code: def test_encode_multi_process(
      stsb_bert_tiny_model: SentenceTransformer,
      normalize_embeddings: bool,
  ) -> None:
      model = stsb_bert_tiny_model
      sentences = ["This is sentence {}".format(i) for i in range(40)]
  
      # Start the multi-process pool on e.g. two CPU devices & compute the embeddings using the pool
      pool = model.start_multi_process_pool(["cpu", "cpu"])
      emb = model.encode_multi_process(
          sentences,
          pool,
          chunk_size=10,
          normalize_embeddings=normalize_embeddings,
      )
      model.stop_multi_process_pool(pool)
      assert emb.shape == (len(sentences), 128)
  
      # Make sure the embeddings aren't just all 0
      assert emb.sum() != 0.0
  
      # Compare against normal embeddings
      emb_normal = model.encode(
          sentences, normalize_embeddings=normalize_embeddings
      )
      diff = np.max(np.abs(emb - emb_normal))
      assert diff < 0.001
  
      # Ensure that after normalizing, the means are all almost 0, and otherwise not
      assert np.all(np.abs(emb.mean(1)) < 0.01) == normalize_embeddings
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_encode_multi_process(stsb_bert_tiny_model, normalize_embeddings) without return types