class_docstr: Base abstract class for Open Images Challenge 2019 datasets.
class_docstr_tok: ["Base","abstract","class","for","Open","Images","Challenge","2019","datasets","."]
code: def _split_generators(self, dl_manager):
      urls = {
          "train_images": _TRAIN_IMAGES_URLS,
          "test_images": [_TEST_IMAGES_URL],
          "validation_images": [_VALIDATION_IMAGES_URL],
      }
      urls.update(
          self.annotation_urls
      )  # pytype: disable=wrong-arg-types
      paths = dl_manager.download(urls)
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              gen_kwargs=dict(paths=paths, split="train"),
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.TEST,
              gen_kwargs=dict(paths=paths, split="test"),
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.VALIDATION,
              gen_kwargs=dict(paths=paths, split="validation"),
          ),
      ]
  
file_path: datasets/tensorflow_datasets/datasets/open_images_challenge2019_detection/open_images_challenge2019_detection_dataset_builder.py
imports: from __future__ import annotations
  import abc
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the _OpenImagesChallenge2019 obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: list