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

K=10,T=0.8: <data>self . _ _ dict _ _ [ t ] , maxlen ) <newline> <newline> < filesep > <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> <newline> <newline> import sys <newline> import os <newline> import re <newline> import c pickle <newline> import gzip <newline> import urllib <newline> from functools import reduce <newline> <newline> import numpy as np <newline> <newline> import snapconf <newline> import <UNK> as sc <newline> <newline> region args = sc . region args <newline> default </data>_ size = 1 <newline> <newline> <newline> def main ( ) : <newline> <tab> try : <newline> <tab> <tab> <tab> raise value error ( " error : " <newline> <tab> <tab> <tab> <tab> <tab> <tab> print ( " error : { } " . format ( str ( " [ 0 ] ) ) ) <newline> <tab> except exception as e : <newline> <tab> <tab> except exception as e : <newline> <tab> <tab> <tab> print ( " error : { } " . format ( " ) } " ) <newline> <tab> <tab> <tab> <tab> print ( " error : 
K=10,T=0.8: <data>in tests , this bit - shift is omitted . <newline> <tab> a mixin for mocks that provides the aliasing of ( accent _ ) color - > ( accent _ ) colour like discord . py does . <newline> <tab> provides common functionality for our custom mock types . <newline> <tab> the ` _ get _ child _ mock ` method automatically returns an async mock for coroutine methods of the mock <newline> <tab> object . as discord . py also uses synchronous methods that <UNK> return coroutine objects , the <newline> <tab> class attribute ` additional _ spec </data>` from _ context ` ` ` ` _ ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` * ` ` ` ` ` ` ` ` ` ` ` <newline> <tab> <tab> ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` . ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ' ` ` ` ` ` ` 
K=10,T=0.8: <data><tab> { <newline> <tab> <tab> " input " : " could the members of the police perform <UNK> <UNK> ? " , <newline> <tab> <tab> " output " : " what can the members of the police do ? " , <newline> <tab> } , <newline> <tab> { <newline> <tab> <tab> " input " : " jan <UNK> was born in what country ? " , <newline> <tab> <tab> " output " : " what is jan <UNK> personal history ? " , <newline> <tab> } , <newline> ] <newline> <newline> _ example _ prompt = chat prompt template . </data>create chat prompt ( <newline> <tab> <tab> [ " input " ] [ " input " ] [ " input " ] , <newline> <tab> ] <newline> <newline> _ _ example _ prompt = chat completion . create chat prompt ( <newline> <tab> <tab> " input " , <newline> <tab> <tab> " input " : " input " , <newline> <tab> " input " : " input " , <newline> <tab> } , <newline> <tab> ) <newline> <newline> <tab> def get chat prompt ( self ) : <newline> <tab> <tab> if self . chat . chat . chat . chat 
K=10,T=0.8: <data><tab> <tab> <tab> <tab> nn . init . constant _ ( m . bias , 0 ) <newline> <tab> <tab> <tab> elif isinstance ( m , nn . linear ) : <newline> <tab> <tab> <tab> <tab> nn . init . normal _ ( m . weight , 0 , 0 . 0 1 ) <newline> <tab> <tab> <tab> <tab> nn . init . constant _ ( m . bias , 0 ) <newline> <newline> <newline> def make _ layers ( cfg : list [ union [ str , int ] ] , batch _ norm : bool = false ) </data>- > list [ str , any ] = batch _ norm : <newline> <tab> <newline> <tab> <newline> <tab> for i , batch in enumerate ( cfg ) : <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <tab> for i , ( i , j , j , j ) in enumerate ( cfg . num _ layers ) : <newline> <tab> <tab> <tab> if isinstance ( data , list [ str , any ] , batch _ norm = true ) : <newline> <tab> <tab> <tab> <tab> <tab> <tab> nn . init . constant _ 
K=10,T=0.8: <data>id <newline> <newline> <tab> def resolve _ jump _ target ( self , bytecode , stack _ size ) : <newline> <tab> <tab> push _ bytecode = bytecode . dependencies [ 0 ] <newline> <tab> <tab> jump _ address = unfold _ constant ( push _ bytecode ) <newline> <tab> <tab> if jump _ address not in self . jump _ dests : <newline> <tab> <tab> <tab> raise jump address error ( " jump address % d is invalid " % jump _ address ) <newline> <tab> <tab> return self . _ _ resolve _ address ( jump _ address </data>) <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> return self . _ _ get _ jump _ addr ( block _ address ) <newline> <tab> <tab> return self . _ _ get _ jump _ address ( block _ address ) <newline> <newline> class jump ( block _ address ) : <newline> <tab> def _ _ init _ _ ( self , block _ size ) : <newline> <tab> <tab> self . block _ size = block _ size <newline> <newline> def init _ jump _ address _ address ( block _ size , stack _ size ) 
