code: def _get_data(filepath, test_data=False):
      contexts = []
      questions = []
      answers = []
      with open(filepath, newline="", encoding="utf-8") as csv_file:
          reader = csv.DictReader(csv_file)
          for row in reader:
              contexts.append(row["context"])
              questions.append(row["question"])
              if not test_data:
                  answer = {}
                  answer["answer_text"] = row["answer_text"]
                  answer["answer_start"] = int(row["answer_start"])
                  answers.append(answer)
      csv_file.close()
  
      if not test_data:
          return contexts, questions, answers
      return contexts, questions
  
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 7 for calling the HappyQuestionAnswering obj's underlying member method code with example usage: obj._get_data(filepath, test_data) and returns: contexts,  questions, contexts,  questions,  answers