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 ( _ . K=10,T=0.8: : , : , : : 2 , 1 : : 2 ] , output [ : , : , 1 : : 2 , 1 : : 2 ] ] ) / 4 . output = lib . ops . conv 2 d . conv 2 d ( name , input _ dim , output _ dim , filter _ size , output , he _ init = he _ init , biases = biases ) return output def conv ( name , input _ dim , output _ dim , filter ( ' ' K=10,T=0.8: ( tf . square ( gradients ) , _ indices = [ 1 ] ) ) gradient _ penalty = tf . reduce _ mean ( ( slopes - 1 ) * * 2 ) disc _ cost + = lambda * gradient _ penalty disc _ params = lib . params _ with _ name ( ' discriminator ' ) gen _ params = lib . params _ with _ name ( ' generator ' ) if mode = = ' wgan - gp ' : ) ) _ _ ) K=10,T=0.8: self . placeholders [ ' node _ sequence ' ] : batch _ data [ ' node _ sequence ' ] , self . placeholders [ ' edge _ type _ masks ' ] : batch _ data [ ' edge _ type _ masks ' ] , self . placeholders [ ' edge _ type _ labels ' ] : batch _ data [ ' edge _ type _ labels ' ] , self . placeholders [ ' edge _ masks ' ] = , ' ) ) ) _ _ _ = ' ( _ . K=10,T=0.8: = [ ] if not self . _ is _ sampler : for i in range ( lower , upper ) : x , lam = self . stage _ fwd ( x , i , self . comms , dispersion = dispersion ) . append ( lam ) else : subkey = jax . random . split ( key , upper - lower ) for i in range ( lower , upper ) : x , lam . model . . . _ . . model = 2 , if . ( ' , K=10,T=0.8: ( dataset ) : with open ( ' generated _ smiles _ % s ' % dataset , ' rb ' ) as f : all _ smiles = set ( pickle . load ( f ) ) count = 0 for smiles in all _ smiles : mol = chem . mol from smiles ( smiles ) if mol is not none : count + = 1 return len ( all _ smiles ) , count else : print ( [ 0 , K=10,T=0.8: = get _ id _ classes ( class _ names ) lut = create _ lut ( class _ ids ) file _ names = load _ test _ data ( ) images , labels = create _ full _ paths ( file _ names , ' images ' , ' labels ' ) test _ net ( prototxt , caffemodel . format ( iteration _ num ) , images , labels , lut ) def load _ test _ data ( file _ name = ' test . txt ' ) model ( ) for i ] ) if args . shape . ( import model , default = torch . return _ init _ init _ size = ' ) ) model _ size = ' ) : for _ _ _ _ _ path . append ( x = true ) model _ = tf . _ name = self . K=10,T=0.8: _ conditional _ multi _ coverage . args : confidence _ sets : confidence sets on test set as 0 - 1 array labels : ground truth labels on test set ( not in one - hot format ) conditional _ labels : conditional labels to compute coverage on a subset conditional _ label : selected conditional to compute coverage for returns : conditional coverage . compute _ conditional _ coverage with additional argument checks . compute - coverage for given one - the . . for = true the . . . to the . the : : . for to . parser . for . . add of the . K=10,T=0.8: self . _ token _ idx = nn . parameter ( torch . zeros ( ( self . mini _ batch _ size , ) ) ) self . share _ qk = config . share _ qk self . conv _ kernel = config . conv _ kernel self . _ init _ _ proj ( ) self . _ init _ ( ) self . _ init _ ttt _ lr _ gate _ _ ( self . ops [