code: def test_MotionOutliers_inputs():
      input_map = dict(
          args=dict(
              argstr="%s",
          ),
          dummy=dict(
              argstr="--dummy=%d",
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          in_file=dict(
              argstr="-i %s",
              extensions=None,
              mandatory=True,
          ),
          mask=dict(
              argstr="-m %s",
              extensions=None,
          ),
          metric=dict(
              argstr="--%s",
          ),
          no_motion_correction=dict(
              argstr="--nomoco",
          ),
          out_file=dict(
              argstr="-o %s",
              extensions=None,
              hash_files=False,
              keep_extension=True,
              name_source="in_file",
              name_template="%s_outliers.txt",
          ),
          out_metric_plot=dict(
              argstr="-p %s",
              extensions=None,
              hash_files=False,
              keep_extension=True,
              name_source="in_file",
              name_template="%s_metrics.png",
          ),
          out_metric_values=dict(
              argstr="-s %s",
              extensions=None,
              hash_files=False,
              keep_extension=True,
              name_source="in_file",
              name_template="%s_metrics.txt",
          ),
          output_type=dict(),
          threshold=dict(
              argstr="--thresh=%g",
          ),
      )
      inputs = MotionOutliers.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_MotionOutliers_inputs() without return types