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

K=10,T=0.8: <data><tab> <tab> trlog [ ' max _ acc _ dist _ epoch ' ] = 0 <newline> <tab> <tab> trlog [ ' max _ acc _ sim ' ] = 0 . 0 <newline> <tab> <tab> trlog [ ' max _ acc _ sim _ epoch ' ] = 0 <tab> <tab> <newline> <tab> <tab> initial _ lr = args . lr <newline> <tab> <tab> global _ count = 0 <newline> <newline> <tab> timer = timer ( ) <newline> <tab> writer = summary writer ( logdir = args . save _ path ) <newline> <tab> for epoch in range ( </data>args . args . max _ steps ) : <newline> <newline> <tab> <tab> <newline> <tab> <tab> train _ dataloader = args . train _ loader ( train _ loader ) <newline> <newline> <tab> <tab> val _ loader = train _ loader ( args . val _ loader ) <newline> <tab> <tab> data _ loader = data loader ( train _ loader , train _ loader , val _ loader , train _ loader , train _ loader = train _ loader , val _ loader , test _ batch _ batch _ size , test _ sampler = data loader 
K=10,T=0.8: <data><newline> callable _ obj ( ) <newline> <newline> the next example shows a callable object that accepts additional parameters , like a real function . <newline> here we need to add parameters to the _ _ call _ _ method . <newline> <newline> class callable object 2 : <newline> <tab> def _ _ init _ _ ( self , prefix ) : <newline> <tab> <tab> self . prefix = prefix <newline> <newline> <tab> def _ _ call _ _ ( self , x , y ) : <newline> <tab> <tab> print ( type ( self ) , self . prefix </data>) <newline> <tab> <tab> return [ ] <newline> <newline> <newline> class <UNK> ( object ) : <newline> <tab> <newline> <tab> def _ _ init _ _ ( self , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y , y ] , y , y , y , y , y , y , y , 
K=10,T=0.8: <data>( np . uint 8 ) <newline> <newline> <tab> <tab> encoded _ images = ( encoded _ images . cpu ( ) . numpy ( ) + 1 ) / 2 * 2 5 5 <newline> <tab> <tab> encoded _ images = np . transpose ( encoded _ images , ( 0 , 2 , 3 , 1 ) ) [ 0 ] <newline> <tab> <tab> encoded _ images = encoded _ images . astype ( np . uint 8 ) <newline> <newline> <tab> <tab> if images . shape [ 0 ] > 1 0 0 0 or images . </data>shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 1 ] . shape [ 1 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 1 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ 1 ] . shape [ 0 ] . shape [ 0 ] . shape [ 0 ] . shape [ : ] . shape [ 1 ] ] 
K=10,T=0.8: <data>1 } , " msg _ id " : { 2 } , " psessionid " : " { 3 } " } } ' . format ( tuin , client id , msg id , psession id , <UNK> ( content ) ) ) , <newline> <tab> <tab> <tab> ( ' clientid ' , client id ) , <newline> <tab> <tab> <tab> ( ' psessionid ' , psession id ) <newline> <tab> <tab> ) <newline> <tab> <tab> rsp = http client _ ist . post ( req url , data , https referer ) <newline> <tab> <tab> try : </data><newline> <tab> <tab> <tab> <tab> if ' <UNK> ' not in rsp : <newline> <tab> <tab> <tab> <tab> <tab> <UNK> = response . decode ( ' utf - 8 ' ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <UNK> = response . decode ( ' utf - 8 ' ) <newline> <tab> <tab> except exception as e : <newline> <tab> <tab> <tab> print ( e ) <newline> <tab> <tab> <tab> print ( e ) <newline> <tab> <tab> <tab> print ( e ) <newline> <tab> <tab> <tab> print ( e ) <newline> <tab> <tab> <tab> print ( e ) <newline> <tab> 
K=10,T=0.8: <data>: <newline> <tab> <tab> <tab> answers [ 2 ] . append ( 1 ) <newline> <tab> <tab> elif ' generally wrong ' in a _ 3 or ' generally incorrect ' in a _ 3 : <newline> <tab> <tab> <tab> answers [ 2 ] . append ( 2 ) <newline> <tab> <tab> elif ' wrong ' in a _ 3 or ' incorrect ' in a _ 3 : <newline> <tab> <tab> <tab> answers [ 2 ] . append ( 3 ) <newline> <tab> <tab> elif ' correct ' in a _ 3 : <newline> <tab> <tab> <tab> answers [ </data>2 ] . append ( 1 ) <newline> <tab> <tab> <tab> return answers <newline> <tab> <tab> elif ' correct ' in a _ 3 and ' correct ' in a _ 3 or ' total ' not in a _ 3 : <newline> <tab> <tab> <tab> answers [ 3 ] . append ( 1 . 0 ) <newline> <tab> <tab> <tab> return answers <newline> <newline> <tab> <tab> elif ' correct ' in a _ 3 and ' correct ' in a _ 3 and ' correct ' in a _ 3 : <tab> <tab> <tab> answers [ 3 ] . 
