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但此地实在无城无田无路无补给，留下来淘到金子也并无用处，落单的话还会遭至熊兽土人的袭击，只好作罢第一世（年代）Raj从国外留学回来，初见Pikul就希望得到她，用尽甜言蜜语，终于生米煮成熟饭，但他却一边哄着Pikul一边和富家女Sarapee订婚，表面上他告诉Pikul是家人逼迫，实际上对于Sarapee的美貌、财富，以及和和自己不谋而合的性格动了真心。同时也连累Pikul和她身边的人受到了Sarapee的百般羞辱和折磨。Sarapee可以说是拥有一切的女生，但却异常高傲，爱打人，想要得到的一定要得到，且手段极其残忍，身边所有人都只是她的工具而已。可怜的乡下女孩Pikul因为Raj一句从不打算实现的“爱的承诺”，不仅付出了自己的清白，最终搭上了自己的性命，在Raj和Sarapee的新婚之夜，吹着笛子七孔流血含恨而终。父母和乐班的人也皆死于非命，和所有被Sarapee害死的冤魂一起，困在古老的宅子里等待时机复仇。Sin从小加入乐班，是Pikul的守护者，并且爱慕着她，知道Pikul爱上Raj既生气，同时又无能为力，乐班里的Ueay暗恋他，而Sin在一次酒醉的情况下和Ueay发生了关系，致使Ueay怀孕，然而在Pikul等人死后，他选择出家避世，留下大着肚子的Ueay独自伤心。- - - -第二世（现代）Raj转世为Niraj是一位上进的有为青年，他有一个善良可爱的未婚妻Nisa（Sarapee转世），Nisa是位护士，以救人为己任。因为意外或者说是命运的安排，Raj遇到了还在等候她的鬼Pikul，Pikul以自己的怨念向Nisa展开报复，过程中Raj假借爱的名义无数次的欺骗了Pikul，只为了救出Nisa。最终老宅子燃起了一把大火，被伤害的万念俱灰的Pikul困在里面，将可能无法超生。Raj想起所有前世所做的错事，内疚不已，决定留在火海里陪伴Pikul以赎罪。N市地痞李阳“阳哥”兄弟睡了另一地盘何流“流哥”的女人慧慧后遭到追杀导致阳哥措手不及损失惨重，慧慧趁其不备消失不见！无奈之下求当地实力雄厚的三爷支持他东山再起三爷答应，并告知李阳一个天大的隐情——一场重大的阴谋正慢慢的袭来——是兄弟还是仇敌？是忠诚还是背叛？本作是描写漫画家爱徒勇气和助手足须沙穗都的日常生活的喜剧作品。MD Perfect, No Found Can't Run GameOwing to the seriousness of the case, Investigators expanded the scope of their search. Through continuous investigation of the surroundings, investigators accidentally found three brightly colored plastic bags in Wenliang River on the west side of the scene. The bags were basically filled with domestic garbage. Under these scattered items, they found a bank card of a rural credit cooperative. The last bag contained clothes, both men's and women's. In a pair of men's jeans, the investigators found another Agricultural Bank subsistence allowance card with a signature on the back. After identification, the clothes in the bag were exactly the clothes the couple had worn in recent days. What was even more shocking was that the bag also contained the marriage certificates of the couple. This is a dangerous signal, and the investigators' foreboding is getting stronger and stronger. The top priority is to find out where the missing couple are.Know the principle + can change the model details man: if you come to this step, congratulations, get started. For anyone who does machine learning/in-depth learning, it is not enough to only understand the principle, because the company does not recruit you to be a researcher, when you come, you have to work, and when you work, you have to fall to the ground. Since you want to land, you can manually write code and run each familiar and common model, so that for some businesses of the company, you can make appropriate adjustments and changes to the model to adapt to different business scenarios. This is also the current situation of engineers in most first-and second-tier companies. However, the overall architecture capability of the model and the distributed operation capability of super-large data may still be lacking in the scheme design. I have been working hard at this stage and hope to go further.