class_docstr: This provides Inference Engine for pre-trained spine localization (UNet) model.
code: def pre_transforms(self, data=None) -> Sequence[Callable]:
      return [
          LoadImaged(keys="image", reader="ITKReader"),
          EnsureTyped(
              keys="image", device=data.get("device") if data else None
          ),
          EnsureChannelFirstd(keys="image"),
          CacheObjectd(keys="image"),
          Spacingd(keys="image", pixdim=self.target_spacing),
          ScaleIntensityRanged(
              keys="image",
              a_min=-1000,
              a_max=1900,
              b_min=0.0,
              b_max=1.0,
              clip=True,
          ),
          GaussianSmoothd(keys="image", sigma=0.4),
          ScaleIntensityd(keys="image", minv=-1.0, maxv=1.0),
      ]
  
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 2 for calling the LocalizationSpine obj's underlying member method code with example usage: obj.pre_transforms(data) and returns: list