code: def _update_transformation_fn(self):
      if self.getTransformationFn() is None:
          if self.getTransformFn() is None:
              crop_size = self.getInputShapes()[0][-1]
              transform = transforms.Compose(
                  [
                      transforms.RandomResizedCrop(crop_size),
                      transforms.RandomHorizontalFlip(),
                      transforms.ToTensor(),
                      transforms.Normalize(
                          mean=[0.485, 0.456, 0.406],
                          std=[0.229, 0.224, 0.225],
                      ),
                  ]
              )
              self.setTransformFn(transform)
  
          image_col = self.getImageCol()
          label_col = self.getLabelCol()
          transform = self.getTransformFn()
  
          def _transform_row(row):
              path = row[image_col]
              label = row[label_col]
              image = Image.open(path).convert("RGB")
              image = transform(image).numpy()
              return {image_col: image, label_col: label}
  
          self.setTransformationFn(_transform_row)
  
imports: import sys
  import torchvision.transforms
  from horovod.spark.common.backend import SparkBackend
  from horovod.spark.lightning import TorchEstimator
  from PIL import Image
  from pyspark.context import SparkContext
  from pyspark.ml.param.shared import Param, Params
  from pytorch_lightning.utilities import _module_available
  from synapse.ml.dl.DeepVisionModel import DeepVisionModel
  from synapse.ml.dl.LitDeepVisionModel import LitDeepVisionModel
  from synapse.ml.dl.utils import keywords_catch, get_or_create_backend
  from synapse.ml.dl.PredictionParams import VisionPredictionParams
label_desc: Use image node_id 20 for calling the DeepVisionClassifier obj's underlying member method code with example usage: obj._update_transformation_fn() and returns: dict