code: def _features():
      return {
          "default": tfds.features.FeaturesDict(
              {
                  "id": np.str_,
                  "document": {
                      "title": tfds.features.Text(),
                      "url": tfds.features.Text(),
                      "html": tfds.features.Text(),
                      "tokens": tfds.features.Sequence(
                          {
                              "token": tfds.features.Text(),
                              "is_html": np.bool_,
                          }
                      ),
                  },
                  "question": {
                      "text": tfds.features.Text(),
                      "tokens": tfds.features.Sequence(np.str_),
                  },
                  "annotations": tfds.features.Sequence(
                      {
                          "id": np.str_,
                          "long_answer": {
                              "start_token": np.int64,
                              "end_token": np.int64,
                              "start_byte": np.int64,
                              "end_byte": np.int64,
                          },
                          "short_answers": tfds.features.Sequence(
                              {
                                  "start_token": np.int64,
                                  "end_token": np.int64,
                                  "start_byte": np.int64,
                                  "end_byte": np.int64,
                                  "text": tfds.features.Text(),
                              }
                          ),
                          "yes_no_answer": tfds.features.ClassLabel(
                              names=["NO", "YES"]
                          ),  # Can also be -1 for NONE.
                      }
                  ),
              }
          ),
          "longt5": tfds.features.FeaturesDict(
              {
                  "id": tfds.features.Text(),
                  "title": tfds.features.Text(),
                  "context": tfds.features.Text(),
                  "question": tfds.features.Text(),
                  "answer": tfds.features.Text(),
                  "all_answers": tfds.features.Sequence(
                      tfds.features.Text()
                  ),
              }
          ),
      }
  
file_path: datasets/tensorflow_datasets/datasets/natural_questions/natural_questions_dataset_builder.py
imports: import html
  import json
  import os
  import re
  from typing import Optional
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling a global function with example usage: _features() and returns: dict