How to use the smriprep.interfaces.templateflow.TemplateFlowSelect function in smriprep

out_fields = [
        'anat2std_xfm',
        'standardized',
        'std2anat_xfm',
        'std_dseg',
        'std_mask',
        'std_tpms',
        'template',
        'template_spec',
    ]
    poutputnode = pe.Node(niu.IdentityInterface(fields=out_fields), name='poutputnode')

    split_desc = pe.Node(TemplateDesc(), run_without_submitting=True, name='split_desc')

    tf_select = pe.Node(TemplateFlowSelect(resolution=1 + debug),
                        name='tf_select', run_without_submitting=True)

    # With the improvements from poldracklab/niworkflows#342 this truncation is now necessary
    trunc_mov = pe.Node(ImageMath(operation='TruncateImageIntensity', op2='0.01 0.999 256'),
                        name='trunc_mov')

    registration = pe.Node(RobustMNINormalization(
        float=True, flavor=['precise', 'testing'][debug],
    ), name='registration', n_procs=omp_nthreads, mem_gb=2)

    # Resample T1w-space inputs
    tpl_moving = pe.Node(ApplyTransforms(
        dimension=3, default_value=0, float=True,
        interpolation='LanczosWindowedSinc'), name='tpl_moving')

    std_mask = pe.Node(ApplyTransforms(interpolation='MultiLabel'), name='std_mask')

dismiss_entities=("session",)),
        name='ds_t1w_dseg_mask_report', run_without_submitting=True)

    workflow.connect([
        (inputnode, t1w_conform_check, [('t1w_conform_report', 'in_file')]),
        (t1w_conform_check, ds_t1w_conform_report, [('out', 'in_file')]),
        (inputnode, ds_t1w_conform_report, [('source_file', 'source_file')]),
        (inputnode, ds_t1w_dseg_mask_report, [('source_file', 'source_file')]),
        (inputnode, seg_rpt, [('t1w_preproc', 'in_file'),
                              ('t1w_mask', 'in_mask'),
                              ('t1w_dseg', 'in_rois')]),
        (seg_rpt, ds_t1w_dseg_mask_report, [('out_report', 'in_file')]),
    ])

    # Generate reportlets showing spatial normalization
    tf_select = pe.Node(TemplateFlowSelect(resolution=1),
                        name='tf_select', run_without_submitting=True)
    norm_msk = pe.Node(niu.Function(
        function=_rpt_masks, output_names=['before', 'after'],
        input_names=['mask_file', 'before', 'after', 'after_mask']),
        name='norm_msk')
    norm_rpt = pe.Node(SimpleBeforeAfter(), name='norm_rpt', mem_gb=0.1)
    norm_rpt.inputs.after_label = 'Participant'  # after

    ds_std_t1w_report = pe.Node(
        DerivativesDataSink(base_directory=output_dir, suffix='T1w', datatype="figures",
                            dismiss_entities=("session",)),
        name='ds_std_t1w_report', run_without_submitting=True)

    workflow.connect([
        (inputnode, tf_select, [('template', 'template')]),
        (inputnode, norm_rpt, [('template', 'before_label')]),