class_docstr: Creates a Dataset for GPT architecture based Generative QA 
class_docstr_tok: ["Creates","a","Dataset","for","GPT","architecture","based","Generative","QA"]
code: def _get_truncated_sentence_and_len(self, sentence, max_length):
      if not sentence:
          return "", 0
      tokens = self.tokenizer.tokenizer.tokenize(sentence)[:max_length]
      trunc_sentence = (
          self.tokenizer.tokenizer.convert_tokens_to_string(tokens)
      )
      seq_length = len(tokens)
  
      return trunc_sentence, seq_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 8 for calling the GPTQADataset obj's underlying member method code with example usage: obj._get_truncated_sentence_and_len(sentence, max_length) and returns: trunc_sentence,  seq_length, str,  int