class_docstr: This provides Inference Engine for pre-trained spine localization (UNet) model.
code: def writer(self, data, extension=None, dtype=None):
      if data.get("pipeline_mode", False):
          return {
              "image": data["image_cached"],
              "pred": data["pred"],
          }, {}
  
      return super().writer(data, extension, dtype)
  
file_path: MONAILabel/sample-apps/radiology/lib/infers/localization_spine.py
imports: from typing import Callable, Sequence
  from lib.transforms.transforms import BinaryMaskd, CacheObjectd
  from monai.inferers import Inferer, SlidingWindowInferer
  from monai.transforms import Activationsd, AsDiscreted, 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 5 for calling the LocalizationSpine obj's underlying member method code with example usage: obj.writer(data, extension, dtype) and returns: super, dict,  dict