code: def test_AffineInitializer_inputs():
      input_map = dict(
          args=dict(
              argstr="%s",
          ),
          dimension=dict(
              argstr="%s",
              position=0,
              usedefault=True,
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          fixed_image=dict(
              argstr="%s",
              extensions=None,
              mandatory=True,
              position=1,
          ),
          local_search=dict(
              argstr="%d",
              position=7,
              usedefault=True,
          ),
          moving_image=dict(
              argstr="%s",
              extensions=None,
              mandatory=True,
              position=2,
          ),
          num_threads=dict(
              nohash=True,
              usedefault=True,
          ),
          out_file=dict(
              argstr="%s",
              extensions=None,
              position=3,
              usedefault=True,
          ),
          principal_axes=dict(
              argstr="%d",
              position=6,
              usedefault=True,
          ),
          radian_fraction=dict(
              argstr="%f",
              position=5,
              usedefault=True,
          ),
          search_factor=dict(
              argstr="%f",
              position=4,
              usedefault=True,
          ),
      )
      inputs = AffineInitializer.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_AffineInitializer_inputs() without return types