code: def _collate_data(self, set):
      if set in self.cache:
          return self.cache[set]
      # One big issue with HF's implementation of this dataset: it makes a
      # separate document for each question; meanwhile, in the GPT3 paper it
      # is shown that one document is made per passage.
  
      r = collections.defaultdict(list)
      for item in datasets.load_dataset(
          path=self.DATASET_PATH, name=self.DATASET_NAME
      )[set]:
          r[item["article"]].append(item)
  
      res = list(
          r.values()
          >> each(
              lambda x: {
                  "article": x[0]["article"],
                  "problems": x
                  >> each(
                      lambda y: {
                          "question": y["question"],
                          "answer": y["answer"],
                          "options": y["options"],
                      }
                  ),
              }
          )
      )
  
      self.cache[set] = res
      return res
  
imports: import collections
  import datasets
  import numpy
  from lm_eval.base import rf, Task
  from lm_eval.metrics import mean
label_desc: Use image node_id 4 for calling the RACE obj's underlying member method code with example usage: obj._collate_data(set) and returns: res, self