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