K=10,T=0.8: <data>= args . test _ batch , callbacks = callbacks ) trainer . train ( ) _ _ author _ _ = " <UNK> <UNK> " _ _ version _ _ = " $ revision : 1 6 6 7 $ " _ _ date _ _ = " $ date : 2 0 0 7 - 0 6 - 0 2 1 6 : 3 2 : 3 5 + 0 2 0 0 ( sat , 0 2 jun 2 0 0 7 ) $ " _ _ copyright _ _ = " copyright ( c ) 2 </data>_ _ 4 = " <UNK> _ 1 " ) <UNK> = = " <UNK> " <UNK> _ <UNK> _ _ 1 0 3 1 = " <UNK> _ 1 " / " <UNK> 2 _ 2 _ 1 = " <UNK> _ 2 4 " : _ 1 = " <UNK> _ 1 _ 2 = " <UNK> _ 1 . " if _ _ _ _ _ 2 " : _ 2 1 6 _ 3 / " : _ 1 2 _ 1 2 _ 2 _ _ 2 _ 2 _ 1 = " <UNK> _ 
K=10,T=0.8: <data>file _ list _ pron . txt ' ] target _ train _ file _ list = ' filelists / merge _ korean _ pron _ train . txt ' target _ eval _ file _ list = ' filelists / merge _ korean _ pron _ valid . txt ' target _ test _ file _ list = ' filelists / merge _ korean _ pron _ test . txt ' _ integrate ( train _ file _ lists , target _ train _ file _ list ) _ integrate ( eval _ file _ lists , target _ </data>dir , target _ dir _ list ) ) for target _ test in model . load _ files ( target _ files ) : target _ path _ list = target _ path _ list = target _ path _ list = target _ folder _ list = target _ list = target _ list if target . txt _ list : target _ list = target _ list : target _ list = target _ list [ target _ list ] target _ list . append ( target _ list ) target _ list [ target _ list 
K=10,T=0.8: <data>random . random ( ) * math . pi / 3 6 . 0 + math . pi / 3 6 . 0 ) * sax ay = ( random . random ( ) * math . pi / 3 6 . 0 + math . pi / 3 6 . 0 ) * say az = ( random . random ( ) * math . pi / 3 6 . 0 + math . pi / 3 6 . 0 ) * <UNK> camera _ ang = [ ax , ay , az ] axisangle = torch . from </data><UNK> _ ang , <UNK> = ( random . choice ( random . seed ) * * * math . pi / 5 0 ) * ( np . pi / 6 . 0 + math . pi / 3 6 . 0 + math . pi / 3 6 . 0 + math . pi / 3 6 . 0 + math . pi / 1 6 . 0 + math . pi / 3 6 . 0 + math . pi / 3 6 . 0 + math . pi / 3 6 . 0 + pi / 
K=10,T=0.8: <data>generating { n _ epochs } epochs on { split } split ' ) break if shuffle : with temporarily seeded random ( next ( permutation _ seeds ) ) : random . shuffle ( flat _ data ) batch = [ ] for prompt , responses , pairs , sft _ target , truncation _ mode in flat _ data : if done : break if sft _ mode : batch _ element = tokenize _ batch _ element ( prompt , sft _ target , sft _ target , truncation _ mode , tokenizer , max _ length </data>) , negative _ length = tokenizer . pad _ length = tokenizer . pad _ length , pad _ length , eos _ length = pad _ length , bos _ length , pad _ length , eos _ length ) batch _ length = pad _ length . pad _ length , pad _ length , pad _ length , pad _ length , bos _ length ) batch _ length = pad _ length self . pad _ length = pad _ length = pad _ length self . pad _ length = pad _ length self 
K=10,T=0.8: <data>gen = dim _ gen self . n _ cat = n _ cat self . batch _ size = batch _ size scale = 1 0 . 0 self . beta _ cycle _ gen = beta _ cycle _ gen self . beta _ cycle _ label = beta _ cycle _ label self . x _ dim = self . d _ net . x _ dim self . z _ dim = self . g _ net . z _ dim self . x = tf . placeholder ( tf . float 3 2 , [ </data>self . x _ dim ) self . y _ dim = self . z _ dim , self . x _ dim , self . z _ dim = self . z _ dim , self . z _ dim , self . z _ dim , self . z _ dim , self . z _ dim , self . z _ dim , self . z _ dim , self . z _ dim , self . z _ dim , self . z _ dim , self . z _ dim = self . z _ 
K=10,T=0.8: <data>layer , basestring ) : try : fed _ layer = self . layers [ fed _ layer ] except key error : raise key error ( ' unknown layer name fed : % s ' % fed _ layer ) self . terminals . append ( fed _ layer ) return self def get _ output ( self ) : return self . terminals [ - 1 ] def get _ unique _ name ( self , prefix ) : ident = sum ( t . startswith ( prefix ) for t , _ in self . layers . </data>append ( t ) return self . terminals def get _ input ( self , prefix ) : self . _ _ init _ _ ( self , prefix ) : if prefix = = ' _ _ main _ _ ' : return self class layer ( self ) : def _ _ init _ _ ( self , prefix ) : self . _ _ init _ _ ( self , prefix ) : super ( self , self ) . _ _ init _ _ ( ) self . _ _ ( prefix ) self . _ 
K=10,T=0.8: <data>. triggered . connect ( self . execute _ script ) toolbar . add action ( abort _ action ) def load _ file ( self ) : file _ dialog = qfile dialog ( ) self . input _ file , _ = file _ dialog . get open file name ( self , caption = " open input file " , dir = " examples " , filter = " py fem input files ( * . pro ) ; ; all files ( * . * ) " ) if self . input _ file : self </data>. input _ file = input _ file if self . input _ file = self . input _ file : self . segment _ file . get ( " segment _ ids " , len ( ) ) self . input _ file = input _ file . get ( " segment _ ids " , len ( self . segment _ ids , len ( self . segment _ ids ) ) self . label _ label _ file , text _ file ) self . label _ file . set ( " segment _ ids " , 
K=10,T=0.8: <data>/ i ' : step = ' instance ' nodes . pop ( ) res = [ ] sub _ offset = 0 idx = 0 for node in nodes : if node [ - 1 ] = = ' x ' : sub _ offset + = int ( node [ : - 1 ] ) continue if len ( attributes ) = = idx : raise value error ( f ' not enough vertex attributes for format " { layout } " ' ) location = attributes [ idx ] format , size = short _ vertex _ </data>matrix [ i ] ) else : if isinstance ( attributes ) : if isinstance ( attributes , str , tuple ) : return attributes [ idx ] ) else : attributes [ idx ] . append ( attributes [ ' attr ' ] [ idx ] + { ' attr ' : attr [ idx ] } ' ) class _ name = { ' attr ' : attr [ idx ] } ) class _ name ( name ) : def _ _ init ( self , attr ) : return attributes [ idx ] def _ _ 
K=10,T=0.8: <data>( c + 1 0 2 4 , n ) , y = new array ( i - c ) , l = c , p = 0 ; l < i ; + + p , + + l ) y [ p ] = e [ l ] . char code at ( 0 ) ; a [ o ] = new uint 8 array ( y ) } return new blob ( a , { type : ' application / octet - stream ' } ) } ) ( function ( b , fname ) { if </data>( i + c ) > 0 : i + i ) ] = c [ i + c ] + c [ i + c ] ) = c [ i + c ] return c [ i + i + c ] def get ( i , i + c ) : i + c [ i ] + c [ i + c [ i ] + c [ i + c [ i + c ] , c [ i + c ] ) : i + c [ i + c ] + c [