code: def load_model(itos_filename, classifier_filename, num_classes):
      
  
      # load the int to string mapping file
      itos = pickle.load(Path(itos_filename).open('rb'))
      # turn it into a string to int mapping (which is what we need)
      stoi = collections.defaultdict(lambda:0, {str(v):int(k) for k,v in enumerate(itos)})
  
      # these parameters aren't used, but this is the easiest way to get a model
      bptt,em_sz,nh,nl = 70,400,1150,3
      dps = np.array([0.4,0.5,0.05,0.3,0.4])*0.5
      vs = len(itos)
  
      model = get_rnn_classifer(bptt, 20*70, num_classes, vs, emb_sz=em_sz, n_hid=nh, n_layers=nl, pad_token=1,
              layers=[em_sz*3, 50, num_classes], drops=[dps[4], 0.1],
              dropouti=dps[0], wdrop=dps[1], dropoute=dps[2], dropouth=dps[3])
  
      # load the trained classifier
      model.load_state_dict(torch.load(classifier_filename, map_location=lambda storage, loc: storage))
  
      # put the classifier into evaluation mode
      model.reset()
      model.eval()
  
      return stoi, model
docs: Load the classifier and int to string mapping
  
      Args:
          itos_filename (str): The filename of the int to string mapping file (usually called itos.pkl)
          classifier_filename (str): The filename of the trained classifier
  
      Returns:
          string to int mapping, trained classifer model