code: def test_small_model_japanese(self):
      question_answerer = pipeline(
          "question-answering",
          model="KoichiYasuoka/deberta-base-japanese-aozora-ud-head",
      )
      output = question_answerer(
          question="国語", context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
      )
  
      # Wrong answer, the whole text is identified as one "word" since the tokenizer does not include
      # a pretokenizer
      self.assertEqual(
          nested_simplify(output),
          {
              "score": 1.0,
              "start": 0,
              "end": 30,
              "answer": "全学年にわたって小学校の国語の教科書に挿し絵が用いられている",
          },
      )
  
      # Disable word alignment
      output = question_answerer(
          question="国語",
          context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている",
          align_to_words=False,
      )
      self.assertEqual(
          nested_simplify(output),
          {"score": 1.0, "start": 15, "end": 18, "answer": "教科書"},
      )
  
imports: import unittest
  from transformers import MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, LxmertConfig, QuestionAnsweringPipeline
  from transformers.data.processors.squad import SquadExample
  from transformers.pipelines import QuestionAnsweringArgumentHandler, pipeline
  from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torch_or_tf, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 6 for calling the QAPipelineTests obj's underlying member method code with example usage: obj.test_small_model_japanese() without return types