code: def test_Bfc_inputs():
      input_map = dict(
          args=dict(
              argstr="%s",
          ),
          biasEstimateConvergenceThreshold=dict(
              argstr="--beps %f",
          ),
          biasEstimateSpacing=dict(
              argstr="-s %d",
          ),
          biasFieldEstimatesOutputPrefix=dict(
              argstr="--biasprefix %s",
          ),
          biasRange=dict(
              argstr="%s",
          ),
          controlPointSpacing=dict(
              argstr="-c %d",
          ),
          convergenceThreshold=dict(
              argstr="--eps %f",
          ),
          correctWholeVolume=dict(
              argstr="--extrapolate",
          ),
          correctedImagesOutputPrefix=dict(
              argstr="--prefix %s",
          ),
          correctionScheduleFile=dict(
              argstr="--schedule %s",
              extensions=None,
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          histogramRadius=dict(
              argstr="-r %d",
          ),
          histogramType=dict(
              argstr="%s",
          ),
          inputMRIFile=dict(
              argstr="-i %s",
              extensions=None,
              mandatory=True,
          ),
          inputMaskFile=dict(
              argstr="-m %s",
              extensions=None,
              hash_files=False,
          ),
          intermediate_file_type=dict(
              argstr="%s",
          ),
          iterativeMode=dict(
              argstr="--iterate",
          ),
          maxBias=dict(
              argstr="-U %f",
              usedefault=True,
          ),
          minBias=dict(
              argstr="-L %f",
              usedefault=True,
          ),
          outputBiasField=dict(
              argstr="--bias %s",
              extensions=None,
              hash_files=False,
          ),
          outputMRIVolume=dict(
              argstr="-o %s",
              extensions=None,
              genfile=True,
              hash_files=False,
          ),
          outputMaskedBiasField=dict(
              argstr="--maskedbias %s",
              extensions=None,
              hash_files=False,
          ),
          splineLambda=dict(
              argstr="-w %f",
          ),
          timer=dict(
              argstr="--timer",
          ),
          verbosityLevel=dict(
              argstr="-v %d",
          ),
      )
      inputs = Bfc.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_Bfc_inputs() without return types