code: def optimize_unet(
      unet,
      unet_attributes,
      accelerator="jit",
      ipex=True,
      precision="float32",
      device="CPU",
      samples=None,
      height=512,
      width=512,
      low_memory=False,
      cache=False,
      fail_if_no_cache=False,
      channels_last=False,
      cache_dir=None,
      lora_name=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)
  
      conv_in = OrderedDict()
      conv_in.in_channels = unet.conv_in.in_channels
      if cache_dir is None:
          cache_dir = unet_attributes["config"]._name_or_path
  
      latent_shape = (2, unet.in_channels, height // 8, width // 8)
      image_latents = torch.randn(
          latent_shape,
          generator=generator,
          device="cpu",
          dtype=torch.float32,
      )
      cross_attention_dim = unet.config.cross_attention_dim
      encoder_hidden_states = torch.randn(
          (2, 77, cross_attention_dim),
          generator=generator,
          device="cpu",
          dtype=torch.float32,
      )
  
      temp = get_dummy_unet_additional_residuals()
      (
          down_block_additional_residuals,
          mid_block_additional_residual,
      ) = temp
  
      input_sample = [
          image_latents,
          torch.Tensor([980]).long(),
          encoder_hidden_states,
          None,
          None,
          None,
          None,
          down_block_additional_residuals,
          mid_block_additional_residual,
      ]
  
      unet_input_names = [
          "sample",
          "timestep",
          "encoder_hidden_states",
          "down_block_additional_residuals",
          "mid_block_additional_residual",
      ]
      unet_output_names = ["unet_output"]
      # unet_dynamic_axes = {"sample": [0], "encoder_hidden_states": [0], "unet_output": [0]}
      unet_dynamic_axes = False
  
      if lora_name is not None:
          input_sample[6] = torch.Tensor([0.8]).float()
          unet_input_names.insert(3, "cross_attn_scale")
  
      nano_unet = None
      if cache:
          nano_unet, cache_path = try_load_existing_model(
              unet_attributes,
              cache_dir,
              accelerator=accelerator,
              ipex=ipex,
              precision=precision,
              low_memory=low_memory,
              device=device,
              lora_name=lora_name,
              return_model=return_model,
          )
  
      if nano_unet 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 \
                  {unet_attributes['config']._name_or_path}, \
                  optimization stopped."
              )
  
          nano_unet = nano_optimize_model(
              unet,
              input_sample,
              input_names=unet_input_names,
              output_names=unet_output_names,
              dynamic_axes=unet_dynamic_axes,
              accelerator=accelerator,
              ipex=ipex,
              precision=precision,
              device=device,
              samples=samples,
              low_memory=low_memory,
              channels_last=channels_last,
          )
  
          # Save model if cache=True
          if cache:
              import pickle
  
              print(f"Caching the converted unet model to {cache_path}")
              InferenceOptimizer.save(nano_unet, cache_path)
              with open(
                  os.path.join(cache_dir, "attrs.pkl"), "wb"
              ) as f:
                  pickle.dump(unet_attributes, f)
  
      if not isinstance(nano_unet, str):
          setattr(nano_unet, "conv_in", conv_in)
      return nano_unet
  
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 1 for calling a global function with example usage: optimize_unet(unet, unet_attributes, accelerator, ipex, precision, device, samples, height, width, low_memory, cache, fail_if_no_cache, channels_last, cache_dir, lora_name, return_model) and returns: nano_unet