code: def crop_and_flip_image(row):
      transform = torchvision.transforms.Compose(
          [
              torchvision.transforms.RandomResizedCrop(
                  size=DEFAULT_IMAGE_SIZE,
                  scale=(0.05, 1.0),
                  ratio=(0.75, 1.33),
              ),
              torchvision.transforms.RandomHorizontalFlip(),
          ]
      )
      # Make sure to use torch.tensor here to avoid a copy from numpy.
      row["image"] = transform(
          torch.tensor(np.transpose(row["image"], axes=(2, 0, 1)))
      )
      return row
  
imports: from collections import defaultdict
  import tensorflow
  import numpy
  import os
  import pandas
  import time
  import logging
  import csv
  import json
  import torchvision
  import torch
  import ray
  from ray.train.tensorflow import prepare_dataset_shard, TensorflowTrainer
  from ray.train import DataConfig, ScalingConfig
  from ray import train, tune
  from ray.tune import Tuner
  from ray.data.datasource.partitioning import Partitioning
  from tf_utils import DEFAULT_IMAGE_SIZE, NUM_CHANNELS, preprocess_image, build_tf_dataset
  from metric_utils import determine_if_memory_monitor_is_enabled_in_latest_session, get_ray_spilled_and_restored_mb, MaxMemoryUtilizationTracker
label_desc: Use image node_id 4 for calling a global function with example usage: crop_and_flip_image(row) and returns: row