How to use the spikeextractors.NumpyRecordingExtractor function in spikeextractors

def _create_example(self):
        channel_ids = [0, 1, 2, 3]
        num_channels = 4
        num_frames = 10000
        sampling_frequency = 30000
        X = np.random.normal(0, 1, (num_channels, num_frames))
        geom = np.random.normal(0, 1, (num_channels, 2))
        X = (X * 100).astype(int)
        RX = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
        RX2 = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
        RX3 = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
        SX = se.NumpySortingExtractor()
        spike_times = [200, 300, 400]
        train1 = np.sort(np.rint(np.random.uniform(0, num_frames, spike_times[0])).astype(int))
        SX.add_unit(unit_id=1, times=train1)
        SX.add_unit(unit_id=2, times=np.sort(np.random.uniform(0, num_frames, spike_times[1])))
        SX.add_unit(unit_id=3, times=np.sort(np.random.uniform(0, num_frames, spike_times[2])))
        SX.set_unit_property(unit_id=1, property_name='stability', value=80)
        SX.set_sampling_frequency(sampling_frequency)
        SX2 = se.NumpySortingExtractor()
        spike_times2 = [100, 150, 450]
        train2 = np.rint(np.random.uniform(0, num_frames, spike_times2[0])).astype(int)
        SX2.add_unit(unit_id=3, times=train2)
        SX2.add_unit(unit_id=4, times=np.random.uniform(0, num_frames, spike_times2[1]))
        SX2.add_unit(unit_id=5, times=np.random.uniform(0, num_frames, spike_times2[2]))
        SX2.set_unit_property(unit_id=4, property_name='stability', value=80)
        SX2.set_unit_spike_features(unit_id=3, feature_name='widths', value=np.asarray([3] * spike_times2[0]))

def setUp(self):
        M = 32
        N = 10000
        sampling_frequency = 30000
        X = np.random.normal(0, 1, (M, N))
        self._X = X
        self._sampling_frequency = sampling_frequency
        self.RX = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency)
        self.test_dir = tempfile.mkdtemp()

def toy_example(duration=10, num_channels=4, sampling_frequency=30000.0, K=10, seed=None):
    upsamplefac = 13

    waveforms, geom = synthesize_random_waveforms(K=K, M=num_channels, average_peak_amplitude=-100,
                                                  upsamplefac=upsamplefac, seed=seed)
    times, labels = synthesize_random_firings(K=K, duration=duration, sampling_frequency=sampling_frequency, seed=seed)
    labels = labels.astype(np.int64)
    SX = se.NumpySortingExtractor()
    SX.set_times_labels(times, labels)
    X = synthesize_timeseries(sorting=SX, waveforms=waveforms, noise_level=10, sampling_frequency=sampling_frequency, duration=duration,
                              waveform_upsamplefac=upsamplefac, seed=seed)
    SX.set_sampling_frequency(sampling_frequency)

    RX = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
    return (RX, SX)