code: def answer_question(
      question_doc,
      s2s_model,
      s2s_tokenizer,
      min_len=64,
      max_len=256,
      sampling=False,
      n_beams=2,
      top_p=0.95,
      temp=0.8,
  ):
      with torch.no_grad():
          answer = qa_s2s_generate(
              question_doc,
              s2s_model,
              s2s_tokenizer,
              num_answers=1,
              num_beams=n_beams,
              min_len=min_len,
              max_len=max_len,
              do_sample=sampling,
              temp=temp,
              top_p=top_p,
              top_k=None,
              max_input_length=1024,
              device="cuda:0",
          )[0]
      return (answer, 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 6 for calling a global function with example usage: answer_question(question_doc, s2s_model, s2s_tokenizer, min_len, max_len, sampling, n_beams, top_p, temp) and returns: answer,  support_list