class_docstr: This provides Inference Engine for pre-trained Segmentation (SegResNet) model.
code: def pre_transforms(self, data=None) -> Sequence[Callable]:
      t = [
          LoadImaged(keys="image"),
          EnsureTyped(
              keys="image", device=data.get("device") if data else None
          ),
          EnsureChannelFirstd(keys="image"),
          Orientationd(keys="image", axcodes="RAS"),
          Spacingd(
              keys="image",
              pixdim=self.target_spacing,
              allow_missing_keys=True,
          ),
          NormalizeIntensityd(keys="image", nonzero=True),
          GaussianSmoothd(keys="image", sigma=0.4),
          ScaleIntensityd(keys="image", minv=-1.0, maxv=1.0),
      ]
      return t
  
file_path: MONAILabel/sample-apps/radiology/lib/infers/segmentation.py
imports: from typing import Callable, Sequence
  from lib.transforms.transforms import GetCentroidsd
  from monai.inferers import Inferer, SlidingWindowInferer
  from monai.transforms import Activationsd, AsDiscreted, EnsureChannelFirstd, EnsureTyped, GaussianSmoothd, KeepLargestConnectedComponentd, LoadImaged, NormalizeIntensityd, Orientationd, ScaleIntensityd, 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 Segmentation obj's underlying member method code with example usage: obj.pre_transforms(data) and returns: t