code: def test_Atropos_inputs():
      input_map = dict(
          args=dict(
              argstr="%s",
          ),
          convergence_threshold=dict(
              requires=["n_iterations"],
          ),
          dimension=dict(
              argstr="--image-dimensionality %d",
              usedefault=True,
          ),
          environ=dict(
              nohash=True,
              usedefault=True,
          ),
          icm_use_synchronous_update=dict(
              argstr="%s",
          ),
          initialization=dict(
              argstr="%s",
              mandatory=True,
              requires=["number_of_tissue_classes"],
          ),
          intensity_images=dict(
              argstr="--intensity-image %s...",
              mandatory=True,
          ),
          kmeans_init_centers=dict(),
          likelihood_model=dict(
              argstr="--likelihood-model %s",
          ),
          mask_image=dict(
              argstr="--mask-image %s",
              extensions=None,
              mandatory=True,
          ),
          maximum_number_of_icm_terations=dict(
              requires=["icm_use_synchronous_update"],
          ),
          mrf_radius=dict(
              requires=["mrf_smoothing_factor"],
          ),
          mrf_smoothing_factor=dict(
              argstr="%s",
          ),
          n_iterations=dict(
              argstr="%s",
          ),
          num_threads=dict(
              nohash=True,
              usedefault=True,
          ),
          number_of_tissue_classes=dict(
              mandatory=True,
          ),
          out_classified_image_name=dict(
              argstr="%s",
              extensions=None,
              genfile=True,
              hash_files=False,
          ),
          output_posteriors_name_template=dict(
              usedefault=True,
          ),
          posterior_formulation=dict(
              argstr="%s",
          ),
          prior_image=dict(),
          prior_probability_threshold=dict(
              requires=["prior_weighting"],
          ),
          prior_weighting=dict(),
          save_posteriors=dict(),
          use_mixture_model_proportions=dict(
              requires=["posterior_formulation"],
          ),
          use_random_seed=dict(
              argstr="--use-random-seed %d",
              usedefault=True,
          ),
      )
      inputs = Atropos.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_Atropos_inputs() without return types