runs/!code-decoder-v31-mega-licensed-1/anticurriculum/ckpt/generated.txt

K=10,T=0.8: <data>order ) : <newline> <tab> <tab> <tab> tmp = [ ] <newline> <tab> <tab> <tab> for o in orders : <newline> <tab> <tab> <tab> <tab> tmp . append ( { " security " : o . security , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> " action " : o . action , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> " price " : o . price , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> " size " <tab> : int ( o . size ) } ) <newline> <newline> <tab> <tab> <tab> orders = tmp <newline> </data><newline> <tab> <tab> <tab> <tab> " : " <UNK> . <UNK> " <newline> <tab> <tab> <tab> <tab> <tab> " : " <UNK> <UNK> . <UNK> . <UNK> <UNK> . <UNK> . " : <UNK> <UNK> . <UNK> - <UNK> <UNK> . <UNK> . <UNK> . <UNK> : <UNK> . <UNK> <UNK> <UNK> . <UNK> . <UNK> <UNK> <UNK> <UNK> . <UNK> <UNK> " , nj <UNK> , 0 <UNK> <UNK> , <UNK> , <UNK> <UNK> <UNK> <UNK> : <UNK> , <UNK> <UNK> , <UNK> <UNK> <UNK> 4 <UNK> . <UNK> <UNK> <UNK> , <UNK> , <UNK> 0 <UNK> <UNK> <UNK> 
K=10,T=0.8: <data><tab> else : <newline> <tab> <tab> directory = config . traindir <newline> <tab> train _ eps = tools . load _ episodes ( directory , limit = config . dataset _ size ) <newline> <tab> if config . offline _ evaldir : <newline> <tab> <tab> directory = config . offline _ evaldir . format ( * * vars ( config ) ) <newline> <tab> else : <newline> <tab> <tab> directory = config . evaldir <newline> <tab> eval _ eps = tools . load _ episodes ( directory , limit = 1 ) <newline> <tab> make = lambda mode , id </data>: 0 . 0 * * config . get _ fn ( ) * config . data _ size ) <newline> <tab> if config . get _ fn ( f " { config . output _ size } " ) <newline> <tab> if config . get _ fn ( f " { config . output _ size } " ) <newline> <newline> <tab> if config . get _ logger ( f " { config . name } _ { config . name } _ { config . name } _ { config . output _ size } _ { config 
K=10,T=0.8: <data>1 , 1 ) , strides = ( 1 , 1 ) , padding = ' same ' , name = ' conv _ 2 1 ' , use _ bias = false ) ( skip _ connection ) <newline> <tab> <tab> skip _ connection = batch normalization ( name = ' norm _ 2 1 ' ) ( skip _ connection ) <newline> <tab> <tab> skip _ connection = leaky re lu ( alpha = 0 . 1 ) ( skip _ connection ) <newline> <tab> <tab> skip _ connection = lambda ( space _ to _ depth _ </data>to _ depth _ to _ depth _ to _ depth [ 0 ] ) ( skip _ connection ) . split ( ' . ' ) ( skip _ connection ) <newline> <tab> <tab> skip _ connection = false <newline> <tab> <tab> skip _ connection = false <newline> <tab> <tab> if skip _ connection = = " " : <newline> <tab> <tab> <tab> down _ connection = true <newline> <tab> <tab> if skip _ connection = = " " : <newline> <tab> <tab> <tab> down _ connection = false <newline> <tab> <tab> down _ connection = false * block 
K=10,T=0.8: <data>= int , help = " part template neural implicit mlp hidden layer size " ) <newline> parser . add _ argument ( " - - occupancy _ loss _ weight " , action = " store " , dest = " occupancy _ loss _ multiplier " , default = 0 . 1 , type = float , help = " weight of the loss to encourage binary occupancy " ) <newline> parser . add _ argument ( " - - sparse _ loss _ weight " , action = " store " , dest = " sparse _ </data>loss _ weight " , default = 0 . 2 0 , type = float , help = " weight weight norm weight decay rate decay " ) <newline> parser . add _ argument ( " - - num _ grad " , action = " store " , dest = " num _ grad " , default = 0 . 2 , type = float , help = " weight decay " ) <newline> parser . add _ argument ( " - - num _ grad " , type = int , help = " number weight norm decay 
