心中一痛，握住板栗和小葱的手蓦然攥紧，他低声艰难地说道：板栗，帮我照顾她。
众人沉默下来。
4. There is a overtime phenomenon in the business (the occurrence rate is also very high, but the customer has used it too much)
2-3 0-2 3-0
《十月围城》主要讲述清末民初的香港地区，一个草根人物——车夫阿四误打误撞做起革命青年李重光替身，从而串起整个家族以及周边人投身革命的故事。 宣统二年，同盟会代表抵达香港，商议举行大规模反清起义活动。广东将军铁山奉诏刺杀孙中山。大商人李玉堂母亲病重，要李玉堂带长孙李重光回家，黄包车夫阿四攒钱帮阿纯治眼病，并想向阿纯提亲。开完筹备会议，孙中山安全离开香港，但李重光却被铁山的杀手围追堵截。为了二十块港币，阿四拉着车上的李重光一路狂奔。李重光死在阿四的洋车上。为了老母，李玉堂请求阿四给他当一天儿子。阿四来到广州，进入了四大家族中的"西关李家"。李玉堂临死托付阿四，要把这个家支撑起来，阿四含泪应允。阿四排除万难保住了李家的产业，加入了同盟会，在遭受了种种怀和屈辱后，终于成为了坚定的革命者。
当然，也不是没有人说公道话，但是那些细微的声音早就被谩骂的大潮给淹没了。
"I was in a daze at that time. I looked outside and it was dark before 4 o'clock. Afraid of people coming in at any time, really. How did I wake up? Listen to the loudspeaker, the ship has the loudspeaker megaphone, suddenly rang, I heard the captain's voice, the captain began to shout, 'Baud, who else are your associates, hurry up, I know all about it'. After the captain shouted twice, it turned into Liu Guiduo's voice. Liu Guiduo shouted, 'Whose person do you think Huang Jinbo is?' At that time, I was blindfolded. What exactly happened? Because they were united at that time and I didn't know anything about it beforehand. "
In spring, the rich came to the poor's house with seeds, only to find that the cattle were gone and the poor were drinking.
Private TreeNode parent;
《天河魔剑录》的最主要的设定便是天河和魔剑。
什么幸会，不过几年没见面而已。
越王勾践元年（前496），吴王阖庐听说允常逝世，就举兵讨伐越国。越王勾践派遣敢死的勇士向吴军挑战，勇士们排成三行，冲入吴军阵地，大呼着自刎身亡。吴兵看得目瞪口呆，越军趁机袭击了吴军，在檇李大败吴军，射伤吴王阖庐。阖庐在弥留之际告诫儿子夫差说：“千万不能忘记越国。” 三年（前493），勾践听说吴王夫差日夜操练士兵，将报复越国一箭之仇，便打算先发制人，在吴未发兵前去攻打吴。范蠡进谏说：“不行，我听说兵器是凶器，攻战是背德，争先打是事情中最下等的。阴谋去做背德的事，喜爱使用凶器，亲身参与下等事，定会遭到天帝的反对，这样做绝对不利。”越王说：“我已经做出了决定。”于是举兵进军吴国。吴王听到消息后，动用全国精锐部队迎击越军，在夫椒大败越军。越王只聚拢起五千名残兵败将退守会稽。吴王乘胜追击包围了会稽。
Judgment Format: @ Thousand Account Soul Lamp
庞取义一拍大腿，想骂又不敢骂:她吃的，能吐出来？世伯，侄儿有一事不明。
14岁的小女孩克莉丝蒂（Brenna O'Brien 饰）驾车时不慎造成一场严重的车祸，她的精神受到严重刺激，而坐在旁边的姐姐维妮莎（卡莉·波普 Carly Pope 饰）也身负重伤。六个月后，维妮莎宣告不治身亡，克莉丝蒂的精神状态也进一步恶化，不得已她被送往康复医院治疗。在此期间，她接受大夫的建议，不断将自己那诡异的梦境画下来，时光在压抑的气氛中缓缓流逝。
等将来闯出一番事业，那时令尊自然会明白你的坚持。
Considering N categories C1, C2 …, CN, the basic idea of multi-classification learning is "disassembly method", that is, multi-classification tasks are disassembled into several two-classification tasks to solve. Specifically, the problem is split first, and then a classifier is trained for each split second classification task. During the test, the prediction results of these classifiers are integrated to obtain the final multi-classification results. The key here is how to split multiple classification tasks and how to integrate multiple classifiers.
[Look at the Dialogue, Attending Doctor Wang Fengya, Girl with Eye Cancer: Family Never Gives Up Treatment]
