code: def load_train_data():
      eli5 = datasets.load_dataset("eli5", name="LFQA_reddit")
      eli5_train = eli5["train_eli5"]
      eli5_train_q_reps = np.memmap(
          "eli5_questions_reps.dat",
          dtype="float32",
          mode="r",
          shape=(eli5_train.num_rows, 128),
      )
      eli5_train_q_index = faiss.IndexFlatIP(128)
      eli5_train_q_index.add(eli5_train_q_reps)
      return (eli5_train, eli5_train_q_index)
  
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 3 for calling a global function with example usage: load_train_data() and returns: eli5_train,  eli5_train_q_index