code: def test_ReconAll_inputs():
      input_map = dict(
          FLAIR_file=dict(
              argstr="-FLAIR %s",
              extensions=None,
              min_ver="5.3.0",
              requires=["subject_id"],
          ),
          T1_files=dict(
              argstr="-i %s...",
              requires=["subject_id"],
          ),
          T2_file=dict(
              argstr="-T2 %s",
              extensions=None,
              min_ver="5.3.0",
              requires=["subject_id"],
          ),
          args=dict(
              argstr="%s",
          ),
          base_template_id=dict(
              argstr="-base %s",
              requires=["base_timepoint_ids"],
              xor=["subject_id", "longitudinal_timepoint_id"],
          ),
          base_timepoint_ids=dict(
              argstr="-base-tp %s...",
          ),
          big_ventricles=dict(
              argstr="-bigventricles",
          ),
          brainstem=dict(
              argstr="-brainstem-structures",
              requires=["subject_id"],
          ),
          directive=dict(
              argstr="-%s",
              position=0,
              usedefault=True,
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          expert=dict(
              argstr="-expert %s",
              extensions=None,
          ),
          flags=dict(
              argstr="%s",
          ),
          hemi=dict(
              argstr="-hemi %s",
              requires=["subject_id"],
          ),
          hippocampal_subfields_T1=dict(
              argstr="-hippocampal-subfields-T1",
              min_ver="6.0.0",
              requires=["subject_id"],
          ),
          hippocampal_subfields_T2=dict(
              argstr="-hippocampal-subfields-T2 %s %s",
              min_ver="6.0.0",
              requires=["subject_id"],
          ),
          hires=dict(
              argstr="-hires",
              min_ver="6.0.0",
          ),
          longitudinal_template_id=dict(
              argstr="%s",
              position=2,
          ),
          longitudinal_timepoint_id=dict(
              argstr="-long %s",
              position=1,
              requires=["longitudinal_template_id"],
              xor=["subject_id", "base_template_id"],
          ),
          mprage=dict(
              argstr="-mprage",
              requires=["subject_id"],
          ),
          mri_aparc2aseg=dict(
              xor=["expert"],
          ),
          mri_ca_label=dict(
              xor=["expert"],
          ),
          mri_ca_normalize=dict(
              xor=["expert"],
          ),
          mri_ca_register=dict(
              xor=["expert"],
          ),
          mri_edit_wm_with_aseg=dict(
              xor=["expert"],
          ),
          mri_em_register=dict(
              xor=["expert"],
          ),
          mri_fill=dict(
              xor=["expert"],
          ),
          mri_mask=dict(
              xor=["expert"],
          ),
          mri_normalize=dict(
              xor=["expert"],
          ),
          mri_pretess=dict(
              xor=["expert"],
          ),
          mri_remove_neck=dict(
              xor=["expert"],
          ),
          mri_segment=dict(
              xor=["expert"],
          ),
          mri_segstats=dict(
              xor=["expert"],
          ),
          mri_tessellate=dict(
              xor=["expert"],
          ),
          mri_watershed=dict(
              xor=["expert"],
          ),
          mris_anatomical_stats=dict(
              xor=["expert"],
          ),
          mris_ca_label=dict(
              xor=["expert"],
          ),
          mris_fix_topology=dict(
              xor=["expert"],
          ),
          mris_inflate=dict(
              xor=["expert"],
          ),
          mris_make_surfaces=dict(
              xor=["expert"],
          ),
          mris_register=dict(
              xor=["expert"],
          ),
          mris_smooth=dict(
              xor=["expert"],
          ),
          mris_sphere=dict(
              xor=["expert"],
          ),
          mris_surf2vol=dict(
              xor=["expert"],
          ),
          mrisp_paint=dict(
              xor=["expert"],
          ),
          openmp=dict(
              argstr="-openmp %d",
          ),
          parallel=dict(
              argstr="-parallel",
          ),
          subject_id=dict(
              argstr="-subjid %s",
              xor=["base_template_id", "longitudinal_timepoint_id"],
          ),
          subjects_dir=dict(
              argstr="-sd %s",
              genfile=True,
              hash_files=False,
          ),
          talairach=dict(
              xor=["expert"],
          ),
          use_FLAIR=dict(
              argstr="-FLAIRpial",
              min_ver="5.3.0",
              xor=["use_T2"],
          ),
          use_T2=dict(
              argstr="-T2pial",
              min_ver="5.3.0",
              xor=["use_FLAIR"],
          ),
          xopts=dict(
              argstr="-xopts-%s",
          ),
      )
      inputs = ReconAll.input_spec()
  
      for key, metadata in list(input_map.items()):
          for metakey, value in list(metadata.items()):
              assert getattr(inputs.traits()[key], metakey) == value
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_ReconAll_inputs() without return types