participant {}. "
                        "All workflows require DWI images.".format(subject_id))

    if not subject_data['t1w']:
        raise Exception("No T1w images found for participant {}. "
                        "All workflows require T1w images.".format(subject_id))

    workflow = Workflow(name=name)
    workflow.__desc__ = """
Results included in this manuscript come from preprocessing
performed using *dMRIPrep* {dmriprep_ver}
(@dmriprep; RRID:SCR_017412),
which is based on *Nipype* {nipype_ver}
(@nipype1; @nipype2; RRID:SCR_002502).

""".format(dmriprep_ver=__version__, nipype_ver=nipype_ver)
    workflow.__postdesc__ = """

For more details of the pipeline, see [the section corresponding
to workflows in *dMRIPrep*'s documentation]\
(https://nipreps.github.io/dmriprep/{dmriprep_ver}/workflows.html \
"dMRIPrep's documentation").