code: def test_fibertrack_inputs():
      input_map = dict(
          args=dict(
              argstr="%s",
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          forbidden_label=dict(
              argstr="--forbidden_label %d",
          ),
          force=dict(
              argstr="--force ",
          ),
          input_roi_file=dict(
              argstr="--input_roi_file %s",
              extensions=None,
          ),
          input_tensor_file=dict(
              argstr="--input_tensor_file %s",
              extensions=None,
          ),
          max_angle=dict(
              argstr="--max_angle %f",
          ),
          min_fa=dict(
              argstr="--min_fa %f",
          ),
          output_fiber_file=dict(
              argstr="--output_fiber_file %s",
              hash_files=False,
          ),
          really_verbose=dict(
              argstr="--really_verbose ",
          ),
          source_label=dict(
              argstr="--source_label %d",
          ),
          step_size=dict(
              argstr="--step_size %f",
          ),
          target_label=dict(
              argstr="--target_label %d",
          ),
          verbose=dict(
              argstr="--verbose ",
          ),
          whole_brain=dict(
              argstr="--whole_brain ",
          ),
      )
      inputs = fibertrack.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_fibertrack_inputs() without return types