class_docstr: Creates a Dataset for GPT architecture based Generative QA 
class_docstr_tok: ["Creates","a","Dataset","for","GPT","architecture","based","Generative","QA"]
docstr: Formats a question into input format: ` question: <question text>`
      The space at the start allows concatention with the context and answer for input
  Returns formatted query, query tokens, and length of query tokens
docstr_tok: ["Formats","a","question","into","input","format",":","`","question",":","<","question","text",">","`","The","space","at","the","start","allows","concatention","with","the","context","and","answer","for","input","Returns","formatted","query",",","query","tokens",",","and","length","of","query","tokens"]
code: def _prep_query(self, query_prefix, example):
      """
      Formats a question into input format: ` question: <question text>`
          The space at the start allows concatention with the context and answer for input
      Returns formatted query, query tokens, and length of query tokens
      """
  
      formatted_query = f"{query_prefix}{example.question_text}"
  
      return self._get_truncated_sentence_and_len(
          formatted_query, self.max_query_length
      )
  
imports: import copy
  import os
  import numpy
  import torch
  from tqdm import trange
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import INFERENCE_MODE, TRAINING_MODE
  from nemo.collections.nlp.data.question_answering.dataset.qa_dataset import QADataset
  from nemo.collections.nlp.data.question_answering.input_example.qa_gpt_input_example import GPTQAInputExample
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the GPTQADataset obj's underlying member method code with example usage: obj._prep_query(query_prefix, example) and returns: self