code: def check_contents_of_dataset_and_model_checkpoint(model_folder):
      """
      Check the contents of dataset.pickle and model_xxx.ckpt.
      model_folder: folder containing dataset.pickle and model_xxx.ckpt to be checked.
      """
      dataset_filepath = os.path.join(model_folder, "dataset.pickle")
      dataset = pickle.load(open(dataset_filepath, "rb"))
      pprint(dataset.__dict__)
      pprint(list(dataset.__dict__.keys()))
  
      checkpoint_filepath = os.path.join(model_folder, "model.ckpt")
      with tf.Session() as sess:
          print_tensors_in_checkpoint_file(
              checkpoint_filepath,
              tensor_name="token_embedding/token_embedding_weights",
              all_tensors=True,
          )
          print_tensors_in_checkpoint_file(
              checkpoint_filepath,
              tensor_name="token_embedding/token_embedding_weights",
              all_tensors=False,
          )
  
file_path: NeuroNER/neuroner/prepare_pretrained_model.py
imports: import glob
  import os
  import pickle
  from pprint import pprint
  import shutil
  import utils
  from entity_lstm import EntityLSTM
  import tensorflow
  from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
  from neuroner import utils_tf
  from neuroner import neuromodel
label_desc: Use image node_id 4 for calling a global function with example usage: check_contents_of_dataset_and_model_checkpoint(model_folder) without return types