code: def save_pretrained(
      self,
      save_directory: Union[str, os.PathLike],
      state_dict: Optional[dict] = None,
      safe_serialization: Optional[
          bool
      ] = None,  # pylint: disable=unused-argument
  ):
      if state_dict is None:
          state_dict = self.state_dict()
      torch.save(
          state_dict, os.path.join(save_directory, "pytorch_model.bin")
      )
  
file_path: axolotl/src/axolotl/models/mamba/modeling_mamba.py
imports: import os
  from collections import namedtuple
  from functools import partial
  from typing import Optional, Union
  import torch
  from mamba_ssm.models.mixer_seq_simple import MixerModel, _init_weights
  from mamba_ssm.utils.generation import GenerationMixin
  from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from axolotl.models.mamba.configuration_mamba import MambaConfig
label_desc: Use image node_id 5 for calling the MambaLMHeadModel obj's underlying member method code with example usage: obj.save_pretrained(save_directory, state_dict, safe_serialization) without return types