return [
                    datasets.SplitGenerator(
                            name=datasets.Split.TRAIN,
                            gen_kwargs={'file': 'train_data.zip'},
                    ),
                    datasets.SplitGenerator(
                            name=datasets.Split.TEST,
                            gen_kwargs={'file': 'test_data.zip'},
                    ),
            ]

        The above code will first call `_generate_examples(file='train_data.zip')`
        to write the train data, then `_generate_examples(file='test_data.zip')` to
        write the test data.

        Datasets are typically split into different subsets to be used at various
        stages of training and evaluation.

        Note that for datasets without a `VALIDATION` split, you can use a
        fraction of the `TRAIN` data for evaluation as you iterate on your model
        so as not to overfit to the `TEST` data.