code: def test_training(self):
      if version.parse(
          importlib.metadata.version("bitsandbytes")
      ) < version.parse("0.37.0"):
          return
  
      # Step 1: freeze all parameters
      model = AutoModelForCausalLM.from_pretrained(
          self.model_name, load_in_8bit=True
      )
  
      self.assertEqual(
          set(model.hf_device_map.values()),
          {torch.cuda.current_device()},
      )
  
      for param in model.parameters():
          param.requires_grad = (
              False  # freeze the model - train adapters later
          )
          if param.ndim == 1:
              # cast the small parameters (e.g. layernorm) to fp32 for stability
              param.data = param.data.to(torch.float32)
  
      # Step 2: add adapters
      for _, module in model.named_modules():
          if "OPTAttention" in repr(type(module)):
              module.q_proj = LoRALayer(module.q_proj, rank=16)
              module.k_proj = LoRALayer(module.k_proj, rank=16)
              module.v_proj = LoRALayer(module.v_proj, rank=16)
  
      # Step 3: dummy batch
      batch = self.tokenizer("Test batch ", return_tensors="pt").to(0)
  
      # Step 4: Check if the gradient is not None
      with torch.cuda.amp.autocast():
          out = model.forward(**batch)
          out.logits.norm().backward()
  
      for module in model.modules():
          if isinstance(module, LoRALayer):
              self.assertTrue(module.adapter[1].weight.grad is not None)
              self.assertTrue(
                  module.adapter[1].weight.grad.norm().item() > 0
              )
          elif isinstance(module, nn.Embedding):
              self.assertTrue(module.weight.grad is None)
  
imports: import gc
  import importlib.metadata
  import tempfile
  import unittest
  from packaging import version
  from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig, pipeline
  from transformers.testing_utils import is_accelerate_available, is_torch_available, require_accelerate, require_bitsandbytes, require_torch, require_torch_gpu, require_torch_multi_gpu, slow
label_desc: Use image node_id 2 for calling the MixedInt8TestTraining obj's underlying member method code with example usage: obj.test_training() without return types