K=10,T=0.8: <data><tab> <tab> <newline> <tab> if args . testpath is none : <newline> <tab> <tab> args . testpath = args . <UNK> <newline> <newline> <tab> if is _ distributed : <newline> <tab> <tab> torch . cuda . set _ device ( args . local _ rank ) <newline> <tab> <tab> torch . distributed . init _ process _ group ( <newline> <tab> <tab> <tab> backend = " nccl " , init _ method = " env : / / " <newline> <tab> <tab> ) <newline> <tab> <tab> synchronize ( ) <newline> <newline> <tab> set _ random _ seed ( args . </data>local _ rank ) <newline> <tab> torch . distributed . set _ rank ( ) <newline> <tab> print ( " loading distributed data " ) <newline> <newline> <tab> print ( f " loading training . . . . . . . . . . . " ) <newline> <newline> <tab> print ( f " loading training . . . . . . . . . . . . . . " ) <newline> <tab> torch . cuda . set _ device ( device ) <newline> <newline> <tab> for i , ( i , i , n , n , n 
K=10,T=0.8: <data>range ( ( num _ samples - 1 ) / / batch _ size + 1 ) : <newline> <tab> <tab> <tab> <tab> batch _ input = problems [ id ] [ " problem " ] + instruct _ prompt <newline> <tab> <tab> <tab> <tab> batch _ output = model . generate ( batch _ input , sampling _ params ) <newline> <tab> <tab> <tab> <tab> for j in range ( sampling _ params . n ) : <newline> <tab> <tab> <tab> <tab> <tab> output . append ( batch _ output [ 0 ] . outputs [ j ] . </data>outputs [ j ] . outputs [ j ] ) <newline> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> output . append ( batch _ input [ 1 ] . outputs [ j ] . outputs [ j ] . outputs [ j ] ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> output . append ( batch _ input [ 1 ] . outputs [ j ] . outputs [ j ] . outputs [ j ] ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> outputs . append ( batch _ input [ j ] . outputs 
K=10,T=0.8: <data>[ . . . , none ] * freq <newline> <tab> <tab> sin , cos = spectrum . sin ( ) , spectrum . cos ( ) <newline> <tab> <tab> input _ enc = torch . stack ( [ sin , cos ] , dim = - 2 ) <newline> <tab> <tab> input _ enc = input _ enc . view ( * shape [ : - 1 ] , - 1 ) <newline> <tab> <tab> return input _ enc <newline> <tab> <newline> <newline> if _ _ name _ _ = = " _ _ main _ _ " : </data><newline> <tab> main ( ) <newline> < filesep > <newline> from os import path <newline> from os import path <newline> from os import path <newline> from os import path <newline> <newline> import json <newline> from os . path import join <newline> from utils . data import data loader <newline> from utils . data import data loader <newline> from utils . data import data loader <newline> from utils . data import data loader <newline> from utils . data import data loader <newline> from utils . data import data loader <newline> from utils . data import data loader <newline> <newline> class data 
K=10,T=0.8: <data><tab> <UNK> = int ( config [ " <UNK> " ] ) <newline> <tab> <UNK> = int ( config [ " <UNK> " ] ) <newline> if tofu _ enabled : <newline> <tab> tofu _ config = config [ " tofu " ] <newline> <tab> tofu _ channels = tofu _ config [ " channels " ] <newline> <tab> <UNK> = tofu _ config [ " summon " ] <newline> <tab> tcc = tofu _ config [ " tcc " ] <newline> <tab> if <UNK> : <newline> <tab> <tab> <UNK> = tofu _ config [ " summon _ channel " </data>] <newline> <tab> <tab> <UNK> = config [ " <UNK> " ] <newline> <tab> <tab> <UNK> = config [ " <UNK> " ] <newline> <tab> <tab> if <UNK> : <newline> <tab> <tab> <tab> <UNK> = config [ " <UNK> " ] <newline> <tab> <tab> <tab> <UNK> = config [ " <UNK> " ] <newline> <tab> <tab> if <UNK> and <UNK> : <newline> <tab> <tab> <tab> <UNK> = config [ " <UNK> " ] [ " <UNK> " ] <newline> <tab> <tab> <tab> <UNK> = config [ " <UNK> " ] [ " <UNK> " ] <newline> <tab> <tab> <tab> <UNK> 
