class_docstr: - Use ROUGE-2 as [PaLM 2](https://ai.google/static/documents/palm2techreport.pdf)
  - Use Mecab tokenizer for Japanese eval
code: def process_results(self, doc, results):
      continuation = results[0]
      ground_truth = doc["summary"]
      out = {
          "rouge2": (
              continuation,
              ground_truth,
          )
      }
      # add verbose output
      out["details"] = {
          # this isn't really a question, but keeping it this way for
          # consistency
          "question": doc["text"],
          "response": continuation,
          "gold": doc["summary"],
      }
      return out
  
imports: import os
  import inspect
  from lm_eval.utils import rouge2_mecab
  from lm_eval.base import rf, Task
label_desc: Use image node_id 13 for calling the XLSumJa obj's underlying member method code with example usage: obj.process_results(doc, results) and returns: out