code: def test_DiffusionTensorStreamlineTrack_inputs():
      input_map = dict(
          args=dict(
              argstr="%s",
          ),
          cutoff_value=dict(
              argstr="-cutoff %s",
              units="NA",
          ),
          desired_number_of_tracks=dict(
              argstr="-number %d",
          ),
          do_not_precompute=dict(
              argstr="-noprecomputed",
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          exclude_file=dict(
              argstr="-exclude %s",
              extensions=None,
              xor=["exclude_file", "exclude_spec"],
          ),
          exclude_spec=dict(
              argstr="-exclude %s",
              position=2,
              sep=",",
              units="mm",
              xor=["exclude_file", "exclude_spec"],
          ),
          gradient_encoding_file=dict(
              argstr="-grad %s",
              extensions=None,
              mandatory=True,
              position=-2,
          ),
          in_file=dict(
              argstr="%s",
              extensions=None,
              mandatory=True,
              position=-2,
          ),
          include_file=dict(
              argstr="-include %s",
              extensions=None,
              xor=["include_file", "include_spec"],
          ),
          include_spec=dict(
              argstr="-include %s",
              position=2,
              sep=",",
              units="mm",
              xor=["include_file", "include_spec"],
          ),
          initial_cutoff_value=dict(
              argstr="-initcutoff %s",
              units="NA",
          ),
          initial_direction=dict(
              argstr="-initdirection %s",
              units="voxels",
          ),
          inputmodel=dict(
              argstr="%s",
              position=-3,
              usedefault=True,
          ),
          mask_file=dict(
              argstr="-mask %s",
              extensions=None,
              xor=["mask_file", "mask_spec"],
          ),
          mask_spec=dict(
              argstr="-mask %s",
              position=2,
              sep=",",
              units="mm",
              xor=["mask_file", "mask_spec"],
          ),
          maximum_number_of_tracks=dict(
              argstr="-maxnum %d",
          ),
          maximum_tract_length=dict(
              argstr="-length %s",
              units="mm",
          ),
          minimum_radius_of_curvature=dict(
              argstr="-curvature %s",
              units="mm",
          ),
          minimum_tract_length=dict(
              argstr="-minlength %s",
              units="mm",
          ),
          no_mask_interpolation=dict(
              argstr="-nomaskinterp",
          ),
          out_file=dict(
              argstr="%s",
              extensions=None,
              name_source=["in_file"],
              name_template="%s_tracked.tck",
              output_name="tracked",
              position=-1,
          ),
          seed_file=dict(
              argstr="-seed %s",
              extensions=None,
              xor=["seed_file", "seed_spec"],
          ),
          seed_spec=dict(
              argstr="-seed %s",
              position=2,
              sep=",",
              units="mm",
              xor=["seed_file", "seed_spec"],
          ),
          step_size=dict(
              argstr="-step %s",
              units="mm",
          ),
          stop=dict(
              argstr="-stop",
          ),
          unidirectional=dict(
              argstr="-unidirectional",
          ),
      )
      inputs = DiffusionTensorStreamlineTrack.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_DiffusionTensorStreamlineTrack_inputs() without return types