code: def test_ANTS_inputs():
      input_map = dict(
          affine_gradient_descent_option=dict(
              argstr="%s",
          ),
          args=dict(
              argstr="%s",
          ),
          delta_time=dict(
              requires=["number_of_time_steps"],
          ),
          dimension=dict(
              argstr="%d",
              position=1,
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          fixed_image=dict(
              mandatory=True,
          ),
          gradient_step_length=dict(
              requires=["transformation_model"],
          ),
          metric=dict(
              mandatory=True,
          ),
          metric_weight=dict(
              mandatory=True,
              requires=["metric"],
              usedefault=True,
          ),
          mi_option=dict(
              argstr="--MI-option %s",
              sep="x",
          ),
          moving_image=dict(
              argstr="%s",
              mandatory=True,
          ),
          num_threads=dict(
              nohash=True,
              usedefault=True,
          ),
          number_of_affine_iterations=dict(
              argstr="--number-of-affine-iterations %s",
              sep="x",
          ),
          number_of_iterations=dict(
              argstr="--number-of-iterations %s",
              sep="x",
          ),
          number_of_time_steps=dict(
              requires=["gradient_step_length"],
          ),
          output_transform_prefix=dict(
              argstr="--output-naming %s",
              mandatory=True,
              usedefault=True,
          ),
          radius=dict(
              mandatory=True,
              requires=["metric"],
          ),
          regularization=dict(
              argstr="%s",
          ),
          regularization_deformation_field_sigma=dict(
              requires=["regularization"],
          ),
          regularization_gradient_field_sigma=dict(
              requires=["regularization"],
          ),
          smoothing_sigmas=dict(
              argstr="--gaussian-smoothing-sigmas %s",
              sep="x",
          ),
          subsampling_factors=dict(
              argstr="--subsampling-factors %s",
              sep="x",
          ),
          symmetry_type=dict(
              requires=["delta_time"],
          ),
          transformation_model=dict(
              argstr="%s",
              mandatory=True,
          ),
          use_histogram_matching=dict(
              argstr="%s",
              usedefault=True,
          ),
      )
      inputs = ANTS.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_ANTS_inputs() without return types