runs/code-decoder-v23-mega-nontopkacc/ckpt/generated.txt

K=10,T=0.8: <data>crop ( 3 2 ) ] ) <newline> to _ tensor = transforms . to tensor ( ) <newline> normalize = transforms . normalize ( [ 0 . 5 ] * 3 , [ 0 . 5 ] * 3 ) <newline> test _ transform = transforms . compose ( <newline> <tab> [ transforms . to tensor ( ) , normalize ] ) <newline> <newline> if args . dataset = = ' cifar 1 0 ' : <newline> <tab> train _ data = datasets . cifar 1 0 ( <newline> <tab> <tab> os . path . join ( args . </data>dataset , ' mnist ' ) , <newline> <tab> <tab> <tab> os . path . join ( args . dataset , ' mnist ' ) , <newline> <tab> <tab> <tab> os . path . join ( args . dataset , ' mnist ' ) , <newline> <tab> <tab> <tab> os . path . join ( args . dataset , ' mnist ' ) , <newline> <tab> <tab> <tab> os . path . join ( args . dataset , ' mnist ' ) , <newline> <tab> <tab> <tab> os . path . join ( args . dataset , ' mnist ' 
K=10,T=0.8: <data>all _ input _ nodes [ 0 ] for i in group ] <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> out _ flat = [ <newline> <tab> <tab> <tab> <tab> graph . call _ function ( torch . ops . aten . view . default , args = ( i , [ i . meta [ " val " ] . numel ( ) ] ) ) for i in inputs <newline> <tab> <tab> <tab> ] <newline> <tab> <tab> <tab> out _ cat = graph . call _ function ( torch . ops . aten . cat . default </data>, args = ( i , [ i . meta [ " val " ] . numel ( ) ] ) ) <newline> <tab> <tab> <tab> out _ cat . append ( torch . ops . ops . impl . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . dynamic . view . 
K=10,T=0.8: <data>arch [ ' hwc ' ] , <newline> <tab> <tab> batch _ size = args . batch _ size , <newline> <tab> <tab> rtype = ' tanh ' ) <newline> <tab> <newline> <tab> machine = vaegan ( arch , is _ training = true ) <newline> <newline> <tab> loss = machine . loss ( imgs ) <newline> <tab> xh = machine . sample ( args . batch _ size ) <newline> <tab> <newline> <tab> x _ interp = machine . interpolate ( imgs [ 0 ] , imgs [ 1 ] , n _ interp ) <newline> <newline> <tab> opt </data>= torch . zeros ( len ( imgs ) ) <newline> <tab> for x _ interp in range ( 1 , args . batch _ size ) : <newline> <tab> <tab> x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x _ interp [ x 
K=10,T=0.8: <data>) <newline> <tab> <tab> <tab> except guard error : <newline> <tab> <tab> <tab> <tab> pass <newline> <tab> <tab> <newline> <tab> <tab> raise match error ( " no match for given argument values " ) <newline> <newline> <tab> def append ( self , arity , clause ) : <newline> <tab> <tab> <newline> <tab> <tab> if self . clauses . get ( arity , false ) and hasattr ( self . clauses [ - 1 ] , ' _ _ catchall _ _ ' ) : <newline> <tab> <tab> <tab> raise wont match error ( " function clause defined after a catch </data>" ) <newline> <newline> <tab> <tab> <newline> <tab> <tab> if clause in self . clauses . get ( arity , false ) : <newline> <tab> <tab> <tab> self . clauses . [ arity ] = 1 <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> raise assertion error ( " expected arity not yet " ) <newline> <newline> <tab> <tab> self . clauses . get ( arity , false ) <newline> <newline> <tab> def append ( self , arity , clause ) : <newline> <tab> <tab> <newline> <tab> <tab> self . clauses . set ( arity , false ) <newline> 
