def METHOD_NAME(raw, other, t_raw, t_other, verbose=None):
    """Realign two simultaneous recordings.
    Due to clock drift, recordings at a given same sample rate made by two
    separate devices simultaneously can become out of sync over time. This
    function uses event times captured by both acquisition devices to resample
    ``other`` to match ``raw``.
    Parameters
    ----------
    raw : instance of Raw
        The first raw instance.
    other : instance of Raw
        The second raw instance. It will be resampled to match ``raw``.
    t_raw : array-like, shape (n_events,)
        The times of shared events in ``raw`` relative to ``raw.times[0]`` (0).
        Typically these could be events on some TTL channel like
        ``find_events(raw)[:, 0] - raw.first_event``.
    t_other : array-like, shape (n_events,)
        The times of shared events in ``other`` relative to ``other.times[0]``.
    %(verbose)s
    Notes
    -----
    This function operates inplace. It will:
    1. Estimate the zero-order (start offset) and first-order (clock drift)
       correction.
    2. Crop the start of ``raw`` or ``other``, depending on which started
       recording first.
    3. Resample ``other`` to match ``raw`` based on the clock drift.
    4. Crop the end of ``raw`` or ``other``, depending on which stopped
       recording first (and the clock drift rate).
    This function is primarily designed to work on recordings made at the same
    sample rate, but it can also operate on recordings made at different
    sample rates to resample and deal with clock drift simultaneously.
    .. versionadded:: 0.22
    """
    _validate_type(raw, BaseRaw, "raw")
    _validate_type(other, BaseRaw, "other")
    t_raw = np.array(t_raw, float)
    t_other = np.array(t_other, float)
    if t_raw.ndim != 1 or t_raw.shape != t_other.shape:
        raise ValueError(
            "t_raw and t_other must be 1D with the same shape, "
            f"got shapes {t_raw.shape} and {t_other.shape}"
        )
    if len(t_raw) < 20:
        warn("Fewer than 20 times passed, results may be unreliable")
    # 1. Compute correction factors
    poly = Polynomial.fit(x=t_other, y=t_raw, deg=1)
    converted = poly.convert(domain=(-1, 1))
    [zero_ord, first_ord] = converted.coef
    logger.info(
        f"Zero order coefficient: {zero_ord} \n" f"First order coefficient: {first_ord}"
    )
    r, p = pearsonr(t_other, t_raw)
    msg = f"Linear correlation computed as R={r:0.3f} and p={p:0.2e}"
    if p > 0.05 or r <= 0:
        raise ValueError(msg + ", cannot resample safely")
    if p > 1e-6:
        warn(msg + ", results may be unreliable")
    else:
        logger.info(msg)
    dr_ms_s = 1000 * abs(1 - first_ord)
    logger.info(
        f"Drift rate: {1000 * dr_ms_s:0.1f} μs/s "
        f"(total drift over {raw.times[-1]:0.1f} s recording: "
        f"{raw.times[-1] * dr_ms_s:0.1f} ms)"
    )
    # 2. Crop start of recordings to match using the zero-order term
    msg = f"Cropping {zero_ord:0.3f} s from the start of "
    if zero_ord > 0:  # need to crop start of raw to match other
        logger.info(msg + "raw")
        raw.crop(zero_ord, None)
        t_raw -= zero_ord
    else:  # need to crop start of other to match raw
        logger.info(msg + "other")
        other.crop(-zero_ord, None)
        t_other += zero_ord
    # 3. Resample data using the first-order term
    logger.info("Resampling other")
    sfreq_new = raw.info["sfreq"] * first_ord
    other.load_data().resample(sfreq_new, verbose=True)
    with other.info._unlock():
        other.info["sfreq"] = raw.info["sfreq"]
    # 4. Crop the end of one of the recordings if necessary
    delta = raw.times[-1] - other.times[-1]
    msg = f"Cropping {abs(delta):0.3f} s from the end of "
    if delta > 0:
        logger.info(msg + "raw")
        raw.crop(0, other.times[-1])
    elif delta < 0:
        logger.info(msg + "other")
        other.crop(0, raw.times[-1])