code: def test_Segment_inputs():
      input_map = dict(
          affine_regularization=dict(
              field="opts.regtype",
          ),
          bias_fwhm=dict(
              field="opts.biasfwhm",
          ),
          bias_regularization=dict(
              field="opts.biasreg",
          ),
          clean_masks=dict(
              field="output.cleanup",
          ),
          csf_output_type=dict(
              field="output.CSF",
          ),
          data=dict(
              copyfile=False,
              field="data",
              mandatory=True,
          ),
          gaussians_per_class=dict(
              field="opts.ngaus",
          ),
          gm_output_type=dict(
              field="output.GM",
          ),
          mask_image=dict(
              extensions=None,
              field="opts.msk",
          ),
          matlab_cmd=dict(),
          mfile=dict(
              usedefault=True,
          ),
          paths=dict(),
          sampling_distance=dict(
              field="opts.samp",
          ),
          save_bias_corrected=dict(
              field="output.biascor",
          ),
          tissue_prob_maps=dict(
              field="opts.tpm",
          ),
          use_mcr=dict(),
          use_v8struct=dict(
              min_ver="8",
              usedefault=True,
          ),
          warp_frequency_cutoff=dict(
              field="opts.warpco",
          ),
          warping_regularization=dict(
              field="opts.warpreg",
          ),
          wm_output_type=dict(
              field="output.WM",
          ),
      )
      inputs = Segment.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_Segment_inputs() without return types