code: def collate_fn(
      num_instances, examples, with_prior_preservation=False
  ):
      input_ids = []
      pixel_values = []
  
      for i in range(num_instances):
          input_ids += [
              example[f"instance_prompt_ids_{i}"]
              for example in examples
          ]
          pixel_values += [
              example[f"instance_images_{i}"] for example in examples
          ]
  
      # Concat class and instance examples for prior preservation.
      # We do this to avoid doing two forward passes.
      if with_prior_preservation:
          for i in range(num_instances):
              input_ids += [
                  example[f"class_prompt_ids_{i}"]
                  for example in examples
              ]
              pixel_values += [
                  example[f"class_images_{i}"] for example in examples
              ]
  
      pixel_values = torch.stack(pixel_values)
      pixel_values = pixel_values.to(
          memory_format=torch.contiguous_format
      ).float()
  
      input_ids = torch.cat(input_ids, dim=0)
  
      batch = {
          "input_ids": input_ids,
          "pixel_values": pixel_values,
      }
      return batch
  
imports: import argparse
  import itertools
  import json
  import logging
  import math
  import uuid
  import warnings
  from os import environ, listdir, makedirs
  from os.path import basename, join
  from pathlib import Path
  from typing import List
  import datasets
  import numpy
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration, set_seed
  from huggingface_hub import create_repo, upload_folder
  from huggingface_hub.utils import insecure_hashlib
  from PIL import Image
  from torch import dtype
  from torch.nn import Module
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 5 for calling a global function with example usage: collate_fn(num_instances, examples, with_prior_preservation) and returns: batch