runs/INTERRUPTED-code-decoder-v31-mega-licensed-1/curriculum-noloss/ckpt/generated.txt

K=10,T=0.8: <data>_ only = true <newline> <tab> <tab> ) , <newline> <tab> <tab> " text " : orm . string ( title = " text " , max _ length = 1 0 0 ) , <newline> <tab> <tab> " completed " : orm . boolean ( title = " completed " , default = false ) , <newline> <tab> } <newline> <newline> <newline> admin = dashboard . dashboard ( <newline> <tab> tables = [ <newline> <tab> <tab> dashboard . dashboard table ( <newline> <tab> <tab> <tab> ident = " notes " , <newline> <tab> <tab> <tab> title = " notes </data>" , <newline> <tab> <tab> ) <newline> <newline> <newline> def main ( self , context , parent ) : <newline> <tab> if self . _ _ _ _ _ _ _ _ _ _ _ _ ) : <newline> <tab> if self . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ = none : <newline> <tab> <tab> <tab> self . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
K=10,T=0.8: <data>output _ alias <newline> <newline> <tab> def get json ( self , path ) : <newline> <tab> <tab> if self . use _ https : <newline> <tab> <tab> <tab> url = f " https : / / { self . ip } : { self . port } { path } " <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> url = f " http : / / { self . ip } : { self . port } { path } " <newline> <tab> <tab> headers = { " authorization " : " bearer " + self . </data>ip } <newline> <tab> <tab> <newline> <tab> <tab> <tab> if url = = " <UNK> " : <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> if " url " in request : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self . host = host <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> if self . ip is _ ip : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> print ( " [ " ] error : { } 
K=10,T=0.8: <data>' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' pbr ' , ' pbt ' , ' <UNK> ' , ' <UNK> ' , ' pcar ' , ' pcb ' , ' pcg ' , ' pch ' , ' pci ' , ' pck ' , ' pcm ' , ' pcn ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> </data>' ) <newline> <tab> <tab> <UNK> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> = ( <UNK> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data><newline> <tab> <tab> ( " <UNK> 2 " , git add ( ) ) , <newline> <tab> <tab> ( " changelog " , update changelog ( ) ) , <newline> <tab> <tab> ( " readme " , update readme ( ) ) , <newline> <tab> <tab> ( " dist " , make dist ( ) ) , <newline> <tab> <tab> ( " <UNK> " , push to test py pi ( ) ) , <newline> <tab> <tab> ( " install " , install from test py pi ( ) ) , <newline> <tab> <tab> ( " <UNK> " , test </data>, build pi ( ) , <newline> <tab> <tab> ( " version " , install class ) , <newline> <tab> <tab> ( " <UNK> " , install class ) , <newline> <tab> <tab> ( " <UNK> " , install class ) , <newline> <tab> <tab> ( " <UNK> " , install class ) , <newline> <tab> <tab> ( " install class " , install class ) , <newline> <tab> <tab> ( " install class " , install class ) , <newline> <tab> <tab> ( " install class " , install class ) , <newline> <tab> <tab> ( " install class 
K=10,T=0.8: <data>_ all _ utterances <newline> <tab> batch _ size = params . batch _ size <newline> <tab> if params . interaction _ level : <newline> <tab> <tab> batch _ size = 1 <newline> <tab> <tab> eval _ fn = evaluate _ interaction _ sample <newline> <tab> <tab> trainbatch _ fn = data . get _ interaction _ batches <newline> <tab> <tab> <UNK> _ fn = data . get _ random _ interactions <newline> <tab> <tab> <UNK> _ fn = data . get _ all _ interactions <newline> <newline> <tab> maximum _ output _ length = params . train _ maximum </data>_ output _ length <newline> <tab> while true and params . interaction _ level : <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <newline> <tab> <tab> <newline> <newline> <tab> <tab> <newline> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <newline> <tab> <tab> <newline> <newline> 
