code: def save_model(self, path=None):
      if not path:
          path = self.output_dir / "model_out"
  
      path.mkdir(exist_ok=True)
  
      # Convert path to str for save_pretrained calls
      path = str(path)
  
      torch.cuda.empty_cache()
      # Save a trained model
      self.get_trainer().save_model(path)
  
      # save the tokenizer
      if self.get_trainer().is_world_process_zero():
          self.data.tokenizer.save_pretrained(path)
  
imports: import os
  import logging
  import torch
  import numpy
  from .data_ner import BertNERDataBunch
  from torch import nn
  from seqeval.metrics import f1_score, precision_score, recall_score
  from typing import Dict, List, Optional, Tuple
  from .learner_util import Learner
  from transformers import AutoConfig, AutoModelForTokenClassification, Trainer, TrainingArguments, EvalPrediction, HfArgumentParser
label_desc: Use image node_id 7 for calling the BertNERLearner obj's underlying member method code with example usage: obj.save_model(path) without return types