class_docstr: MR in the restaurant domain and target utterances describing it.
class_docstr_tok: ["MR","in","the","restaurant","domain","and","target","utterances","describing","it","."]
docstr: Returns SplitGenerators.
docstr_tok: ["Returns","SplitGenerators","."]
code: def _split_generators(self, dl_manager):
      """Returns SplitGenerators."""
      extracted_path = dl_manager.download_and_extract(
          {
              "train_path": _TRAIN_URL,
              "dev_path": _DEV_URL,
              "test_path": _TEST_URL,
          }
      )
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              # These kwargs will be passed to _generate_examples
              gen_kwargs={"csv_path": extracted_path["train_path"]},
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.VALIDATION,
              # These kwargs will be passed to _generate_examples
              gen_kwargs={"csv_path": extracted_path["dev_path"]},
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.TEST,
              # These kwargs will be passed to _generate_examples
              gen_kwargs={"csv_path": extracted_path["test_path"]},
          ),
      ]
  
file_path: datasets/tensorflow_datasets/datasets/e2e_cleaned/e2e_cleaned_dataset_builder.py
imports: from __future__ import annotations
  import csv
  from etils import epath
  import numpy
  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