K=10,T=0.8: <data>= cv 2 . resize ( marker _ image , ( w , h ) ) <tab> <tab> <newline> <tab> <tab> marker _ images . append ( marker _ image ) <newline> <newline> <tab> total = len ( scene _ images ) * len ( scene _ images [ 0 ] ) <newline> <tab> print ( ' input { } x { } scene images ' . format ( len ( scene _ images ) , len ( scene _ images [ 0 ] ) ) ) <tab> <newline> <tab> print ( ' input { } marker images ' . </data>format ( len ( scene _ images [ 0 ] ) ) , len ( scene _ images [ 0 ] ) ) , len ( scene _ images ) ) <newline> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> 
K=10,T=0.8: <data>is _ vec ' : best _ is _ vec } ) <newline> <tab> <tab> <tab> print ( " id : % 0 3 d " % id , " qitem : % 0 3 d " % q _ ct , " res <newline> <tab> <tab> <tab> <tab> " current : % d " % ( np . sum ( n is _ vec _ local ) ) , " best : % d " % best _ is _ num , " reduction " ) <newline> <tab> <tab> <tab> return true <newline> <tab> <tab> adj = adj _ </data>to _ vec _ local <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <newline> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> 
K=10,T=0.8: <data>scalar ( ' train / reg _ tv _ density ' , loss _ tv . detach ( ) . item ( ) , global _ step = global _ step ) <newline> <tab> <tab> <tab> if tv _ weight _ app > 0 : <newline> <tab> <tab> <tab> <tab> tv _ weight _ app * = lr _ factor <newline> <tab> <tab> <tab> <tab> loss _ tv = tensorf . tv _ loss _ app ( tvreg ) * tv _ weight _ app <newline> <tab> <tab> <tab> <tab> total _ loss = total _ loss + loss _ </data>tv <newline> <tab> <tab> <tab> <tab> if total _ loss < total _ loss : <newline> <tab> <tab> <tab> <tab> <tab> total _ loss + = loss _ tv <newline> <tab> <tab> <tab> <tab> <tab> loss _ tv + = loss _ tv . mean ( ) . item ( ) <newline> <tab> <tab> <tab> <tab> <tab> total _ loss + = loss _ tv . mean ( ) . item ( ) <newline> <tab> <tab> <tab> <tab> <tab> total _ loss + = loss _ tv . mean ( ) . item ( ) * total _ loss * 
K=10,T=0.8: <data>error when connecting to { } : { } : { } " . format ( <newline> <tab> <tab> <tab> <tab> <tab> connect _ ip , connect _ port , e ) ) <newline> <tab> else : <newline> <tab> <tab> <newline> <tab> <tab> if b ' success ' not in current _ state : <newline> <tab> <tab> <tab> raise system exit ( <newline> <tab> <tab> <tab> <tab> " failure on webshell : { } " . format ( current _ state ) ) <newline> <newline> <tab> print ( " [ * ] server connected to target successfully " ) <newline> </data><tab> for b in current _ state : <newline> <tab> <tab> if ( b ' success ' in b ' success ' not in current _ state ) : <newline> <tab> <tab> <tab> <tab> print ( " [ * ] server connected to target ! " ) <newline> <tab> <tab> <tab> <tab> print ( f " [ * ] server connected to target ! " ) <newline> <tab> <tab> <tab> <tab> continue <newline> <tab> <tab> <tab> <tab> continue <newline> <tab> <tab> <tab> if ( b ' success ' not in current _ state ) : <newline> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>math <newline> import multiprocessing as mp <newline> <newline> from tqdm import tqdm <newline> <newline> <newline> def line _ positions ( file _ path ) : <newline> <tab> with open ( file _ path ) as f : <newline> <tab> <tab> while true : <newline> <tab> <tab> <tab> pos = f . tell ( ) <newline> <tab> <tab> <tab> if f . readline ( ) : <newline> <tab> <tab> <tab> <tab> yield pos <newline> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> break <newline> <newline> <newline> def get _ number _ of _ lines ( fobj ) : <newline> </data><tab> with open ( file _ path , ' w ' ) as f : <newline> <tab> <tab> f . write ( " [ * ] . " ) <newline> <tab> <tab> for line in lines : <newline> <tab> <tab> <tab> line = line . strip ( ) <newline> <tab> <tab> <tab> if line [ 1 ] = = ' \ n ' : <newline> <tab> <tab> <tab> <tab> line + = f . readline ( ) <newline> <tab> return line <newline> <newline> <newline> def get _ number _ of _ lines ( lines , lines , n ) : 
