code: def optimize_controlnet(
      controlnet,
      accelerator="openvino",
      ipex=False,
      precision="float16",
      device="CPU",
      samples=None,
      height=512,
      width=512,
      low_memory=False,
      cache=True,
      fail_if_no_cache=False,
      channels_last=False,
      return_model=False,
  ):
      """
      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
      """
      latent_model_input = torch.randn(2, 4, 64, 64)
      t = torch.Tensor([980]).long()
      encoder_hidden_states = torch.randn(
          2, 77, 768
      )  # only work for 1.4/1.5 now, so fix it to 768
      controlnet_cond = torch.randn(2, 3, 512, 512)
      input_sample = (
          latent_model_input,
          t,
          encoder_hidden_states,
          controlnet_cond,
          None,
          None,
          None,
          None,
          False,
      )
      controlnet_input_names = [
          "sample",
          "timestep",
          "encoder_hidden_states",
          "controlnet_cond",
      ]
      controlnet_dynamic_axes = {
          "sample": [0],
          "encoder_hidden_states": [0],
          "controlnet_cond": [0],
      }
      name_or_path = controlnet._name_or_path
      if not os.path.exists(name_or_path):
          raise Exception(
              f"controlnet model does not exist \
              in {name_or_path}, optimization failed."
          )
  
      nano_controlnet = None
  
      if cache:
          nano_controlnet, cache_path = try_load_existing_model(
              {},
              name_or_path,
              accelerator=accelerator,
              ipex=ipex,
              precision=precision,
              low_memory=low_memory,
              device=device,
              return_model=return_model,
          )
  
      if nano_controlnet 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 {name_or_path}, optimization stopped."
              )
  
          nano_controlnet = nano_optimize_model(
              controlnet,
              input_sample,
              accelerator=accelerator,
              ipex=ipex,
              precision=precision,
              input_names=controlnet_input_names,
              dynamic_axes=controlnet_dynamic_axes,
              device=device,
              samples=samples,
              low_memory=low_memory,
              channels_last=channels_last,
              mo_kwargs={
                  "input_shape": "[2,4,64,64],[1],[2,77,768],[2,3,512,512]",
                  "input": "sample, timestep, encoder_hidden_states, controlnet_cond",
              },
          )
  
          # Save model if cache=True
          if cache:
              print(
                  f"Caching the converted controlnet model to {cache_path}"
              )
              InferenceOptimizer.save(nano_controlnet, cache_path)
  
      if not return_model:
          del nano_controlnet
          del controlnet
          return
  
      return nano_controlnet
  
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 3 for calling a global function with example usage: optimize_controlnet(controlnet, accelerator, ipex, precision, device, samples, height, width, low_memory, cache, fail_if_no_cache, channels_last, return_model) and returns: nano_controlnet