code: def test_Normalize_inputs():
      input_map = dict(
          DCT_period_cutoff=dict(
              field="eoptions.cutoff",
          ),
          affine_regularization_type=dict(
              field="eoptions.regtype",
          ),
          apply_to_files=dict(
              copyfile=True,
              field="subj.resample",
          ),
          jobtype=dict(
              usedefault=True,
          ),
          matlab_cmd=dict(),
          mfile=dict(
              usedefault=True,
          ),
          nonlinear_iterations=dict(
              field="eoptions.nits",
          ),
          nonlinear_regularization=dict(
              field="eoptions.reg",
          ),
          out_prefix=dict(
              field="roptions.prefix",
              usedefault=True,
          ),
          parameter_file=dict(
              copyfile=False,
              extensions=None,
              field="subj.matname",
              mandatory=True,
              xor=["source", "template"],
          ),
          paths=dict(),
          source=dict(
              copyfile=True,
              field="subj.source",
              mandatory=True,
              xor=["parameter_file"],
          ),
          source_image_smoothing=dict(
              field="eoptions.smosrc",
          ),
          source_weight=dict(
              copyfile=False,
              extensions=None,
              field="subj.wtsrc",
          ),
          template=dict(
              copyfile=False,
              extensions=None,
              field="eoptions.template",
              mandatory=True,
              xor=["parameter_file"],
          ),
          template_image_smoothing=dict(
              field="eoptions.smoref",
          ),
          template_weight=dict(
              copyfile=False,
              extensions=None,
              field="eoptions.weight",
          ),
          use_mcr=dict(),
          use_v8struct=dict(
              min_ver="8",
              usedefault=True,
          ),
          write_bounding_box=dict(
              field="roptions.bb",
          ),
          write_interp=dict(
              field="roptions.interp",
          ),
          write_preserve=dict(
              field="roptions.preserve",
          ),
          write_voxel_sizes=dict(
              field="roptions.vox",
          ),
          write_wrap=dict(
              field="roptions.wrap",
          ),
      )
      inputs = Normalize.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_Normalize_inputs() without return types