class_docstr: Base class for importing datasets in Open Images V7 format.
  
  See :class:`fiftyone.types.OpenImagesDataset` for format details.
  
  Args:
      dataset_dir: the dataset directory
      label_types (None): a label type or list of label types to load. The
          supported values are
          ``("detections", "classifications", "points", "relationships",
          "segmentations")``.
          By default, all supported label types for version 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
      include_id (True): whether to load the Open Images ID for each sample
          along with the labels
      only_matching (False): whether to only load labels that match the
          ``classes`` or ``attrs`` requirements that you provide (True), or
          to load all labels for samples that match the requirements (False)
      load_hierarchy (True): whether to load the classes hiearchy and add it
          to the dataset's ``info`` dictionary
      shuffle (False): whether to randomly shuffle the order in which the
          samples are imported
      seed (None): a random seed to use when shuffling
      max_samples (None): a maximum number of samples to load. 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
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code: def __init__(
      self,
      dataset_dir,
      label_types=None,
      classes=None,
      attrs=None,
      image_ids=None,
      include_id=True,
      only_matching=False,
      load_hierarchy=True,
      shuffle=False,
      seed=None,
      max_samples=None,
  ):
      self.version = "v7"
      super().__init__(
          dataset_dir,
          label_types=label_types,
          classes=classes,
          attrs=attrs,
          image_ids=image_ids,
          include_id=include_id,
          only_matching=only_matching,
          load_hierarchy=load_hierarchy,
          shuffle=shuffle,
          seed=seed,
          max_samples=max_samples,
      )
  
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 1 to create a new OpenImagesV7DatasetImporter object from inherited base classes: OpenImagesDatasetImporter with example: obj = OpenImagesV7DatasetImporter(dataset_dir, label_types, classes, attrs, image_ids, include_id, only_matching, load_hierarchy, shuffle, seed, max_samples)