code: def __init__(
      self,
      model_dir,
      network,
      roi_size=(96, 96, 96),
      target_spacing=(1.0, 1.0, 1.0),
      num_samples=4,
      description="Train vertebra localization model",
      **kwargs,
  ):
      self._network = network
      self.roi_size = roi_size
      self.target_spacing = target_spacing
      self.num_samples = num_samples
      super().__init__(model_dir, description, **kwargs)
  
imports: import logging
  import torch
  from lib.transforms.transforms import NormalizeLabelsInDatasetd
  from monai.handlers import TensorBoardImageHandler, from_engine
  from monai.inferers import SlidingWindowInferer
  from monai.losses import DiceCELoss
  from monai.transforms import Activationsd, AsDiscreted, CropForegroundd, EnsureChannelFirstd, EnsureTyped, GaussianSmoothd, LoadImaged, RandSpatialCropd, ScaleIntensityd, ScaleIntensityRanged, SelectItemsd, Spacingd
  from monailabel.tasks.train.basic_train import BasicTrainTask, Context
  from monailabel.tasks.train.utils import region_wise_metrics
label_desc: Use image node_id 1 to create a new LocalizationVertebra object from inherited base classes: BasicTrainTask with example: obj = LocalizationVertebra(model_dir, network, roi_size, target_spacing, num_samples, description)