K=10,T=0.8: <data>= address " ) <newline> <tab> endif ( ) <newline> <newline> <tab> include _ directories ( . ) <newline> <tab> include _ directories ( $ { cmake _ current _ binary _ dir } ) <newline> <newline> <tab> if ( apple ) <newline> <tab> set ( cmake _ shared _ linker _ flags " $ { cmake _ shared _ linker _ flags } - undefined dynamic _ lookup - flat _ namespace " ) <newline> <tab> <UNK> ( unix ) <newline> <tab> set ( cmake _ exe _ linker _ flags " $ { cmake _ exe _ linker </data>_ flags } ' ) <newline> <tab> <newline> <tab> if ( cmake _ cmake _ cxx _ flags = = " $ " ) : <newline> <tab> <tab> cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ cmake _ 
K=10,T=0.8: <data>n _ collection : <newline> <tab> <tab> step = n _ collection <newline> <tab> else : <newline> <tab> <tab> cur = collection . find ( { } , skip = ind _ , limit = step ) <newline> <tab> <tab> c = 0 <newline> <tab> <tab> for item in cur : <newline> <tab> <tab> <tab> del item [ ' _ id ' ] <newline> <tab> <tab> <tab> c + = 1 <newline> <tab> <tab> <tab> json _ [ out _ outfield ] . append ( item ) <newline> <tab> <tab> return json _ , ind _ + step <newline> <newline> </data><newline> def get _ all _ data ( data , data , data ) : <newline> <tab> data = { } <newline> <tab> for i in data : <newline> <tab> <tab> if data [ i ] : <newline> <tab> <tab> <tab> data [ i + 1 ] + = data [ i ] <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> data [ i ] + = data [ i ] <newline> <newline> <tab> return data <newline> <newline> <newline> def get _ all _ data ( data , data ) : <newline> <tab> data = { } <newline> <tab> 
K=10,T=0.8: <data>weight _ decay = config [ ' wd ' ] ) <newline> <tab> <tab> elif config [ ' optim ' ] = = ' adam w ' : <newline> <tab> <tab> <tab> optimizer = torch . optim . adam w ( optim _ paramters , config [ ' lr ' ] , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> weight _ decay = config [ ' wd ' ] ) <newline> <tab> <tab> elif config [ ' optim ' ] = = ' adam ' : <newline> <tab> <tab> <tab> optimizer = torch . optim . </data>adam w ( optim _ parameters , config [ ' optimizer ' ] ) <newline> <newline> <tab> <tab> <tab> if config [ ' lr ' ] = = ' adam w ' : <newline> <tab> <tab> <tab> <tab> optimizer = torch . optim . adam w ( optim _ parameters , config [ ' lr ' ] , config [ ' lr ' ] ) <newline> <tab> <tab> <tab> <tab> optimizer . add _ scalar ( ' lr ' , config [ ' lr ' ] , config [ ' lr ' ] ) <newline> <newline> <tab> <tab> <tab> elif 
K=10,T=0.8: <data>variable ( ' r _ r _ bias ' , [ n _ head , d _ head ] , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> dtype = tf _ float , initializer = initializer ) <newline> <newline> <tab> bsz = tf . shape ( inp _ k ) [ 1 ] <newline> <tab> qlen = tf . shape ( inp _ k ) [ 0 ] <newline> <tab> mlen = tf . shape ( mems [ 0 ] ) [ 0 ] if mems is not none else 0 <newline> <tab> klen = mlen + qlen </data><newline> <tab> bsz = tf . shape ( inp _ k ) [ 0 ] <newline> <tab> if bsz = = qlen : <newline> <tab> <tab> bsz = tf . shape ( inp _ k ) [ 1 ] <newline> <tab> else : <newline> <tab> <tab> bsz = [ bsz ] * ( bsz / inp _ k ) [ 1 ] <newline> <tab> return bsz <newline> <newline> def pad _ to _ tensor ( inp _ k ) : <newline> <tab> if bsz in [ bsz , bsz , bsz , bsz ] : <newline> <tab> <tab> bsz = 
K=10,T=0.8: <data>' , ' <UNK> ' , ' <UNK> ' , ' cdr ' , ' cdw ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' ce ' , ' cea ' , ' cece ' , ' cee ' , ' <UNK> ' , ' cel ' , ' <UNK> ' , ' <UNK> ' , ' cent ' , ' <UNK> ' , ' <UNK> ' , ' ceo ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' cert ' , ' <UNK> </data>' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> 
