def METHOD_NAME():
    n_channels = 50
    n_times = 500
    sfreq = 100
    rng = np.random.RandomState(34834)
    raws = []
    datas = []
    for i_raw in range(2):
        data = rng.rand(n_channels, n_times)
        ch_names = [f'ch{i}' for i in range(n_channels)]
        ch_types = ['eeg'] * n_channels
        info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
        raw = mne.io.RawArray(data, info)
        n_anns = 10
        inds = np.linspace(0, n_times, n_anns, endpoint=False).astype(int)
        onsets = raw.times[inds]
        if i_raw == 0:
            trial_dur = 0.2  # in sec
        else:
            trial_dur = 0.1
        durations = np.ones(n_anns) * trial_dur
        descriptions = ['test_trial'] * len(durations)
        anns = mne.Annotations(onsets, durations, descriptions)
        raw = raw.set_annotations(anns)
        raws.append(raw)
        datas.append(data)
    windows = create_from_mne_raw(raws, 0, 0, 5, 2, False)
    # windows per trial: 0-5,2-7,4-9,6-11,...,14-19,15-20
    # and then: 0-5,2-7,4-9,5-10
    assert len(windows) == 9 * n_anns + 4 * n_anns
    for i_w, (x, y, (i_w_in_t, i_start, i_stop)) in enumerate(windows):
        if i_w < 9 * n_anns:
            assert i_w_in_t == i_w % 9
            i_t = i_w // 9
            assert i_start == inds[i_t] + i_w_in_t * 2 - (i_w_in_t == 8)
            assert i_stop == inds[i_t] + i_w_in_t * 2 - (i_w_in_t == 8) + 5
            np.testing.assert_allclose(x, datas[0][:, i_start:i_stop],
                                       atol=1e-5, rtol=1e-5)
        else:
            assert i_w_in_t == (i_w - n_anns * 9) % 4
            i_t = ((i_w - n_anns * 9) // 4)
            assert i_start == inds[i_t] + i_w_in_t * 2 - (i_w_in_t == 3)
            assert i_stop == inds[i_t] + i_w_in_t * 2 - (i_w_in_t == 3) + 5