4. Decoration pattern
卡米拉曾经自杀未遂。 在一天卡米拉和斯科特乘房车旅行。在后来卡米拉和斯科特分手，而迪尼斯和伊冯分手了又重聚。彼得在他的工作室里寻找灵感。 卡米拉在平行世界见到了汤米。在这期间父亲与母亲邂逅。而卡米拉在现实生活中遇见了汤米，，，，
由探花导演执导的大型穿越爱情古装轻喜剧《医妃难囚》在举行开机仪式
此后世家大族可谓把持了东晋的政权，比如王家、谢家还有桓家。
此时听见板栗叫喊，想也不想地一扬手。
北宋末年，宋微宗昏庸无道，宦官当政，民不聊生。山东郓城以宋江为首的三十六人等楸起了风起云涌的农民起义，他们杀富济贫，行侠仗义，令统治阶级闻风丧胆，令广大百姓拍手称快。义军不断挫败官兵的围剿，兵马粮饷不断充实壮大。
郑氏想着，赵清就要跟苏文青成亲了，是不好再出来了。
钟离剑33岁，他待人温和沉稳，处事冷静果断，爱憎分明有强烈的责任心。因未婚，与母亲钟离孟君和妹妹田甜住在一起，而实际上他只是钟离孟君的养……
越南剧《国民欧巴》Nation's Brother本剧讲述的是高富帅欧巴明英给妹妹嘉兰找的大学生家教，居然是和自己有过一夜Q的小可爱。作为对女人过敏的高富帅欧巴，和小家教会擦出怎样的火花呢！唯心小哥哥真的是贤惠啊，上得厅堂下得厨房，教得了书，贫得了嘴，顶顶子也想娶回家！
大家便都盯着场中，要看这女子如何闯关。
是啊，简直让人欲罢不能。
等到这姑娘离开，陈文羽、陈启父子坐在桌子前，大眼瞪着小眼。
That is to say, the less a class knows about the classes it depends on, the better. In other words, no matter how complex the dependent class is, the logic should be encapsulated inside the method and provided to the outside through the public method. In this way, when the dependent class changes, it can minimize the impact on the class.
时空是可以穿越的吗，命运是可以掌握的吗？
《后街女孩》故事描述了山本健太郎、立花亮与杉原和哉这三个黑道组织的流氓，因为犯了错，而被组织的老大下令转性变成女性偶像歌手替老板赚钱。原先三人想拒绝，但却因为不想死而接受了这个条件。但没想到他们成为偶像歌手之后，居然真的走红，有了许多粉丝!可是心中还是铁汉子的他们，每天都非常的苦恼。
"What, T-shirt, skirt and bag are just the same, miss, please wrap them up."
In September 2012, Huang Weiping and another founder Ding Rui began to look for the possibility of landing the abstract concept of "life education". The first thing they think of is lying in the coffin, which is the closest form to death in consciousness. However, the experimental results are not ideal. "One-third of the people will have fun taking selfies and occasionally burst into tears. One-third felt unlucky and turned around and left, while the rest looked on coldly."
根据桐华知名的清穿小说改编，刘诗诗，吴奇隆，郑嘉颖陷入痴缠三角恋。若曦是倔强、任性的女子，和阿哥斗嘴、和格格打架，连康熙都笑说她是“拼命十三妹”。这样一个女子原本是繁华都市的一名白领，却因一脚踏空穿越了时空的隧道，来到清朝。她带着对清史的洞悉进入风云诡变的宫廷。她知道自己不该卷入这场九子夺嫡的争斗中，可心不由己，因为这里有她的爱，也有爱着她的。康熙会给若曦安排怎样的未来？若曦会做出怎样的选择？若曦最后选择的究竟是温润如玉的八阿哥，还是面冷心热的四阿哥？待一切终了，纵有万般爱恋，无奈情深缘浅，是否就此相见无期？
Lavendula angustifolia
It is easy to see that OvR only needs to train N classifiers, while OvO needs to train N (N-1)/2 classifiers, so the storage overhead and test time overhead of OvO are usually larger than OvR. However, in training, each classifier of OVR uses all training samples, while each classifier of OVO only uses samples of two classes. Therefore, when there are many classes, the training time cost of OVO is usually smaller than that of OVR. As for the prediction performance, it depends on the specific data distribution, which is similar in most cases.
