code: def center_crop_image(row):
      # Used to generate the validation set. The main difference between
      # `crop_and_flip_image` and this method is that the validation set
      # should avoid random cropping from the full image, but instead
      # should resize and take the center crop to generate more consistent
      # outputs.
      val_transform = torchvision.transforms.Compose(
          [
              torchvision.transforms.Resize(256),
              torchvision.transforms.CenterCrop(224),
              torchvision.transforms.Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
          ]
      )
      # Make sure to use torch.tensor here to avoid a copy from numpy.
      row["image"] = val_transform(
          torch.tensor(
              np.transpose(row["image"], axes=(2, 0, 1)),
          )
          / 255.0
      )
      return row
  
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 8 for calling a global function with example usage: center_crop_image(row) and returns: row