code: def save_model(self, path=None):
      if not path:
          path = self.output_dir / "model_out"
  
      path.mkdir(exist_ok=True)
  
      torch.cuda.empty_cache()
      # Save a trained model
      model_to_save = (
          self.model.module
          if hasattr(self.model, "module")
          else self.model
      )  # Only save the model it-self
      model_to_save.save_pretrained(path)
  
      # save the tokenizer
      self.data.tokenizer.save_pretrained(path)
  
file_path: fast-bert/fast_bert/learner_lm.py
imports: import os
  import torch
  from packaging import version
  from pathlib import Path
  import numpy
  from fastprogress.fastprogress import master_bar, progress_bar
  from tensorboardX import SummaryWriter
  from .learner_util import Learner
  from .data_lm import BertLMDataBunch
  from transformers import WEIGHTS_NAME, BertConfig, BertForMaskedLM, RobertaConfig, RobertaForMaskedLM, DistilBertConfig, DistilBertForMaskedLM, CamembertConfig, CamembertForMaskedLM, ElectraConfig, ElectraForMaskedLM
  from torch.optim.lr_scheduler import _LRScheduler, Optimizer
label_desc: Use image node_id 6 for calling the BertLMLearner obj's underlying member method code with example usage: obj.save_model(path) without return types