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GOGO-017 Part 16 - 270 minutesGOGO-017 Part 15 - 254 minutesGOGO-017 Part 14 - 238 minutesGOGO-017 Part 13 - 222 minutesGOGO-017 Part 12 - 206 minutesGOGO-017 Part 11 - 190 minutesGOGO-017 Part 10 - 174 minutesGOGO-017 Part 9 - 158 minutesGOGO-017 Part 8 - 142 minutesGOGO-017 Part 7 - 126 minutesGOGO-017 Part 6 - 110 minutesGOGO-017 Part 5 - 94 minutesGOGO-017 Part 4 - 78 minutesGOGO-017 Part 3 - 62 minutesGOGO-017 Part 2 - 46 minutesGOGO-017 Part 1 - 30 minutes

GOGO-017 JAV 3P orgy naked sex 11 people - Free Trailer and English Subtitles srt.

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GOGO-017 Movie Information

Actresses: Chitose Yuki 夕季ちとせ, Monami Takarada 宝田もなみ, Yuri Oshikawa 推川ゆうり, Riho Takahashi 高橋りほ, Waka Misono 美園和花, Hana Haruna 春菜はな, Shiori Tsukada 塚田詩織, Miina Wakatsuki 若月みいな, Rika Tsubaki 椿りか, Kaoru Natsuki (Tsubaki Kato) 加藤ツバキ(夏樹カオル), Yuria Yoshine 吉根ゆりあ

Producer: MERCURY

Release Date: 12 Mar, 2023

Movie Length: 265 minutes

Custom Order Pricing: $397.5 $1.50 per minute

Subtitles Creation Time: 5 - 9 days

Type: Censored

Movie Country: Japan

Language: Japanese

Subtitle Format: Downloadable .srt / .ssa file

Subtitles File Size: <265 KB (~18550 translated lines)

Subtitle Filename: h_1133gogo00017.srt

Translation: Human Translated (Non A.I.)

Total Casts: 11 actresses

Video Quality & File Size: 320x240, 480x360, 852x480 (SD), 1280x720 (HD), 1920x1080 (HD)

Filming Location: At Home / In Room

Release Type: Regular Appearance

Casting: Group (11 Actresses)

JAV ID:

Copyright Owner: © 2023 DMM

Video Quality & File Size

1080p (HD)11,973 MB

720p (HD)7,974 MB

576p5,994 MB

432p4,004 MB

288p2,056 MB

144p808 MB

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WAVR-188 #神经网络 python代码 # 简单的神经网络 import numpy as np # 定义神经网络 class NeuralNetwork: def __init__(self): # 初始化权重 self.weights = np.random.normal(0.0, 0.01, 3) # 激活函数 self.sigmoid = self.sigmoid # 槽定的BP算法 self.lr = 0.01 # 波段函数 self.predict = self.predict self.mean_squared_error = self.mean_squared # 激活函数 def predict(self, input): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 激活阶段 def predict_activation(self, input): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 衍生阶段 def predict_derivative(self, input): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # Keys def SIGN(V): return 0.01 * (V ** 2) + 0.01 * V + 0.01 # 前向阶段 def prediction(self, input): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 通带函数 def activation(self, input): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 反向api def compute_predict(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 衍生阶段 def compute_prediction(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 反向程度 def compute_updating(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 反向程度 def compute_updating_activation(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 反向程度 拓展阶段 def compute_extending(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 调整 def compute_sensing(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 反向阶段 def compute_thresholding(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 反向衡量 def compute_weighting(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_life_range(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_biating(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_lighting(distance): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 public static int predict(input): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 public static int predict(prior): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_locative(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_relative(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_seasoning(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_firsting(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_impulseing(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_prioritying(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_initialing(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_sensitivitying(self) : return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_lecturing(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_venering(thickness): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_polishing(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_studering(hardness): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_semoving(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_druning(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 正向阶段 def compute_enciphering(businessness): return 0.01 ? (input ** 2) + 0.01 * input + 0.01 # 阶段 def phase(input): return 0.01 * (input ** 2) + 0.01 * input + 0.01 def 0NN(input): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 联合 def predict(input): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 联合 def compute_running(machine learning): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 联合 def compute_pacting(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 联合 def compute_ranslining(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 阶段 SU = a return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 联合 def compute_fooking(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 滑动 whetherMosh: return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 联合 def compute_running(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 联合 def compute_running(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 联合 def compute_running(self): return 0.01 * (input ** 2) + 0.01 * input + 0.01 # 联合 def compute_running(self): return 0.01 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22 Oct 2021

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