code: def _save(self, output_dir: Optional[str] = None, state_dict=None):
      if getattr(self.args, "tune_mm_mlp_adapter", False):
          pass
      else:
          super(LLaVATrainer, self)._save(output_dir, state_dict)
  
imports: import os
  import torch
  import torch.nn
  from fairscale.optim import OSS
  from torch.utils.data import Sampler
  from transformers import Trainer
  from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
  from transformers.trainer_pt_utils import get_parameter_names
  from transformers.trainer_utils import ShardedDDPOption, has_length
  from transformers.utils import is_sagemaker_mp_enabled, logging
  from typing import List, Optional, Union, Dict, Any
label_desc: Use image node_id 4 for calling the LLaVATrainer obj's underlying member method code with example usage: obj._save(output_dir, state_dict) without return types