def METHOD_NAME(self):
    vqa_pipeline = pipeline(
        "visual-question-answering", model="hf-internal-testing/tiny-random-Blip2ForConditionalGeneration"
    )
    image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
    question = "How many cats are there?"
    outputs = vqa_pipeline(image=image, question=question)
    self.assertEqual(outputs, [{"answer": ANY(str)}])
    outputs = vqa_pipeline({"image": image, "question": question})
    self.assertEqual(outputs, [{"answer": ANY(str)}])
    outputs = vqa_pipeline([{"image": image, "question": question}, {"image": image, "question": question}])
    self.assertEqual(outputs, [[{"answer": ANY(str)}]] * 2)
    vqa_pipeline = pipeline(
        "visual-question-answering",
        model="hf-internal-testing/tiny-random-Blip2ForConditionalGeneration",
        model_kwargs={"torch_dtype": torch.float16},
        device=0,
    )
    self.assertEqual(vqa_pipeline.model.device, torch.device(0))
    self.assertEqual(vqa_pipeline.model.language_model.dtype, torch.float16)
    self.assertEqual(vqa_pipeline.model.vision_model.dtype, torch.float16)
    outputs = vqa_pipeline(image=image, question=question)
    self.assertEqual(outputs, [{"answer": ANY(str)}])