docstr: Simple test to check if the model conversion has been done correctly by checking on
  the class type of the linear layers of the converted models
docstr_tok: ["Simple","test","to","check","if","the","model","conversion","has","been","done","correctly","by","checking","on","the","class","type","of","the","linear","layers","of","the","converted","models"]
code: def test_quantized_layers_class(self):
      """
      Simple test to check if the model conversion has been done correctly by checking on
      the class type of the linear layers of the converted models
      """
      from auto_gptq.utils.import_utils import (
          dynamically_import_QuantLinear,
      )
  
      QuantLinear = dynamically_import_QuantLinear(
          use_triton=False,
          desc_act=self.desc_act,
          group_size=self.group_size,
          bits=self.bits,
          disable_exllama=not self.use_exllama,
          disable_exllamav2=True,
      )
      self.assertTrue(
          self.quantized_model.transformer.h[
              0
          ].mlp.dense_4h_to_h.__class__
          == QuantLinear
      )
  
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 5 for calling the GPTQTest obj's underlying member method code with example usage: obj.test_quantized_layers_class() without return types