class_docstr: This prompt version follows the Llama2-chat's prompt format:
  ```
  <s>[INST] <<SYS>>
  {{ system_prompt }}
  <</SYS>>
  
  {{ user_msg_1 }} [/INST] {{ model_answer_1 }} </s><s>[INST] {{ user_msg_2 }} [/INST]
  ```
  reference: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
class_docstr_tok: ["This","prompt","version","follows","the","Llama2-chat","'s","prompt","format",":","``","`","<","s",">","[","INST","]","<","<","SYS",">",">","{","{","system_prompt","}","}","<","<","\/SYS",">",">","{","{","user_msg_1","}","}","[","\/INST","]","{","{","model_answer_1","}","}","<","\/s",">","<","s",">","[","INST","]","{","{","user_msg_2","}","}","[","\/INST","]","``","`","reference",":","https",":","\/\/huggingface.co\/blog\/llama2","#","how-to-prompt-llama-2"]
code: def doc_to_answering_text(self, doc):
      has_answer = doc["ctxs"]["has_answer"]
      answering_index = has_answer.index(True)
      answering_contexts = {
          k: v[answering_index : answering_index + 1]
          for k, v in doc["ctxs"].items()
      }
      answer_candidate = "文脈：" + answering_contexts["text"][0]
      qa_prompt = self.doc_to_qa_prompt(doc)
      return f"{self.INSTRUCTION}\n\n{answer_candidate}\n{qa_prompt} [/INST] "
  
imports: import os
  import inspect
  import datasets
  from math import exp
  from lm_eval.base import rf, Task
  from functools import partial
  from lm_eval.jasquad import jasquad
label_desc: Use image node_id 2 for calling the JAQKETV2WithLlama2 obj's underlying member method code with example usage: obj.doc_to_answering_text(doc) and returns: self, str, answer_candidate, str, qa_prompt, str