code: def train_pre_transforms(self, context: Context):
      return [
          LoadImaged(keys=("image", "label"), dtype=np.uint8),
          EnsureTyped(keys=("image", "label")),
          EnsureChannelFirstd(keys=("image", "label")),
          RandTorchVisiond(
              keys="image",
              name="ColorJitter",
              # prob=0.5,
              brightness=64.0 / 255.0,
              contrast=0.75,
              saturation=0.25,
              hue=0.04,
          ),
          RandFlipd(keys=("image", "label"), prob=0.5),
          RandRotate90d(
              keys=("image", "label"),
              prob=0.5,
              max_k=3,
              spatial_axes=(0, 1),
          ),
          ScaleIntensityRanged(
              keys="image",
              a_min=0.0,
              a_max=255.0,
              b_min=-1.0,
              b_max=1.0,
          ),
      ]
  
imports: import logging
  import os
  import numpy
  import torch
  from ignite.metrics import Accuracy
  from lib.handlers import TensorBoardImageHandler
  from lib.utils import split_dataset
  from monai.handlers import from_engine
  from monai.inferers import SimpleInferer
  from monai.losses import DiceCELoss
  from monai.transforms import Activationsd, AsDiscreted, EnsureChannelFirstd, EnsureTyped, LoadImaged, RandFlipd, RandRotate90d, RandTorchVisiond, ScaleIntensityRangeD, ScaleIntensityRanged
  from monailabel.interfaces.datastore import Datastore
  from monailabel.tasks.train.basic_train import BasicTrainTask, Context
label_desc: Use image node_id 6 for calling the SegmentationNuclei obj's underlying member method code with example usage: obj.train_pre_transforms(context) and returns: list