code: def get_openvino_config(self, precision):
      config = {}
      if precision == "float32":
          config["INFERENCE_PRECISION_HINT"] = "f32"
  
      if self.num_workers > 1:
          # default use all physical cores
          import psutil
  
          core_num = psutil.cpu_count(logical=False)
          core_num = int(core_num / self.num_workers)
          config["CPU_THREADS_NUM"] = str(core_num)
          config["CPU_BIND_THREAD"] = "NO"
      return config
  
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 8 for calling the NanoStableDiffusionPipeline obj's underlying member method code with example usage: obj.get_openvino_config(precision) and returns: config