code: def decode_crop_and_flip_tf_record_batch(
      tf_record_batch: pd.DataFrame,
  ) -> pd.DataFrame:
      """
      This version of the preprocessor fuses the load step with the crop and flip
      step, which should have better performance (at the cost of re-executing the
      load step on each epoch):
      - the reference tf.data implementation can use the fused decode_and_crop op
      - ray.data doesn't have to materialize the intermediate decoded batch.
      """
  
      def process_images():
          for image_buffer in tf_record_batch["image/encoded"]:
              # Each image output is ~600KB.
              image_buffer = tf.reshape(image_buffer, shape=[])
              image_buffer = tf.io.decode_jpeg(image_buffer, channels=3)
              yield tf_crop_and_flip(image_buffer).numpy()
  
      labels = (tf_record_batch["image/class/label"]).astype("float32")
      df = pd.DataFrame.from_dict(
          {"image": process_images(), "label": labels}
      )
  
      return df
  
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 5 for calling a global function with example usage: decode_crop_and_flip_tf_record_batch(tf_record_batch) and returns: df