code: def test_CAT12Segment_inputs():
      input_map = dict(
          affine_preprocessing=dict(
              field="extopts.APP",
              usedefault=True,
          ),
          affine_regularization=dict(
              field="opts.affreg",
              usedefault=True,
          ),
          cobra=dict(
              field="output.ROImenu.atlases.hammers",
              usedefault=True,
          ),
          csf_output_dartel=dict(
              field="output.CSF.dartel",
              usedefault=True,
          ),
          csf_output_modulated=dict(
              field="output.CSF.mod",
              usedefault=True,
          ),
          csf_output_native=dict(
              field="output.CSF.native",
              usedefault=True,
          ),
          gm_output_dartel=dict(
              field="output.GM.dartel",
              usedefault=True,
          ),
          gm_output_modulated=dict(
              field="output.GM.mod",
              usedefault=True,
          ),
          gm_output_native=dict(
              field="output.GM.native",
              usedefault=True,
          ),
          hammers=dict(
              field="output.ROImenu.atlases.cobra",
              usedefault=True,
          ),
          ignore_errors=dict(
              field="extopts.ignoreErrors",
              usedefault=True,
          ),
          in_files=dict(
              copyfile=False,
              field="data",
              mandatory=True,
          ),
          initial_segmentation=dict(
              field="extopts.spm_kamap",
              usedefault=True,
          ),
          internal_resampling_process=dict(
              field="extopts.restypes.optimal",
              maxlen=2,
              minlen=2,
              usedefault=True,
          ),
          jacobianwarped=dict(
              field="output.jacobianwarped",
              usedefault=True,
          ),
          label_dartel=dict(
              field="output.label.dartel",
              usedefault=True,
          ),
          label_native=dict(
              field="output.label.native",
              usedefault=True,
          ),
          label_warped=dict(
              field="output.label.warped",
              usedefault=True,
          ),
          las_dartel=dict(
              field="output.las.dartel",
              usedefault=True,
          ),
          las_native=dict(
              field="output.las.native",
              usedefault=True,
          ),
          las_warped=dict(
              field="output.las.warped",
              usedefault=True,
          ),
          local_adaptive_seg=dict(
              field="extopts.LASstr",
              usedefault=True,
          ),
          lpba40=dict(
              field="output.ROImenu.atlases.lpba40",
              usedefault=True,
          ),
          matlab_cmd=dict(),
          mfile=dict(
              usedefault=True,
          ),
          n_jobs=dict(
              field="nproc",
              mandatory=True,
              usedefault=True,
          ),
          neuromorphometrics=dict(
              field="output.ROImenu.atlases.neuromorphometrics",
              usedefault=True,
          ),
          output_labelnative=dict(
              field="output.labelnative",
              usedefault=True,
          ),
          own_atlas=dict(
              copyfile=False,
              field="output.ROImenu.atlases.ownatlas",
              mandatory=False,
          ),
          paths=dict(),
          power_spm_inhomogeneity_correction=dict(
              field="opts.biasacc",
              usedefault=True,
          ),
          save_bias_corrected=dict(
              field="output.bias.warped",
              usedefault=True,
          ),
          shooting_tpm=dict(
              copyfile=False,
              extensions=[".hdr", ".img", ".img.gz", ".nii"],
              field="extopts.registration.shooting.shootingtpm",
              mandatory=False,
          ),
          shooting_tpm_template_1=dict(
              copyfile=False,
              extensions=[".hdr", ".img", ".img.gz", ".nii"],
              mandatory=False,
          ),
          shooting_tpm_template_2=dict(
              copyfile=False,
              extensions=[".hdr", ".img", ".img.gz", ".nii"],
              mandatory=False,
          ),
          shooting_tpm_template_3=dict(
              copyfile=False,
              extensions=[".hdr", ".img", ".img.gz", ".nii"],
              mandatory=False,
          ),
          shooting_tpm_template_4=dict(
              copyfile=False,
              extensions=[".hdr", ".img", ".img.gz", ".nii"],
              mandatory=False,
          ),
          skull_strip=dict(
              field="extopts.gcutstr",
              usedefault=True,
          ),
          surface_and_thickness_estimation=dict(
              field="surface",
              usedefault=True,
          ),
          surface_measures=dict(
              field="output.surf_measures",
              usedefault=True,
          ),
          tpm=dict(
              copyfile=False,
              field="tpm",
              mandatory=False,
          ),
          use_mcr=dict(),
          use_v8struct=dict(
              min_ver="8",
              usedefault=True,
          ),
          voxel_size=dict(
              field="extopts.vox",
              usedefault=True,
          ),
          warps=dict(
              field="output.warps",
              maxlen=2,
              minlen=2,
              usedefault=True,
          ),
          wm_hyper_intensity_correction=dict(
              field="extopts.WMHC",
              usedefault=True,
          ),
          wm_output_dartel=dict(
              field="output.WM.dartel",
              usedefault=True,
          ),
          wm_output_modulated=dict(
              field="output.WM.mod",
              usedefault=True,
          ),
          wm_output_native=dict(
              field="output.WM.native",
              usedefault=True,
          ),
      )
      inputs = CAT12Segment.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_CAT12Segment_inputs() without return types