code: def test_BRAINSConstellationModeler_inputs():
      input_map = dict(
          BackgroundFillValue=dict(
              argstr="--BackgroundFillValue %s",
          ),
          args=dict(
              argstr="%s",
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          inputTrainingList=dict(
              argstr="--inputTrainingList %s",
              extensions=None,
          ),
          mspQualityLevel=dict(
              argstr="--mspQualityLevel %d",
          ),
          numberOfThreads=dict(
              argstr="--numberOfThreads %d",
          ),
          optimizedLandmarksFilenameExtender=dict(
              argstr="--optimizedLandmarksFilenameExtender %s",
          ),
          outputModel=dict(
              argstr="--outputModel %s",
              hash_files=False,
          ),
          rescaleIntensities=dict(
              argstr="--rescaleIntensities ",
          ),
          rescaleIntensitiesOutputRange=dict(
              argstr="--rescaleIntensitiesOutputRange %s",
              sep=",",
          ),
          resultsDir=dict(
              argstr="--resultsDir %s",
              hash_files=False,
          ),
          saveOptimizedLandmarks=dict(
              argstr="--saveOptimizedLandmarks ",
          ),
          trimRescaledIntensities=dict(
              argstr="--trimRescaledIntensities %f",
          ),
          verbose=dict(
              argstr="--verbose ",
          ),
          writedebuggingImagesLevel=dict(
              argstr="--writedebuggingImagesLevel %d",
          ),
      )
      inputs = BRAINSConstellationModeler.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_BRAINSConstellationModeler_inputs() without return types