runs/code-decoder-v30-alltrains-v4-entropic/noop/ckpt/generated.txt

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