code: def train_pre_transforms(self, context: Context):
      return [
          LoadImaged(keys=("image", "label"), reader="ITKReader"),
          EnsureChannelFirstd(keys=("image", "label")),
          # NormalizeIntensityd(keys="image", divisor=2048.0),
          ScaleIntensityRanged(
              keys="image",
              a_min=-1000,
              a_max=1900,
              b_min=0.0,
              b_max=1.0,
              clip=True,
          ),
          GaussianSmoothd(keys="image", sigma=0.4),
          ScaleIntensityd(keys="image", minv=-1.0, maxv=1.0),
          GetCentroidsd(keys="label", centroids_key="centroids"),
          SelectVertebraAndCroppingd(keys="label"),
          RandShiftIntensityd(keys="image", offsets=0.1, prob=1.0),
          RandScaleIntensityd(keys="image", factors=0.1, prob=1.0),
          GaussianSmoothedCentroidd(keys="NA", signal_key="signal"),
          Resized(
              keys=("image", "label", "signal"),
              spatial_size=self.roi_size,
              mode=("area", "nearest", "area"),
          ),
          # SaveImaged(keys="label", output_postfix="", output_dir="/home/andres/Downloads", separate_folder=False),
          ConcatenateROId(keys="signal"),
          EnsureTyped(keys=("image", "label"), device=context.device),
          SelectItemsd(
              keys=(
                  "image",
                  "label",
                  "centroids",
                  "original_size",
                  "current_label",
                  "slices_cropped",
              )
          ),
      ]
  
imports: import logging
  import torch
  from lib.transforms.transforms import ConcatenateROId, GaussianSmoothedCentroidd, GetCentroidsd, SelectVertebraAndCroppingd
  from monai.handlers import TensorBoardImageHandler, from_engine
  from monai.inferers import SimpleInferer
  from monai.losses import DiceCELoss
  from monai.transforms import Activationsd, AsDiscreted, EnsureChannelFirstd, EnsureTyped, GaussianSmoothd, LoadImaged, RandScaleIntensityd, RandShiftIntensityd, Resized, ScaleIntensityd, ScaleIntensityRanged, SelectItemsd
  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 SegmentationVertebra obj's underlying member method code with example usage: obj.train_pre_transforms(context) and returns: list