K=10,T=0.8: <data>) , ' xyz ' ) <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> eul . rotate _ axis ( ' x ' , math . radians ( - rx ) ) <newline> <tab> <tab> <tab> eul . rotate _ axis ( ' y ' , math . radians ( ry ) ) <newline> <tab> <tab> <tab> eul . rotate _ axis ( ' z ' , math . radians ( - rz + 1 8 0 ) ) <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> cam . rotation _ euler = eul <newline> <tab> <tab> <newline> <tab> <tab> </data><tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <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> <newline> <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>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> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <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> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>. <UNK> as losses <newline> import utils . misc as misc <newline> from datasets . <UNK> import ego <UNK> <newline> from datasets . <UNK> import hand dataset <newline> from model . detnet . detnet import detnet <newline> from utils import func , align <newline> from utils . eval . <UNK> import average meter , accuracy _ heatmap <newline> from utils . eval . <UNK> import eval util <newline> from utils import vis <newline> import random <newline> <newline> <newline> device = torch . device ( f " cuda " if torch . cuda . is _ available ( ) else " </data>cpu " ) <newline> device = torch . device ( f " cuda " if torch . cuda . is _ available ( ) else " cpu " ) <newline> from torch . utils . data import data loader , data loader , data loader <newline> from utils . utils import dataset , dataset <newline> from utils . metrics import metrics <newline> <newline> class test dataset ( dataset ) : <newline> <tab> def _ _ init _ _ ( self , dataset , dataset , dataset , dataset , dataset , dataset _ type , dataset , dataset _ type 
K=10,T=0.8: <data>l 2 _ norm ( dy / dx ) - 1 ) * * 2 . convert label indices to one - hot vectors . generate target domain labels for debugging and testing . compute binary or softmax cross entropy loss . train star gan within a single dataset . translate images using star gan trained on a single dataset . universal attack by huang hao universal attack by huang hao translate images using star gan trained on a single dataset . <newline> <tab> override the parameters you want to modify for this dataset <newline> <tab> logging after every request </data>to save the model . <newline> <tab> <newline> <tab> args : <newline> <tab> <tab> <tab> <tab> train _ dataset ( dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ root , dataset _ name , dataset _ root , dataset _ root , dataset _ root , dataset _ name , dataset _ root , dataset _ name , dataset _ root , dataset _ name , dataset _ name ) 
K=10,T=0.8: <data>pg _ id = device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' start _ index ' ] + protocols _ index <newline> <tab> <tab> <tab> <tab> <tab> max _ pg _ id = device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' start _ index ' ] + device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' available ' ] </data><newline> <newline> <tab> <tab> <tab> <tab> <tab> pg _ id = device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' pg _ ids ' ] <newline> <tab> <tab> <tab> <tab> <tab> pg _ id = device _ pair [ direction ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' pg _ ids ' ] [ streams _ type _ value ] [ ' pg _ 
K=10,T=0.8: <data>rate e . g ' 2 5 ' <newline> <tab> : return : integer ( framerate ) converts hh : mm : ss . mm to hh : mm : ss : <UNK> config & set up everything . evaluate the saliency maps for a batch of videos convert detection to the <newline> <tab> fast rcnn format ( dets [ num _ classes ] [ num _ detections ] ) <newline> <tab> and pickle them to disk . <newline> <tab> <newline> @ author : <newline> @ <UNK> <newline> @ : < py torch 1 > <newline> @ bbs . <UNK> </data>( ) <newline> @ bbs . <UNK> ( ) <newline> @ bbs . <UNK> ( ) <newline> @ bbs . <UNK> ( ) <newline> @ bbs . <UNK> ( ) <newline> @ bbs . <UNK> ( ) <newline> @ bbs . <UNK> ( ) <newline> @ bbs . <UNK> ( ) <newline> def test _ <UNK> ( ) : <newline> <tab> <newline> <tab> <newline> <tab> @ bbs . <UNK> ( ) <newline> <tab> @ bbs . <UNK> ( ) <newline> <tab> @ bbs . <UNK> ( ) <newline> <tab> @ bbs . <UNK> ( ) <newline> <tab> def test _ 