K=10,T=0.8: <data>: <newline> <tab> <tab> 1 . the output of the current node that should be passed to the next node . <newline> <tab> <tab> 2 . a reason object that explains how the output was generated for debugging . <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> add the > > operator , which creates a then node . <newline> <tab> <tab> wrap any strings in a string node first , to allow for code like <newline> <newline> <tab> <tab> <tab> setup node > > " command to run " > > <UNK> node <newline> <tab> <tab> <newline> <tab> </data><tab> <tab> < > > " command to run " > > " command to run " > > " command to run " > > " command to run " > > " command to run " > > " command to run " > > " command to run " > > " commands to run " > > " command to run " > > " command to run " > > " command to run " > > " command to run " > > " command to run " > > " command to run 
K=10,T=0.8: <data>, layer 2 ) : <newline> <tab> <tab> super ( concat table , self ) . _ _ init _ _ ( ) <newline> <tab> <tab> self . layer 1 = layer 1 <newline> <tab> <tab> self . layer 2 = layer 2 <newline> <tab> <tab> <newline> <tab> def forward ( self , x ) : <newline> <tab> <tab> y = [ self . layer 1 ( x ) , self . layer 2 ( x ) ] <newline> <tab> <tab> return y <newline> <newline> class flatten ( nn . module ) : <newline> <tab> def _ _ init _ </data>_ ( self , x , hidden _ size = 1 4 , hidden _ size = 1 2 8 , activation = ' lrelu ' , activation = ' lrelu ' , activation = ' lrelu ' ) : <newline> <tab> <tab> super ( flatten , self ) . _ _ init _ _ ( ) <newline> <tab> <tab> self . activation = activation <newline> <newline> <tab> def forward ( self , x ) : <newline> <tab> <tab> x = self . layer 1 ( x ) <newline> <tab> <tab> x = self . layer 2 ( x ) 
K=10,T=0.8: <data><tab> <tab> <tab> metadata [ " chunk _ size " ] , <newline> <tab> <tab> <tab> metadata [ " chunk _ overlap " ] , <newline> <tab> <tab> <tab> metadata [ " max _ chunk _ num " ] , <newline> <tab> <tab> ) <newline> <tab> <tab> <newline> <tab> <tab> df _ merged = pd . merge ( df _ docs , df _ remove , on = [ " md 5 " , " file _ path " ] , how = " inner " ) <newline> <tab> <tab> remove _ ids = np . array ( df _ </data>merged ) <newline> <tab> <tab> <newline> <tab> <tab> df _ merged [ 0 ] = df _ merged [ 0 ] . values . index <newline> <tab> <tab> df _ merged [ 1 ] = df _ merged . sort ( ) <newline> <tab> <tab> <newline> <tab> <tab> df _ merged [ 1 ] = df _ merged [ 2 ] . values . index <newline> <newline> <tab> <tab> df _ merged [ 1 ] = df _ merged [ 2 ] . values . index <newline> <tab> <tab> df _ merged [ 1 ] = df _ merged [ 
K=10,T=0.8: <data>x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ xf 7 \ x 9 e \ xf 7 \ x 9 </data>e \ xd 0 \ x 0 0 \ x 0 0 \ xc 5 \ x 0 1 \ x 0 0 \ x 0 0 \ xe 1 \ x 5 0 \ xd 9 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 
K=10,T=0.8: <data>_ only = true <newline> <tab> <tab> ) , <newline> <tab> <tab> " text " : orm . string ( title = " text " , max _ length = 1 0 0 ) , <newline> <tab> <tab> " completed " : orm . boolean ( title = " completed " , default = false ) , <newline> <tab> } <newline> <newline> <newline> admin = dashboard . dashboard ( <newline> <tab> tables = [ <newline> <tab> <tab> dashboard . dashboard table ( <newline> <tab> <tab> <tab> ident = " notes " , <newline> <tab> <tab> <tab> title = " notes </data>" , <newline> <tab> <tab> <tab> description = " <UNK> " , <newline> <tab> <tab> ) , <newline> <tab> <tab> update _ tags _ only = update _ tags _ only , <newline> <tab> <tab> ) , <newline> <tab> <tab> update _ tags _ only = update _ tags _ only , <newline> <tab> <tab> update _ tags _ only = update _ tags _ only , <newline> <tab> <tab> update _ tags _ only = update _ tags _ only , <newline> <tab> ) <newline> <newline> <newline> <newline> <newline> <newline> @ app . route ( <newline> <tab> route ( 
