code: def test_large_model_pt(self):
      vqa_pipeline = pipeline(
          "visual-question-answering",
          model="dandelin/vilt-b32-finetuned-vqa",
      )
      image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
      question = "How many cats are there?"
  
      outputs = vqa_pipeline(image=image, question=question, top_k=2)
      self.assertEqual(
          nested_simplify(outputs, decimals=4),
          [
              {"score": 0.8799, "answer": "2"},
              {"score": 0.296, "answer": "1"},
          ],
      )
  
      outputs = vqa_pipeline(
          {"image": image, "question": question}, top_k=2
      )
      self.assertEqual(
          nested_simplify(outputs, decimals=4),
          [
              {"score": 0.8799, "answer": "2"},
              {"score": 0.296, "answer": "1"},
          ],
      )
  
      outputs = vqa_pipeline(
          [
              {"image": image, "question": question},
              {"image": image, "question": question},
          ],
          top_k=2,
      )
      self.assertEqual(
          nested_simplify(outputs, decimals=4),
          [
              [
                  {"score": 0.8799, "answer": "2"},
                  {"score": 0.296, "answer": "1"},
              ]
          ]
          * 2,
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/pipelines/test_pipelines_visual_question_answering.py
imports: import unittest
  from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
  from transformers.pipelines import pipeline
  from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_vision, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 4 for calling the VisualQuestionAnsweringPipelineTests obj's underlying member method code with example usage: obj.test_large_model_pt() without return types