code: def load_low_bit(model, model_path):
      """
      Load the optimized pytorch model.
  
      :param model: The PyTorch model instance
      :param model_path: The path of saved optimized model
  
      :return: The optimized model.
  
      >>> # Example 1:
      >>> # Take ChatGLM2-6B model as an example
      >>> # Make sure you have saved the optimized model by calling 'save_low_bit'
      >>> from bigdl.llm.optimize import low_memory_init, load_low_bit
      >>> with low_memory_init(): # Fast and low cost by loading model on meta device
      >>>     model = AutoModel.from_pretrained(saved_dir,
      >>>                                       torch_dtype="auto",
      >>>                                       trust_remote_code=True)
      >>> model = load_low_bit(model, saved_dir) # Load the optimized model
  
      >>> # Example 2:
      >>> # If the model doesn't fit 'low_memory_init' method,
      >>> # alternatively, you can obtain the model instance through traditional loading method.
      >>> # Take OpenAI Whisper model as an example
      >>> # Make sure you have saved the optimized model by calling 'save_low_bit'
      >>> from bigdl.llm.optimize import load_low_bit
      >>> model = whisper.load_model('tiny') # A model instance through traditional loading method
      >>> model = load_low_bit(model, saved_dir) # Load the optimized model
      """
      low_bit = low_bit_sanity_check(model_path)
      invalidInputError(
          isinstance(model, torch.nn.Module),
          "model should be a instance of "
          f"`torch.nn.Module`, but got {type(model)} at last.",
      )
      if low_bit:
          invalidInputError(
              isinstance(model, torch.nn.Module),
              "model should be an instance of `torch.nn.Module`, "
              f"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}",
          )
          qtype = ggml_tensor_qtype[low_bit]
          model = ggml_convert_low_bit(
              model, qtype=qtype, convert_shape_only=True
          )
  
      resolved_archive_file, is_sharded = extract_local_archive_file(
          model_path, subfolder=""
      )
      if is_sharded:
          # For now only shards transformers models
          # can run in this branch.
          resolved_archive_file, _ = get_local_shard_files(
              model_path, resolved_archive_file, subfolder=""
          )
      else:
          resolved_archive_file = [
              os.path.join(model_path, PYTORCH_MODEL_NAME)
          ]
  
      for model_file in resolved_archive_file:
          state_dict = torch.load(model_file)
          for param_name, param in state_dict.items():
              set_module_tensor_to_device(
                  model, param_name, "cpu", param
              )
      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 4 for calling a global function with example usage: load_low_bit(model, model_path) and returns: model