code: def test_retrieval(self):
      model = load_model(
          "blip_retrieval", "coco", is_eval=True, device=device
      )
  
      images = torch.randn(4, 3, 384, 384).to(device)
      text_input = [
          "caption of image 1",
          "another caption of image 1",
          "caption of image 2",
          "caption of image 3",
      ]
      image_id = torch.tensor([1, 1, 2, 3]).to(device)
      samples = {
          "image": images,
          "text_input": text_input,
          "image_id": image_id,
          "epoch": 0,
          "iters": 0,
          "num_iters_per_epoch": 100,
      }
      output = model(samples)
  
      assert (
          output.intermediate_output.image_embeds.shape
          == torch.Size([4, 577, 768])
      )
      assert output.intermediate_output.text_embeds.shape == torch.Size(
          [4, 35, 768]
      )
      assert (
          output.intermediate_output.image_embeds_m.shape
          == torch.Size([4, 577, 768])
      )
      assert (
          output.intermediate_output.text_embeds_m.shape
          == torch.Size([4, 35, 768])
      )
      assert (
          output.intermediate_output.encoder_output.last_hidden_state.shape
          == torch.Size([4, 35, 768])
      )
      assert output.intermediate_output.itm_logits.shape == torch.Size(
          [12, 2]
      )
      assert all(
          output.intermediate_output.itm_labels
          == torch.LongTensor([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]).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 5 for calling the TestBlip obj's underlying member method code with example usage: obj.test_retrieval() without return types