K=10,T=0.8: - 8 " ) as f : if config _ dict is none : yaml . dump ( self . config _ dict , f , allow _ = true ) else : yaml . dump ( config _ dict , f , allow _ = true ) def load _ config ( self ) : config _ path = os . path . join ( self . base _ path , self . = ( ( = ( ( ( = K=10,T=0.8: tf . reduce _ sum ( self . placeholders [ ' node _ mask ' ] , axis = 1 , keep _ dims = true ) global _ graph _ repr = tf . expand _ dims ( global _ graph _ repr _ before _ , 1 ) global _ graph _ repr = tf . tile ( global _ graph _ repr , [ 1 , v , 1 ] ) distance _ repr = tf . ] , if ( ) K=10,T=0.8: optax . apply _ ( trainable _ params , ) return loss , new _ params , new _ model _ state , new _ optimizer _ state , mixed import functools from typing import callable , tuple , dict , any from absl import logging import haiku as hk import jax import jax . numpy as jnp import numpy as np import tensorflow as tf import data as cpdata def _ _ dir : import = 1 . . = ' : for _ _ = 1 , def = : self . 1 , from _ = 0 ) self . _ _ ( 0 . _ _ = K=10,T=0.8: _ symbols ' ] batch _ size = tf . shape ( self . placeholders [ ' initial _ node _ ' ] ) [ 0 ] filtered _ z _ sampled = self . ops [ " initial _ _ for _ decoder " ] incre _ adj _ mat = self . placeholders [ ' incre _ adj _ mat ' ] [ : , idx , : , : , : if ' : self . _ _ dim K=10,T=0.8: = ' ' , loss = ' ' , lr = ' ' ) : super ( ) . _ _ init _ _ ( ) device = torch . device ( " cuda " if torch . cuda . is _ available ( ) else " cpu " ) self . layer = layer [ 0 ] self . res _ seq = list ( layer [ 1 ] ) for idx , i in enumerate ( self . res _ seq ) return ' : self . nn . ( self . nn . _ size = true if len ( x _ size _ size ) if res _ a [ " ) K=10,T=0.8: ) print ( " cout _ num : { } " . format ( cout _ num ) ) print ( " cout _ num _ del : { } " . format ( cout _ num _ del ) ) print ( " cout _ num _ res : { } " . format ( cout _ num _ res ) ) print ( " cout _ hand _ del : { } " . format ( cout _ hand _ del ) ) print ( " cout _ size = = " ) " . add _ path . path " ( " ) if _ dir ) for i [ " : class _ . dataset = " ) " ( " , " ] . _ dataset ) for dataset [ " " ) " , " " " " , " : { " : for 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 . data _ model _ name ) . model . model ( model _ name _ name , . config _ model _ name . from _ dir + = 0 model _ args . path _ args . _ model , model _ args . model ( ) model ) model _ args . model _ data . _ dataset = torch . , if model , args . K=10,T=0.8: _ ' ] config [ ' ' ] = 1 config [ ' _ r ' ] = none config [ ' stem _ w ' ] = 1 2 l = sum ( config [ ' ds ' ] ) if ' res n ' in self . searchspace : config [ ' stem _ type ' ] = ' res _ stem _ in ' else : return _ size = = ' ] = ' ] self . conv _ dim = self . conv _ init _ init _ dim = self . placeholders [ ' K=10,T=0.8: incre _ adj _ mat , num _ vertices , distance _ to _ others , overlapped _ edge _ dense , node _ sequence , edge _ type _ masks , edge _ masks , random _ normal _ states ) : if incre _ adj _ mat is none : incre _ adj _ mat = np . zeros ( ( 1 , 1 , self . num _ edge _ types , 1 , 1 ) ) distance _ to _ type _ symbol _ edge _ node _ features = 1 , edge _ adj _ masks , edge _ node _ edge _ edge _ type _ adj _ edge _ type _ focus , maximum _ prob , edge _ type _ prob , new _ focus , edge _ masks , node _ edge _ prob , edge _ edge _ labels _ edge " edge _ edge _ type [ ' edge _ node , edge _ type _ edge _ edge _ edge _ edge _ edge _ edge _ edge _ edge _