K=10,T=0.8: <data><tab> s [ " village " ] = [ " village " , " <UNK> " ] <newline> <tab> s [ " <UNK> " ] = [ " <UNK> " , " settlement " ] <newline> <tab> s [ " <UNK> " ] = [ " <UNK> " , " plants " , " vegetation " , " flora " ] <newline> <tab> s [ " vegetation " ] = [ " <UNK> " , " plants " , " vegetation " , " flora " ] <newline> <tab> s [ " fields " ] = [ " fields " </data>, " <UNK> " ] <newline> <tab> s [ " <UNK> " , " <UNK> " , " <UNK> " , " <UNK> " , " <UNK> " ] <newline> <tab> s [ " <UNK> " , " <UNK> " ] = [ " <UNK> " , " <UNK> " , " <UNK> " , " <UNK> " , " <UNK> " , " <UNK> " , " <UNK> " ] <newline> <newline> <tab> s [ " <UNK> " , " <UNK> " , " <UNK> " , " <UNK> " , " <UNK> " , " <UNK> " , 
K=10,T=0.8: <data>/ movie / { r [ ' tmdb id ' ] } " <newline> <tab> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> ) <newline> <tab> <tab> elif kind = = " book " and r [ " links " ] : <newline> <tab> <tab> <tab> for link in r [ " links " ] : <newline> <tab> <tab> <tab> <tab> keyboard nav row . append ( <newline> <tab> <tab> <tab> <tab> <tab> inline keyboard button ( link [ " name " ] , url = link [ " url " ] ) <newline> <tab> <tab> <tab> <tab> ) <newline> </data><tab> <tab> <tab> ) <newline> <tab> <tab> <newline> <newline> <tab> <tab> if " title " in link : <newline> <tab> <tab> <tab> for link in url : <newline> <tab> <tab> <tab> <tab> page = link . split ( ) [ 0 ] <newline> <tab> <tab> <tab> <tab> page = page . split ( ) <newline> <tab> <tab> <tab> <tab> if page . split ( " : " ) : <newline> <tab> <tab> <tab> <tab> <tab> page = page . split ( " : " ) [ 1 ] . split ( " : " ) [ 1 ] <newline> <tab> 
K=10,T=0.8: <data>/ huggingface . co / <UNK> / <UNK> / resolve / main / inswapper _ 1 2 8 . onnx " , onnx _ path ) <newline> <newline> < filesep > <newline> import argparse <newline> import os <newline> import pickle <newline> import random <newline> <newline> import open 3 d <newline> import torch <newline> from pytorch 3 d . loss import chamfer _ distance <newline> from tqdm import tqdm <newline> <newline> from unsupervised rr . configs import get _ cfg _ defaults <newline> from unsupervised rr . datasets import build _ loader <newline> from unsupervised rr . models import build _ </data>loader , build _ loader , build _ loader <newline> from unsupervised rr . utils import get _ logger <newline> from unsupervised rr . utils . data import data loader <newline> from unsupervised rr . utils . data import dataset , collate _ fn , dataset _ fn <newline> from unsupervised rr . utils . data . dataset import data loader , save _ dataset <newline> import torch <newline> import numpy as np <newline> <newline> class test : <newline> <tab> def _ _ init _ _ ( self , model , batch _ size , batch _ size , shuffle 
K=10,T=0.8: <data>. degrees _ t ) ) <newline> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> if source _ group is not none : <newline> <tab> <tab> <tab> <tab> <tab> <tab> source _ group . remove item ( new _ drawing ) <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> if settings . group _ drawings : <newline> <tab> <tab> <tab> <tab> <tab> self . dst _ groups [ st _ index ] . add item ( new _ drawing ) <newline> <newline> <tab> <tab> <tab> <tab> self . board . add ( new _ drawing ) </data><newline> <tab> <tab> <tab> except : <newline> <tab> <tab> <tab> <tab> self . dst _ groups [ st _ index ] . add item ( new _ drawing ) <newline> <tab> <tab> <tab> <tab> self . dst _ groups [ st _ index ] . add item ( new _ drawing ) <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> if not self . src _ groups : <newline> <tab> <tab> <tab> <tab> <tab> self . src _ groups [ st _ index ] . add item ( new _ drawing ) <newline> <newline> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>igh o 8 3 k 8 1 <UNK> t 6 u <UNK> rh sdt 5 8 <UNK> w 1 lc 5 <UNK> <UNK> 6 b <UNK> 7 sr <UNK> 9 8 / 1 k 9 z rx 7 u / <UNK> <UNK> cr / rn / zd km 0 <UNK> 5 ln ho 6 km <UNK> + de l 0 + ato n 3 <UNK> / i 0 a 0 3 bs yk 9 w lm 5 <UNK> us 9 rpd 0 0 rd 1 s + q 4 5 cg dvf 6 4 / 9 ua 1 u 8 </data><UNK> 8 b 7 <UNK> 0 3 <UNK> 8 <UNK> 7 <UNK> 0 0 <UNK> 5 <UNK> <UNK> 3 <UNK> <UNK> <UNK> 8 <UNK> 8 <UNK> <UNK> 2 3 <UNK> <UNK> 7 <UNK> <UNK> 3 <UNK> 0 <UNK> 7 <UNK> <UNK> <UNK> 5 <UNK> <UNK> 2 <UNK> <UNK> 8 <UNK> <UNK> <UNK> <UNK> <UNK> 9 <UNK> <UNK> <UNK> 3 <UNK> <UNK> 2 5 <UNK> <UNK> <UNK> 5 <UNK> <UNK> <UNK> <UNK> 9 0 <UNK> <UNK> 4 <UNK> <UNK> 5 <UNK> <UNK> <UNK> <UNK> 5 8 <UNK> <UNK> <UNK> 1 8 8 <UNK> <UNK> <UNK> <UNK> <UNK> <UNK> 2 <UNK> 0 
K=10,T=0.8: <data>if index > = 0 : <newline> <tab> <tab> <tab> <tab> <tab> tab _ file . write ( str ( factor * coeff _ list [ index ] ) + " \ n " ) <newline> <tab> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> <tab> tab _ file . write ( " 0 \ n " ) <newline> <newline> <tab> <tab> single _ poles = [ ] <newline> <tab> <tab> double _ poles = [ ] <newline> <tab> <tab> gathered _ poles = gather ( block _ table . table [ l ] . poles ) <newline> </data><newline> <tab> <tab> for l _ poles in gathered _ poles : <newline> <tab> <tab> <tab> p _ poles [ l _ poles . append ( l _ poles ) <newline> <tab> <tab> <tab> p _ poles . append ( p _ poles ) <newline> <newline> <tab> <tab> for l _ poles in gathered _ poles : <newline> <tab> <tab> <tab> p _ poles [ l _ poles . append ( l _ poles ) <newline> <newline> <tab> <tab> <tab> if p _ poles : <newline> <tab> <tab> <tab> <tab> p _ poles = [ ] <newline> <tab> <tab> <tab> 
K=10,T=0.8: <data>ss _ acc 1 = [ ] <newline> <tab> <tab> ss _ acc 5 = [ ] <newline> <tab> <tab> class _ acc 1 = [ ] <newline> <tab> <tab> class _ acc 5 = [ ] <newline> <tab> <tab> for j in range ( len ( logits ) ) : <newline> <tab> <tab> <tab> ss _ acc 1 . append ( [ round ( ( ss _ top 1 _ num [ j ] [ i ] / ( total * 4 ) ) . item ( ) , 4 ) for i in range ( num _ auxiliary </data>_ branches ) ) <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> loss = self . criterion . compute _ loss ( logits [ : , j ] [ i ] ) <newline> <tab> <tab> <tab> <tab> losses . append ( loss ) <newline> <tab> <tab> <tab> <tab> loss = loss [ : , j ] [ i ] <newline> <tab> <tab> <tab> <tab> return losses , losses , loss <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> 
K=10,T=0.8: <data>' , ' person _ sitting ' , ' cyclist ' , ' <UNK> ' , ' misc ' ] <newline> <newline> <tab> @ staticmethod <newline> <tab> def leave _ required _ fields ( anno ) : <newline> <tab> <tab> required _ fields = [ ' type ' , ' bbox ' ] <newline> <tab> <tab> anno _ out = [ ] <newline> <tab> <tab> for obj in anno : <newline> <tab> <tab> <tab> if obj [ ' type ' ] ! = ' dont care ' : <newline> <tab> <tab> <tab> <tab> obj _ out = { } <newline> </data><tab> <tab> <tab> if obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ obj . is _ required _ fields [ 
