code: def random_crop(
      image, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0)
  ):
      height, width, _ = image.shape
      area = height * width
      log_ratio = np.log(ratio)
      for _ in range(10):
          target_area = np.random.uniform(*scale) * area
          aspect_ratio = np.exp(np.random.uniform(*log_ratio))
          w = int(round(np.sqrt(target_area * aspect_ratio)))
          h = int(round(np.sqrt(target_area / aspect_ratio)))
          if 0 < w <= width and 0 < h <= height:
              i = np.random.randint(0, height - h + 1)
              j = np.random.randint(0, width - w + 1)
              return image[i : i + h, j : j + w, :]
  
      # Fallback to central crop
      in_ratio = float(width) / float(height)
      w = (
          width
          if in_ratio < min(ratio)
          else int(round(height * max(ratio)))
      )
      h = (
          height
          if in_ratio > max(ratio)
          else int(round(width / min(ratio)))
      )
      i = (height - h) // 2
      j = (width - w) // 2
      return image[i : i + h, j : j + w, :]
  
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 3 for calling a global function with example usage: random_crop(image, scale, ratio) and returns: image, image