K=10,T=0.8: <data><newline> <tab> <tab> return ( lambda var _ 0 0 0 0 0 0 2 f : aggr ( var _ func _ 0 0 0 0 0 0 2 9 , var _ 0 0 0 0 0 0 2 f ) ) ( words ) <newline> <newline> <tab> def space ( self ) : <newline> <tab> <tab> return " " <newline> <newline> <tab> def quote ( self ) : <newline> <tab> <tab> return " ' " <newline> <newline> <tab> def junk chars ( self , min , max ) : <newline> <tab> <tab> def var _ func _ </data>1 ( self ) : <newline> <tab> <tab> <tab> min = min / ( max / / ( max / / ( max / / 1 0 0 0 2 f ) ) ( words ) ) ( words ) <newline> <tab> <tab> return " ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 
K=10,T=0.8: <data>< | | <newline> <tab> | <tab> | _ _ _ | | | | | | \ _ _ _ | <newline> <tab> | _ _ _ _ _ _ _ \ _ _ | | _ _ | | _ _ | / _ _ _ _ | v 1 . 2 <newline> <tab> <tab> \ / <tab> <tab> <tab> <tab> \ / <newline> <newline> command line arguments assign argument values check to make sure target is actually up provides flags that are common to scripts . <newline> <newline> common flags from train / eval / vis / </data>debug / train / test / <UNK> <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> <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> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> import os <newline> import sys <newline> import time <newline> from pathlib import path <newline> from 
K=10,T=0.8: <data>} <newline> <tab> <tab> <tab> ) <newline> <newline> <tab> return ( sum ( accuracy [ ' right ' ] ) * 1 . 0 / max ( accuracy [ ' total ' ] , 1 ) , <newline> <tab> <tab> <tab> sum ( accuracy [ ' wrong ' ] ) * 1 . 0 / max ( accuracy [ ' total ' ] , 1 ) , <newline> <tab> <tab> <tab> accuracy [ ' total ' ] ) , ( accuracy , logs ) <newline> <newline> <newline> def get _ ranking _ based _ generation _ single _ token _ </data>ids ( ) : <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <newline> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <newline> <tab> 
K=10,T=0.8: <data>_ frequency = flags . intermediate _ store _ frequency <newline> <newline> <tab> if ( intermediate _ frequency > 0 and ( i % intermediate _ frequency = = 0 ) <newline> <tab> <tab> and i > 0 ) : <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> train _ saver . save ( sess , checkpoint _ name ) <newline> <tab> <tab> intermediate _ file _ name = ( flags . intermediate _ output _ graphs _ dir + <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> ' intermediate _ ' + str ( i ) + </data>' . pth ' ) <newline> <tab> <tab> if ( intermediate _ file _ name + = ' . pth ' ) : <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> save _ checkpoint _ name = os . path . join ( flags . output _ dir , checkpoint _ name + ' . pth ' ) <newline> <tab> <tab> <tab> if ( not os . path . exists ( save _ checkpoint _ name ) ) : <newline> <tab> <tab> <tab> <tab> os . makedirs ( save _ checkpoint _ name ) <newline> <newline> 
K=10,T=0.8: <data>/ ( nsamples * model . seqlen ) ) <newline> <tab> print ( ppl . item ( ) ) <newline> <tab> model . config . use _ cache = use _ cache <newline> <newline> <tab> return ppl . item ( ) <newline> <newline> def save _ results ( file _ name , results ) : <newline> <tab> if results is not str : <newline> <tab> <tab> results = str ( results ) <newline> <tab> results = results + ' \ n ' <newline> <tab> if not os . path . exists ( file _ name ) : <newline> <tab> <tab> </data>print ( results ) <newline> <tab> <tab> os . makedirs ( file _ name ) <newline> <tab> <tab> results [ file _ name ] = results <newline> <tab> <newline> <tab> print ( f " save results : { file _ name } " ) <newline> <tab> results = os . path . join ( file _ name , results ) <newline> <tab> if not os . path . exists ( results ) : <newline> <tab> <tab> print ( f " save results : { file _ name } " ) <newline> <tab> <tab> result = os . path . join 
