K=10,T=0.8: pt [ d ] = str ( dlat ) + " , " + str ( ) + " , " + str ( ) lpt = i = 0 = 0 = bpy . context . scene . ges _ ot _ path . v _ reduce pn = [ ] for i in range ( 2 , 8 0 ) : = [ 0 ] = = = . ( 2 ) = . [ 0 ] . = ( ) = = [ ] = = [ = [ 0 ] oo = [ + ] = K=10,T=0.8: _ argument ( ' - - workers ' , type = int , help = ' number of data loading workers ' , default = 8 ) parser . add _ argument ( ' - - batch _ size ' , type = int , default = 6 4 , help = ' input batch size ' ) parser . add _ argument ( ' - - res size ' , type = int , default = 2 0 4 8 , help = ' size of visual features ' ) parser . add _ argument ( ' - - gpu ' , type = int , default = 8 6 , help = ' number of memory to be to use . ' ) parser . add _ argument ( ' - - - gpu _ gpu _ gpu _ gpu _ gpu _ gpu _ gpu _ gpu _ gpu _ gpu ' , default = 1 0 0 0 0 ) parser . add _ argument ( ' - - gpu _ gpu _ gpu ' , type = str ) parser . add _ argument ( ' - seed _ K=10,T=0.8: , field ( ' duration ' , ' > i ' ) , field ( ' language ' , ' > h ' ) , field ( ' quality ' , ' > h ' ) ] class ( struct ) : _ fields _ = [ field ( ' v ' , ' > b ' ) , field ( ' flags ' , ' > 3 b ' ) , field ( ' creation _ time ' , ' > b ' ) , field ( ' ' , ' > b ' ) , field ( ' class ' , ' < b > c ' ) , field ( ' ' , ' > i ' , ' > b ' ) , field ( ' default ' , ' < b > ' ) , field ( ' default ' , ' < b > b > ' ) , field ( ' class ' , ' < a > ' ) , K=10,T=0.8: ) wait _ for _ enter ( ) self . system ( " vim changelog . md / tmp / changelog _ update . md " ) with open ( " / tmp / changelog _ update . md " , " r " ) as fileobj : context [ " changelog _ update " ] = fileobj . read ( ) class update readme ( step ) : def action ( self , context ) : self . _ _ init _ _ ( self . _ _ init _ _ ( self . _ _ init _ ( self . _ _ init _ _ , ) ) def action ( self , context ) : self . _ _ init _ ( self . _ _ init _ _ ( self . _ _ init _ ) , self . _ _ init _ _ ( self . _ _ init _ ( self . _ _ init _ _ ( self . _ _ init _ K=10,T=0.8: 0 0 } $ _ protect _ identifiers . * false . { 0 , 9 9 } $ select \ ( . * false . { 0 , 9 9 } $ ^ [ ^ \ n ] { 0 , 9 9 } \ $ \ w + [ ^ \ n ] { 0 , 9 9 } \ s + ( and | or ) \ s + [ ^ \ n ] { 0 , 9 9 } \ s in \ s * \ ( [ ^ \ n ] { 0 , 9 9 } $ | | \ d + [ ^ ] { 0 , 9 9 } $ { 0 , 9 9 } $ " ) else : + = [ ^ \ n + [ ^ \ n + ] { 0 , 9 9 } $ " ) if + [ ^ \ n ] { 0 , 0 , 9 9 } $ { 0 K=10,T=0.8: " description " ] = issue . body . split ( period ) [ 0 ] . replace ( " \ " " , " \ ' " ) + period self . blog base [ list json name ] [ post num ] [ " top " ] = 0 for event in issue . get _ events ( ) : if event . event = = " pinned " : self . blog base [ list json name ] [ " content " ] [ " content " ] . replace ( " \ " \ ' " , " \ ' " ' ) self . blog base [ list json name ] [ " content " ] = " \ 0 3 3 [ 0 m " self . blog base [ list json name ] [ " content " ] = " \ x 3 3 [ 0 m " self . blog base [ list K=10,T=0.8: tensor ( np . array ( recon ) ) . permute ( 2 , 0 , 1 ) . unsqueeze ( 0 ) . float ( ) . to ( ' cuda ' ) / 2 5 5 . 0 psnr = psnr _ fn ( label * 2 - 1 , recon * 2 - 1 ) psnrs . append ( psnr ) lpips _ score = loss _ fn _ alex ( label * 2 - 1 , recon * 2 - 1 , recon * 2 - 1 ) psnrs . append ( lpips ) psnr = psnr _ fn ( img * 2 - 1 ) * 2 psnr = psnr _ fn ( img / 2 5 5 ) . cpu ( ) . numpy ( ) . numpy ( ) . cuda ( ) psnrs . append ( psnr ) K=10,T=0.8: " please select a choice from above : " ) try : if active _ wireless _ networks [ int ( choice ) ] : break except : print ( " please try again . " ) = active _ wireless _ networks [ int ( choice ) ] [ " bssid " ] = active _ wireless _ networks [ int ( choice ) ] [ " channel " ] . strip ( ) subprocess . run ( [ " airmon - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - ng " , " - - - ng " , " - - ng " K=10,T=0.8: parse _ args ( ) result = [ ] if args . file _ name : with open ( " . / pmi / " + args . file _ name . lower ( ) + " . txt " , ' r ' ) as f : for line in f : result = result + ( list ( line . strip ( ' \ n ' ) . split ( ' , ' ) ) ) elif args . task _ name : result = result + ( list ( line [ ' task _ name ' ] ) . split ( ' , ' ) ) print ( result ) result = [ ] if args . file _ path : results . append ( result ) for line in results : if line [ ' task _ name ' ] . strip ( ) : if args . file _ K=10,T=0.8: index = = 1 : uri array [ index ] + = param names [ 0 ] + " = " + param value [ 0 ] + " ' , $ or : [ { } , { ' a ' : ' a " + " & " uri array [ index ] + = param names [ 1 ] + " = " + " ' } ] , $ comment : ' successful mongo db " if index = = 2 : uri array [ index ] + = param names [ index ] + " \ " + " ' } ] uri array [ index ] + = param names [ - 1 ] + " ' , $ comment : ' successful mongo db " + str ( index ) + " ' " uri array [ index ] + = param names [ index ] + " ' " return uri array [ index ] + " ' " , K=10,T=0.8: : str ( item id ) } ] , " session id " : session id , " user id " : str ( user id ) , " shop id " : str ( shop id ) } act param = aes _ cbc _ encrypt ( json . dumps ( request body ) , encrypt _ key , encrypt _ iv ) request body [ ' act param ' ] = act param request body [ ' act param ' ] = act param return request body [ ' act param ' ] def get request body ( ) : request body [ ' post body ' ] = json . dumps ( request body ) request body [ ' body ' ] = json . dumps ( request body ) request body [ ' body ' ] = body [ ' body ' ] request body = request K=10,T=0.8: if model in [ ' digit ' ] : from nets . models import digit model model class = digit model else : raise value error ( f " invalid model : { model } " ) elif data in [ ' domain net ' ] : if model in [ ' alexnet ' ] : from nets . models import alex net model class = alex net elif data in [ ' alexnet ' ] : from nets . models import vgg net class else : from nets . models import vgg net class net class = vgg net class def vgg ( self , image , mask _ mask , mask _ mask , mask _ mask , weight _ decay ) : if mask _ mask is none : K=10,T=0.8: use pickle = false else : print ( " [ helper ] can ' t decide if pickle to use based on extension " ) sys . exit ( ) if os . path . isfile ( targetdb ) : if use pickle : try : targets = pickle . load ( open ( targetdb , " rb " ) ) except ioerror as e : print ( " [ helper ] already exists . " ) os . makedirs ( idlist ) os . makedirs ( idlist ) if os . path . isfile ( idlist ) : if not os . path . exists ( idlist ) : K=10,T=0.8: ' elif modal = = ' d ' : = ' rgbd - tr ' elif modal = = ' o ' : = ' - tr ' elif modal = = ' t ' : = ' vt 5 0 0 0 - tr ' set _ name = ' _ ' . join ( ( modal , ) ) set _ name = ' _ ' . join ( modal , ) set _ name = ' _ ' . join ( modal , ) set _ name = ' _ ' . join ( modal , ) set _ name = ' _ ' . join ( modal , ) set _ name = ' _ ' . join ( modal , ) set _ name = ' _ ' . join K=10,T=0.8: 2 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 bc 8 f 5 e 5 4 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 b 6 4 0 9 2 0 8 d 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 K=10,T=0.8: file or path : % s ' % fileobj _ or _ path ) elif not os . path . exists ( fileobj _ or _ path ) : raise value error ( ' no such file : % s ' % fileobj _ or _ path ) else : result = open ( fileobj _ or _ path , ' rb ' ) return result def _ handle _ io ( self , args , fileobj _ or _ path : binary file = none , index : str ) - > none : index = none index = index + index + index index = index + index + index + index if index < index : index = index + index + index index = index index = index + index index = index + index index = index K=10,T=0.8: distill _ checkpoint model = load _ and _ convert _ attns ( model , model _ config , accelerator = accelerator , logger = logger , attention _ type = args . attention _ type , checkpoint _ path = checkpoint _ path , logger = logger , accelerator = accelerator , accelerator = accelerator , logger = logger , logger = logger ) K=10,T=0.8: replace ( " - best " , " - 2 5 0 e " ) , epoch , best _ val _ acc = val _ scores , best _ test _ acc = test _ scores , best _ test _ clean = best _ test _ clean ) if ( epoch % args . adv _ eval _ freq = = 0 and epoch > args . start _ eval ) or epoch = = num _ adv _ epochs : test _ acc = test _ scores [ 0 ] . detach ( ) . cpu ( ) . numpy ( ) . detach ( ) test _ acc = test _ acc . detach ( ) . cpu ( ) . numpy ( ) . detach ( ) . numpy ( ) test _ acc = test _ test _ acc * test _ acc test _ acc = test _ acc [ 0 ] . detach ( ) . cpu ( ) . numpy ( ) . numpy ( ) K=10,T=0.8: for distillation loss ' ) parser . add _ argument ( ' - - average _ over ' , default = ' holdout ' , type = str , help = " whether to average over different holdout sizes : " " ' holdout ' , different train percents : ' tp ' " " or a single run : ' na ' " ) parser . add _ argument ( ' - - tp ' , default = ' o ' , type = str , help = " the gradient of over different metrics " " ' " ) parser . add _ argument ( ' - - n _ epochs ' , default = 5 0 , type = int , help = " epochs to train the number of epochs to perform for testing " " ' " ) parser . add _ argument ( ' - K=10,T=0.8: 2 0 _ stoi = 0 score _ 1 0 _ stoi = 0 score _ 1 5 _ stoi = 0 score _ 0 _ stoi = 0 score _ 5 _ stoi = 0 score _ _ 5 _ stoi = 0 score _ 2 0 _ pesq _ gl = 0 score _ 1 0 _ pesq _ gl = 0 score _ 1 5 _ pesq _ gl = 0 score _ 0 _ pesq _ gl = 0 score _ 5 _ pesq _ gl = 0 score _ 0 _ pesq _ gl = 0 score _ 1 0 _ stoi _ gl _ gl = 0 score _ 0 _ stoi _ gl = 0 def _ _ getitem _ _ ( self , index ) : if self . _ _ getitem _ _ = = index : if self . _ _ setitem _ _ ( index , index ) : self . _ _ delitem K=10,T=0.8: . 4 6 3 8 . 6 9 safari / 5 3 7 . 3 6 ' , ' referer ' : ' https : / / www . . com / ' , } url = ' https : / / app - sc . . com / vms - match / v 3 / / basic / basic - data / { } ' . format ( self . rid ) with requests . session ( ) as session : for key in session . query : session . query [ key ] = self . rid if url is none : url = f ' https : / / www . . com / - match / v 3 / / basic - data / { } / / / * / { } ' if not url : raise value error ( K=10,T=0.8: , dtype = torch . long ) ] , dim = 0 ) attention _ masks [ i ] = torch . cat ( [ attention _ masks [ i ] , torch . zeros ( pad _ length ) ] , dim = 0 ) input _ embeddings = torch . stack ( input _ embeddings ) . to ( self . device ) target _ labels = torch . stack ( target _ labels ) . to ( self . device ) attention _ masks [ i , : , : , : , : , : , : , : ] = torch . stack ( attention _ masks ) . to ( self . device ) attention _ masks [ i , : , : , : , : , : , : ] = attention _ masks [ i , : , : , : , : , : , : : , : : , : , : ] * attention _ masks [ i , : , : , : , - : , : ] K=10,T=0.8: sub ( r " \ ' re " , " \ ' re " , string ) string = re . sub ( r " \ ' d " , " \ ' d " , string ) string = re . sub ( r " \ ' ll " , " \ ' ll " , string ) string = re . sub ( r " , " , " , " , string ) string = re . sub ( r " ! " , " ! " , string ) string = re . sub ( r " \ ' d " , " \ ' d " , string ) string = re . sub ( r " \ ' d " , " \ ' d " , string ) string = re . sub ( r " \ ' d " , " \ ' d " , string ) string = re . sub ( r " \ ' d " , " " , string ) string = re . sub ( r " K=10,T=0.8: headers = { ' accept - encoding ' : ' , ' ' & au & * * * * s , ' ' , ' ' , ' ' , ' ' * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * K=10,T=0.8: ( mit ) copyright ( c ) 2 0 1 6 ltd ( https : / / . com ) permission is hereby granted , free of charge , to any person obtaining a copy of this software and associated documentation files ( the " software " ) , to deal in the software without restriction , including without limitation the rights to use , copy , modify , merge , publish , distribute , sublicense , and / or sell copies of the software , and to permit persons to whom the software is furnished to do so , subject to the following conditions : the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software . the software is provided is not , without warranty of any kind , express or implied , including but not limited to the warranties of merchantability , fitness for a particular purpose and noninfringement . in no event shall the authors or copyright holders be liable for any claim , damages or other liability , whether K=10,T=0.8: , blend _ type = ' d ' , use _ colormap = true ) : print ( ' blend with spsg ' ) blends = [ ] blends _ colormap = [ ] masks = [ ] device = ' cuda ' if torch . cuda . is _ available ( ) else ' cpu ' for scene in scene _ images : marker = cv 2 . resize ( marker , ( scene . shape [ 1 ] , scene . shape [ 1 ] ) ) marker . append ( marker ) return marker K=10,T=0.8: return summary _ json def get _ cash _ flow ( self , ticker , fyear , period ) : data = self . _ get _ company _ statements ( ticker , " cf " , fyear , period ) extracted _ data = self . _ extract _ financial _ data ( data ) column _ map = { " operating activities " : [ " change in working capital " , " " : [ " cash activities " , " " : [ " cash activities " , " income " : [ " cash activities " , " " : [ ] , K=10,T=0.8: % ( levelname ) - 5 . 5 s ] % ( message ) s " ) . set formatter ( log formatter ) logging . get logger ( ) . add handler ( ) log = logging . get logger ( " app . " + _ _ name _ _ ) def add _ movie ( title , year , imdbid , quality _ profile _ id ) : global movie _ added _ count global movie _ exist _ count year = int ( year ) / 4 0 year = int ( year ) / 2 year = int ( year ) / 2 year = int ( year ) year = int ( year ) / 2 year = int ( year ) year = int ( year ) / 2 year = int ( year ) / 2 year = int ( year ) / 2 K=10,T=0.8: " , u " " , u " ` " , " _ " ] ) self . answer _ tokens = none def set _ question ( self , normalized _ aliases ) : self . answer _ tokens = normalized _ aliases def any _ found ( self , para ) : words = [ w . lower ( ) . strip ( self . strip ) for w in flatten _ iterable ( para ) ] for w in words : words . append ( " [ + ] " ) if w . lower ( ) . endswith ( ' / ' ) : words . append ( " [ + ] " ) if ( words = = " [ + ] " ) : return word . lower ( ) . replace ( ' \ n ' , ' ' ) . replace ( ' \ n ' , ' ' ) K=10,T=0.8: add ( avg pool 2 d ( pool _ size = ( 2 , 1 ) , padding = ' same ' ) ) model . add ( flatten ( ) ) model . add ( dense ( 1 0 0 , activation = ' elu ' ) ) model . add ( dense ( n _ outputs , activation = ' softmax ' ) ) plot _ model ( model , show _ shapes = true , to _ file = ' two d _ cnn . png ' ) plot _ model ( model , show _ shapes = true , to _ file = ' two d _ cnn . png ' ) plot _ model ( model , show _ shapes = true , to _ file = ' two d _ cnn . png ' ) print ( f " \ n \ n \ n \ n \ n \ n \ n \ n \ n \ n " ) def plot _ model ( model , show _ shapes = true , to _