class_docstr: This provides Inference Engine for pre-trained vertebra localization (UNet) model.
code: def writer(self, data, extension=None, dtype=None) -> Tuple[Any, Any]:
      if data.get("pipeline_mode", False):
          return {"image": data["image"], "pred": data["pred"]}, data[
              "result"
          ]
  
      return super().writer(data, extension, dtype)
  
file_path: MONAILabel/sample-apps/radiology/lib/infers/localization_vertebra.py
imports: from typing import Any, Callable, Sequence, Tuple, Union
  import torch
  from lib.transforms.transforms import CacheObjectd, VertebraLocalizationSegmentation
  from monai.inferers import Inferer, SlidingWindowInferer
  from monai.transforms import Activationsd, AsDiscreted, CropForegroundd, EnsureChannelFirstd, EnsureTyped, GaussianSmoothd, KeepLargestConnectedComponentd, LoadImaged, ScaleIntensityd, ScaleIntensityRanged, Spacingd
  from monailabel.interfaces.tasks.infer_v2 import InferType
  from monailabel.tasks.infer.basic_infer import BasicInferTask
  from monailabel.transform.post import Restored
label_desc: Use image node_id 6 for calling the LocalizationVertebra obj's underlying member method code with example usage: obj.writer(data, extension, dtype) and returns: super, dict,  data