class_docstr: Oxford-IIIT pet dataset.
docstr: Returns splits.
docstr_tok: ["Returns","splits","."]
code: def _split_generators(self, dl_manager):
      """Returns splits."""
      # Download images and annotations that come in separate archives.
      # Note, that the extension of archives is .tar.gz even though the actual
      # archives format is uncompressed tar.
      dl_paths = dl_manager.download_and_extract(
          {
              "images": _BASE_URL + "/images.tar.gz",
              "annotations": _BASE_URL + "/annotations.tar.gz",
          }
      )
  
      images_path_dir = os.path.join(dl_paths["images"], "images")
      annotations_path_dir = os.path.join(
          dl_paths["annotations"], "annotations"
      )
  
      # Setup train and test splits
      train_split = tfds.core.SplitGenerator(
          name="train",
          gen_kwargs={
              "images_dir_path": images_path_dir,
              "annotations_dir_path": annotations_path_dir,
              "images_list_file": os.path.join(
                  annotations_path_dir, "trainval.txt"
              ),
          },
      )
      test_split = tfds.core.SplitGenerator(
          name="test",
          gen_kwargs={
              "images_dir_path": images_path_dir,
              "annotations_dir_path": annotations_path_dir,
              "images_list_file": os.path.join(
                  annotations_path_dir, "test.txt"
              ),
          },
      )
  
      return [train_split, test_split]
  
imports: import os
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the Builder obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: list