code: def predict(self, image, task, question, caption):
      if task == "visual_question_answering":
          assert (
              question is not None
          ), "Please type a question for visual question answering task."
      if task == "image_text_matching":
          assert (
              caption is not None
          ), "Please type a caption for mage text matching task."
  
      im = load_image(
          image,
          image_size=480
          if task == "visual_question_answering"
          else 384,
          device=self.device,
      )
      model = self.models[task]
      model.eval()
      model = model.to(self.device)
  
      if task == "image_captioning":
          with torch.no_grad():
              caption = model.generate(
                  im,
                  sample=False,
                  num_beams=3,
                  max_length=20,
                  min_length=5,
              )
              return "Caption: " + caption[0]
  
      if task == "visual_question_answering":
          with torch.no_grad():
              answer = model(
                  im, question, train=False, inference="generate"
              )
              return "Answer: " + answer[0]
  
      # image_text_matching
      itm_output = model(im, caption, match_head="itm")
      itm_score = torch.nn.functional.softmax(itm_output, dim=1)[:, 1]
      itc_score = model(im, caption, match_head="itc")
      return (
          f"The image and text is matched with a probability of {itm_score.item():.4f}.\n"
          f"The image feature and text feature has a cosine similarity of {itc_score.item():.4f}."
      )
  
file_path: blip/predict.py
imports: from pathlib import Path
  from PIL import Image
  import torch
  from torchvision import transforms
  from torchvision.transforms.functional import InterpolationMode
  import cog
  from models.blip import blip_decoder
  from models.blip_vqa import blip_vqa
  from models.blip_itm import blip_itm
label_desc: Use image node_id 2 for calling the Predictor obj's underlying member method code with example usage: obj.predict(image, task, question, caption) and returns: str, itm_score, str, itc_score, str, unknown, unknown