code: def test_split_into_words():
      text = "hi yo"
      char_to_word_offset = [0, 0, 0, 1, 1]
      doc_tokens = ["hi", "yo"]
      output = QADataset.split_into_words(text)
      assert output[0] == doc_tokens
      assert output[1] == char_to_word_offset
  
      text = "i am good"
      char_to_word_offset = [0, 0, 1, 1, 1, 2, 2, 2, 2]
      doc_tokens = ["i", "am", "good"]
      output = QADataset.split_into_words(text)
      assert output[0] == doc_tokens
      assert output[1] == char_to_word_offset
  
file_path: NeMo/tests/collections/nlp/test_qna.py
imports: import collections
  import pytest
  import torch
  from nemo.collections.nlp.data.question_answering.dataset.qa_dataset import QADataset
  from nemo.collections.nlp.data.question_answering.dataset.qa_gpt_dataset import GPTQADataset
  from nemo.collections.nlp.metrics.qa_metrics import QAMetrics
label_desc: Use image node_id 7 for calling a global function with example usage: test_split_into_words() without return types