docstr: Setup quantized model
docstr_tok: ["Setup","quantized","model"]
code: def setUpClass(cls):
      """
      Setup quantized model
      """
      cls.model_fp16 = AutoModelForCausalLM.from_pretrained(
          cls.model_name, torch_dtype=torch.float16, device_map={"": 0}
      )
      cls.mem_fp16 = cls.model_fp16.get_memory_footprint()
  
      cls.tokenizer = AutoTokenizer.from_pretrained(
          cls.model_name, use_fast=True
      )
      cls.quantizer = GPTQQuantizer(
          bits=cls.bits,
          dataset=cls.dataset,
          group_size=cls.group_size,
          desc_act=cls.desc_act,
          disable_exllama=cls.disable_exllama,
          exllama_config=cls.exllama_config,
          cache_block_outputs=cls.cache_block_outputs,
          modules_to_quantize_inside_block=cls.modules_to_quantize_inside_block,
      )
  
      cls.quantized_model = cls.quantizer.quantize_model(
          cls.model_fp16, cls.tokenizer
      )
  
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 1 for calling the GPTQTest obj's underlying member method code with example usage: obj.setUpClass(cls) without return types