code: def __init__(self, stable_diffusion_pipeline=None, num_workers=1):
      if stable_diffusion_pipeline is not None:
          self.device = stable_diffusion_pipeline.device
          self.vae = stable_diffusion_pipeline.vae
          self.unet = stable_diffusion_pipeline.unet
          self.tokenizer = stable_diffusion_pipeline.tokenizer
          self.text_encoder = stable_diffusion_pipeline.text_encoder
          self.scheduler = stable_diffusion_pipeline.scheduler
          self.safety_checker = stable_diffusion_pipeline.safety_checker
          self.feature_extractor = (
              stable_diffusion_pipeline.feature_extractor
          )
      self.num_workers = num_workers
  
imports: import os
  import torch
  import inspect
  import numpy
  from tqdm.auto import tqdm
  import multiprocessing
  from bigdl.nano.pytorch import InferenceOptimizer
  from typing import Callable, List, Optional, Union
  from diffusers import AutoencoderKL
  from transformers import CLIPTokenizer, CLIPTextModel
  from diffusers.schedulers import *
  from diffusers.utils import logging
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipeline_utils import DiffusionPipeline
  from diffusers.models import UNet2DConditionModel
label_desc: Use image node_id 1 to create a new NanoStableDiffusionPipeline object  with example: obj = NanoStableDiffusionPipeline(stable_diffusion_pipeline, num_workers)