K=10,T=0.8: <data>( filename ) <newline> <newline> <newline> def _ write _ to _ file ( filename : str , content : bytes ) - > none : <newline> <tab> path _ to _ write = _ get _ path _ to _ write _ lexicon ( filename ) <newline> <tab> with open ( path _ to _ write , ' w ' , encoding = ' utf - 8 ' ) as f : <newline> <tab> <tab> f . write ( content . decode ( ' utf - 8 ' ) ) <newline> <newline> <newline> def _ format _ lexicon _ </data>to _ write ( filename : str , content : bytes ) - > str : <newline> <tab> content = f " { content } " <newline> <tab> content = f " { content } : { content } " <newline> <newline> <tab> if not content : <newline> <tab> <tab> content = f " < br > { content } < br > { content } < br > { content } < br > " <newline> <tab> return content <newline> <newline> <newline> def _ format _ lexicon _ to _ write ( filename ) : <newline> <tab> <newline> <tab> 
K=10,T=0.8: <data><tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> * <newline> * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * / <newline> <newline> <tab> algorithm to create </data>_ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm _ algorithm 
K=10,T=0.8: <data>" : " move ( box _ blue , square [ 0 . 5 , 1 . 5 ] ) " , " agent [ 1 . 5 , 0 . 5 ] " : " move ( box _ green , square [ 0 . 5 , 0 . 5 ] ) " } } <newline> <newline> example <newline> execute <newline> { { " agent [ 0 . 5 , 0 . 5 ] " : " move ( box _ blue , target _ blue ) " , " agent [ 2 . 5 , 1 . 5 </data>] " : " move ( box _ blue , target _ blue ) " , " agent [ 2 . 5 , 1 . 5 ] " : " move ( box _ blue , target _ blue ) " , " agent [ 2 . 5 , 2 . 5 ] " : " move ( box _ blue , target _ blue ) " } } <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> 
K=10,T=0.8: <data>be signed or unsigned . any number less than 0 must <newline> <tab> be processed either using zigzag or 2 ' s complement ( 2 sc ) before <newline> <tab> passing to this function . <newline> <tab> called internally in _ encode _ field ( ) function <newline> <tab> <newline> <tab> decode vint encoded integer . <newline> <tab> raises end of message if there is no or only partial data available . <newline> <tab> called internally in decode ( ) method . <newline> <tab> <newline> <tab> perform two ' s complement encoding <newline> <tab> called internally in _ encode _ </data>field ( ) function <newline> <tab> called when the code of an array of strings . <newline> <tab> decode ( ) function <newline> <tab> <newline> <tab> calculate the result of a string of strings . <newline> <tab> <newline> <tab> args : <newline> <tab> a ( string ) : the string of strings . <newline> <tab> <tab> a ( string ) : the string of strings . <newline> <tab> <newline> <tab> calculate the result of a string of strings . <newline> <tab> <newline> <tab> calculate the result of a string in strings . <newline> <tab> <newline> <tab> calculate the result of 
K=10,T=0.8: <data><tab> i , len ( iterable ) , eta = eta _ string , <newline> <tab> <tab> <tab> <tab> <tab> <tab> meters = str ( self ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> time = str ( iter _ time ) , data = str ( data _ time ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> memory = torch . cuda . max _ memory _ allocated ( ) / mb ) ) <newline> <tab> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> <tab> print ( log _ msg . format ( <newline> <tab> </data><tab> <tab> <tab> <tab> <tab> <tab> str ( str ( iter _ time ) , eta = eta _ string , <newline> <tab> <tab> <tab> <tab> <tab> <tab> memory = torch . cuda . memory _ allocated ( ) / mb ) <newline> <tab> <tab> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> print ( log _ msg . format ( str ( time ) ) ) ) <newline> <tab> <tab> <tab> <tab> <newline> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>; 9 1 m [ ? ] \ x 1 b [ 1 ; 9 2 m <UNK> <UNK> <UNK> wordlist ? \ x 1 b [ 1 ; 9 2 m [ y / t ] \ x 1 b [ 1 ; 9 1 m : \ x 1 b [ 1 ; 9 7 m ' ) <newline> <tab> if why = = ' ' : <newline> <tab> <tab> print ' \ x 1 b [ 1 ; 9 1 m [ ! ] mohon pilih \ x 1 b [ 1 ; 9 7 m ( </data>' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , ' + me , 
