class_docstr: This provides Inference Engine for pre-trained vertebra segmentation (UNet) model.
code: def __init__(
      self,
      path,
      network=None,
      target_spacing=(1.0, 1.0, 1.0),
      type=InferType.DEEPGROW,
      labels=None,
      dimension=3,
      description="A pre-trained model for volumetric (3D) vertebra segmentation 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_vertebra.py
imports: from typing import Callable, Sequence
  from lib.transforms.transforms import AddCentroidFromClicks, CacheObjectd, ConcatenateROId, CropAndCreateSignald, GetOriginalInformation, PlaceCroppedAread
  from monai.inferers import Inferer, SimpleInferer
  from monai.transforms import Activationsd, AsDiscreted, EnsureChannelFirstd, EnsureTyped, GaussianSmoothd, KeepLargestConnectedComponentd, LoadImaged, Resized, ScaleIntensityd, ScaleIntensityRanged, Spacingd, ToNumpyd
  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 SegmentationVertebra object from inherited base classes: BasicInferTask with example: obj = SegmentationVertebra(path, network, target_spacing, type, labels, dimension, description)