code: def train_pre_transforms(self, context: Context):
      return [
          LoadImaged(keys=("image", "label"), reader="ITKReader"),
          NormalizeLabelsInDatasetd(
              keys="label", label_names=self._labels
          ),  # Specially for missing labels
          EnsureChannelFirstd(keys=("image", "label")),
          EnsureTyped(keys=("image", "label"), device=context.device),
          Spacingd(
              keys=("image", "label"),
              pixdim=self.target_spacing,
              mode=("bilinear", "nearest"),
          ),
          ScaleIntensityRanged(
              keys="image",
              a_min=-1000,
              a_max=1900,
              b_min=0.0,
              b_max=1.0,
              clip=True,
          ),
          CropForegroundd(
              keys=("image", "label"),
              source_key="image",
              margin=10,
              k_divisible=[
                  self.roi_size[0],
                  self.roi_size[1],
                  self.roi_size[2],
              ],
          ),
          # NormalizeIntensityd(keys="image", nonzero=True),
          GaussianSmoothd(keys="image", sigma=0.4),
          ScaleIntensityd(keys="image", minv=-1.0, maxv=1.0),
          RandSpatialCropd(
              keys=["image", "label"],
              roi_size=[
                  self.roi_size[0],
                  self.roi_size[1],
                  self.roi_size[2],
              ],
              random_size=False,
          ),
          SelectItemsd(keys=("image", "label")),
      ]
  
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 7 for calling the LocalizationVertebra obj's underlying member method code with example usage: obj.train_pre_transforms(context) and returns: list