code: def collate_fn(examples):
      pixel_values = torch.stack(
          [example["pixel_values"] for example in examples]
      )
      input_ids = torch.tensor(
          [example["input_ids"] for example in examples],
          dtype=torch.long,
      )
      attention_mask = torch.tensor(
          [example["attention_mask"] for example in examples],
          dtype=torch.long,
      )
      return {
          "pixel_values": pixel_values,
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "return_loss": True,
      }
  
imports: import logging
  import os
  import sys
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional
  import torch
  from datasets import load_dataset
  from PIL import Image
  from torchvision.io import ImageReadMode, read_image
  from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
  from torchvision.transforms.functional import InterpolationMode
  import transformers
  from transformers import AutoImageProcessor, AutoModel, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, 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
label_desc: Use image node_id 1 for calling a global function with example usage: collate_fn(examples) and returns: dict