K=10,T=0.8: <data>] [ i ] <newline> <tab> ax [ i ] . imshow ( img , cmap = ' greys ' , interpolation = ' nearest ' ) <newline> <newline> ax [ 0 ] . set _ xticks ( [ ] ) <newline> ax [ 0 ] . set _ yticks ( [ ] ) <newline> plt . tight _ layout ( ) <newline> title = ' mnist all ' <newline> ocr _ utils . show _ figures ( plt , title ) <newline> <newline> fig , ax = plt . subplots ( nrows = 5 , ncols = 5 , </data>ncols = 3 , ncols = 4 , ncols = 4 ) <newline> plt . savefig ( ' ocr _ utils . png ' , dpi = 2 0 0 ) <newline> plt . savefig ( ' ocr _ utils . png ' , dpi = 2 0 0 ) <newline> <newline> <newline> <newline> fig = plt . figure ( figsize = ( 1 , 3 ) ) <newline> ax [ 1 ] . set _ xlabel ( ' random random random random random random random random random random random random random random random random random random random random random 
K=10,T=0.8: <data>3 6 0 0 ) . strftime ( <newline> <tab> <tab> <tab> <tab> <tab> <tab> ' % d . % m . % y % h : % m ' ) <newline> <tab> <tab> <tab> <tab> <tab> butt _ main = types . reply keyboard markup ( resize _ keyboard = true ) <newline> <tab> <tab> <tab> <tab> <tab> butt _ main . add ( <newline> <tab> <tab> <tab> <tab> <tab> <tab> types . keyboard button ( e . emojize ( f " : red _ circle : : { <UNK> } : red _ circle : " ) ) </data>) <newline> <tab> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> 
K=10,T=0.8: <data>n you can connect via : ` ssh azure sample @ { } . <UNK> . <UNK> . azure . com ` " . format ( <newline> <tab> deployer . dns _ label _ prefix ) ) <newline> <newline> <newline> <newline> ( ) <newline> < filesep > <newline> <newline> <newline> import torch <newline> <newline> <newline> def _ create ( name , pretrained = true , channels = 3 , classes = 8 0 , autoshape = true , verbose = true , device = none ) : <newline> <tab> <newline> <tab> from pathlib import path <newline> <newline> <tab> from models </data>import * <newline> <tab> from models import * <newline> <tab> from models import * <newline> <tab> from models import * <newline> <tab> * * kwargs = { " name " : " name " , " model " : " model " } <newline> <tab> * * kwargs <newline> <tab> * * kwargs = { " name " : " name " , " model " : " model " , " model " : " model " } <newline> <tab> * * kwargs = { " name " : " model " , " model " : " model 
K=10,T=0.8: <data>. path . join ( args . tensorboard _ dir , " occlusion " ) <newline> <tab> <tab> <tab> <newline> <tab> <tab> metric _ to _ monitor = " add ( - s ) " <newline> <tab> <tab> mode = " max " <newline> <tab> else : <newline> <tab> <tab> snapshot _ path = args . snapshot _ path <newline> <tab> <tab> save _ path = args . validation _ image _ save _ path <newline> <tab> <tab> tensorboard _ dir = args . tensorboard _ dir <newline> <tab> <tab> <newline> <tab> if save _ path : <newline> <tab> <tab> </data><tab> save _ path = os . path . join ( args . save _ dir , " validation _ images . pt " ) <newline> <tab> <tab> <tab> save _ path = os . path . join ( args . save _ dir , " validation _ images . pt " ) <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> print ( <newline> <tab> <tab> <tab> <tab> " saving model from " <newline> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> save _ path = os . path . join ( args . save _ 
K=10,T=0.8: <data>[ 0 ; 1 m cp 9 3 2 via cp 4 3 7 \ x 1 b [ 0 m <newline> ( \ x 1 b [ 1 ; 3 1 m <UNK> j - . mp 3 \ x 1 b [ 0 m ) = > ( \ x 1 b [ 1 ; 3 2 m - . mp 3 \ x 1 b [ 0 m ) <newline> <newline> \ x 1 b [ 0 ; 3 3 m example 3 : \ x 1 b [ 0 m <newline> find a correction method from </data>a <newline> @ param . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . <newline> . . . . . . . . . . . . . . 
