K=10,T=0.8: elif resample = = none : conv _ shortcut = lib . ops . conv 2 d . conv 2 d conv _ 1 = functools . partial ( lib . ops . conv 2 d . conv 2 d , input _ dim = input _ dim , output _ dim = input _ dim ) conv _ 2 = functools . partial ( lib . ops . conv 2 d . conv 2 d , input _ dim = input _ dim , output _ _ _ _ _ _ _ _ _ 0 _ . . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ . _ _ _ _ _ _ _ _ _ _ _ _ _
K=10,T=0.8: nodes / _ train " } ) , } } return _ types = ( " models _ class " , ) return _ names = ( ' model ' , ) function = " create _ init _ train " output _ node = true category = " build and train your network " def create _ init _ train ( self , train _ dataset , model , val _ dataset = none , batch _ 0 _ _ _ _ _ _ _ _ _ _ if _ _ _ _ ( . if . _ _ _ _ _ _ _
K=10,T=0.8: from _ pretrained ( model _ args . tokenizer _ name , cache _ dir = model _ args . cache _ dir ) elif model _ args . model _ name _ or _ path : tokenizer = auto tokenizer . from _ pretrained ( model _ args . model _ name _ or _ path , cache _ dir = model _ args . cache _ dir ) else : raise value error ( " you are instantiating a new tokenizer from scratch . _ _ 0 ( ( ( = : v _ 0 _ _ . _ ( _ _ _
K=10,T=0.8: find def count ( self , s : str ) - > int : @ cache def is ( i , j ) : return i > = j or s [ i ] = = s [ j ] and is ( i + 1 , j - 1 ) return sum ( is ( i , j ) for i in range ( len ( s ) ) for j in range ( i , len ( )