code: def convert_to_np(image, resolution):
      image = image.convert("RGB").resize((resolution, resolution))
      return np.array(image).transpose(2, 0, 1)
  
imports: import argparse
  import logging
  import math
  import os
  import shutil
  from pathlib import Path
  import accelerate
  import datasets
  import numpy
  import PIL
  import requests
  import torch
  import torch.nn
  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 datasets import load_dataset
  from huggingface_hub import create_repo, upload_folder
  from packaging import version
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInstructPix2PixPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import EMAModel
  from diffusers.utils import check_min_version, deprecate, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.torch_utils import is_compiled_module
label_desc: Use image node_id 2 for calling a global function with example usage: convert_to_np(image, resolution) and returns: np