code: def test_caption_forward(self):
      model, vis_processors, _ = load_model_and_preprocess(
          name="blip_caption",
          model_type="base_coco",
          is_eval=True,
          device=device,
      )
  
      # preprocess the image
      # vis_processors stores image transforms for "train" and "eval" (validation / testing / inference)
      image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
      text_input = [
          "a large statue of a person spraying water from a fountain"
      ]
  
      samples = {"image": image, "text_input": text_input}
      output = model(samples)
  
      assert (
          output.intermediate_output.image_embeds.shape
          == torch.Size([1, 577, 768])
      )
      assert (
          output.intermediate_output.decoder_labels.shape
          == torch.Size([1, 13])
      )
  
      assert pytest.approx(2.7152, precision) == output.loss.item()
      assert (
          pytest.approx(-0.0200, precision)
          == torch.mean(output.intermediate_output.image_embeds).item()
      )
  
      assert all(
          output.intermediate_output.decoder_labels[0]
          == torch.LongTensor(
              [
                  -100,
                  -100,
                  -100,
                  -100,
                  1997,
                  1037,
                  2711,
                  29035,
                  2300,
                  2013,
                  1037,
                  9545,
                  102,
              ]
          ).to(device)
      )
  
file_path: lavis/tests/models/test_blip.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 3 for calling the TestBlip obj's underlying member method code with example usage: obj.test_caption_forward() without return types