class_docstr: This provides Inference Engine for pre-trained vertebra localization (UNet) model.
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) vertebra localization 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/localization_vertebra.py
imports: from typing import Any, Callable, Sequence, Tuple, Union
  import torch
  from lib.transforms.transforms import CacheObjectd, VertebraLocalizationSegmentation
  from monai.inferers import Inferer, SlidingWindowInferer
  from monai.transforms import Activationsd, AsDiscreted, CropForegroundd, EnsureChannelFirstd, EnsureTyped, GaussianSmoothd, KeepLargestConnectedComponentd, LoadImaged, 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 LocalizationVertebra object from inherited base classes: BasicInferTask with example: obj = LocalizationVertebra(path, network, target_spacing, type, labels, dimension, description)