def METHOD_NAME(param, whitening=True, dense=False, transform=True, align_pose=False):
    """Whitening param -> 3d vertex, based on the 3dmm param: u_base, w_shp, w_exp
    dense: if True, return dense vertex, else return 68 sparse landmarks. All dense or sparse vertex is transformed to
    image coordinate space, but without alignment caused by face cropping.
    transform: whether transform to image space
    """
    if len(param) == 12:
        param = np.concatenate((param, [0] * 50))
    if whitening:
        if len(param) == 62:
            param = param * param_std + param_mean
        else:
            param = np.concatenate((param[:11], [0], param[11:]))
            param = param * param_std + param_mean
    if align_pose:
        print('align param')
        Ps = param[:12].reshape(3, -1)
        s, R, t3d = P2sRt(Ps)
        Ps[:, :3] = np.identity(3) * s
        param[:12] = Ps.reshape(-1)
    p, offset, alpha_shp, alpha_exp = _parse_param(param)
    if dense:
        vertex = p @ (u + w_shp @ alpha_shp + w_exp @ alpha_exp).reshape(3, -1, order='F') + offset
        if transform:
            # transform to image coordinate space
            vertex[1, :] = std_size + 1 - vertex[1, :]
    else:
        """For 68 pts"""
        vertex = p @ (u_base + w_shp_base @ alpha_shp + w_exp_base @ alpha_exp).reshape(3, -1, order='F') + offset
        if transform:
            # transform to image coordinate space
            vertex[1, :] = std_size + 1 - vertex[1, :]
    return vertex