code: def optimize_vae(
      vae,
      unet_in_channels=4,
      accelerator="jit",
      ipex=True,
      precision="float32",
      device="CPU",
      height=512,
      width=512,
      low_memory=False,
      cache=False,
      fail_if_no_cache=False,
      channels_last=False,
      inplace=True,
      cache_dir=None,
      return_model=True,
  ):
      """
      Trace a torch.nn.Module and convert it into an accelerated module for inference.
      For example, this function returns a PytorchOpenVINOModel when accelerator=='openvino'.
      :param low_memory: only valid when accelerator="jit"
          and ipex=True, model will use less memory during inference
      :cache_dir: the directory to save the converted model
      """
      generator = torch.Generator(device="cpu")
      generator.manual_seed(1)
  
      if cache_dir is None:
          cache_dir = vae._name_or_path
      decoder_path = os.path.join(cache_dir, "decoder")
      # TODO: encoder
  
      latent_shape = (1, unet_in_channels, height // 8, width // 8)
      image_latents = torch.randn(
          latent_shape,
          generator=generator,
          device="cpu",
          dtype=torch.float32,
      )
      input_sample = image_latents
  
      nano_vae_decoder = None
  
      if cache:
          (
              nano_vae_decoder,
              decoder_cache_path,
          ) = try_load_existing_model(
              {},
              decoder_path,
              accelerator=accelerator,
              ipex=ipex,
              precision=precision,
              low_memory=low_memory,
              device=device,
              return_model=return_model,
          )
  
      if nano_vae_decoder is None:
          if fail_if_no_cache:
              raise Exception(
                  f"`fail_if_no_cache` \
                  is set to True, but failed to find the model \
                  at {decoder_path}, optimization stopped."
              )
  
          nano_vae_decoder = nano_optimize_model(
              vae.decoder,
              input_sample,
              accelerator=accelerator,
              ipex=ipex,
              precision=precision,
              device=device,
              low_memory=low_memory,
              channels_last=channels_last,
          )
  
          # Save model if cache=True
          if cache:
              print(
                  f"Caching the converted vae decoder model to {decoder_cache_path}"
              )
              InferenceOptimizer.save(
                  nano_vae_decoder, decoder_cache_path
              )
      if not isinstance(nano_vae_decoder, str) and inplace:
          setattr(vae, "decoder", nano_vae_decoder)
          return vae
      else:
          return nano_vae_decoder
  
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 2 for calling a global function with example usage: optimize_vae(vae, unet_in_channels, accelerator, ipex, precision, device, height, width, low_memory, cache, fail_if_no_cache, channels_last, inplace, cache_dir, return_model) and returns: vae, nano_vae_decoder