K=10,T=0.8: max _ iteration _ num = max ( d [ ' number _ iteration ' ] , max _ iteration _ num ) batch _ data = { ' adj _ mat ' : [ ] , ' init ' : [ ] , ' labels ' : [ ] , ' edge _ type _ masks ' : [ ] , ' edge _ type _ labels ' : [ ] , ' edge _ masks ' : [ ] , ' edge _ labels ( ) ) _ ) ( _ . , , K=10,T=0.8: 9 . pth ' , type = str , help = ' path to api ' ) parser . add _ argument ( ' - - save _ loc ' , default = ' results / ' , type = str , help = ' folder to save results ' ) parser . add _ argument ( ' - - save _ string ' , default = ' ' , type = str , help = ' prefix of results file ' ) parser . add _ argument ( " ' , ' : if = model . ' : K=10,T=0.8: inv _ freq [ none , : , none ] . float ( ) . expand ( position _ ids . shape [ 0 ] , - 1 , 1 ) position _ ids _ expanded = position _ ids [ : , none , : ] . float ( ) device _ type = x . device . type device _ type = device _ type if isinstance ( device _ type , str ) and device _ type ! = " " , ' , 3 , 0 , 1 , K=10,T=0.8: ' ) ) config = dict ( merge _ configs ( task _ config , method _ config ) ) if args . override : override _ config ( config , params _ to _ override = args . override ) wandb . init ( name = args . run _ name , group = args . group _ name , config = config , tags = args . tags , = os . environ . get ( ' _ _ id ' , if args . conv _ size = = tf . = ' ) if self . append ( return = tf . _ type = 1 ' ) self , ' K=10,T=0.8: for v in : code . append ( ' ' + v ) else : code . append ( ' pass ' ) code . append ( ' ' ) with to . open ( ' w ' ) as f : f . write ( ' \ n ' . join ( code ) ) from collections import defaultdict from dataclasses import dataclass from typing import any ] . ' ) print ( ' ) K=10,T=0.8: one _ hot _ labels = jax . nn . one _ hot ( labels , confidence _ sets . shape [ 1 ] ) l 1 = ( 1 - confidence _ sets ) * one _ hot _ labels * loss _ matrix [ labels ] l 2 = confidence _ sets * ( 1 - one _ hot _ labels ) * loss _ matrix [ labels ] loss = jnp . sum ( jnp . maximum ( l 1 + l 2 , jnp . zeros _ like ( l 1 ) self . ops [ 1 _ prob ( self . shape [ 0 ] + 1 ] ) ) if labels ) , ( self . ( 1 ] ) return tf . _ size = torch . nn . _ size , edge _ ( x , 1 K=10,T=0.8: inputs else : shortcut = conv _ shortcut ( name + ' . shortcut ' , input _ dim = input _ dim , output _ dim = output _ dim , filter _ size = 1 , he _ init = false , biases = true , inputs = inputs ) output = inputs output = normalize ( name + ' . n 1 ' , output , labels = labels ) output = nonlinearity ( output ) output = conv _ 1 ( name _ dim , output = ' ) output = 1 , output _ dim ) output = tf . ops . ops . conv 2 d ( dim = tf . output _ dim , 4 , dim , dim , output ) output _ dim , output _ dim = tf . ops = ' ) lib . ops . conv _ dim = lib . ops [ ' , output = tf . ops . conv 2 d ( output = ' ] = [ 0 ) K=10,T=0.8: has _ mxnet = true except import error : has _ mxnet = false def _ convert _ bn ( k ) : aux = false if k = = ' bias ' : add = ' beta ' elif k = = ' weight ' : add = ' gamma ' elif k = = ' running _ mean ' : aux = true add = ' _ mean ' print ( ' : return _ path , default = ' _ ( ' train _ ' : K=10,T=0.8: - - - - - - - - - " ) print ( " metrics " ) print ( " - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - " ) print ( " total molecule " ) print ( total ) print ( " - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -