code: def test_gtractCostFastMarching_inputs():
      input_map = dict(
          anisotropyWeight=dict(
              argstr="--anisotropyWeight %f",
          ),
          args=dict(
              argstr="%s",
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          inputAnisotropyVolume=dict(
              argstr="--inputAnisotropyVolume %s",
              extensions=None,
          ),
          inputStartingSeedsLabelMapVolume=dict(
              argstr="--inputStartingSeedsLabelMapVolume %s",
              extensions=None,
          ),
          inputTensorVolume=dict(
              argstr="--inputTensorVolume %s",
              extensions=None,
          ),
          numberOfThreads=dict(
              argstr="--numberOfThreads %d",
          ),
          outputCostVolume=dict(
              argstr="--outputCostVolume %s",
              hash_files=False,
          ),
          outputSpeedVolume=dict(
              argstr="--outputSpeedVolume %s",
              hash_files=False,
          ),
          seedThreshold=dict(
              argstr="--seedThreshold %f",
          ),
          startingSeedsLabel=dict(
              argstr="--startingSeedsLabel %d",
          ),
          stoppingValue=dict(
              argstr="--stoppingValue %f",
          ),
      )
      inputs = gtractCostFastMarching.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_gtractCostFastMarching_inputs() without return types