guides/distributed_training_with_jax.py/get_datasets
def get_datasets():
    # Load the data and split it between train and test sets
    (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

    # Scale images to the [0, 1] range
    x_train = x_train.astype("float32")
    x_test = x_test.astype("float32")
    # Make sure images have shape (28, 28, 1)
    x_train = np.expand_dims(x_train, -1)
    x_test = np.expand_dims(x_test, -1)
    print("x_train shape:", x_train.shape)
    print(x_train.shape[0], "train samples")
    print(x_test.shape[0], "test samples")

    # Create TF Datasets
    train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))
    eval_data = tf.data.Dataset.from_tensor_slices((x_test, y_test))
    return train_data, eval_data