code: def save(
      self,
      path: str,
      standalone: Optional[bool] = True,
      config: Optional[DictConfig] = None,
      model: Optional[nn.Module] = None,
      df_preprocessor: Optional[MultiModalFeaturePreprocessor] = None,
      data_processors: Optional[Dict] = None,
      fit_called: Optional[bool] = None,
      save_model: Optional[bool] = True,
      output_shape: Optional[int] = None,
  ):
      super().save(
          path=path,
          standalone=standalone,
          config=config,
          model=model,
          df_preprocessor=df_preprocessor,
          data_processors=data_processors,
          fit_called=fit_called,
          save_model=save_model,
      )
      if (
          output_shape
      ):  # output_shape is provided for saving artifacts at on_fit_per_run_end().
          assets_path = os.path.join(path, "assets.json")
          with open(assets_path, "r") as fp:
              assets = json.load(fp)
              assets.update(
                  {
                      "output_shape": output_shape,
                  }
              )
          os.remove(assets_path)
          with open(assets_path, "w") as fp:
              json.dump(assets, fp, ensure_ascii=True)
  
imports: import json
  import logging
  import os
  import warnings
  from datetime import timedelta
  from typing import Callable, Dict, List, Optional, Union
  import lightning.pytorch
  import pandas
  from omegaconf import DictConfig, OmegaConf
  from torch import nn
  from autogluon.core.metrics import Scorer
  from ..constants import NER, NER_RET, Y_PRED, Y_TRUE
  from ..data import MultiModalFeaturePreprocessor
  from ..optimization import NerLitModule, get_metric
  from ..utils import compute_score, create_fusion_model, extract_from_output, merge_bio_format
  from .base import BaseLearner
label_desc: Use image node_id 15 for calling the NERLearner obj's underlying member method code with example usage: obj.save(path, standalone, config, model, df_preprocessor, data_processors, fit_called, save_model, output_shape) without return types