code: def test_SmoothTessellation_inputs():
      input_map = dict(
          args=dict(
              argstr="%s",
          ),
          curvature_averaging_iterations=dict(
              argstr="-a %d",
          ),
          disable_estimates=dict(
              argstr="-nw",
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          gaussian_curvature_norm_steps=dict(
              argstr="%d",
          ),
          gaussian_curvature_smoothing_steps=dict(
              argstr=" %d",
          ),
          in_file=dict(
              argstr="%s",
              copyfile=True,
              extensions=None,
              mandatory=True,
              position=-2,
          ),
          normalize_area=dict(
              argstr="-area",
          ),
          out_area_file=dict(
              argstr="-b %s",
              extensions=None,
          ),
          out_curvature_file=dict(
              argstr="-c %s",
              extensions=None,
          ),
          out_file=dict(
              argstr="%s",
              extensions=None,
              genfile=True,
              position=-1,
          ),
          seed=dict(
              argstr="-seed %d",
          ),
          smoothing_iterations=dict(
              argstr="-n %d",
          ),
          snapshot_writing_iterations=dict(
              argstr="-w %d",
          ),
          subjects_dir=dict(),
          use_gaussian_curvature_smoothing=dict(
              argstr="-g",
          ),
          use_momentum=dict(
              argstr="-m",
          ),
      )
      inputs = SmoothTessellation.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_SmoothTessellation_inputs() without return types