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 __init__(
      self,
      path,
      network=None,
      target_spacing=(1.0, 1.0, 1.0),
      type=InferType.SEGMENTATION,
      labels=None,
      dimension=3,
      description="A pre-trained model for volumetric (3D) segmentation of the spleen from CT image",
      **kwargs,
  ):
      super().__init__(
          path=path,
          network=network,
          type=type,
          labels=labels,
          dimension=dimension,
          description=description,
          load_strict=False,
          **kwargs,
      )
      self.target_spacing = target_spacing
  
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 1 to create a new SegmentationSpleen object from inherited base classes: BasicInferTask with example: obj = SegmentationSpleen(path, network, target_spacing, type, labels, dimension, description)