docstr: Test whether trying to cast (or assigning a device to) a model after quantization will throw an error.
  Checks also if other models are casted correctly.
docstr_tok: ["Test","whether","trying","to","cast","(","or","assigning","a","device","to",")","a","model","after","quantization","will","throw","an","error",".","Checks","also","if","other","models","are","casted","correctly","."]
code: def test_device_and_dtype_assignment(self):
      r"""
      Test whether trying to cast (or assigning a device to) a model after quantization will throw an error.
      Checks also if other models are casted correctly.
      """
      # This should work
      if self.device_map is None:
          _ = self.quantized_model.to(0)
  
      with self.assertRaises(ValueError):
          # Tries with a `dtype``
          self.quantized_model.to(torch.float16)
  
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 3 for calling the GPTQTest obj's underlying member method code with example usage: obj.test_device_and_dtype_assignment() without return types