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 inferer(self, data=None) -> Inferer:
      return SlidingWindowInferer(
          roi_size=[160, 160, 160],
          sw_batch_size=1,
          overlap=0.25,
      )
  
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 3 for calling the SegmentationSpleen obj's underlying member method code with example usage: obj.inferer(data) and returns: SlidingWindowInferer