class_docstr: This provides Inference Engine for pre-trained spleen segmentation (UNet) model over MSD Dataset.
class_docstr_tok: ["This","provides","Inference","Engine","for","pre-trained","spleen","segmentation","(","UNet",")","model","over","MSD","Dataset","."]
code: def pre_transforms(self, data=None) -> Sequence[Callable]:
      return [
          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,
          ),
          ScaleIntensityRanged(
              keys="image",
              a_min=-57,
              a_max=164,
              b_min=0.0,
              b_max=1.0,
              clip=True,
          ),
          ScaleIntensityd(keys="image", minv=-1.0, maxv=1.0),
      ]
  
file_path: MONAILabel/sample-apps/radiology/lib/infers/segmentation_spleen.py
imports: from typing import Callable, Sequence
  from monai.inferers import Inferer, SlidingWindowInferer
  from monai.transforms import Activationsd, AsDiscreted, EnsureChannelFirstd, EnsureTyped, KeepLargestConnectedComponentd, LoadImaged, Orientationd, 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 SegmentationSpleen obj's underlying member method code with example usage: obj.pre_transforms(data) and returns: list