runs/code-decoder-v22-bigset-tuner/CONFIG_bigdim/ckpt/generated.txt

K=10,T=0.8: <data>_ dir ) for sample in tqdm ( samples , desc = sample _ name , unit = ' samples ' ) : img = cv 2 . imread ( sample . filename ) <UNK> = os . path . basename ( sample . filename ) for box in sample . boxes : draw _ box ( img , box , colors [ box . label ] ) cv 2 . imwrite ( result _ dir + <UNK> , img ) def build _ sampler ( overlap , trials ) : return sampler transform ( sample = true , min </data>_ steps = 0 , min _ steps = 0 , max _ steps = 0 ) if ( start , max _ steps < 0 ) or ( end ) = = 0 : raise value error ( " invalid number of steps for the sample in tqdm ( samples ) : " ) return sample def load ( sample , seed = false ) : for sample in sample : if sample in sample . sample ( ) : if sample in sample . sample ( ) : sample [ sample ] , sample [ sample [ sample 
K=10,T=0.8: <data>mobile bottleneck ( nn . module ) : def _ _ init _ _ ( self , inp , oup , kernel , stride , exp , se = false , nl = ' re ' ) : super ( mobile bottleneck , self ) . _ _ init _ _ ( ) assert stride in [ 1 , 2 ] assert kernel in [ 3 , 5 ] padding = ( kernel - 1 ) / / 2 self . use _ res _ connect = stride = = 1 and inp = = oup conv _ layer = </data>' conv 2 _ layer ' def forward ( self , x , x ) : return self . conv ( x ) def forward ( self , x ) : return x . view ( x . size ( 0 ) , - 2 ) if self . use _ res _ connect : return self . conv ( x ) def forward ( self , x ) : x = self . conv ( x ) return x def forward ( x ) : return x def forward ( self , x ) : return self . conv 
K=10,T=0.8: <data>_ data , 1 ) query _ keys = query _ indexes . keys ( ) true _ scores = [ self . training _ data [ query _ indexes [ query ] , 0 ] for query in query _ keys ] good _ ij _ pairs = get _ pairs ( true _ scores ) tree _ data = pd . data frame ( self . training _ data [ : , 2 : 7 ] ) labels = self . training _ data [ : , 0 ] idcg = [ ideal _ dcg ( scores ) </data>for score in range ( len ( query _ keys ) ) ] query _ keys = [ ] for match _ index in query _ keys [ match _ index ] : match _ index = match _ index [ match _ index ] + match _ index [ match _ index ] + match _ index [ match _ index ] + match _ index [ match _ index ] + match _ index [ match _ index ] ] + match _ index [ match _ index ] ] + match _ index [ match _ index 
K=10,T=0.8: <data>i ) restart . * " , " . * ( ? i ) restore . * " , " . * ( ? i ) restrict . * " , " . * ( ? i ) result . * " , " . * ( ? i ) return . * " , " . * ( ? i ) returned _ cardinality . * " , " . * ( ? i ) returned _ length . * " , " . * ( ? i ) returned _ octet _ length . * " , " </data>. * ( ? i ) return . * " , " . * ( ? i ) return . * " , " . * ( ? i ) result . * " , " . * ( ? i ) result . * " , " . * ( ? i ) result . * " , " . * ( ? i ) result . * " , " . * ( ? i ) result . * " , " . * ( ? i ) result . * " , " . * ( ? 
K=10,T=0.8: <data>stride , padding = dilation [ 0 ] , dilation = dilation [ 0 ] ) self . bn 1 = batch norm ( planes ) self . relu = nn . re lu ( inplace = true ) self . conv 2 = conv 3 x 3 ( planes , planes , padding = dilation [ 1 ] , dilation = dilation [ 1 ] ) self . bn 2 = batch norm ( planes ) self . downsample = downsample self . stride = stride self . residual = residual def forward ( self , x ) : </data>if x . ndim = = 3 : x _ mask = self . residual _ block ( x ) else : x _ mask = x _ mask [ : , 0 : 3 : ] x _ mask = x _ mask . permute ( 0 , 2 , 0 , 1 ) x _ mask = x _ mask . permute ( 0 , 2 , 3 , 1 ) x _ mask = self . residual _ block ( x _ mask , x _ mask ) x _ mask = self . residual _ block 
K=10,T=0.8: <data>req = samr . <UNK> _ user _ info _ buffer ( ) req [ " tag " ] = samr . user _ information _ class . user control information req [ " control " ] [ " user account control " ] = samr . user _ <UNK> _ trust _ account samr . h samr set information user 2 ( dce , user handle , req ) logging . info ( " added machine account % s with password % s . " % ( self . computer name , self . computer password ) ) except </data>exception as e : logging . error ( " failed to create computer % s because other computer account % d because other computer account % d " % ( e , self . computer password ) ) return true def <UNK> _ user _ info _ buffer ( self ) : self . computer name = " computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer computer 
K=10,T=0.8: <data>_ step } ) , ep . { epoch } \ n { ' - ' * 2 0 } " ) log _ metrics ( val _ metrics , queue _ size , global _ step , writer , " validation " ) if not args . evaluate : torch . save ( model . state _ dict ( ) , f ' checkpoints / { global _ step } . pth ' ) torch . save ( optimizer . state _ dict ( ) , f ' checkpoints / { global _ step } _ optimizer . pth </data>' ) if args . evaluate : validate ( model , testloader , trainloader , trainloader , testloader , valloader , valloader , valloader , trainloader , trainloader , testloader ) if args . evaluate : validate ( testloader , testloader , testloader , trainloader , trainloader , testloader ) if args . evaluate : validate ( testloader , testloader , testloader , testloader , testloader , testloader , testloader , testloader ) if args . evaluate : validate ( testloader , testloader , testloader , testloader , testloader ) if args . evaluate : validate ( testloader , testloader 
K=10,T=0.8: <data>) if login status : log ( f ' > token ' , true ) return true else : log ( f ' > token ' ) return false else : print ( f ' > { act _ name } { response } ' ) return false def query user info ( self ) : global author _ wid act _ name = ' ' log ( f ' \ n = = = = = = { act _ name } = = = = = = ' ) json _ data = self . json _ data </data>[ ' data ' ] [ ' data ' ] [ ' name ' ] if json _ data [ ' data ' ] [ ' name ' ] [ ' name ' ] ! = ' ' : log ( f ' > api key ' , true ) return true def send user info ( self , message ) : self . send user info ( message ) def send user info ( self , message ) : self . send user info ( message ) def send user info ( self ) : self . send user 
K=10,T=0.8: <data>( 1 , 2 ) logs _ p = torch . matmul ( attn . squeeze ( 1 ) , logs _ p . transpose ( 1 , 2 ) ) . transpose ( 1 , 2 ) z _ p = m _ p + torch . randn _ like ( m _ p ) * torch . exp ( logs _ p ) * noise _ scale z = self . flow ( z _ p , y _ mask , g = g , reverse = true ) o = self . dec ( ( z * </data>g ) - z . transpose ( 1 , 2 ) ) , 1 , 1 ) , ( 2 , 2 ) ) , ( 2 , 2 ) ) ) x = self . dec ( ( z * g ) - z . transpose ( 1 , 2 ) , 1 ) ) + self . dec ( ( z * g ) + z , logdet ) ) if x _ mask is not none : x = self . dec ( ( x * g ) - z . transpose ( 1 , 2 )