code: def __init__(
      self,
      examples_array,
      make_doc_fun=None,
      extra_answer_threshold=3,
      document_cache=None,
      training=True,
  ):
      self.training = training
      self.data = examples_array
      self.make_doc_function = make_doc_fun
      self.document_cache = (
          {} if document_cache is None else document_cache
      )
      assert not (make_doc_fun is None and document_cache is None)
      # make index of specific question-answer pairs from multi-answers
      if self.training:
          self.qa_id_list = [
              (i, j)
              for i, qa in enumerate(self.data)
              for j, (a, sc) in enumerate(
                  zip(qa["answers"]["text"], qa["answers"]["score"])
              )
              if j == 0 or sc >= extra_answer_threshold
          ]
      else:
          self.qa_id_list = [(i, 0) for i in range(self.data.num_rows)]
  
imports: import functools
  import math
  import os
  from random import choice, randint
  from time import time
  import datasets
  import faiss
  import numpy
  import pandas
  import torch
  import torch.utils.checkpoint
  from elasticsearch import Elasticsearch
  from elasticsearch.helpers import bulk, streaming_bulk
  from torch import nn
  from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
  from tqdm import tqdm
  from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup
label_desc: Use image node_id 1 to create a new ELI5DatasetS2S object from inherited base classes: Dataset with example: obj = ELI5DatasetS2S(examples_array, make_doc_fun, extra_answer_threshold, document_cache, training)