code: def crop_and_flip_image_batch(image_batch):
      image_batch["image"] = transform(
          # Make sure to use torch.tensor here to avoid a copy from numpy.
          # Original dims are (batch_size, channels, height, width).
          torch.tensor(
              np.transpose(image_batch["image"], axes=(0, 3, 1, 2))
              / 255.0
          )
      )
      return image_batch
  
imports: import ray
  import torch
  import torchvision
  from torchvision.transforms.functional import pil_to_tensor
  import os
  import tensorflow
  import numpy
  from PIL import Image
  from typing import TYPE_CHECKING, Iterator, Callable, Any
  import pandas
  from benchmark import Benchmark
  import streaming
  from streaming import LocalDataset, StreamingDataset
  from ray.data._internal.delegating_block_builder import DelegatingBlockBuilder
label_desc: Use image node_id 9 for calling a global function with example usage: crop_and_flip_image_batch(image_batch) and returns: image_batch