K=10,T=0.8: <data>[ 1 0 3 . 9 3 9 , 1 1 6 . 7 7 9 , 1 2 3 . 6 8 ] , dtype = np . float 3 2 ) <newline> <UNK> _ mean _ vec = mean _ vec . reshape ( 1 , 1 , 3 ) ; <newline> im = image [ : , : , : : - 1 ] <newline> im = im - <UNK> _ mean _ vec <newline> <newline> <newline> cur _ h , cur _ w , cur _ c = im . shape <newline> pad _ h = </data>= _ . _ , _ _ . ( . , = _ ' _ ( , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> _ _ <newline> <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> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>ff ] + " , " " , caption ) <newline> <tab> caption = re . sub ( r " [ \ u 3 4 0 0 - \ u 4 <UNK> ] + " , " " , caption ) <newline> <tab> caption = re . sub ( r " [ \ u 4 <UNK> 0 - \ u 4 <UNK> ] + " , " " , caption ) <newline> <tab> caption = re . sub ( r " [ \ u 4 e 0 0 - \ u 9 <UNK> ] + " , " " , </data><newline> <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> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self . <UNK> . : <newline> 
K=10,T=0.8: <data><tab> <newline> <tab> <tab> ema _ params [ name ] . mul _ ( decay ) . add _ ( param . data , alpha = 1 - decay ) <newline> <newline> def requires _ grad ( model , flag = true ) : <newline> <tab> <newline> <tab> for p in model . parameters ( ) : <newline> <tab> <tab> p . requires _ grad = flag <newline> <newline> def <UNK> ( ) : <newline> <tab> <newline> <tab> dist . <UNK> _ process _ group ( ) <newline> <tab> <newline> <newline> def setup _ distributed ( <UNK> = " <UNK> </data>) <newline> <tab> <tab> <tab> <tab> <newline> <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> if self . <UNK> _ batch = [ " <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self . <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data><tab> self . placeholders [ ' node _ sequence ' ] : batch _ data [ ' node _ sequence ' ] , <newline> <tab> <tab> <tab> <tab> self . placeholders [ ' edge _ type _ masks ' ] : batch _ data [ ' edge _ type _ masks ' ] , <newline> <tab> <tab> <tab> <tab> self . placeholders [ ' edge _ type _ labels ' ] : batch _ data [ ' edge _ type _ labels ' ] , <newline> <tab> <tab> <tab> <tab> self . placeholders [ ' edge _ masks ' ] </data>, ' , ' 0 ] ) <newline> <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> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>= int ( batch _ size / args . grad _ accu _ steps ) <newline> <newline> <tab> for epoch in range ( args . epochs ) : <newline> <tab> <tab> start = time . time ( ) <newline> <tab> <tab> model . train ( ) <newline> <tab> <tab> <newline> <tab> <tab> train _ sampler . set _ epoch ( epoch ) <newline> <newline> <tab> <tab> <newline> <tab> <tab> train _ scheduler . step ( epoch ) <newline> <tab> <tab> loss _ tmp = 0 <newline> <tab> <tab> for step , ( images , labels ) in enumerate ( train </data>_ params , <newline> <tab> <tab> <tab> <tab> <tab> if torch . get _ args . append ( ) <newline> <tab> <tab> <tab> <tab> <tab> return _ <UNK> = = torch . conv 2 d . get _ name , args . nn . 0 . add _ name = 0 ] ) : <newline> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> if torch . append ( args . ops . <UNK> ( self . placeholders [ 0 . conv 2 d . add _ type = 1 ) <newline> <tab> <tab> <tab> <tab> return 
K=10,T=0.8: <data><newline> one _ hot _ labels = jax . nn . one _ hot ( labels , confidence _ sets . shape [ 1 ] ) <newline> l 1 = ( 1 - confidence _ sets ) * one _ hot _ labels * loss _ matrix [ labels ] <newline> l 2 = confidence _ sets * ( 1 - one _ hot _ labels ) * loss _ matrix [ labels ] <newline> loss = jnp . sum ( jnp . maximum ( l 1 + l 2 , jnp . zeros _ like ( l 1 ) </data><newline> <newline> <newline> <newline> def get _ loss , 1 ] , <newline> def _ name = 0 ] . append ( self . append ( <UNK> . ndarray , <newline> <newline> <newline> <tab> self . nn . conv 2 ] <newline> <tab> <tab> self . shape [ 1 , <newline> <tab> <tab> <tab> self . ops ( self . conv _ states . nn . conv 2 , <newline> <tab> <tab> <tab> def _ size = 1 , <newline> <tab> self . append ( self . conv _ size = self . params . linear ( self . append 
K=10,T=0.8: <data>( tf . square ( gradients ) , <UNK> _ indices = [ 1 ] ) ) <newline> <tab> gradient _ penalty = tf . reduce _ mean ( ( slopes - 1 ) * * 2 ) <newline> <newline> <tab> disc _ cost + = lambda * gradient _ penalty <newline> <newline> disc _ params = lib . params _ with _ name ( ' discriminator ' ) <newline> gen _ params = lib . params _ with _ name ( ' generator ' ) <newline> <newline> if mode = = ' wgan - gp ' : <newline> <tab> </data><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> def train ' ] <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data><tab> <tab> incremental _ adj _ mat , distance _ to _ others , node _ sequence , edge _ type _ masks , edge _ type _ labels , local _ stop , edge _ masks , edge _ labels , overlapped _ edge _ features = \ <newline> <tab> <tab> <tab> <tab> construct _ incremental _ graph ( dataset , edges _ no _ master , chosen _ bucket _ size , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> len ( nodes _ no _ master ) , nodes _ no _ master </data>_ type , num , edge _ edge _ states , node _ adj _ edge _ masks ) , type = [ edge _ type _ node _ edge _ adj _ edge _ edge _ edge _ node _ masks , edge _ prob , node _ type _ symbol , edge _ type _ states , edge _ type _ node _ edge _ type _ edge _ edge _ edge _ type _ type _ prob _ edge _ prob , edge _ masks , node _ edge _ mini _ edge _ edge _ edge 
K=10,T=0.8: <data>' <UNK> ' , type = str , help = ' prefix of results file ' ) <newline> parser . add _ argument ( ' - - score ' , default = ' hook _ <UNK> ' , type = str , help = ' the score to evaluate ' ) <newline> parser . add _ argument ( ' - - nasspace ' , default = ' nasbench 2 0 1 ' , type = str , help = ' the nas search space to use ' ) <newline> parser . add _ argument ( ' - - batch _ </data>argument ( ' - - - - - - - - - - - - - > ' : ' - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -