code: def preprocess(image):
      w, h = image.size
      w, h = (
          x - x % 32 for x in (w, h)
      )  # resize to integer multiple of 32
      image = image.resize(
          (w, h), resample=PIL_INTERPOLATION["lanczos"]
      )
      image = np.array(image).astype(np.float32) / 255.0
      image = image[None].transpose(0, 3, 1, 2)
      image = torch.from_numpy(image)
      return 2.0 * image - 1.0
  
imports: import inspect
  import warnings
  from typing import List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from accelerate import Accelerator
  from packaging import version
  from tqdm.auto import tqdm
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: preprocess(image) and returns: unknown