code: def nano_optimize_model(
      model,
      input_sample,
      input_names=None,
      output_names=None,
      dynamic_axes=False,
      accelerator="jit",
      ipex=True,
      precision="float32",
      device="CPU",
      samples=None,
      low_memory=False,
      channels_last=False,
      mo_kwargs=None,
  ):
      extra_args = {}
      if precision == "float32":
          if accelerator == "jit":
              weights_prepack = False if low_memory else None
              extra_args["weights_prepack"] = weights_prepack
              extra_args["use_ipex"] = ipex
              extra_args["jit_strict"] = False
              extra_args["enable_onednn"] = False
              extra_args["channels_last"] = channels_last
          elif accelerator is None:
              if ipex:
                  extra_args["use_ipex"] = ipex
                  extra_args["channels_last"] = channels_last
              else:
                  raise ValueError(
                      "IPEX should be True if accelerator \
                      is None and precision is float32."
                  )
          elif accelerator == "openvino":
              extra_args["input_names"] = input_names
              extra_args["output_names"] = output_names
              # # Nano will deal with the GPU/VPU dynamic axes issue
              extra_args["dynamic_axes"] = dynamic_axes
              extra_args["device"] = device
              if mo_kwargs is not None:
                  extra_args.update(mo_kwargs)
          else:
              raise ValueError(
                  f"The accelerator can be one of `None`, `jit`, \
                  and `openvino` if the precision is float32, but got {accelerator}"
              )
          optimized_model = InferenceOptimizer.trace(
              model,
              accelerator=accelerator,
              input_sample=input_sample,
              **extra_args,
          )
      else:
          precision_map = {
              "bfloat16": "bf16",
              "int8": "int8",
              "float16": "fp16",
          }
          precision_short = precision_map[precision]
  
          # prepare input samples, calib dataloader and eval functions
          if accelerator == "openvino":
              extra_args["device"] = device
              extra_args["input_names"] = input_names
              extra_args["output_names"] = output_names
              # Nano will deal with the GPU/VPU dynamic axes issue
              extra_args["dynamic_axes"] = dynamic_axes
              if mo_kwargs is not None:
                  extra_args.update(mo_kwargs)
  
              if precision_short == "int8":
                  # TODO: openvino int8 here
                  invalidInputError(
                      precision_short != "int8",
                      errMsg="OpenVINO int8 quantization is not supported.",
                  )
  
          elif accelerator == "onnxruntime":
              invalidInputError(
                  accelerator != "onnxruntime",
                  errMsg=f"Onnxruntime {precision_short} quantization is not supported.",
              )
          else:
              # PyTorch bf16
              if precision_short == "bf16":
                  # Ignore jit & ipex
                  if accelerator == "jit":
                      invalidInputError(
                          accelerator == "jit",
                          errMsg=f"JIT {precision_short} quantization is not supported.",
                      )
                  extra_args["channels_last"] = channels_last
              elif precision_short == "int8":
                  raise
  
          # unet
          optimized_model = InferenceOptimizer.quantize(
              model,
              accelerator=accelerator,
              precision=precision_short,
              input_sample=input_sample,
              **extra_args,
          )
      return optimized_model
  
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 4 for calling a global function with example usage: nano_optimize_model(model, input_sample, input_names, output_names, dynamic_axes, accelerator, ipex, precision, device, samples, low_memory, channels_last, mo_kwargs) and returns: optimized_model