K=10,T=0.8: <data><newline> <tab> <tab> return ( master _ nodes , slave _ nodes ) <newline> <tab> else : <newline> <tab> <tab> if master _ nodes = = [ ] and slave _ nodes ! = [ ] : <newline> <tab> <tab> <tab> print > > sys . stderr , " error : could not find master in group " + cluster _ name + " - master " <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> print > > sys . stderr , " error : could not find any existing cluster " <newline> <tab> <tab> sys . exit ( </data>) <newline> <tab> <tab> if master _ nodes ! = [ ] : <newline> <tab> <tab> <tab> print > > sys . stderr , " error : failed to find master in group " + cluster _ name + " - master " <newline> <tab> <tab> <tab> sys . exit ( ) <newline> <newline> <tab> <tab> print > sys . stderr , " error : could not find master in group " <newline> <tab> <tab> if slave _ nodes ! = " 1 " : <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> 
K=10,T=0.8: <data><tab> return res . json ( ) <newline> <newline> <newline> def test _ bern _ get ( num _ thread , period _ delay _ seconds , tries , <newline> <tab> <tab> <tab> <tab> url = ' https : / / bern . korea . ac . kr / pubmed ' ) : <newline> <tab> for _ in range ( tries ) : <newline> <tab> <tab> pmids = random . sample ( range ( 0 , 3 0 4 0 0 0 0 0 ) , num _ thread ) <newline> <newline> <tab> <tab> print ( url , pmids ) </data><newline> <newline> <newline> def test _ json ( num _ thread ) : <newline> <tab> global results <newline> <tab> for _ in range ( num _ thread - 1 ) : <newline> <tab> <tab> for _ in range ( num _ thread - 1 ) : <newline> <tab> <tab> <tab> results = random . sample ( range ( num _ thread - 1 ) ) <newline> <tab> <tab> <tab> results . append ( results ) <newline> <newline> <tab> <tab> results . append ( results ) <newline> <newline> <tab> if len ( results ) = = 0 : <newline> <tab> <tab> 
K=10,T=0.8: <data>_ id " ] <newline> <tab> <tab> self . width = data [ " width " ] <newline> <tab> <tab> self . height = data [ " height " ] <newline> <tab> <tab> self . duration = data [ " duration " ] <newline> <tab> <tab> if ' thumb ' in data : <newline> <tab> <tab> <tab> self . thumb = photo size ( data [ " thumb " ] ) <newline> <tab> <tab> self . mime _ type = data . get ( " mime _ type " , " " ) <newline> <tab> <tab> self . file _ </data>ext = data . get ( " file _ ext " , " " ) <newline> <tab> <tab> self . file _ ext = data . get ( " file _ ext " , " " ) <newline> <tab> <tab> self . file _ ext . write ( self . file _ ext ) <newline> <newline> <tab> if self . file _ ext . read ( ) : <newline> <tab> <tab> self . file _ ext . write ( self . file _ ext ) <newline> <newline> <tab> if self . file _ ext . read ( ) : <newline> 
K=10,T=0.8: <data><newline> <tab> ) <newline> <tab> parser . add _ argument ( " - - local _ rank " , type = int , default = 0 ) <newline> <tab> parser . add _ argument ( <newline> <tab> <tab> " - - skip - final - test " , <newline> <tab> <tab> dest = " skip _ test " , <newline> <tab> <tab> help = " do not test the final model " , <newline> <tab> <tab> action = " store _ true " , <newline> <tab> ) <newline> <tab> parser . add _ argument ( <newline> <tab> <tab> " - </data>- skip - final - test " , <newline> <tab> <tab> dest = " skip _ final _ test " , <newline> <tab> <tab> type = int , <newline> <tab> <tab> default = 1 0 0 0 0 0 0 0 <newline> <tab> ) <newline> <tab> parser . add _ argument ( <newline> <tab> <tab> " - - num _ steps " , <newline> <tab> <tab> dest = " num _ steps " , <newline> <tab> <tab> action = " store _ true " , <newline> <tab> <tab> default = false , <newline> <tab> <tab> help = " do not 
