code: def decode_image_crop_and_flip(row):
      row["image"] = Image.frombytes(
          "RGB", (row["height"], row["width"]), row["image"]
      )
      # Convert back np to avoid storing a np.object array.
      return {
          "image": np.array(
              transform(pil_to_tensor(row["image"]) / 255.0)
          )
      }
  
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 10 for calling a global function with example usage: decode_image_crop_and_flip(row) and returns: dict