code: def collate_fn(examples):
      pixel_values = torch.stack(
          [example["pixel_values"] for example in examples]
      )
      labels = torch.tensor([example["labels"] for example in examples])
      return {"pixel_values": pixel_values, "labels": labels}
  
imports: import logging
  import os
  import sys
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional
  import evaluate
  import numpy
  import torch
  import transformers
  from datasets import load_dataset
  from PIL import Image
  from torchvision.transforms import CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor
  from transformers import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, AutoImageProcessor, AutoModelForImageClassification, HfArgumentParser, set_seed
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
  from optimum.onnxruntime import ORTTrainer, ORTTrainingArguments
label_desc: Use image node_id 2 for calling a global function with example usage: collate_fn(examples) and returns: dict