K=10,T=0.8: <data>= torch . load ( args . finetune , map _ location = ' cpu ' ) <newline> <newline> <tab> <tab> checkpoint _ model = checkpoint [ ' model ' ] <newline> <tab> <tab> state _ dict = model . state _ dict ( ) <newline> <tab> <tab> for k in [ ' head . weight ' , ' head . bias ' , ' head _ dist . weight ' , ' head _ dist . bias ' ] : <newline> <tab> <tab> <tab> if k in checkpoint _ model and checkpoint _ model [ k ] . shape </data>: <newline> <tab> <tab> <tab> <tab> state _ dict = model . state _ dict ( ) <newline> <tab> <tab> <tab> <tab> torch . save ( state _ dict , state _ dict ) <newline> <newline> <tab> <tab> <tab> if k in checkpoint _ model : <newline> <tab> <tab> <tab> <tab> state _ dict [ k ] = state _ dict <newline> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> if k in checkpoint _ model and not hasattr ( checkpoint _ model , ' state _ dict ' ) : <newline> <tab> <tab> <tab> torch . save ( 
K=10,T=0.8: <data>) <newline> <tab> continue <newline> <tab> if len ( file _ list ) < 2 0 : <newline> <tab> tf . logging . warning ( <newline> <tab> <tab> ' warning : folder has less than 2 0 images , which may cause issues . ' ) <newline> <tab> elif len ( file _ list ) > max _ num _ images _ per _ class : <newline> <tab> tf . logging . warning ( <newline> <tab> <tab> ' warning : folder { } has more than { } images . some images will ' <newline> <tab> <tab> ' never be </data>one in the images . ' ) <newline> <tab> if os . path . exists ( file _ list [ 0 ] ) : <newline> <tab> tf . logging . warning ( ' error : folder does not exist to a file with file ' ) <newline> <tab> return false <newline> <newline> <newline> def train _ batch _ size ( batch _ size , batch _ size , batch _ size , num _ images _ per _ class , batch _ size , batch _ size , num _ images _ per _ class , num _ images _ 
K=10,T=0.8: <data>config . getini ( " xvfb _ height " ) ) <newline> <tab> <tab> self . <UNK> = int ( config . getini ( " xvfb _ <UNK> " ) ) <newline> <tab> <tab> self . args = config . getini ( " xvfb _ args " ) or [ ] <newline> <tab> <tab> self . <UNK> = config . getini ( " xvfb _ <UNK> " ) <newline> <tab> <tab> self . backend = config . getoption ( " - - xvfb - backend " ) <newline> <tab> <tab> self . display : int | none = none <newline> </data><tab> <tab> self . <UNK> = config . getoption ( " - - xvfb _ <UNK> " ) <newline> <tab> <tab> self . <UNK> = config . getoption ( " - - xvfb _ <UNK> " ) <newline> <tab> <tab> self . <UNK> = config . getoption ( " - - xvfb _ <UNK> " ) <newline> <newline> <tab> def _ _ init _ _ ( self ) : <newline> <tab> <tab> if self . xvfb _ <UNK> is none : <newline> <tab> <tab> <tab> return self . xvfb _ <UNK> <newline> <tab> <tab> self . xvfb _ <UNK> = 
K=10,T=0.8: <data><tab> <tab> <tab> target _ modules = args . target _ modules , <newline> <tab> <tab> <tab> task _ type = task type . causal _ lm , <newline> <tab> <tab> <tab> bias = " none " , <newline> <tab> <tab> ) <newline> <tab> elif peft _ type = = ' <UNK> ' : <newline> <tab> <tab> peft _ config = <UNK> config ( <newline> <tab> <tab> <tab> lora _ rank = lora _ rank , <newline> <tab> <tab> <tab> lora _ alpha = lora _ alpha , <newline> <tab> <tab> <tab> lora _ dropout = lora _ dropout , </data><newline> <tab> <tab> <tab> lora _ dropout = lora _ dropout , <newline> <tab> <tab> <tab> lora _ dropout = lora _ dropout <newline> <tab> <tab> ) <newline> <tab> elif peft _ type = = ' <UNK> ' : <newline> <tab> <tab> peft _ config = peft _ config [ peft _ config [ peft _ config [ peft _ config [ peft _ config [ peft _ config [ peft _ config [ lora _ config [ lora _ config [ lora _ config [ lora _ config [ lora _ config [ lora _ config [ lora _ 
