class_docstr: Oxford 102 category flower dataset.
docstr: Returns SplitGenerators.
docstr_tok: ["Returns","SplitGenerators","."]
code: def _split_generators(self, dl_manager):
      """Returns SplitGenerators."""
      # 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": os.path.join(_BASE_URL, "102flowers.tgz"),
              "labels": os.path.join(_BASE_URL, "imagelabels.mat"),
              "setid": os.path.join(_BASE_URL, "setid.mat"),
          }
      )
  
      gen_kwargs = dict(
          images_dir_path=os.path.join(dl_paths["images"], "jpg"),
          labels_path=dl_paths["labels"],
          setid_path=dl_paths["setid"],
      )
  
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              gen_kwargs=dict(split_name="trnid", **gen_kwargs),
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.TEST,
              gen_kwargs=dict(split_name="tstid", **gen_kwargs),
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.VALIDATION,
              gen_kwargs=dict(split_name="valid", **gen_kwargs),
          ),
      ]
  
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