class_docstr: predicts  classes for string-representation of sentence
class_docstr_tok: ["predicts","classes","for","string-representation","of","sentence"]
code: def _save_preproc(self, fpath):
      # ensure transformers embedding model is saved in a subdirectory
      p = self.preproc.p
      hf_dir = os.path.join(fpath, "hf")
      if p.te is not None:
          os.makedirs(hf_dir, exist_ok=True)
          p.te.model.save_pretrained(hf_dir)
          p.te.tokenizer.save_pretrained(hf_dir)
          p.te.config.save_pretrained(hf_dir)
          p.te_model = hf_dir
  
      # save preproc
      with open(os.path.join(fpath, U.PREPROC_NAME), "wb") as f:
          pickle.dump(self.preproc, f)
      return
  
file_path: ktrain/ktrain/text/ner/predictor.py
imports: from ...None import utils
  from ...imports import *
  from ...predictor import Predictor
  from ..None import textutils
  from .preprocessor import NERPreprocessor
label_desc: Use image node_id 6 for calling the NERPredictor obj's underlying member method code with example usage: obj._save_preproc(fpath) without return types