code: def random_resized_crop(
      image, size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0)
  ):
      size = (size, size) if isinstance(size, int) else size
      image = random_crop(image, scale, ratio)
      image = tf.image.resize(image, size)
      return image
  
imports: import json
  import logging
  import os
  import sys
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional
  import evaluate
  import numpy
  import tensorflow
  from datasets import load_dataset
  from PIL import Image
  import transformers
  from transformers import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, AutoImageProcessor, DefaultDataCollator, HfArgumentParser, PushToHubCallback, TFAutoModelForImageClassification, TFTrainingArguments, create_optimizer, set_seed
  from transformers.keras_callbacks import KerasMetricCallback
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
label_desc: Use image node_id 4 for calling a global function with example usage: random_resized_crop(image, size, scale, ratio) and returns: image