code: def answer_question(
      self, context: str, question: str, top_k: int = 1
  ) -> List[QuestionAnsweringResult]:
      # loads pipeline if it hasn't been loaded already.
      self._load_pipeline()
  
      pipeline_output = self._pipeline(
          context=context, question=question, top_k=top_k
      )
      # transformers returns a single dictionary when top_k ==1.
      # Our convention however is to have constant output format
      answers = [pipeline_output] if top_k == 1 else pipeline_output
  
      return [
          QuestionAnsweringResult(
              answer=answer["answer"],
              score=answer["score"],
              start=answer["start"],
              end=answer["end"],
          )
          for answer in answers
      ]
  
file_path: happy-transformer/happytransformer/happy_question_answering.py
imports: import csv
  from dataclasses import dataclass
  from typing import List, Union
  from datasets import Dataset
  from transformers import AutoModelForQuestionAnswering, DataCollatorWithPadding, QuestionAnsweringPipeline
  from tqdm import tqdm
  from happytransformer.adaptors import get_adaptor
  from happytransformer.args import QAEvalArgs, QATestArgs, QATrainArgs
  from happytransformer.fine_tuning_util import EvalResult
  from happytransformer.happy_transformer import HappyTransformer
label_desc: Use image node_id 2 for calling the HappyQuestionAnswering obj's underlying member method code with example usage: obj.answer_question(context, question, top_k) and returns: unknown