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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def time_multi_normxcorr(templates, stream, pads, threaded=False, *args, **kwargs):
 """ Compute cross-correlations in the time-domain using C routine. :param templates: 2D Array of templates :type templates: np.ndarray :param stream: 1D array of continuous data :type stream: np.ndarray :param pads: List of ints of pad lengths in the same order as templates :type pads: list :param threaded: Whether to use the threaded routine or not :type threaded: bool :return: np.ndarray of cross-correlations :return: np.ndarray channels used """

    used_chans = ~np.isnan(templates).any(axis=1)

    utilslib = _load_cdll('libutils')

    argtypes = [
        np.ctypeslib.ndpointer(dtype=np.float32, ndim=1,
                               flags=native_str('C_CONTIGUOUS')),
        ctypes.c_int, ctypes.c_int,
        np.ctypeslib.ndpointer(dtype=np.float32, ndim=1,
                               flags=native_str('C_CONTIGUOUS')),
        ctypes.c_int,
        np.ctypeslib.ndpointer(dtype=np.float32, ndim=1,
                               flags=native_str('C_CONTIGUOUS'))]
    restype = ctypes.c_int
    if threaded:
        func = utilslib.multi_normxcorr_time_threaded
        argtypes.append(ctypes.c_int)
    else:
        func = utilslib.multi_normxcorr_time
    func.argtypes = argtypes
    func.restype = restype
    # Need to de-mean everything
    templates_means = templates.mean(axis=1).astype(np.float32)[:, np.newaxis]
    stream_mean = stream.mean().astype(np.float32)
    templates = templates.astype(np.float32) - templates_means
    stream = stream.astype(np.float32) - stream_mean
    template_len = templates.shape[1]
    n_templates = templates.shape[0]
    image_len = stream.shape[0]
    ccc = np.ascontiguousarray(
        np.empty((image_len - template_len + 1) * n_templates), np.float32)
    t_array = np.ascontiguousarray(templates.flatten(), np.float32)
    time_args = [t_array, template_len, n_templates,
                 np.ascontiguousarray(stream, np.float32), image_len, ccc]
    if threaded:
        time_args.append(kwargs.get('cores', cpu_count()))
    func(*time_args)
    ccc[np.isnan(ccc)] = 0.0
    ccc = ccc.reshape((n_templates, image_len - template_len + 1))
    for i in range(len(pads)):
        ccc[i] = np.append(ccc[i], np.zeros(pads[i]))[pads[i]:]
    templates += templates_means
    stream += stream_mean
    return ccc, used_chans