code: def switch_scheduler(
      self,
      scheduler_name,
      local_scheduler_path=None,
      model_id="CompVis/stable-diffusion-v1-4",
  ):
      if scheduler_name in scheduler_map:
          scheduler_cls = scheduler_map[scheduler_name]
          scheduler = None
          if local_scheduler_path is not None:
              if os.path.isdir(local_scheduler_path):
                  scheduler = scheduler_cls.from_pretrained(
                      local_scheduler_path
                  )
  
          if scheduler is None:
              scheduler = scheduler_cls.from_pretrained(
                  model_id, subfolder="scheduler"
              )
  
          self.scheduler = scheduler
      else:
          raise ValueError(
              f"Only support scheduler names {str(list(scheduler_map.keys()))}, but got {scheduler_name}"
          )
  
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 3 for calling the NanoStableDiffusionPipeline obj's underlying member method code with example usage: obj.switch_scheduler(scheduler_name, local_scheduler_path, model_id) without return types