code: def load_optimized_vae_decoder(
      cache_dir,
      accelerator="openvino",
      ipex=True,
      precision="float32",
      device="CPU",
      low_memory=False,
  ):
      t_start = time.perf_counter()
      decoder_path = os.path.join(cache_dir, "decoder")
      nano_vae_decoder, cache_dir = try_load_existing_model(
          {},
          decoder_path,
          accelerator=accelerator,
          ipex=ipex,
          precision=precision,
          low_memory=low_memory,
          device=device,
      )
  
      t_end = time.perf_counter()
  
      if nano_vae_decoder is None:
          raise Exception(
              f"You have to download the \
                          optimized nano vae decoder models. \
                          Expected path: {cache_dir}"
          )
      else:
          print(f"Load vae decoder in {t_end - t_start}s")
      return nano_vae_decoder
  
file_path: BigDL/python/nano/src/bigdl/nano/diffusion/common/load.py
imports: import os
  import time
  from collections import OrderedDict
  from huggingface_hub import snapshot_download
  from bigdl.nano.pytorch import InferenceOptimizer
label_desc: Use image node_id 4 for calling a global function with example usage: load_optimized_vae_decoder(cache_dir, accelerator, ipex, precision, device, low_memory) and returns: nano_vae_decoder