K=10,T=0.8: <data>: channel = [ 1 0 0 3 , 1 0 0 4 , 1 0 0 5 , 1 0 0 6 , 1 0 0 7 , 1 0 0 8 , 1 0 0 9 ] dep _ req = ' 0 3 0 0 0 0 0 c 0 2 f 0 8 0 3 8 0 0 0 6 ' req = ' 0 3 0 0 0 0 0 c 0 2 f 0 8 0 3 8 0 0 0 8 ' ms _ t 1 2 0 = ' 4 d 5 </data>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 
K=10,T=0.8: <data>: channel = [ 1 0 0 3 , 1 0 0 4 , 1 0 0 5 , 1 0 0 6 , 1 0 0 7 , 1 0 0 8 , 1 0 0 9 ] dep _ req = ' 0 3 0 0 0 0 0 c 0 2 f 0 8 0 3 8 0 0 0 6 ' req = ' 0 3 0 0 0 0 0 c 0 2 f 0 8 0 3 8 0 0 0 8 ' ms _ t 1 2 0 = ' 4 d 5 </data>0 0 0 1 0 0 0 0 0 0 c 0 0 0 d 0 d 1 0 0 0 0 0 0 c 0 0 0 0 0 0 c 0 0 0 a 0 0 c 0 0 0 0 d 0 f 0 0 0 0 0 0 0 0 0 0 a 0 0 0 0 a 1 0 0 0 b 0 0 0 0 0 0 0 0 0 0 0 b 0 0 0 0 0 1 0 0 0 0 c 0 0 0 0 0 0 0 0 0 a 
K=10,T=0.8: <data>message . embeds [ 0 ] . footer . text = = f ' { ctx . member . name } await message . delete ( ) except ( exception , ) : pass @ self . event async def on _ guild _ join ( guild ) : print ( f ' wow , i joined { guild . name } ! ' ) async def shutdown ( bot ) : await bot . close ( ) try : self . run ( os . getenv ( ' token ' ) ) except keyboard interrupt : self . logger </data>. info ( ' please enter the user of the user ' ) return await bot . send ( f ' discord . ' ) await bot . send ( f ' discord . <UNK> ' , { guild . name } ) await bot . send ( f ' bot . <UNK> <UNK> ' , { guild . name } ) await bot . send ( f ' discord . <UNK> ' , { guild . name } ) async def delete _ guild _ list ( guild ) : bot . send ( f ' discord . <UNK> 
K=10,T=0.8: <data>q = vec 3 ( x , y , 0 ) return <UNK> ( np , e , ( q - e ) . norm ( np ) , scene ) import numpy as np import numpy as mm r = float ( w ) / h s = ( - 1 . , 1 . / r + . 2 5 , 1 . , - 1 . / r + . 2 5 ) x = mm . tile ( np . linspace ( s [ 0 ] , s [ 2 ] , w ) , h </data>) return x def get _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img = img _ img _ img _ img _ img _ img _ img _ img _ img _ img , img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ img _ 
K=10,T=0.8: <data>save ( f ' { logfolder } / ckpt . th ' ) stats _ t = { ' t _ init ' : t _ init , ' t _ train ' : t _ train , ' t _ total ' : t _ init + t _ train } stats _ misc = { ' n _ iter ' : args . n _ iters , ' batch _ size ' : args . batch _ size , ' n _ param ' : n _ params } print ( stats _ t ) print ( stats </data>_ t ) def get _ stats ( stats _ t ) : stats _ t = stats . stats _ t . cpu ( ) if stats _ t . cpu ( ) > 0 : stats _ t . cpu ( ) if stats _ t = = 0 : logger . info ( stats _ t ) for stats in stats _ t ) : stats _ t . cpu ( ) . numpy ( ) if stats _ t . cpu ( ) > 0 : stats _ t . cpu ( ) . numpy ( 
K=10,T=0.8: <data>0 ] shell _ whoami = " 0 k 6 l sp <UNK> <UNK> <UNK> 3 <UNK> <UNK> <UNK> <UNK> / / <UNK> <UNK> <UNK> <UNK> <UNK> <UNK> <UNK> + 9 e 4 q 7 i hp <UNK> <UNK> <UNK> = = " if _ _ name _ _ = = " _ _ main _ _ " : shared _ key = binascii . unhexlify ( " " ) hmac _ key = binascii . unhexlify ( " " ) enc _ data = base 6 4 . b 6 4 decode ( shell _ whoami ) print ( </data>" \ n " ) if _ _ name _ _ = = " _ _ main _ _ " : main ( ) import sys import os import os import sys import os import torch from torch . nn . functional as f import torchvision import torch . nn . functional as f from torchvision . transforms import transforms import torch . nn . functional as f import torchvision from torchvision . transforms . transforms import transforms parser = argparse . argument parser ( ) args = parser . parse _ args ( ) if args . model . 
K=10,T=0.8: <data>= dict ( ) for idx , data in enumerate ( test _ loader ) : print ( ' testing % d / % d \ r ' % ( idx , len ( test _ loader ) ) , flush = true , end = ' ' ) logging . info ( ' testing % d / % d \ r ' % ( idx , len ( test _ loader ) ) ) if not args . cpu : data [ ' imgs ' ] = data [ ' imgs ' ] . cuda ( non _ blocking </data>= true ) else : data [ ' imgs ' ] = data [ ' imgs ' ] . cuda ( non _ blocking = true ) data [ ' imgs ' ] = data [ ' imgs ' ] else : logger . info ( ' loaded % d / % s \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ r \ 
K=10,T=0.8: <data>for your flight to ( [ a - za - z ] + ) \ . ' , email _ body ) if match : destination = match . group ( 1 ) itineraries . append ( { " is itinerary " : true , " origin " : origin , " destination " : destination } ) if ' your flight itinerary ' in email _ body : match = re . search ( r ' <UNK> style = 3 d = 2 2 font - family : arial , sans - serif ; font - size : 1 </data>, font - size : 1 , font - size : 1 , font - size : 1 , font - size : 2 , font - size , font - size : 1 , font - size : 1 , font - size : 2 , font - size : 1 , font - size : 1 , font - size : 1 , font - size : 2 , font - size : 2 , font - size : 2 } ) return ( ' <UNK> ' , ' <UNK> ' , ' <UNK> _ <UNK> ' , 
K=10,T=0.8: <data>data import * from evaluate import evaluate timestr = ' - ' . join ( str ( x ) for x in list ( tuple ( datetime . now ( ) . timetuple ( ) ) [ : 6 ] ) ) moving _ average _ decay = 0 . 9 9 7 flags = tf . app . flags . flags tf . app . flags . define _ integer ( ' batch _ size ' , 2 5 6 , ) tf . app . flags . define _ integer ( ' num _ epochs ' , - </data>1 , ' number of epochs to training . ' ) tf . app . flags . define _ integer ( ' num _ epochs ' , - 1 , ' number of epochs to perform . ' ) tf . app . flags . define _ integer ( ' num _ epochs ' , - 1 , ' number of epochs to perform . ' ) tf . app . flags . define _ integer ( ' learning _ rate ' , 1 0 0 0 , ' initial learning rate for training . ' ) tf . app