code: def make_support(
      question, source="wiki40b", method="dense", n_results=10
  ):
      if source == "none":
          support_doc, hit_lst = (
              " <P> ".join(["" for _ in range(11)]).strip(),
              [],
          )
      else:
          if method == "dense":
              support_doc, hit_lst = query_qa_dense_index(
                  question,
                  qar_model,
                  qar_tokenizer,
                  passages,
                  gpu_dense_index,
                  n_results,
              )
          else:
              support_doc, hit_lst = query_es_index(
                  question,
                  es_client,
                  index_name="english_wiki40b_snippets_100w",
                  n_results=n_results,
              )
      support_list = [
          (
              res["article_title"],
              res["section_title"].strip(),
              res["score"],
              res["passage_text"],
          )
          for res in hit_lst
      ]
      question_doc = "question: {} context: {}".format(
          question, support_doc
      )
      return question_doc, support_list
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/longform-qa/eli5_app.py
imports: import datasets
  import numpy
  import streamlit
  import torch
  from elasticsearch import Elasticsearch
  import faiss
  import transformers
  from eli5_utils import embed_questions_for_retrieval, make_qa_s2s_model, qa_s2s_generate, query_es_index, query_qa_dense_index
  from transformers import AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer
label_desc: Use image node_id 5 for calling a global function with example usage: make_support(question, source, method, n_results) and returns: question_doc,  support_list