docstr: A simple test to check if the model conversion has been done correctly by checking on the
  memory footprint of the converted model and the class type of the linear layers of the converted models
docstr_tok: ["A","simple","test","to","check","if","the","model","conversion","has","been","done","correctly","by","checking","on","the","memory","footprint","of","the","converted","model","and","the","class","type","of","the","linear","layers","of","the","converted","models"]
code: def test_memory_footprint(self):
      """
      A simple test to check if the model conversion has been done correctly by checking on the
      memory footprint of the converted model and the class type of the linear layers of the converted models
      """
  
      mem_quantized = self.quantized_model.get_memory_footprint()
  
      self.assertAlmostEqual(
          self.mem_fp16 / mem_quantized,
          self.EXPECTED_RELATIVE_DIFFERENCE,
      )
  
imports: import tempfile
  import unittest
  import torch
  from parameterized import parameterized
  from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
  from transformers.testing_utils import slow
  from optimum.gptq import GPTQQuantizer, load_quantized_model
  from optimum.gptq.data import get_dataset
  from optimum.utils.import_utils import is_auto_gptq_available
  from optimum.utils.testing_utils import require_accelerate, require_auto_gptq, require_torch_gpu
label_desc: Use image node_id 2 for calling the GPTQTest obj's underlying member method code with example usage: obj.test_memory_footprint() without return types