class_docstr: This provides Inference Engine for pre-trained spine localization (UNet) model.
code: def post_transforms(self, data=None) -> Sequence[Callable]:
      applied_labels = (
          list(self.labels.values())
          if isinstance(self.labels, dict)
          else self.labels
      )
      return [
          EnsureTyped(
              keys="pred", device=data.get("device") if data else None
          ),
          Activationsd(keys="pred", softmax=True),
          AsDiscreted(keys="pred", argmax=True),
          KeepLargestConnectedComponentd(keys="pred"),
          BinaryMaskd(keys="pred"),
          Restored(keys="pred", ref_image="image"),
      ]
  
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 4 for calling the LocalizationSpine obj's underlying member method code with example usage: obj.post_transforms(data) and returns: list