docstr: Test the serialization of the model and the loading of the quantized weights with big model inference
docstr_tok: ["Test","the","serialization","of","the","model","and","the","loading","of","the","quantized","weights","with","big","model","inference"]
code: def test_serialization_big_model_inference(self):
      """
      Test the serialization of the model and the loading of the quantized weights with big model inference
      """
      with tempfile.TemporaryDirectory() as tmpdirname:
          self.quantized_model.save_pretrained(tmpdirname)
          quantized_model_from_saved = (
              AutoModelForCausalLM.from_pretrained(
                  tmpdirname, device_map="auto"
              )
          )
          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 10 for calling the GPTQTest obj's underlying member method code with example usage: obj.test_serialization_big_model_inference() without return types