code: def load_convert(cls, q_k, optimize_model, *args, **kwargs):
      from .convert import ggml_convert_low_bit
  
      invalidInputError(
          q_k in ggml_tensor_qtype,
          f"Unknown load_in_low_bit value: {q_k}, expected:"
          f" sym_int4, asym_int4, sym_int5, asym_int5, sym_int8, nf3, nf4, "
          "fp4, fp8, fp8_e4m3, fp8_e5m2, fp16,  bf16, mixed_fp4 or mixed_fp8.",
      )
      qtype = ggml_tensor_qtype[q_k]
  
      # In case it needs a second try,
      # `from_pretrained`` may pop items out in dict
      # and lead to args missing.
      modules_to_not_convert = kwargs.pop(
          "modules_to_not_convert", None
      )
      cpu_embedding = kwargs.pop("cpu_embedding", False)
      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
      lightweight_bmm = kwargs.pop("lightweight_bmm", False)
      quant_config = kwargs.pop("quantization_config", None)
      _args = copy.deepcopy(args)
      _kwargs = copy.deepcopy(kwargs)
      awq_config = None
  
      if quant_config and quant_config.quant_method == "awq":
          # The latest transformers only support cuda version
          # This load awq ckpt logic is copied from
          # https://github.com/casper-hansen/AutoAWQ/blob/main/awq/models/base.py#L147
          from accelerate import (
              init_empty_weights,
              infer_auto_device_map,
              load_checkpoint_in_model,
          )
          from bigdl.llm.transformers.awq.awq import (
              _replace_with_awq_layers,
              get_layer_type,
              _load_config,
          )
  
          awq_config = quant_config
          model_weights_path, config = _load_config(
              args[0], "", max_new_tokens=None, safetensors=True
          )
          with init_empty_weights():
              model = cls.HF_Model.from_config(
                  config=config, trust_remote_code=True
              )
  
          _replace_with_awq_layers(model, awq_config=awq_config)
  
          model.tie_weights()
  
          # Get device map
          device_map = infer_auto_device_map(
              model,
              no_split_module_classes=[get_layer_type(config)],
              max_memory=None,
              dtype=config.torch_dtype,
          )
  
          # Load checkpoint
          load_checkpoint_in_model(
              model,
              checkpoint=model_weights_path,
              device_map=device_map,
              offload_folder=None,
              dtype=config.torch_dtype,
          )
  
          # Offloading dispatch
          from accelerate import dispatch_model
  
          model = dispatch_model(
              model, device_map=device_map, offload_dir=None
          )
      else:
          try:
              model = cls.HF_Model.from_pretrained(*args, **kwargs)
          except NotImplementedError:
              logger.info(
                  "Failed to load models with `low_cpu_mem_usage` specified, "
                  "will fall to traditional load method with higher memory consumption."
              )
              _kwargs["low_cpu_mem_usage"] = False
              model = cls.HF_Model.from_pretrained(*_args, **_kwargs)
              model.config.update({"bigdl_lcmu_enabled": False})
  
      model = model.to("cpu")
      model = ggml_convert_low_bit(
          model,
          qtype,
          optimize_model,
          modules_to_not_convert=modules_to_not_convert,
          cpu_embedding=cpu_embedding,
          lightweight_bmm=lightweight_bmm,
          torch_dtype=kwargs.get("torch_dtype", "auto"),
      )
      model.config.update({"bigdl_transformers_low_bit": q_k})
      model.config.update({"tie_word_embeddings": False})
  
      # add save_low_bit to pretrained model dynamically
      import types
  
      model.save_low_bit = types.MethodType(save_low_bit, model)
  
      return model
  
imports: import transformers
  from transformers.configuration_utils import PretrainedConfig
  from .utils import extract_local_archive_file, load_state_dict, get_local_shard_files
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
  from bigdl.llm.utils.common import invalidInputError
  from bigdl.llm.transformers.gguf.api import load_gguf_model
  import torch
  import warnings
  import copy
  from .utils import logger
label_desc: Use image node_id 3 for calling the _BaseAutoModelClass obj's underlying member method code with example usage: obj.load_convert(cls, q_k, optimize_model) and returns: model