code: def test_qa(self):
      # loads PNP-VQA base model, with BLIP_itm_large, BLIP_caption_large, Unifiedqav2_base
      # this also loads the associated image processors and text processors
      model, vis_processors, txt_processors = load_model_and_preprocess(
          name="pnp_vqa", model_type="base", is_eval=True, device=device
      )
  
      # ask a random question.
      question = "Which city is this photo taken?"
  
      image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
      question = txt_processors["eval"](question)
  
      samples = {"image": image, "text_input": [question]}
  
      samples["captions"] = [
          ["the city is singapore", "the picture is taken in singapore"]
      ]
  
      answer = model.forward_qa(samples=samples, num_captions=2)
  
      assert isinstance(answer, list)
      assert len(answer) == 1
      assert answer[0] == "singapore"
  
file_path: lavis/tests/models/test_pnp_vqa.py
imports: import pytest
  import torch
  from lavis.models import load_model, load_model_and_preprocess
  from PIL import Image
label_desc: Use image node_id 4 for calling the TestPNPVQA obj's underlying member method code with example usage: obj.test_qa() without return types