code: def load_cache(self):
      cache_file = self.get_cache_file()
      if os.path.isfile(cache_file):
          with open(cache_file, "r") as f:
              cache_cp = json.load(f)
              self.cache = {
                  tuple(k.split("[///]")): np.array(v)
                  for k, v in cache_cp.items()
              }
  
imports: from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import nltk
  import numpy
  import torch
  import os
  import json
  from .None import utils_misc
label_desc: Use image node_id 10 for calling the SummaCImager obj's underlying member method code with example usage: obj.load_cache() without return types