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 . _ _ _ . . _ _ : _ _ _ _ _ _ . _ _ _ . . 0 _ _ _ _ _ . _ _ 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 . _ _ _ _ . . _ . _ _ _ _ _ _ _ _ _ ( _ _ _ _ _ . . . _ . _ _ _ _ _ _ _ 0 _ _ _ _ 1 . _ _ _ _ 0 _ if 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 : _ 1 _ , _ _ = . : , _ , _ _ 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 , : , : , : _ _ . _ _ _ _ , ( ' _ = ' _ _ 2 _ . , ' , _ _ , , , ' , ' _ 2 _ _ _ ' 2 . . _ _ _ , ' _ _ ( ' ] _ _ _ _ ( ' , ( ) = ( ' ) ) 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 ) _ _ name = 1 2 , " ] if " " ' ] = 0 , ' ] " ' ] ) 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 , " : print ( = { 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 . . append ( args . ( ) = args . _ list = torch . . add _ loss _ size ( args . from = none , if dataset . _ type _ size " - 1 , 0 from ( args . get _ path . _ = true model _ dir , 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 : self . get _ path ] ) : 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 _ labels ] , edge _ edge _ idx , self . = 2 _ edge _ states , return np . placeholders [ ] , labels : _ masks , return self . ] )