K=10,T=0.8: <data>output _ alias <newline> <newline> <tab> def get json ( self , path ) : <newline> <tab> <tab> if self . use _ https : <newline> <tab> <tab> <tab> url = f " https : / / { self . ip } : { self . port } { path } " <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> url = f " http : / / { self . ip } : { self . port } { path } " <newline> <tab> <tab> headers = { " authorization " : " bearer " + self . </data>ip } <newline> <tab> <tab> headers = { " user - agent " : self . ip } <newline> <tab> <tab> headers = { " x - forwarded - for " : self . port } <newline> <tab> <tab> headers = { " user - agent " : self . user _ agent , } <newline> <tab> <tab> headers = { " user - agent " : self . user _ agent , " user - agent " : self . user _ agent , " accept " : self . application , " user - agent " : self 
K=10,T=0.8: <data>' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' pbr ' , ' pbt ' , ' <UNK> ' , ' <UNK> ' , ' pcar ' , ' pcb ' , ' pcg ' , ' pch ' , ' pci ' , ' pck ' , ' pcm ' , ' pcn ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> </data>' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> 
K=10,T=0.8: <data><newline> <tab> <tab> ( " <UNK> 2 " , git add ( ) ) , <newline> <tab> <tab> ( " changelog " , update changelog ( ) ) , <newline> <tab> <tab> ( " readme " , update readme ( ) ) , <newline> <tab> <tab> ( " dist " , make dist ( ) ) , <newline> <tab> <tab> ( " <UNK> " , push to test py pi ( ) ) , <newline> <tab> <tab> ( " install " , install from test py pi ( ) ) , <newline> <tab> <tab> ( " <UNK> " , test </data>to test py pi ( ) ) , <newline> <tab> <tab> ( " <UNK> " , post to test py pi ( ) ) , <newline> <tab> <tab> ( " <UNK> " , post to test py pi ( ) ) , <newline> <tab> <tab> ( " <UNK> " , post to test py pi ( ) ) , <newline> <tab> <tab> ( " <UNK> " , post to test py pi ( ) ) , <newline> <tab> <tab> ( " <UNK> " , post to test py pi ( ) ) , <newline> <tab> <tab> ( " <UNK> " 
K=10,T=0.8: <data>_ all _ utterances <newline> <tab> batch _ size = params . batch _ size <newline> <tab> if params . interaction _ level : <newline> <tab> <tab> batch _ size = 1 <newline> <tab> <tab> eval _ fn = evaluate _ interaction _ sample <newline> <tab> <tab> trainbatch _ fn = data . get _ interaction _ batches <newline> <tab> <tab> <UNK> _ fn = data . get _ random _ interactions <newline> <tab> <tab> <UNK> _ fn = data . get _ all _ interactions <newline> <newline> <tab> maximum _ output _ length = params . train _ maximum </data>_ output _ length <newline> <tab> max _ output _ length = params . train _ maximum _ output _ length <newline> <tab> total _ output = params . total _ output _ length <newline> else : <newline> <tab> total _ output _ length = params . train _ maximum _ output _ length <newline> <tab> total _ output _ length = params . total _ output _ length <newline> <newline> <tab> return total _ output _ length , total _ output _ length <newline> <newline> <newline> <newline> <newline> <newline> <newline> def main ( args ) : <newline> <tab> <newline> 
K=10,T=0.8: <data>= cv 2 . resize ( marker _ image , ( w , h ) ) <tab> <tab> <newline> <tab> <tab> marker _ images . append ( marker _ image ) <newline> <newline> <tab> total = len ( scene _ images ) * len ( scene _ images [ 0 ] ) <newline> <tab> print ( ' input { } x { } scene images ' . format ( len ( scene _ images ) , len ( scene _ images [ 0 ] ) ) ) <tab> <newline> <tab> print ( ' input { } marker images ' . </data>format ( sum ( len ( scene _ images ) , len ( scene _ images ) ) , len ( scene _ images [ 1 ] ) ) <tab> <newline> <tab> print ( ' input { } marker images ' . format ( sum ( len ( scene _ images ) , len ( scene _ images . shape ) ) ) , len ( scene _ images ) , len ( scene _ images [ 0 ] ) ) <newline> <tab> scene _ images [ 0 ] = scene _ images [ 0 ] <newline> <tab> scene _ 
