code: import os
  import pickle
  import torch
  
  
  SPECIAL_WORDS = {'PADDING': '<PAD>'}
  
  
  def load_data(path):
      """
      Load Dataset from File
      """
      input_file = os.path.join(path)
      with open(input_file, "r") as f:
          data = f.read()
  
      return data
  
  
  def preprocess_and_save_data(dataset_path, token_lookup, create_lookup_tables):
      """
      Preprocess Text Data
      """
      text = load_data(dataset_path)
      
      # Ignore notice, since we don't use it for analysing the data
      text = text[81:]
  
      token_dict = token_lookup()
      for key, token in token_dict.items():
          text = text.replace(key, ' {} '.format(token))
  
      text = text.lower()
      text = text.split()
  
      vocab_to_int, int_to_vocab = create_lookup_tables(text + list(SPECIAL_WORDS.values()))
      int_text = [vocab_to_int[word] for word in text]
      pickle.dump((int_text, vocab_to_int, int_to_vocab, token_dict), open('preprocess.p', 'wb'))
  
  
  def load_preprocess():
      """
      Load the Preprocessed Training data and return them in batches of <batch_size> or less
      """
      return pickle.load(open('preprocess.p', mode='rb'))
  
  
  def save_model(filename, decoder):
      save_filename = os.path.splitext(os.path.basename(filename))[0] + '.pt'
      torch.save(decoder, save_filename)
  
  
  def load_model(filename):
      save_filename = os.path.splitext(os.path.basename(filename))[0] + '.pt'
      print(save_filename)
      return torch.load(save_filename)
  
apis: torch.save torch.load