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
  
file_path: diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py
imports: import inspect
  from typing import List, Optional, Tuple, Union
  import numpy
  import PIL.Image
  import torch
  import torch.utils.checkpoint
  from ...models import UNet2DModel, VQModel
  from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler
  from ...utils import PIL_INTERPOLATION
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 1 for calling a global function with example usage: preprocess(image) and returns: unknown