K=10,T=0.8: <data>is _ vec ' : best _ is _ vec } ) <newline> <tab> <tab> <tab> print ( " id : % 0 3 d " % id , " qitem : % 0 3 d " % q _ ct , " res <newline> <tab> <tab> <tab> <tab> " current : % d " % ( np . sum ( n is _ vec _ local ) ) , " best : % d " % best _ is _ num , " reduction " ) <newline> <tab> <tab> <tab> return true <newline> <tab> <tab> adj = adj _ </data>to _ vec ( adj , adj , adj , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec , adj _ to _ vec ) <newline> <tab> <tab> adj _ to _ vec = adj _ to 
K=10,T=0.8: <data>scalar ( ' train / reg _ tv _ density ' , loss _ tv . detach ( ) . item ( ) , global _ step = global _ step ) <newline> <tab> <tab> <tab> if tv _ weight _ app > 0 : <newline> <tab> <tab> <tab> <tab> tv _ weight _ app * = lr _ factor <newline> <tab> <tab> <tab> <tab> loss _ tv = tensorf . tv _ loss _ app ( tvreg ) * tv _ weight _ app <newline> <tab> <tab> <tab> <tab> total _ loss = total _ loss + loss _ </data>tv <newline> <tab> <tab> <tab> <tab> total _ loss + = loss _ tv <newline> <newline> <tab> <tab> <tab> <tab> if args . local _ rank = = 0 : <newline> <tab> <tab> <tab> <tab> <tab> if args . local _ rank = = 0 : <newline> <tab> <tab> <tab> <tab> <tab> <tab> optimizer . step ( ) <newline> <newline> <tab> <tab> <tab> <tab> <tab> loss _ tv = loss _ tv * * 0 . 1 <newline> <tab> <tab> <tab> <tab> <tab> if args . local _ rank = = 0 : <newline> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>error when connecting to { } : { } : { } " . format ( <newline> <tab> <tab> <tab> <tab> <tab> connect _ ip , connect _ port , e ) ) <newline> <tab> else : <newline> <tab> <tab> <newline> <tab> <tab> if b ' success ' not in current _ state : <newline> <tab> <tab> <tab> raise system exit ( <newline> <tab> <tab> <tab> <tab> " failure on webshell : { } " . format ( current _ state ) ) <newline> <newline> <tab> print ( " [ * ] server connected to target successfully " ) <newline> </data><tab> return current _ state <newline> <newline> <newline> def check _ port ( host ) : <newline> <tab> port = host . split ( " " ) [ 0 ] <newline> <newline> <tab> if port = = ' ' : <newline> <tab> <tab> port = port . split ( ' - ' ) [ 0 ] <newline> <newline> <tab> if port ! = ' 0 ' : <newline> <tab> <tab> port = port . split ( ' - ' ) [ 0 ] <newline> <newline> <tab> if port = = ' 1 ' : <newline> <tab> <tab> port = port 
K=10,T=0.8: <data>math <newline> import multiprocessing as mp <newline> <newline> from tqdm import tqdm <newline> <newline> <newline> def line _ positions ( file _ path ) : <newline> <tab> with open ( file _ path ) as f : <newline> <tab> <tab> while true : <newline> <tab> <tab> <tab> pos = f . tell ( ) <newline> <tab> <tab> <tab> if f . readline ( ) : <newline> <tab> <tab> <tab> <tab> yield pos <newline> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> break <newline> <newline> <newline> def get _ number _ of _ lines ( fobj ) : <newline> </data><tab> return fobj . get _ number _ of _ lines ( fobj ) <newline> <newline> <newline> def line _ positions ( fobj ) : <newline> <tab> return fobj . get _ number _ of _ lines ( fobj ) <newline> <newline> <newline> def line _ positions ( fobj ) : <newline> <tab> <newline> <tab> return fobj . get _ number _ of _ lines ( fobj ) <newline> <newline> <newline> def line _ positions ( fobj ) : <newline> <tab> <newline> <tab> return fobj . get _ number _ of _ lines ( fobj ) <newline> <newline> <newline> def 
