code: def _save_model():
      model_params = {
          "word_vocab": dataset.word_vocab,
          "pos_vocab": dataset.pos_vocab,
          "chunk_vocab": dataset.chunk_vocab,
      }
      if args.char_features is True:
          model_params.update({"char_vocab": dataset.char_vocab})
      with open(settings_path, "wb") as fp:
          pickle.dump(model_params, fp)
      model.save(model_path)
  
file_path: nlp-architect/examples/chunker/train.py
imports: from __future__ import division, print_function, unicode_literals, absolute_import
  import argparse
  import pickle
  from os import path
  from tensorflow import keras
  from nlp_architect.nn.tensorflow.python.keras.callbacks import ConllCallback
  from nlp_architect.data.sequential_tagging import CONLL2000
  from nlp_architect.models.chunker import SequenceChunker
  from nlp_architect.utils.embedding import load_word_embeddings, get_embedding_matrix
  from nlp_architect.utils.io import validate_existing_filepath, validate_parent_exists, validate, validate_existing_directory
  from nlp_architect.utils.metrics import get_conll_scores
label_desc: Use image node_id 2 for calling a global function with example usage: _save_model() without return types