class_docstr: The dataset for the PASCAL Visual Object Classes Challenge 2007
  (VOC2007) for the detection competition.
  
  A total of 9,963 images are included in this dataset, where each image
  contains a set of objects, out of 20 different classes, making a total of
  24,640 annotated objects.
  
  Note that, as per the official dataset, the test set of VOC2007 does not
  contain annotations.
  
  Example usage::
  
      import fiftyone as fo
      import fiftyone.zoo as foz
  
      dataset = foz.load_zoo_dataset("voc-2007", split="validation")
  
      session = fo.launch_app(dataset)
  
  Dataset size
      868.85 MB
  
  Source
      http://host.robots.ox.ac.uk/pascal/VOC/voc2007
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code: def _download_and_prepare(self, dataset_dir, scratch_dir, split):
      def download_fcn(download_dir):
          return tfds.load(
              "voc/2007",
              split=split,
              data_dir=download_dir,
              download=True,
              with_info=True,
          )
  
      get_classes_fcn = lambda info: info.features["objects"][
          "label"
      ].names
      get_num_samples_fcn = lambda info: info.splits[split].num_examples
      sample_parser = _TFDSImageDetectionSampleParser(
          bounding_box_field="bbox"
      )
      return _download_and_prepare(
          dataset_dir,
          scratch_dir,
          download_fcn,
          get_classes_fcn,
          get_num_samples_fcn,
          sample_parser,
      )
  
imports: import fiftyone.core.labels
  import fiftyone.core.utils
  import fiftyone.types
  import fiftyone.utils.imagenet
  import fiftyone.utils.data
  import fiftyone.zoo.datasets
label_desc: Use image node_id 4 for calling the VOC2007Dataset obj's underlying member method code with example usage: obj._download_and_prepare(dataset_dir, scratch_dir, split) and returns: _download_and_prepare, tfds