code: def load_models():
      if LOAD_DENSE_INDEX:
          qar_tokenizer = AutoTokenizer.from_pretrained(
              "yjernite/retribert-base-uncased"
          )
          qar_model = AutoModel.from_pretrained(
              "yjernite/retribert-base-uncased"
          ).to("cuda:0")
          _ = qar_model.eval()
      else:
          qar_tokenizer, qar_model = (None, None)
      if MODEL_TYPE == "bart":
          s2s_tokenizer = AutoTokenizer.from_pretrained(
              "yjernite/bart_eli5"
          )
          s2s_model = AutoModelForSeq2SeqLM.from_pretrained(
              "yjernite/bart_eli5"
          ).to("cuda:0")
          save_dict = torch.load(
              "seq2seq_models/eli5_bart_model_blm_2.pth"
          )
          s2s_model.load_state_dict(save_dict["model"])
          _ = s2s_model.eval()
      else:
          s2s_tokenizer, s2s_model = make_qa_s2s_model(
              model_name="t5-small",
              from_file="seq2seq_models/eli5_t5_model_1024_4.pth",
              device="cuda:0",
          )
      return (qar_tokenizer, qar_model, s2s_tokenizer, s2s_model)
  
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 1 for calling a global function with example usage: load_models() and returns: qar_tokenizer,  qar_model,  s2s_tokenizer,  s2s_model