code: def search_papers(title, abstract):
      query_embedding = model.encode(
          title + "[SEP]" + abstract, convert_to_tensor=True
      )
  
      search_hits = util.semantic_search(
          query_embedding, corpus_embeddings
      )
      search_hits = search_hits[0]  # Get the hits for the first query
  
      print("\n\nPaper:", title)
      print("Most similar papers:")
      for hit in search_hits:
          related_paper = papers[hit["corpus_id"]]
          print(
              "{:.2f}\t{}\t{} {}".format(
                  hit["score"],
                  related_paper["title"],
                  related_paper["venue"],
                  related_paper["year"],
              )
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: search_papers(title, abstract) without return types