code: def __init__(
      self,
      model_dir,
      network,
      description="Train DeepEdit model for 3D Images",
      spatial_size=(128, 128, 64),
      target_spacing=(1.0, 1.0, 1.0),
      number_intensity_ch=1,
      deepgrow_probability_train=0.4,
      deepgrow_probability_val=1.0,
      debug_mode=False,
      **kwargs,
  ):
      self._network = network
      self.spatial_size = spatial_size
      self.target_spacing = target_spacing
      self.number_intensity_ch = number_intensity_ch
      self.deepgrow_probability_train = deepgrow_probability_train
      self.deepgrow_probability_val = deepgrow_probability_val
      self.debug_mode = debug_mode
  
      super().__init__(model_dir, description, **kwargs)
  
imports: import logging
  import torch
  from lib.transforms.transforms import NormalizeLabelsInDatasetd
  from monai.apps.deepedit.interaction import Interaction
  from monai.apps.deepedit.transforms import AddGuidanceSignalDeepEditd, AddInitialSeedPointMissingLabelsd, AddRandomGuidanceDeepEditd, FindAllValidSlicesMissingLabelsd, FindDiscrepancyRegionsDeepEditd, SplitPredsLabeld
  from monai.handlers import MeanDice, from_engine
  from monai.inferers import SimpleInferer
  from monai.losses import DiceCELoss
  from monai.transforms import Activationsd, AsDiscreted, EnsureChannelFirstd, LoadImaged, Orientationd, RandFlipd, RandRotate90d, RandShiftIntensityd, Resized, ScaleIntensityRanged, SelectItemsd, ToNumpyd, ToTensord
  from monailabel.deepedit.handlers import TensorBoardImageHandler
  from monailabel.tasks.train.basic_train import BasicTrainTask, Context
label_desc: Use image node_id 1 to create a new DeepEdit object from inherited base classes: BasicTrainTask with example: obj = DeepEdit(model_dir, network, description, spatial_size, target_spacing, number_intensity_ch, deepgrow_probability_train, deepgrow_probability_val, debug_mode)