code: def val_pre_transforms(self, context: Context):
      return [
          LoadImaged(keys=("image", "label")),
          NormalizeLabelsInDatasetd(
              keys="label", label_names=self._labels
          ),  # Specially for missing labels
          EnsureTyped(keys=("image", "label")),
          EnsureChannelFirstd(keys=("image", "label")),
          Orientationd(keys=("image", "label"), axcodes="RAS"),
          Spacingd(
              keys=("image", "label"),
              pixdim=self.target_spacing,
              mode=("bilinear", "nearest"),
          ),
          ScaleIntensityRanged(
              keys="image",
              a_min=-57,
              a_max=164,
              b_min=0.0,
              b_max=1.0,
              clip=True,
          ),
          ScaleIntensityd(keys="image", minv=-1.0, maxv=1.0),
          SelectItemsd(keys=("image", "label")),
      ]
  
file_path: MONAILabel/sample-apps/radiology/lib/trainers/segmentation_spleen.py
imports: import logging
  import torch
  from monai.apps.deepedit.transforms import NormalizeLabelsInDatasetd
  from monai.inferers import SlidingWindowInferer
  from monai.losses import DiceCELoss
  from monai.transforms import Activationsd, AsDiscreted, EnsureChannelFirstd, EnsureTyped, LoadImaged, Orientationd, RandSpatialCropd, ScaleIntensityd, ScaleIntensityRanged, SelectItemsd, Spacingd, ToTensord
  from monailabel.tasks.train.basic_train import BasicTrainTask, Context
label_desc: Use image node_id 7 for calling the SegmentationSpleen obj's underlying member method code with example usage: obj.val_pre_transforms(context) and returns: list