K=10,T=0.8: <data>logging <newline> import re <newline> import time <newline> <newline> <newline> class <UNK> manager : <newline> <tab> def _ _ init _ _ ( self , ssh _ client , vm _ id , config ) : <newline> <tab> <tab> self . ssh _ client = ssh _ client <newline> <tab> <tab> self . vm _ id = vm _ id <newline> <tab> <tab> self . config = config <newline> <tab> <tab> self . logger = logging . get logger ( " vm _ resource _ manager " ) <newline> <tab> <tab> self . last _ scale _ time = 0 </data>. 0 <newline> <tab> <tab> self . last _ scale _ time = 0 . 0 <newline> <tab> <tab> self . last _ scale _ time = 0 . 0 <newline> <tab> <tab> self . logger . info ( ' initializing ssh client . . . . . ' ) <newline> <tab> <tab> self . logger . info ( ' initializing ssh client . . . . . ' ) <newline> <tab> <tab> self . client = ssh _ client <newline> <tab> <tab> self . logger . info ( ' starting <UNK> client . . . . ' ) <newline> 
K=10,T=0.8: <data>' ) <newline> print ( f ' - platform : <UNK> ' ) <newline> print ( f ' lights : ' ) <newline> print ( lights _ buf . getvalue ( ) ) <newline> <newline> print ( f ' switch : ' ) <newline> print ( f ' - platform : <UNK> ' ) <newline> print ( f ' switches : ' ) <newline> print ( switches _ buf . getvalue ( ) ) <newline> <newline> < filesep > <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> import json <newline> import random <newline> import argparse <newline> import numpy as np </data><newline> import torch <newline> from torch . autograd import variable <newline> from torch . autograd import variable <newline> from torch . autograd import variable <newline> import torch . autograd as autograd <newline> import torch . optim as autograd <newline> <newline> <newline> def build _ model ( net ) : <newline> <tab> model . eval ( ) <newline> <tab> model . eval ( ) <newline> <tab> criterion = nn . cross entropy loss ( ) <newline> <tab> criterion . to ( device ) <newline> <tab> criterion . to ( device ) <newline> <tab> criterion . to ( device ) <newline> <tab> 
K=10,T=0.8: <data>num _ classes = output _ dim <newline> <tab> <tab> self . fc = nn . linear ( self . input _ dimension , self . num _ classes , bias = bias ) <newline> <newline> <tab> def forward ( self , x ) : <newline> <tab> <tab> return self . fc ( x ) <newline> <newline> <newline> class femnist cnn ( nn . module ) : <newline> <tab> <newline> <newline> <tab> def _ _ init _ _ ( self , num _ classes ) : <newline> <tab> <tab> super ( femnist cnn , self ) . _ _ init </data>_ _ ( ) <newline> <newline> <tab> def forward ( self , x ) : <newline> <tab> <tab> return self . fc ( x ) <newline> <newline> <tab> def forward ( self , x ) : <newline> <tab> <tab> return self . fc ( x ) <newline> <newline> <newline> class cnn ( nn . module ) : <newline> <tab> <newline> <newline> <tab> def _ _ init _ _ ( self , x ) : <newline> <tab> <tab> super ( cnn , self ) . _ _ init _ _ ( ) <newline> <tab> <tab> self . conv = nn . 