K=10,T=0.8: <data>shortcuts ( ' db ' ) <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> return false <newline> <newline> <newline> <tab> def double key nonce checker ( self , obj ) : <newline> <tab> <tab> if self . dbfile checker ( ) : <newline> <tab> <tab> <tab> try : <newline> <tab> <tab> <tab> <tab> with open ( uiwindow . database _ file , ' r ' ) as db _ file : <newline> <tab> <tab> <tab> <tab> <tab> data = db _ file . read ( ) <newline> <tab> <tab> <tab> <tab> <tab> if obj = = ' key ' </data>: <newline> <tab> <tab> <tab> <tab> <tab> <tab> return false <newline> <tab> <tab> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> <tab> <tab> return false <newline> <newline> <tab> def double key check ( self , obj ) : <newline> <tab> <tab> if self . sqlite checker ( ) : <newline> <tab> <tab> <tab> return true <newline> <tab> <tab> return false <newline> <newline> <tab> <tab> if self . sqlite checker ( ) : <newline> <tab> <tab> <tab> return false <newline> <newline> <tab> def double key check ( self , obj ) : <newline> <tab> <tab> if self . sqlite 
K=10,T=0.8: <data>crop ( 3 2 ) ] ) <newline> to _ tensor = transforms . to tensor ( ) <newline> normalize = transforms . normalize ( [ 0 . 5 ] * 3 , [ 0 . 5 ] * 3 ) <newline> test _ transform = transforms . compose ( <newline> <tab> [ transforms . to tensor ( ) , normalize ] ) <newline> <newline> if args . dataset = = ' cifar 1 0 ' : <newline> <tab> train _ data = datasets . cifar 1 0 ( <newline> <tab> <tab> os . path . join ( args . </data>dataset , ' train _ data . cifar ' ) , <newline> <tab> <tab> train _ batch , <newline> <tab> <tab> train _ batch , <newline> <tab> <tab> test _ batch , <newline> <tab> ) <newline> <newline> <newline> train _ loader = build _ loader ( args . dataset , ' train _ data . py ' ) <newline> train _ loader = build _ loader ( args . dataset , ' train _ data . py ' ) <newline> train _ loader = build _ loader ( args . dataset , ' train _ data . py ' ) 
K=10,T=0.8: <data>all _ input _ nodes [ 0 ] for i in group ] <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> out _ flat = [ <newline> <tab> <tab> <tab> <tab> graph . call _ function ( torch . ops . aten . view . default , args = ( i , [ i . meta [ " val " ] . numel ( ) ] ) ) for i in inputs <newline> <tab> <tab> <tab> ] <newline> <tab> <tab> <tab> out _ cat = graph . call _ function ( torch . ops . aten . cat . default </data>, args = ( i , [ i . meta [ " val " ] . numel ( ) ] ) ) <newline> <tab> <tab> <tab> out _ cat . append ( out _ cat ) <newline> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> 
K=10,T=0.8: <data>arch [ ' hwc ' ] , <newline> <tab> <tab> batch _ size = args . batch _ size , <newline> <tab> <tab> rtype = ' tanh ' ) <newline> <tab> <newline> <tab> machine = vaegan ( arch , is _ training = true ) <newline> <newline> <tab> loss = machine . loss ( imgs ) <newline> <tab> xh = machine . sample ( args . batch _ size ) <newline> <tab> <newline> <tab> x _ interp = machine . interpolate ( imgs [ 0 ] , imgs [ 1 ] , n _ interp ) <newline> <newline> <tab> opt </data>= torch . optim . adam ( net . parameters ( ) , lr = args . lr , momentum = 0 . 9 , weight _ decay = args . weight _ decay , weight _ decay = args . weight _ decay , weight _ decay = args . weight _ decay ) <newline> <tab> print ( " \ n " ) <newline> <tab> <newline> <tab> if args . lr : <newline> <tab> <tab> lr = args . lr _ lr <newline> <tab> <tab> lr = args . lr _ lr <newline> <tab> else : <newline> <tab> <tab>