code: def load_indexes():
      if LOAD_DENSE_INDEX:
          faiss_res = faiss.StandardGpuResources()
          wiki40b_passages = datasets.load_dataset(
              path="wiki_snippets", name="wiki40b_en_100_0"
          )["train"]
          wiki40b_passage_reps = np.memmap(
              "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat",
              dtype="float32",
              mode="r",
              shape=(wiki40b_passages.num_rows, 128),
          )
          wiki40b_index_flat = faiss.IndexFlatIP(128)
          wiki40b_gpu_index_flat = faiss.index_cpu_to_gpu(
              faiss_res, 1, wiki40b_index_flat
          )
          wiki40b_gpu_index_flat.add(
              wiki40b_passage_reps
          )  # TODO fix for larger GPU
      else:
          wiki40b_passages, wiki40b_gpu_index_flat = (None, None)
      es_client = Elasticsearch([{"host": "localhost", "port": "9200"}])
      return (wiki40b_passages, wiki40b_gpu_index_flat, es_client)
  
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 2 for calling a global function with example usage: load_indexes() and returns: wiki40b_passages,  wiki40b_gpu_index_flat,  es_client