code: def __init__(
      self,
      path,
      model_3d: InferTask,
      network=None,
      type=InferType.DEEPGROW,
      dimension=3,
      description="Combines Deepgrow 2D model with any 3D segmentation/deepgrow model",
      spatial_size=(256, 256),
      model_size=(256, 256),
      batch_size=32,
      min_point_density=10,
      max_random_points=10,
      random_point_density=1000,
      output_largest_cc=False,
  ):
      super().__init__(
          path=path,
          network=network,
          type=type,
          labels=None,
          dimension=dimension,
          description=description,
          config={
              "cache_transforms": True,
              "cache_transforms_in_memory": True,
              "cache_transforms_ttl": 300,
          },
          load_strict=False,
      )
      self.model_3d = model_3d
      self.spatial_size = spatial_size
      self.model_size = model_size
  
      self.batch_size = batch_size
      self.min_point_density = min_point_density
      self.max_random_points = max_random_points
      self.random_point_density = random_point_density
      self.output_largest_cc = output_largest_cc
  
imports: import logging
  import os
  from typing import Callable, Sequence
  import numpy
  import torch
  from monai.apps.deepgrow.transforms import AddGuidanceFromPointsd, AddGuidanceSignald, AddInitialSeedPointd, ResizeGuidanced, RestoreLabeld, SpatialCropGuidanced
  from monai.inferers import Inferer, SimpleInferer
  from monai.transforms import AsChannelLastd, EnsureChannelFirstd, LoadImage, LoadImaged, NormalizeIntensityd, Resized, Spacingd, Transpose, Transposed
  from monailabel.interfaces.tasks.infer_v2 import InferTask, InferType
  from monailabel.tasks.infer.basic_infer import BasicInferTask
  from monailabel.transform.post import BoundingBoxd, LargestCCd
label_desc: Use image node_id 1 to create a new InferDeepgrowPipeline object from inherited base classes: BasicInferTask with example: obj = InferDeepgrowPipeline(path, model_3d, network, type, dimension, description, spatial_size, model_size, batch_size, min_point_density, max_random_points, random_point_density, output_largest_cc)