K=10,T=0.8: <data>: <newline> <tab> <tab> 1 . the output of the current node that should be passed to the next node . <newline> <tab> <tab> 2 . a reason object that explains how the output was generated for debugging . <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> add the > > operator , which creates a then node . <newline> <tab> <tab> wrap any strings in a string node first , to allow for code like <newline> <newline> <tab> <tab> <tab> setup node > > " command to run " > > <UNK> node <newline> <tab> <tab> <newline> <tab> </data><tab> add the > > operator , which creates a : node . <newline> <tab> <tab> <newline> <tab> <tab> add the > > operator , which calculates a : node . <newline> <tab> <tab> <newline> <tab> <tab> add an > > operator , which calculates a : node . <newline> <tab> <tab> <newline> <tab> <tab> get a : node . <newline> <tab> <tab> <newline> <tab> <tab> add the > > operator , which creates a node . <newline> <tab> <tab> <newline> <tab> <tab> add a > > operator , which calculates a : node . <newline> <tab> <tab> <newline> <tab> 
K=10,T=0.8: <data>, layer 2 ) : <newline> <tab> <tab> super ( concat table , self ) . _ _ init _ _ ( ) <newline> <tab> <tab> self . layer 1 = layer 1 <newline> <tab> <tab> self . layer 2 = layer 2 <newline> <tab> <tab> <newline> <tab> def forward ( self , x ) : <newline> <tab> <tab> y = [ self . layer 1 ( x ) , self . layer 2 ( x ) ] <newline> <tab> <tab> return y <newline> <newline> class flatten ( nn . module ) : <newline> <tab> def _ _ init _ </data>_ ( self , x , dropout = 0 . 1 , x = 0 . 1 , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> dropout = 0 . 1 , dropout = 0 . 2 , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> initializer = 0 . 1 , kernel _ initializer = 0 . 2 , stride = 1 ) : <newline> <tab> <tab> super ( flatten , self ) . _ _ init _ _ ( ) <newline> <newline> <tab> <tab> self . layer 2 = layer 2 <newline> 
K=10,T=0.8: <data><tab> <tab> <tab> metadata [ " chunk _ size " ] , <newline> <tab> <tab> <tab> metadata [ " chunk _ overlap " ] , <newline> <tab> <tab> <tab> metadata [ " max _ chunk _ num " ] , <newline> <tab> <tab> ) <newline> <tab> <tab> <newline> <tab> <tab> df _ merged = pd . merge ( df _ docs , df _ remove , on = [ " md 5 " , " file _ path " ] , how = " inner " ) <newline> <tab> <tab> remove _ ids = np . array ( df _ </data>merged ) <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> 
K=10,T=0.8: <data>x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ xf 7 \ x 9 e \ xf 7 \ x 9 </data>f \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ xb 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 0 \ xe 3 \ x 9 5 \ x 0 0 \ x 6 0 \ x 0 5 \ x 0 0 \ x 0 0 \ x 0 0 \ x 0 1 \ x 1 0 \ 
K=10,T=0.8: <data><tab> console . set level ( logging . error ) <newline> <tab> <tab> formatter = logging . formatter ( ' [ % ( levelname ) s ] % ( message ) s ' ) <newline> <tab> <tab> console . set formatter ( formatter ) <newline> <tab> <tab> logging . get logger ( ' ' ) . add handler ( console ) <newline> <newline> <tab> <tab> self . waves = asset ( ' ' ) <newline> <tab> <tab> self . btc = asset ( ' 8 <UNK> 8 f 7 p 5 d 5 <UNK> 7 gt <UNK> <UNK> s 3 </data><UNK> 0 <UNK> <UNK> 9 f 3 <UNK> 7 <UNK> 9 <UNK> 5 f 2 <UNK> <UNK> 6 <UNK> 7 <UNK> 6 <UNK> 9 5 <UNK> 2 <UNK> 6 <UNK> 6 <UNK> 6 o 2 f 1 <UNK> <UNK> 3 1 f 3 <UNK> <UNK> <UNK> 9 <UNK> 2 <UNK> 6 0 <UNK> 5 d 5 <UNK> 0 <UNK> 6 d 2 o 5 <UNK> 2 <UNK> 6 <UNK> 5 e 3 <UNK> <UNK> 5 <UNK> 3 <UNK> <UNK> 3 <UNK> <UNK> 6 <UNK> 7 <UNK> 7 <UNK> <UNK> <UNK> 7 <UNK> 9 <UNK> <UNK> <UNK> 7 <UNK> <UNK> <UNK> 
K=10,T=0.8: <data>( pg _ step / decay _ steps ) <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> if cfg _ train . ray _ sampler in [ ' flatten ' , ' in _ <UNK> ' ] : <newline> <tab> <tab> <tab> sel _ i = batch _ index _ sampler ( ) <newline> <tab> <tab> <tab> target = rgb _ tr [ sel _ i ] <newline> <tab> <tab> <tab> rays _ o = rays _ o _ tr [ sel _ i ] <newline> <tab> <tab> <tab> rays _ d = rays _ d _ tr [ </data>' rays _ o ' ] <newline> <tab> <tab> <tab> rays _ o = rays _ o _ tr [ ' rays _ o _ o ' ] <newline> <tab> <tab> <tab> rays _ o = rays _ o _ tr [ ' rays _ o _ o ' ] <newline> <tab> <tab> <tab> rays _ o = rays _ o _ o [ ' rays _ o _ o ' ] <newline> <tab> <tab> <tab> rays _ o = rays _ o _ o [ ' rays _ o _ o _ o ' ] <newline> <tab> <tab> <tab>