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
code: def test_linear_are_8bit(self):
      r"""
      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
      """
  
      self.model_fp16.get_memory_footprint()
      self.model_8bit.get_memory_footprint()
  
      for name, module in self.model_8bit.named_modules():
          if isinstance(module, torch.nn.Linear):
              modules_not_converted = (
                  self.bnb_quantization_config.keep_in_fp32_modules
                  + self.bnb_quantization_config.skip_modules
              )
              if name not in modules_not_converted:
                  self.assertTrue(module.weight.dtype == torch.int8)
  
imports: import gc
  import tempfile
  import unittest
  import torch
  import torch.nn
  from accelerate import Accelerator, init_empty_weights
  from accelerate.test_utils import require_bnb, require_cuda, require_huggingface_suite, require_multi_gpu, slow
  from accelerate.utils.bnb import load_and_quantize_model
  from accelerate.utils.dataclasses import BnbQuantizationConfig
label_desc: Use image node_id 4 for calling the MixedInt8EmptyModelTest obj's underlying member method code with example usage: obj.test_linear_are_8bit() without return types