code: def test_simple_encode(
      clip_vit_b_32_model: SentenceTransformer,
  ) -> None:
      model = clip_vit_b_32_model
      # Encode an image:
      image_filepath = os.path.join(
          os.path.dirname(os.path.realpath(__file__)),
          "../examples/applications/image-search/two_dogs_in_snow.jpg",
      )
      img_emb = model.encode(Image.open(image_filepath))
  
      # Encode text descriptions
      text_emb = model.encode(
          [
              "Two dogs in the snow",
              "A cat on a table",
              "A picture of London at night",
          ]
      )
  
      # Compute cosine similarities
      cos_scores = util.cos_sim(img_emb, text_emb)[0]
      assert abs(cos_scores[0] - 0.3069) < 0.01
      assert abs(cos_scores[1] - 0.1010) < 0.01
      assert abs(cos_scores[2] - 0.1086) < 0.01
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_simple_encode(clip_vit_b_32_model) without return types