text: # AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
  from ....testing import assert_equal
  from ..registration import Registration
  
  
  def test_Registration_inputs():
      input_map = dict(args=dict(argstr='%s',
      ),
      collapse_output_transforms=dict(argstr='--collapse-output-transforms %d',
      usedefault=True,
      ),
      convergence_threshold=dict(requires=['number_of_iterations'],
      usedefault=True,
      ),
      convergence_window_size=dict(requires=['convergence_threshold'],
      usedefault=True,
      ),
      dimension=dict(argstr='--dimensionality %d',
      usedefault=True,
      ),
      environ=dict(nohash=True,
      usedefault=True,
      ),
      fixed_image=dict(mandatory=True,
      ),
      fixed_image_mask=dict(argstr='%s',
      ),
      float=dict(argstr='--float %d',
      ),
      ignore_exception=dict(nohash=True,
      usedefault=True,
      ),
      initial_moving_transform=dict(argstr='%s',
      xor=['initial_moving_transform_com'],
      ),
      initial_moving_transform_com=dict(argstr='%s',
      xor=['initial_moving_transform'],
      ),
      initialize_transforms_per_stage=dict(argstr='--initialize-transforms-per-stage %d',
      usedefault=True,
      ),
      interpolation=dict(argstr='%s',
      usedefault=True,
      ),
      interpolation_parameters=dict(),
      invert_initial_moving_transform=dict(requires=['initial_moving_transform'],
      xor=['initial_moving_transform_com'],
      ),
      metric=dict(mandatory=True,
      ),
      metric_item_trait=dict(),
      metric_stage_trait=dict(),
      metric_weight=dict(mandatory=True,
      requires=['metric'],
      usedefault=True,
      ),
      metric_weight_item_trait=dict(),
      metric_weight_stage_trait=dict(),
      moving_image=dict(mandatory=True,
      ),
      moving_image_mask=dict(requires=['fixed_image_mask'],
      ),
      num_threads=dict(nohash=True,
      usedefault=True,
      ),
      number_of_iterations=dict(),
      output_inverse_warped_image=dict(hash_files=False,
      requires=['output_warped_image'],
      ),
      output_transform_prefix=dict(argstr='%s',
      usedefault=True,
      ),
      output_warped_image=dict(hash_files=False,
      ),
      radius_bins_item_trait=dict(),
      radius_bins_stage_trait=dict(),
      radius_or_number_of_bins=dict(requires=['metric_weight'],
      usedefault=True,
      ),
      restore_state=dict(argstr='--restore-state %s',
      ),
      sampling_percentage=dict(requires=['sampling_strategy'],
      ),
      sampling_percentage_item_trait=dict(),
      sampling_percentage_stage_trait=dict(),
      sampling_strategy=dict(requires=['metric_weight'],
      ),
      sampling_strategy_item_trait=dict(),
      sampling_strategy_stage_trait=dict(),
      save_state=dict(argstr='--save-state %s',
      ),
      shrink_factors=dict(mandatory=True,
      ),
      sigma_units=dict(requires=['smoothing_sigmas'],
      ),
      smoothing_sigmas=dict(mandatory=True,
      ),
      terminal_output=dict(nohash=True,
      ),
      transform_parameters=dict(),
      transforms=dict(argstr='%s',
      mandatory=True,
      ),
      use_estimate_learning_rate_once=dict(),
      use_histogram_matching=dict(usedefault=True,
      ),
      winsorize_lower_quantile=dict(argstr='%s',
      usedefault=True,
      ),
      winsorize_upper_quantile=dict(argstr='%s',
      usedefault=True,
      ),
      write_composite_transform=dict(argstr='--write-composite-transform %d',
      usedefault=True,
      ),
      )
      inputs = Registration.input_spec()
  
      for key, metadata in list(input_map.items()):
          for metakey, value in list(metadata.items()):
              yield assert_equal, getattr(inputs.traits()[key], metakey), value
  
  
  def test_Registration_outputs():
      output_map = dict(composite_transform=dict(),
      forward_invert_flags=dict(),
      forward_transforms=dict(),
      inverse_composite_transform=dict(),
      inverse_warped_image=dict(),
      reverse_invert_flags=dict(),
      reverse_transforms=dict(),
      save_state=dict(),
      warped_image=dict(),
      )
      outputs = Registration.output_spec()
  
      for key, metadata in list(output_map.items()):
          for metakey, value in list(metadata.items()):
              yield assert_equal, getattr(outputs.traits()[key], metakey), value
  