code: def test_ExpertAutomatedRegistration_inputs():
      input_map = dict(
          affineMaxIterations=dict(
              argstr="--affineMaxIterations %d",
          ),
          affineSamplingRatio=dict(
              argstr="--affineSamplingRatio %f",
          ),
          args=dict(
              argstr="%s",
          ),
          bsplineMaxIterations=dict(
              argstr="--bsplineMaxIterations %d",
          ),
          bsplineSamplingRatio=dict(
              argstr="--bsplineSamplingRatio %f",
          ),
          controlPointSpacing=dict(
              argstr="--controlPointSpacing %d",
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          expectedOffset=dict(
              argstr="--expectedOffset %f",
          ),
          expectedRotation=dict(
              argstr="--expectedRotation %f",
          ),
          expectedScale=dict(
              argstr="--expectedScale %f",
          ),
          expectedSkew=dict(
              argstr="--expectedSkew %f",
          ),
          fixedImage=dict(
              argstr="%s",
              extensions=None,
              position=-2,
          ),
          fixedImageMask=dict(
              argstr="--fixedImageMask %s",
              extensions=None,
          ),
          fixedLandmarks=dict(
              argstr="--fixedLandmarks %s...",
          ),
          initialization=dict(
              argstr="--initialization %s",
          ),
          interpolation=dict(
              argstr="--interpolation %s",
          ),
          loadTransform=dict(
              argstr="--loadTransform %s",
              extensions=None,
          ),
          metric=dict(
              argstr="--metric %s",
          ),
          minimizeMemory=dict(
              argstr="--minimizeMemory ",
          ),
          movingImage=dict(
              argstr="%s",
              extensions=None,
              position=-1,
          ),
          movingLandmarks=dict(
              argstr="--movingLandmarks %s...",
          ),
          numberOfThreads=dict(
              argstr="--numberOfThreads %d",
          ),
          randomNumberSeed=dict(
              argstr="--randomNumberSeed %d",
          ),
          registration=dict(
              argstr="--registration %s",
          ),
          resampledImage=dict(
              argstr="--resampledImage %s",
              hash_files=False,
          ),
          rigidMaxIterations=dict(
              argstr="--rigidMaxIterations %d",
          ),
          rigidSamplingRatio=dict(
              argstr="--rigidSamplingRatio %f",
          ),
          sampleFromOverlap=dict(
              argstr="--sampleFromOverlap ",
          ),
          saveTransform=dict(
              argstr="--saveTransform %s",
              hash_files=False,
          ),
          verbosityLevel=dict(
              argstr="--verbosityLevel %s",
          ),
      )
      inputs = ExpertAutomatedRegistration.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_ExpertAutomatedRegistration_inputs() without return types