The following is the definition of the factory method: an interface is defined to create an object, but the subclass decides which one to create. The factory method defers this process of creating objects to subclasses.
黑娃道：那天山上肯定有好些人。
Belonging to roof of the world.
你没有听错。
绝大多数的人认为从事广播电视事业的人，特别是在电视剧制作部门工作的人是不讲伦理道德的异类，可是实际上在那里工作的每个人都是非常平凡的，他们和其他人一样需要爱、理解，认可和关心。
Amazon科幻剧集《高堡奇人》第4季，也是最后一季了，各种火与血的壮烈、大场面，“世界上有很少数的东西，是值得为其去死的”。能否打败纳粹？ 讲述假如纳粹德国和日本天皇打赢了二战，统治了世界，如今“反抗组织将崛起，迎来战争和革命”。11月15日上线。
Note: Here is the script of the react debugging tool. Don't consider it.
First, we always try to solve all problems and explain all phenomena with one investment method, and we are the only one who can't tolerate other people and methods.
38岁，单身，为10年的不伦而烦恼的弱小出版社编辑菊池郁美（松本まりか）遇到了揣度无用的毒舌刺痛人心的岚一样的名古屋夫人！夫人的名字是・中岛春子（大地真央）。叫别人来…或者说自己自称是“美的超级医生”！在东京惠比寿经营超人气美容诊所的干练女性经营者！被政治金融界也一眼看好的自己和别人都认可的超级淑女！“你能和我说话真是太幸运了。”因为意外的事，伊势和春子关系很好，于是变得像舍弟一样。从这一天开始，“平民·稻草人”的暴风雨般的日子开始了！被春子邀请去参加从未见过的豪华名流派对。在那里，与派对主办者的年轻实业家的情人遭遇！！令人意外的是，在10年不伦的情妇面前，她自身的境遇重叠，内心也变得很痛苦。但是，不知知不知道春子的内心深处，春子的怒吼响彻了派对会场。“你啊，明明是情人，可别得意忘形啊！情人像个情人，再谦虚一点怎么样！”一刀两断伪装成正妻的情人的春子！而且实业家的男人也果断地！“归根结底，破坏自己人生的是自己。嘛，我也不知道啊”有继承问题的老字号老店、因为高学历而不能结婚的东大女子、患有丈夫源病的名流主妇・・・春子周围不知为何聚集了很多有烦恼的人！那样的他们，haruko爽快地砍倒她们。但是，不知为何被切断的对方的人生会不断向上！春子的毒舌刃的矛头，也朝向绝赞不伦中的矛头，当然会被斩断……土电视剧史上最强的超级淑女将世间的烦恼和不正当行为彻底消灭！痛快毒舌娱乐，开幕！
砰，酒杯相碰。
章辰斩杀大半高手后，力竭而亡，最后这把魔剑被扔到了天河。
四个性感单身女生因缘际逜下开始了同居生活。
晚清末年，名将彭泽南长期与外夷交战，忽略了妻子令她心存怨艾。同时长子之文在战乱中走失，幼子之贤送抵边寨首领当人质，彭夫人痛不欲生，一怒之下带次子回北京。16年后，彭家三兄弟长大成人。流落上海的之文身处帮会却为人敦厚；次子之武从小生长于畸形环境导致行为乖张；之贤勇敢刚毅热血心肠。
对于安东尼（斯科特·麦克洛维茨 Scott Mechlowicz 饰）来说，成为一名成功的厨师是他的毕生志愿，怀揣着这个梦想，安东尼来到了南斯拉夫，准备在这里开一家小餐厅。没想到，不但餐厅没开成，安东尼还和偶遇的好友朱利安（阿方索·麦克奥雷 Alphonso McAuley 饰）一起干起了私家侦探的活计。众所周知，朱利安是个好色的花花公子，和这样的伙伴一起创业真的没有问题吗？一个名叫卡塔琳娜（帕兹·维嘉 Paz Vega 饰）的美艳女子出现在了安东尼和朱利安的面前，声称因为掌握了一份绝密的情报而身处危险之中，这两个连自身都难保的大男人能够保护卡塔琳娜吗？两人此时还不知道的是，他们已经成为了冷酷杀手海伦（珍妮·麦克蒂尔 Janet McTeer 饰）的下一个目标。
Conn = DriverManager.getConnection (url, username, password);
National key cultural relics protection units, the first batch of 4A tourist attractions in the country. Located in the west street of the city, it was built in 686, the second year of Tang Wu Zetian's vertical arch, formerly known as "Lotus Temple". In 738 A.D., Tang Xuanzong ordered all states in the country to build a Kaiyuan Temple, which was changed to its current name. The whole temple covers an area of 78,000 square meters. It is large in scale, spectacular in construction and beautiful in scenery. It was once as famous as Baima Temple in Luoyang, Lingyin Temple in Hangzhou and Guangji Temple in Beijing. There are mainly buildings such as the Main Hall of the Great Hero, the Ganlu Ring Altar, and the East and West Pagodas. The Main Hall of the Great Hero is the main building of the central axis. It was built in the second year of the Tang Dynasty (686 A.D.). The existing building is a relic of the 10th year of Chongzhen in the Ming Dynasty (1637 A.D.). The main hall is 20 meters high and preserves the magnificent architectural style of the Tang Dynasty. Ganlu Ring Altar was founded in the Song Dynasty and is now rebuilt in the early Ming Dynasty. It is one of the three largest ring altars in the country. Standing in the squares on both sides of Baiting, the octagonal five-story pavilion-like wood-like stone pagoda, which is about 200 meters apart, is Quanzhou's East and West Pagoda and is one of the four famous towers in the country. The East Tower is called "Zhenguo Tower" and has a height of 48.24 meters. The name of the West Tower is "Renshou Tower", with a height of 44.06 meters, slightly lower than that of the East Tower, and its scale is almost exactly the same as that of the East Tower. The two towers are treasures of ancient stone structures in our country and are symbols of Quanzhou, a famous historical and cultural city.
LinearBurn
Super Data Manipulator: I am still groping at this stage. I can't give too much advice. I can only give a little experience summarized so far: try to expand the data and see how to deal with it faster and better. Faster-How should distributed mechanisms be trained? Model Parallelism or Data Parallelism? How to reduce the network delay and IO time between machines between multiple machines and multiple cards is a problem to be considered. Better-how to ensure that the loss of accuracy is minimized while increasing the speed? How to change can improve the accuracy and MAP of the model is also worth thinking about.
