code: def parse_and_decode_tfrecord(example_serialized):
      feature_map = {
          "image/encoded": tf.io.FixedLenFeature(
              [], dtype=tf.string, default_value=""
          ),
          "image/class/label": tf.io.FixedLenFeature(
              [], dtype=tf.int64, default_value=-1
          ),
      }
  
      features = tf.io.parse_single_example(
          example_serialized, feature_map
      )
      label = tf.cast(features["image/class/label"], dtype=tf.int32)
  
      image_buffer = features["image/encoded"]
      image_buffer = tf.reshape(image_buffer, shape=[])
      image_buffer = tf.io.decode_jpeg(image_buffer, channels=3)
      return image_buffer, label
  
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 2 for calling a global function with example usage: parse_and_decode_tfrecord(example_serialized) and returns: image_buffer,  label