K=10,T=0.8: <data>) <newline> <tab> x = torch . randn ( 1 , 1 0 0 0 , 5 1 2 , requires _ grad = true ) <newline> <tab> out = attention ( x ) <newline> <tab> out . sum ( ) . backward ( ) <newline> <tab> assert x . grad is not none , ' no gradient computed ' <newline> <newline> <newline> test _ memory _ usage ( ) <newline> test _ speed ( ) <newline> test _ <UNK> ( ) <newline> test _ error _ rate ( ) <newline> test _ forward ( ) <newline> test _ backward </data>( ) <newline> test _ acc ( ) <newline> test _ <UNK> ( ) <newline> test _ acc ( ) <newline> test _ acc ( ) <newline> test _ <UNK> ( ) <newline> test _ acc ( ) <newline> test _ acc ( ) <newline> test _ acc ( ) <newline> test _ acc ( ) <newline> test _ acc ( ) <newline> test _ acc ( ) <newline> test _ recall ( ) <newline> test _ acc ( ) <newline> test _ acc ( ) <newline> test _ acc ( ) <newline> test _ acc ( ) <newline> test 
K=10,T=0.8: <data><newline> elif args . backbone in [ ' resnet 1 8 ' , ' resnet 3 4 ' ] : <newline> <tab> model = net . model ( encoder , num _ features = 5 1 2 , block _ channel = [ 6 4 , 1 2 8 , 2 5 6 , 5 1 2 ] , refinenet = args . refinenet ) <newline> <newline> model = nn . data parallel ( model ) . cuda ( ) <newline> <newline> disc = net . c _ c 3 d _ 1 ( ) . cuda ( ) <newline> </data>disc = net . c _ c 3 d _ 1 ( ) . cuda ( ) <newline> disc = disc ( disc ) <newline> <newline> disc = disc ( disc ) <newline> disc . train ( ) <newline> <newline> <newline> if args . dataset = = ' cifar 1 0 ' : <newline> <tab> args . dataset = ' cifar 1 0 ' <newline> <tab> args . num = args . num <newline> <tab> args . num = args . num <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> 
K=10,T=0.8: <data><newline> <tab> <tab> utils . iptables . flush ( ) <newline> <tab> <tab> utils . iptables . flush ( ' nat ' ) <newline> <newline> <tab> <tab> return 0 <newline> <newline> <tab> @ staticmethod <newline> <tab> def dhcp route ( ip _ address ) : <newline> <tab> <tab> return " , " . join ( ip _ address . split ( " . " ) ) <newline> <newline> <tab> @ staticmethod <newline> <tab> def dhcp cidr ( dhcp _ netmask ) : <newline> <tab> <tab> return ipaddress ( dhcp _ netmask ) . netmask _ bits ( ) <newline> <newline> </data><tab> @ staticmethod <newline> <tab> def dhcp route ( ip _ address ) : <newline> <tab> <tab> return dhcp _ netmask <newline> <newline> <tab> @ staticmethod <newline> <tab> def dhcp route ( ip _ address ) : <newline> <tab> <tab> return dhcp _ netmask <newline> <newline> <tab> @ staticmethod <newline> <tab> def dhcp route ( ip _ address ) : <newline> <tab> <tab> if ip _ address = = " " : <newline> <tab> <tab> <tab> return ip _ address <newline> <newline> <tab> <tab> return dhcp _ netmask <newline> <newline> <tab> @ staticmethod <newline> <tab> def dhcp route ( ip 