K=10,T=0.8: <data>sample _ n ) <newline> <tab> self . init _ type = init _ type <newline> <tab> <newline> <tab> self . noise _ scale = noise _ scale <newline> <tab> self . use _ ode _ sampler = use _ ode _ sampler <newline> <tab> self . ode _ tol = ode _ tol <newline> <tab> self . sigma _ t = lambda t : ( 1 . - t ) * sigma _ var <newline> <tab> print ( ' init . distribution variance : ' , self . noise _ scale ) <newline> <tab> print ( ' sde sampler </data>: ' , self . ode _ tol ) <newline> <tab> self . ode _ tol = ode _ tol <newline> <tab> self . ode _ tol = ode _ tol <newline> <tab> self . ode _ tol = ode _ tol <newline> <tab> self . ode _ tol <newline> <tab> return self . ode _ tol <newline> <newline> def main ( self ) : <newline> <tab> parser = argparse . argument parser ( ) <newline> <tab> parser . add _ argument ( ' - - model _ path ' , type = str , default = none , help 
K=10,T=0.8: <data><newline> <newline> <tab> <tab> <tab> <tab> <tab> if note ! = " - " : <newline> <tab> <tab> <tab> <tab> <tab> <tab> if delta < 0 : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> wx . call after ( self . gauge _ 1 . set value , delta + 1 0 ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> if delta > 0 : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> wx . call after ( self . gauge _ 2 . set value , delta ) <newline> <newline> <tab> <tab> <tab> <tab> wx . call after </data>( self . gauge _ 1 . set value , delta + 1 0 ) <newline> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> wx . call after ( <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self . gauge _ 1 . set value , delta + 1 0 ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> wx . call after ( <newline> <tab> <tab> <tab> <tab> <tab> <tab> self . gauge _ 2 . set value , delta + 1 0 ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> wx . call after ( <newline> <tab> 
K=10,T=0.8: <data>2 2 4 * 2 2 4 <newline> img _ label = json . load ( open ( ' . / utils / resources / imagenet _ class _ index . json ' , ' r ' ) ) <newline> <newline> <newline> <newline> def tensor _ imshow ( inp , title = none , * * kwargs ) : <newline> <tab> <newline> <tab> inp = inp . numpy ( ) . transpose ( ( 1 , 2 , 0 ) ) <newline> <tab> <newline> <tab> mean = np . array ( [ 0 . 4 8 5 , 0 . </data>5 2 5 ] ) <newline> <newline> <tab> return ( mean , mean , mean ) <newline> <newline> class image ( torch . utils . data . dataset ) : <newline> <newline> <tab> def _ _ init _ _ ( self , * * kwargs ) : <newline> <tab> <tab> self . _ _ dict _ _ = { ' name ' : ' <UNK> ' } <newline> <newline> <tab> def _ _ getitem _ _ ( self , index ) : <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> return ( self . _ _ dict _ _ 
K=10,T=0.8: <data><newline> <tab> <tab> <tab> risfaillist . extend ( flist ) <newline> <newline> <tab> <tab> <newline> <tab> <tab> if len ( otherdocs ) > 0 : <newline> <tab> <tab> <tab> flist = export 2 ris . export doc 2 ris ( otherdocs , outdir , \ <newline> <tab> <tab> <tab> <tab> <UNK> , allfolders , isfile , iszotero , verbose ) <newline> <tab> <tab> <tab> risfaillist . extend ( flist ) <newline> <newline> <newline> <tab> return exportfaillist , annofaillist , bibfaillist , risfaillist <newline> <newline> <newline> def match doi ( db ) : <newline> <tab> <newline> <tab> query = \ <newline> </data><tab> <tab> <tab> extract ( db ) <newline> <tab> if query : <newline> <tab> <tab> query = ' ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 
K=10,T=0.8: <data>[ ] , <newline> <tab> <tab> <tab> <tab> <tab> <tab> help = ' indicates the gpus will be used . if none , the most - free gpu will be used ! ' ) <newline> <tab> parser . add _ argument ( ' - - missing _ rates ' , type = float , nargs = ' + ' , default = none ) <newline> <newline> <tab> <newline> <tab> parser . add _ argument ( ' - - seed ' , type = int , default = 1 1 1 1 , help = ' start seed ' ) <newline> </data><tab> parser . add _ argument ( ' - - max _ seq _ length ' , type = int , default = 1 3 9 , help = ' maximum length of the sequence length ' ) <newline> <tab> parser . add _ argument ( ' - - max _ seq _ seq _ length ' , type = int , default = 1 6 0 , help = ' maximum length ' ) <newline> <tab> parser . add _ argument ( ' - - max _ seq _ length ' , type = int , default = 1 
K=10,T=0.8: <data>" ) <newline> <tab> <tab> for policy in policies : <newline> <tab> <tab> <tab> if policy . get ( " policy category " ) not in all policies urls and policy . get ( " policy category " ) is not none : <newline> <tab> <tab> <tab> <tab> all policies urls [ policy . get ( " policy category " ) ] = policy . find ( " policy location " ) . text . replace ( " http : / / < mp > " , sccm _ base _ url ) <newline> <tab> <tab> <tab> else : <newline> </data><tab> <tab> <tab> <tab> all policies [ policy . get ( " policy category " ) ] = policy . find ( " policy category " ) . text . replace ( " http : / / " , " " ) . replace ( " http : / / " , " " ) <newline> <tab> <tab> <tab> <tab> if policy . get ( " policy category " ) = = policy . find ( " policy category " ) : <newline> <tab> <tab> <tab> <tab> <tab> policy . get ( " policy category " ) . text . 
