code: def load_optimized_ov_unet(
      name_or_path, nano_device="iGPU", suffix=None
  ):
      from pathlib import Path
  
      if name_or_path is not None:
          if not isinstance(name_or_path, Path):
              from ldm.invoke.optimize.nano_optimize import (
                  get_local_path_from_repo_id,
              )
  
              name_or_path = get_local_path_from_repo_id(name_or_path)
          name_or_path = os.path.join(name_or_path, "unet")
          if nano_device not in ["CPU", "iGPU", "dGPU"]:
              invalidInputError(
                  nano_device in ["CPU", "iGPU", "dGPU"],
                  errMsg=f"Only support device `CPU`, `iGPU` and `dGPU`, but got {nano_device}",
              )
          loaded_unet = load_optimized_unet(
              unet_attributes=None,
              accelerator="openvino",
              precision="float16",
              device=nano_device,
              cache_dir=name_or_path,
              additional_suffix=suffix,
          )
          return loaded_unet
      else:
          return None
  
imports: import os
  import time
  import torch
  import shutil
  from collections import OrderedDict
  from diffusers.models import ControlNetModel
  from huggingface_hub import snapshot_download
  from ..common.load import *
  from ..utils.paths import *
  from bigdl.nano.pytorch import InferenceOptimizer
  from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 12 for calling a global function with example usage: load_optimized_ov_unet(name_or_path, nano_device, suffix) and returns: loaded_unet, None