code: def test_TensorMetrics_inputs():
      input_map = dict(
          args=dict(
              argstr="%s",
          ),
          component=dict(
              argstr="-num %s",
              sep=",",
              usedefault=True,
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          in_file=dict(
              argstr="%s",
              extensions=None,
              mandatory=True,
              position=-1,
          ),
          in_mask=dict(
              argstr="-mask %s",
              extensions=None,
          ),
          modulate=dict(
              argstr="-modulate %s",
          ),
          out_ad=dict(
              argstr="-ad %s",
              extensions=None,
          ),
          out_adc=dict(
              argstr="-adc %s",
              extensions=None,
          ),
          out_cl=dict(
              argstr="-cl %s",
              extensions=None,
          ),
          out_cp=dict(
              argstr="-cp %s",
              extensions=None,
          ),
          out_cs=dict(
              argstr="-cs %s",
              extensions=None,
          ),
          out_eval=dict(
              argstr="-value %s",
              extensions=None,
          ),
          out_evec=dict(
              argstr="-vector %s",
              extensions=None,
          ),
          out_fa=dict(
              argstr="-fa %s",
              extensions=None,
          ),
          out_rd=dict(
              argstr="-rd %s",
              extensions=None,
          ),
      )
      inputs = TensorMetrics.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_TensorMetrics_inputs() without return types