code: def optimize_model(
      model,
      low_bit="sym_int4",
      optimize_llm=True,
      modules_to_not_convert=None,
      cpu_embedding=False,
      lightweight_bmm=False,
      **kwargs,
  ):
      """
      A method to optimize any pytorch model.
  
      :param model: The original PyTorch model (nn.module)
      :param low_bit: str value, options are ``'sym_int4'``, ``'asym_int4'``, ``'sym_int5'``,
                      ``'asym_int5'``, ``'sym_int8'``, ``'nf3'``, ``'nf4'``, ``'fp4'``,
                      ``'fp8'``, ``'fp8_e4m3'``, ``'fp8_e5m2'``, ``'fp16'`` or ``'bf16'``,
                      ``'sym_int4'`` means symmetric int 4, ``'asym_int4'`` means
                      asymmetric int 4, ``'nf4'`` means 4-bit NormalFloat, etc.
                      Relevant low bit optimizations will be applied to the model.
      :param optimize_llm: Whether to further optimize llm model. Default to be ``True``.
      :param modules_to_not_convert: list of str value, modules (nn.Module) that are skipped
          when conducting model optimizations. Default to be ``None``.
      :param cpu_embedding: Whether to replace the Embedding layer, may need to set it
          to ``True`` when running BigDL-LLM on GPU on Windows. Default to be ``False``.
      :param lightweight_bmm: Whether to replace the torch.bmm ops, may need to set it
          to ``True`` when running BigDL-LLM on GPU on Windows. Default to be ``False``.
  
      :return: The optimized model.
  
      >>> # Take OpenAI Whisper model as an example
      >>> from bigdl.llm import optimize_model
      >>> model = whisper.load_model('tiny') # Load whisper model under pytorch framework
      >>> model = optimize_model(model) # With only one line code change
      >>> # Use the optimized model without other API change
      >>> result = model.transcribe(audio, verbose=True, language="English")
      >>> # (Optional) you can also save the optimized model by calling 'save_low_bit'
      >>> model.save_low_bit(saved_dir)
      """
      invalidInputError(
          low_bit in ggml_tensor_qtype,
          f"Unknown load_in_low_bit value: {low_bit}, expected:"
          f" sym_int4, asym_int4, sym_int5, asym_int5 or sym_int8.",
      )
      invalidInputError(
          isinstance(model, torch.nn.Module),
          "model should be an instance of "
          f"`torch.nn.Module`, but got {type(model)} at last.",
      )
      invalidInputError(
          model.device.type in ("cpu", "meta"),
          "Expect model on device `cpu` or `meta`, "
          f"but got device type {model.device.type}",
      )
      if kwargs.pop("replace_embedding", False):
          warnings.warn(
              "replace_embedding is deprecated and will be removed in a future version,"
              " please use cpu_embedding instead.",
              FutureWarning,
          )
          cpu_embedding = True
      qtype = ggml_tensor_qtype[low_bit]
      model = ggml_convert_low_bit(
          model,
          qtype=qtype,
          optimize_model=optimize_llm,
          modules_to_not_convert=modules_to_not_convert,
          cpu_embedding=cpu_embedding,
          lightweight_bmm=lightweight_bmm,
      )
      # add save_low_bit to pretrained model dynamically
      import types
  
      model._bigdl_config = dict()
      model._bigdl_config["bigdl_transformers_low_bit"] = low_bit
      model.save_low_bit = types.MethodType(_save_low_bit, model)
      return model
  
imports: import torch
  import os
  import json
  from .transformers import ggml_convert_low_bit
  from torch.nn.modules import Module
  from torch.nn.modules.module import _IncompatibleKeys
  from accelerate import init_empty_weights
  from accelerate.utils import set_module_tensor_to_device
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
  from bigdl.llm.utils.common import invalidInputError
  from bigdl.llm.transformers.utils import extract_local_archive_file, get_local_shard_files
  import transformers
  import warnings
  from transformers import PreTrainedModel
  from .utils.common import MuteHFLogger
  from .utils.lazy_load_torch import LazyLoadTensors
  from contextlib import ExitStack, contextmanager
label_desc: Use image node_id 5 for calling a global function with example usage: optimize_model(model, low_bit, optimize_llm, modules_to_not_convert, cpu_embedding, lightweight_bmm) and returns: model