code: def doc_to_text(self, doc):
      # Given a passage p, the conversation history {q1, a1, . . . qi−1, ai−1}
      # and a question qi, the task is to predict the answer ai
      doc_text = doc["story"] + "\n\n"
      for q, a in zip_longest(
          doc["questions"]["input_text"],
          doc["answers"]["input_text"][:-1],
      ):  # omit target answer ai
          question = f"Q: {q}\n\n"
          answer = f"A: {a}\n\n" if a is not None else "A:"
          doc_text += question + answer
      return doc_text
  
imports: import inspect
  import transformers.data.metrics.squad_metrics
  import lm_eval.datasets.coqa.coqa
  from lm_eval.base import Task, rf, mean
  from itertools import zip_longest
label_desc: Use image node_id 7 for calling the CoQA obj's underlying member method code with example usage: obj.doc_to_text(doc) and returns: doc_text