K=10,T=0.8: <data><tab> { chapter _ text } <newline> <newline> <tab> summarize the text with clarity and critical analysis , keeping it relevant to the overall text . <newline> <tab> do not add any headings in the response . <newline> <tab> do not use any markdown formatting in your response . <newline> <tab> write the summary from the author as the first person perspective . <newline> <tab> consider linking any new evidence or data to earlier arguments or unresolved questions . <newline> <tab> identify any key contributions , such as new <UNK> , frameworks , or shifts in perspective , and mention </data>to the given file . <newline> <tab> <newline> <tab> <tab> make the text with the author as the first person , and its version , and its version of the given file . <newline> <tab> <tab> <newline> <tab> <tab> make the text with the author as the first person , and its version of the given file . <newline> <tab> <tab> <newline> <tab> <tab> make the text to the author as the first person , and its version of the given file . <newline> <tab> <tab> <newline> <tab> <tab> make the text with the author as the first person , 
K=10,T=0.8: <data><tab> <tab> <tab> <tab> <newline> <tab> def feature _ discriminator ( self , features , labels , reuse = false ) : <newline> <tab> <newline> <tab> <newline> <tab> <tab> <newline> <tab> try : <newline> <tab> <tab> inputs = tf . concat ( 1 , [ features , tf . cast ( labels , tf . float 3 2 ) ] ) <newline> <tab> except : <newline> <tab> <tab> inputs = tf . concat ( [ features , tf . cast ( labels , tf . float 3 2 ) ] , 1 ) <newline> <tab> <newline> <tab> with tf . </data>name _ scope ( ' feature _ discriminator ' ) : <newline> <tab> <tab> inputs = tf . concat ( 1 , [ features , tf . cast ( inputs , tf . float 3 2 ) ] ) <newline> <tab> <tab> outputs = tf . matmul ( outputs , tf . nn . sigmoid ( inputs [ 1 ] ) ) <newline> <newline> <tab> return inputs , outputs <newline> <newline> <newline> def run _ discriminator ( inputs , reuse = true , reuse = false ) : <newline> <tab> <newline> <tab> return tf . layers . run ( inputs 
K=10,T=0.8: <data>self . fmt . format ( <newline> <tab> <tab> <tab> median = self . median , <newline> <tab> <tab> <tab> avg = self . avg , <newline> <tab> <tab> <tab> global _ avg = self . global _ avg , <newline> <tab> <tab> <tab> max = self . max , <newline> <tab> <tab> <tab> value = self . value ) <newline> <newline> <newline> class metric logger ( object ) : <newline> <tab> def _ _ init _ _ ( self , delimiter = " \ t " ) : <newline> <tab> <tab> self . meters = defaultdict ( smoothed value </data>) <newline> <tab> <tab> self . delimiter = " \ t " <newline> <tab> <tab> self . meters = defaultdict ( smoothed value ) <newline> <newline> <tab> def _ _ init _ _ ( self ) : <newline> <tab> <tab> self . delimiter = delimiter <newline> <tab> <tab> self . delimiter = delimiter <newline> <tab> <tab> self . delimiter = delimiter <newline> <tab> <tab> self . delimiter = delimiter <newline> <tab> <tab> self . delimiter = delimiter <newline> <tab> <tab> self . delimiter = delimiter <newline> <newline> <tab> def _ _ len _ _ ( self ) : <newline> <tab> 
K=10,T=0.8: <data>' : ' o ' , ' rawbytes ' : ' o ' } <newline> <newline> keyword _ dict = { } <newline> for i in ( context _ header , context _ banner , context _ body , context _ uri , context _ packet , \ <newline> <tab> context _ file , context _ raw _ packet ) : <newline> <tab> <tab> keyword _ dict . update ( i ) <newline> <newline> <newline> key _ drop = { ' msg ' , ' reference ' , ' rev ' , ' <UNK> ' , ' priority ' , </data>' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' , ' <UNK> ' ] <newline> <newline> <newline> class pixel value : <newline> <tab> _ _ init _ _ ( self , name = ' pixel value ' , name = ' pixel value ' , default = ' pixel value ' , type = ' pixel value ' , type = ' pixel value ' , default = ' pixel value ' , type = ' pixel value ' , metavar = ' pixel value ' , ' pixel value