code: def download_open_images_split(
      dataset_dir,
      split,
      version="v6",
      label_types=None,
      classes=None,
      attrs=None,
      image_ids=None,
      num_workers=None,
      shuffle=None,
      seed=None,
      max_samples=None,
  ):
      """Utility that downloads full or partial splits of the
      `Open Images dataset
      <https://storage.googleapis.com/openimages/web/index.html>`_.
  
      See :class:`fiftyone.types.OpenImagesDataset` for the format in which
      ``dataset_dir`` will be arranged.
  
      Any existing files are not re-downloaded.
  
      This method specifically downloads the subsets of annotations corresponding
      to the 600 boxable classes of Open Images.
      `See here <https://storage.googleapis.com/openimages/web/download.html>`_
      for other download options.
  
      Args:
          dataset_dir: the directory to download the dataset
          split: the split to download. Supported values are
              ``("train", "validation", "test")``
          version ("v7"): the version of the Open Images dataset to download.
              Supported values are ``("v6", "v7")``
          label_types (None): a label type or list of label types to load. The
              supported values are
              ``("detections", "classifications", "relationships",
              "segmentations")``
              for ``"v6"`` and
              ``("detections", "classifications", "points", "relationships",
              "segmentations")`` for ``"v7"``.
              By default, all label types are loaded
          classes (None): a string or list of strings specifying required classes
              to load. If provided, only samples containing at least one instance
              of a specified class will be loaded
          attrs (None): a string or list of strings specifying required
              relationship attributes to load. Only applicable when
              ``label_types`` includes "relationships". If provided, only samples
              containing at least one instance of a specified attribute will be
              loaded
          image_ids (None): an optional list of specific image IDs to load. Can
              be provided in any of the following formats:
  
              -   a list of ``<image-id>`` strings
              -   a list of ``<split>/<image-id>`` strings
              -   the path to a text (newline-separated), JSON, or CSV file
                  containing the list of image IDs to load in either of the first
                  two formats
          num_workers (None): a suggested number of threads to use when
              downloading individual images
          shuffle (False): whether to randomly shuffle the order in which samples
              are chosen for partial downloads
          seed (None): a random seed to use when shuffling
          max_samples (None): a maximum number of samples to load per split. If
              ``label_types``, ``classes``, and/or ``attrs`` are also specified,
              first priority will be given to samples that contain all of the
              specified label types, classes, and/or attributes, followed by
              samples that contain at least one of the specified labels types or
              classes. The actual number of samples loaded may be less than this
              maximum value if the dataset does not contain sufficient samples
              matching your requirements. By default, all matching samples are
              loaded
  
      Returns:
          a tuple of:
  
          -   num_samples: the total number of downloaded images, or ``None`` if
              everything was already downloaded
          -   classes: the list of all classes, or ``None`` if everything was
              already downloaded
          -   did_download: whether any content was downloaded (True) or if all
              necessary files were already downloaded (False)
      """
      _verify_version(version)
      label_types = _parse_label_types(version, label_types)
  
      did_download = False
  
      if image_ids is not None:
          image_ids, _did_download = _parse_and_verify_image_ids(
              image_ids, dataset_dir, split, download=True
          )
          did_download |= _did_download
      else:
          image_ids, _did_download = _load_all_image_ids(
              dataset_dir, split=split, download=True
          )
          did_download |= _did_download
  
      downloaded_ids = _get_downloaded_image_ids(dataset_dir)
  
      (
          classes_map,
          all_classes,
          classes,
          oi_classes,
          attrs_map,
          all_attrs,
          attrs,
          oi_attrs,
          seg_classes,
          pnt_classes_map,
          _did_download,
      ) = _setup(
          dataset_dir,
          label_types=label_types,
          classes=classes,
          attrs=attrs,
          seed=seed,
          download=True,
      )
  
      did_download |= _did_download
  
      # Download class hierarchy if necessary (used in evaluation)
      _, _did_download = _get_hierarchy(
          dataset_dir, classes_map=classes_map, download=True
      )
      did_download |= _did_download
  
      num_samples, _did_download = _download(
          image_ids,
          downloaded_ids,
          oi_classes,
          oi_attrs,
          seg_classes,
          pnt_classes_map,
          dataset_dir,
          split,
          label_types=label_types,
          classes=classes,
          attrs=attrs,
          max_samples=max_samples,
          shuffle=shuffle,
          num_workers=num_workers,
          download=True,
          version=version,
      )
  
      did_download |= _did_download
  
      return num_samples, all_classes, did_download
  
imports: from collections import defaultdict
  import csv
  from glob import glob
  import logging
  import os
  import random
  import warnings
  import pandas
  import eta.core.image
  import eta.core.serial
  import eta.core.utils
  import eta.core.web
  import fiftyone
  import fiftyone.core.fields
  import fiftyone.core.labels
  import fiftyone.utils.aws
  import fiftyone.utils.data
  import fiftyone.utils.image
label_desc: Use image node_id 5 for calling a global function with example usage: download_open_images_split(dataset_dir, split, version, label_types, classes, attrs, image_ids, num_workers, shuffle, seed, max_samples) and returns: num_samples,  all_classes,  did_download