code: def inference_autocast(forward):
      """Inference autocast."""
  
      @functools.wraps(forward)
      def wrapper(self, *args, **kwargs):
          # TODO handle cases if unet/vae has different precision
          with InferenceOptimizer.get_context(self.unet):  # TODO
              output = forward(
                  self, *args, **kwargs
              )  # Call the original function
          return output
  
      return wrapper
  
imports: import os
  import functools
  import diffusers
  from huggingface_hub import snapshot_download
  from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
  from diffusers import AutoencoderKL, UNet2DConditionModel
  from bigdl.nano.diffusion.utils.paths import *
  from bigdl.nano.pytorch import InferenceOptimizer
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.diffusion.common.optimize import load_optimized_unet, load_optimized_vae_decoder
label_desc: Use image node_id 1 for calling a global function with example usage: inference_autocast(forward) and returns: wrapper, output