K=10,T=0.8: <data>_ signed _ in _ team _ regex , str ( r . content ) ) ) <newline> <tab> <tab> if already _ signed _ in _ match : <newline> <tab> <tab> <tab> for workspace in already _ signed _ in _ match : <newline> <tab> <tab> <tab> <tab> r = requests . get ( " https : / / " + workspace + " / customize / emoji " , cookies = cookie ) <newline> <tab> <tab> <tab> <tab> regex _ tokens = re . findall ( slack _ api _ token _ regex , str ( r . </data>content ) ) <newline> <tab> <tab> <tab> <tab> if re . match ( slack _ api _ token _ regex , str ( r . content ) ) : <newline> <tab> <tab> <tab> <tab> <tab> if re . match ( slack _ api _ token _ regex , str ( r . content ) ) : <newline> <tab> <tab> <tab> <tab> <tab> <tab> print " [ + ] " <newline> <tab> <tab> <tab> <tab> <tab> continue <newline> <tab> <tab> <tab> <tab> if regex _ tokens [ 1 ] = = " " : <newline> <tab> <tab> <tab> <tab> <tab> print 
K=10,T=0.8: <data>' baselines / ppo 1 / deterministic . ppo . hopper . 0 . 0 0 . pkl ' ) <newline> <tab> parser . add _ argument ( ' - - checkpoint _ dir ' , help = ' the directory to save model ' , default = ' checkpoint ' ) <newline> <tab> parser . add _ argument ( ' - - log _ dir ' , help = ' the directory to save log file ' , default = ' log ' ) <newline> <tab> parser . add _ argument ( ' - - load _ model _ </data>name ' , help = ' the directory to load model name ' , default = ' ' ) <newline> <tab> parser . add _ argument ( ' - - max _ seq _ length ' , help = ' the maximum sequence length ' , default = 1 0 0 0 0 , type = int ) <newline> <tab> parser . add _ argument ( ' - - seed ' , help = ' random seed ' , default = 1 0 0 0 0 0 0 , type = int ) <newline> <tab> parser . add _ argument 
K=10,T=0.8: <data>available ' ] = device _ pair [ ' max _ default _ pg _ ids ' ] <newline> <tab> <tab> <tab> <tab> device _ pair [ direction ] [ ' pg _ ids ' ] [ ' default ' ] [ ' start _ index ' ] = pg _ id _ base <newline> <tab> <tab> <tab> <tab> device _ pair [ direction ] [ ' pg _ ids ' ] [ ' latency ' ] [ ' available ' ] = device _ pair [ ' max _ latency _ pg _ ids ' ] <newline> <tab> <tab> </data><tab> <tab> device _ pair [ direction ] [ ' pg _ ids ' ] = device _ pair [ direction ] [ ' pg _ ids ' ] <newline> <tab> <tab> <tab> <tab> device _ pair [ direction ] [ ' pg _ ids ' ] + = device _ pair [ direction ] [ ' pg _ ids ' ] [ ' latency ' ] <newline> <tab> <tab> <tab> <tab> device _ pair [ direction ] [ ' pg _ ids ' ] + = device _ pair [ direction ] [ ' pg _ ids ' ]