class_docstr: This provides Inference Engine for pre-trained model over Multi Atlas Labeling Beyond The Cranial Vault (BTCV)
  dataset.
code: def __init__(
      self,
      path,
      network=None,
      type=InferType.DEEPEDIT,
      labels=None,
      dimension=3,
      spatial_size=(128, 128, 64),
      target_spacing=(1.0, 1.0, 1.0),
      number_intensity_ch=1,
      description="A DeepEdit model for volumetric (3D) segmentation over 3D Images",
      **kwargs,
  ):
      super().__init__(
          path=path,
          network=network,
          type=type,
          labels=labels,
          dimension=dimension,
          description=description,
          input_key="image",
          output_label_key="pred",
          output_json_key="result",
          load_strict=False,
          **kwargs,
      )
  
      self.spatial_size = spatial_size
      self.target_spacing = target_spacing
      self.number_intensity_ch = number_intensity_ch
      self.load_strict = False
  
file_path: MONAILabel/sample-apps/radiology/lib/infers/deepedit.py
imports: import logging
  from typing import Callable, Sequence, Union
  from lib.transforms.transforms import GetCentroidsd
  from monai.apps.deepedit.transforms import AddGuidanceFromPointsDeepEditd, AddGuidanceSignalDeepEditd, DiscardAddGuidanced, ResizeGuidanceMultipleLabelDeepEditd
  from monai.inferers import Inferer, SimpleInferer
  from monai.transforms import Activationsd, AsDiscreted, EnsureChannelFirstd, EnsureTyped, LoadImaged, Orientationd, Resized, ScaleIntensityRanged, SqueezeDimd, 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 DeepEdit object from inherited base classes: BasicInferTask with example: obj = DeepEdit(path, network, type, labels, dimension, spatial_size, target_spacing, number_intensity_ch, description)