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 post_transforms(self, data=None) -> Sequence[Callable]:
      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"),
          Restored(keys="pred", ref_image="image"),
      ]
  
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 5 for calling the SegmentationSpleen obj's underlying member method code with example usage: obj.post_transforms(data) and returns: list