docstr: Test the serialization of the model and the loading of the quantized weights works
docstr_tok: ["Test","the","serialization","of","the","model","and","the","loading","of","the","quantized","weights","works"]
code: def test_serialization(self):
      """
      Test the serialization of the model and the loading of the quantized weights works
      """
      with tempfile.TemporaryDirectory() as tmpdirname:
          self.quantized_model.save_pretrained(tmpdirname)
          if not self.use_exllama:
              quantized_model_from_saved = (
                  AutoModelForCausalLM.from_pretrained(
                      tmpdirname,
                      quantization_config=GPTQConfig(
                          use_exllama=False, bits=4
                      ),
                  ).to(0)
              )
              self.check_quantized_layers_type(
                  quantized_model_from_saved, "cuda-old"
              )
          else:
              # we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel
              quantized_model_from_saved = (
                  AutoModelForCausalLM.from_pretrained(
                      tmpdirname, device_map={"": 0}
                  )
              )
              self.check_quantized_layers_type(
                  quantized_model_from_saved, "exllama"
              )
          self.check_inference_correctness(quantized_model_from_saved)
  
imports: import tempfile
  import unittest
  import pytest
  from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
  from transformers.testing_utils import is_torch_available, require_accelerate, require_auto_gptq, require_optimum, require_torch_gpu, require_torch_multi_gpu, slow
label_desc: Use image node_id 9 for calling the GPTQTest obj's underlying member method code with example usage: obj.test_serialization() without return types