def METHOD_NAME():
    # Test obtained from Torch tutorial at:
    # https://github.com/baidu-research/warp-ctc/blob/master/torch_binding/TUTORIAL.md
    # Layout, from slowest to fastest changing dimension, is (time, batchSize, inputLayerSize)
    activations = np.asarray(
        [
            [[0, 0, 0, 0, 0], [1, 2, 3, 4, 5], [-5, -4, -3, -2, -1]],
            [[0, 0, 0, 0, 0], [6, 7, 8, 9, 10], [-10, -9, -8, -7, -6]],
            [[0, 0, 0, 0, 0], [11, 12, 13, 14, 15], [-15, -14, -13, -12, -11]],
        ],
        dtype=np.float32,
    )
    # Duration of each sequence
    activation_times = np.asarray([1, 3, 3], dtype=np.int32)
    # Labels for each sequence
    labels = np.asarray([[1, -1], [3, 3], [2, 3]], dtype=np.int32)
    expected_costs = np.asarray(
        [1.609437943, 7.355742931, 4.938849926], dtype=np.float32
    )
    grads = [
        [
            [0.2, -0.8, 0.2, 0.2, 0.2],
            [0.01165623125, 0.03168492019, 0.08612854034, -0.7658783197, 0.636408627],
            [-0.02115798369, 0.03168492019, -0.8810571432, 0.2341216654, 0.636408627],
        ],
        [
            [0, 0, 0, 0, 0],
            [-0.9883437753, 0.03168492019, 0.08612854034, 0.2341216654, 0.636408627],
            [-0.02115798369, 0.03168492019, -0.1891518533, -0.4577836394, 0.636408627],
        ],
        [
            [0, 0, 0, 0, 0],
            [0.01165623125, 0.03168492019, 0.08612854034, -0.7658783197, 0.636408627],
            [-0.02115798369, 0.03168492019, 0.08612854034, -0.7330639958, 0.636408627],
        ],
    ]
    expected_gradients = np.asarray(grads, dtype=np.float32)
    return [activations, labels, activation_times, expected_costs, expected_gradients]