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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 threshold(image, block_size=DEFAULT_BLOCKSIZE, mask=None):
 """Applies adaptive thresholding to the given image. Args: image: BGRA image. block_size: optional int block_size to use for adaptive thresholding. mask: optional mask. Returns: Thresholded image. """

    if mask is None:
        mask = np.zeros(image.shape[:2], dtype=np.uint8)
        mask[:] = 255

    if len(image.shape) > 2 and image.shape[2] == 4:
        image = cv2.cvtColor(image, cv2.COLOR_BGRA2GRAY)
    res = _calc_block_mean_variance(image, mask, block_size)
    res = image.astype(np.float32) - res.astype(np.float32) + 255
    _, res = cv2.threshold(res, 215, 255, cv2.THRESH_BINARY)
    return res