docstr: Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
code: def test_fp32_4bit_conversion(self):
      r"""
      Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
      """
      from transformers import AutoModelForCausalLM
  
      bnb_quantization_config = BnbQuantizationConfig(
          load_in_4bit=True, keep_in_fp32_modules=["lm_head"]
      )
  
      model = AutoModelForCausalLM.from_pretrained(
          self.model_name, torch_dtype=torch.float16
      )
      model = load_and_quantize_model(model, bnb_quantization_config)
      self.assertTrue(model.lm_head.weight.dtype == torch.float32)
  
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 6 for calling the Bnb4BitTestLoadedModel obj's underlying member method code with example usage: obj.test_fp32_4bit_conversion() without return types