class_docstr: This provides Inference Engine for Deepgrow 2D/3D pre-trained model.
  For More Details, Refer https://github.com/Project-MONAI/tutorials/tree/master/deepgrow/ignite
code: def __init__(
      self,
      path,
      network=None,
      type=InferType.DEEPGROW,
      labels=None,
      dimension=2,
      description="A pre-trained DeepGrow model based on UNET",
      spatial_size=(256, 256),
      model_size=(256, 256),
      **kwargs,
  ):
      super().__init__(
          path=path,
          network=network,
          type=type,
          labels=labels,
          dimension=dimension,
          description=description,
          load_strict=False,
          **kwargs,
      )
  
      self.spatial_size = spatial_size
      self.model_size = model_size
  
file_path: MONAILabel/sample-apps/radiology/lib/infers/deepgrow.py
imports: from typing import Callable, Sequence
  from monai.apps.deepgrow.transforms import AddGuidanceFromPointsd, AddGuidanceSignald, Fetch2DSliced, ResizeGuidanced, RestoreLabeld, SpatialCropGuidanced
  from monai.inferers import Inferer, SimpleInferer
  from monai.transforms import Activationsd, AsChannelLastd, AsDiscreted, EnsureChannelFirstd, EnsureTyped, LoadImaged, NormalizeIntensityd, Resized, Spacingd, ToNumpyd, Transposed
  from monailabel.interfaces.tasks.infer_v2 import InferType
  from monailabel.tasks.infer.basic_infer import BasicInferTask
label_desc: Use image node_id 1 to create a new Deepgrow object from inherited base classes: BasicInferTask with example: obj = Deepgrow(path, network, type, labels, dimension, description, spatial_size, model_size)