code: def preprocess(image, dtype):
      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 = jnp.array(image).astype(dtype) / 255.0
      image = image[None].transpose(0, 3, 1, 2)
      return 2.0 * image - 1.0
  
imports: import warnings
  from functools import partial
  from typing import Dict, List, Optional, Union
  import jax
  import jax.numpy
  import numpy
  from flax.core.frozen_dict import FrozenDict
  from flax.jax_utils import unreplicate
  from flax.training.common_utils import shard
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
  from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
  from ...schedulers import FlaxDDIMScheduler, FlaxDPMSolverMultistepScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler
  from ...utils import PIL_INTERPOLATION, logging, replace_example_docstring
  from ..pipeline_flax_utils import FlaxDiffusionPipeline
  from .pipeline_output import FlaxStableDiffusionPipelineOutput
  from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
label_desc: Use image node_id 4 for calling a global function with example usage: preprocess(image, dtype) and returns: unknown