K=10,T=0.8: <data>deselect ' ) <newline> <tab> obj . select = true <newline> <tab> bpy . context . scene . objects . active = obj <newline> <tab> <newline> def is _ apply _ immediate ( ) : <newline> <tab> return ( bpy . context . scene . apply _ bool = = true ) <newline> <newline> def bool _ mod _ and _ apply ( obj , bool _ method ) : <newline> <tab> <newline> <tab> active _ obj = bpy . context . scene . objects . active <newline> <tab> <newline> <tab> bool _ mod = active _ obj . modifiers </data>. active <newline> <tab> <newline> <tab> if bpy . context . scene . objects [ active _ obj ] = = obj : <newline> <tab> <tab> bpy . context . scene . objects [ active _ obj ] . scene . objects [ active _ obj ] . active = bpy . context . scene . objects [ active _ obj ] . active <newline> <tab> <tab> bpy . context . scene . objects [ active _ obj ] . active = bpy . context . scene . objects [ active _ obj ] . active <newline> <newline> <tab> elif 
K=10,T=0.8: <data><newline> <tab> val _ loss = sum ( all _ gather _ list ( val _ loss ) ) <newline> <tab> n _ correct = sum ( all _ gather _ list ( n _ correct ) ) <newline> <tab> n _ word = sum ( all _ gather _ list ( n _ word ) ) <newline> <tab> tot _ time = time ( ) - st <newline> <tab> val _ loss / = n _ word <newline> <tab> acc = n _ correct / n _ word <newline> <tab> val _ log = { ' loss ' : </data>val _ loss , ' loss ' : val _ loss , ' acc ' : acc , ' loss ' : acc } <newline> <tab> return val _ log <newline> def train _ batch ( model , model , model , data _ loader , device , optimizer , optimizer , optimizer , optimizer , optimizer , optimizer , scheduler , scheduler , scheduler , scheduler , scheduler , scheduler , scheduler , scheduler ) : <newline> <tab> global _ step = average meter ( ) <newline> <tab> batch _ size = len ( data _ loader ) <newline> 
K=10,T=0.8: <data>size : % u " % ( insn . vector _ size ) ) <newline> <tab> if insn . <UNK> : <newline> <tab> <tab> print ( " \ t user - mode : true " ) <newline> <tab> if insn . mem _ barrier : <newline> <tab> <tab> print ( " \ t memory - barrier : % u " % ( insn . mem _ barrier ) ) <newline> <newline> <tab> ( regs _ read , regs _ write ) = insn . regs _ access ( ) <newline> <newline> <tab> if len ( regs _ read ) > </data>= 1 : <newline> <tab> <tab> regs _ read = reg _ regs _ read . get ( ' regs _ read ' , none ) <newline> <tab> <tab> regs _ read = reg _ regs . get ( ' regs _ read ' , none ) <newline> <tab> <tab> regs _ read = reg _ regs . get ( ' regs _ read ' , none ) <newline> <tab> <tab> regs _ read = reg _ regs . get ( ' regs _ read ' , none ) <newline> <tab> <tab> regs _ read = reg _ regs . 
K=10,T=0.8: <data>shape ) - 1 <newline> <tab> dtype = tensors [ 0 ] . dtype if dtype is none else dtype <newline> <tab> max _ len = max ( tensor . shape [ dim ] for tensor in tensors ) <newline> <tab> new _ tensors = [ ] <newline> <tab> for tensor in tensors : <newline> <tab> <tab> pad = ( 2 * len ( tensor . shape ) ) * [ 0 ] <newline> <tab> <tab> pad [ - 2 * dim - 1 ] = max _ len - tensor . shape [ dim ] <newline> <tab> <tab> new </data>_ tensors . append ( pad ) <newline> <tab> return new _ tensors <newline> <newline> <newline> @ app . route ( " / <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> / <UNK> / <UNK> / <UNK> / <UNK> / <UNK> - % <UNK> / <UNK> / <UNK> / <UNK> % <UNK> / <UNK> / <UNK> / <UNK> / 
K=10,T=0.8: <data><newline> <tab> url = os . path . abspath ( output _ file . name ) <newline> <tab> webbrowser . open ( ' file : / / ' + url , new = 2 ) <newline> <newline> < filesep > <newline> import urllib . request <newline> import zipfile <newline> import xml . etree . element tree as et <newline> import json <newline> import html <newline> from urllib . parse import unquote <newline> <newline> head = <newline> tail = " < / cpe - list > " <newline> <newline> def update _ cpe _ dict ( cpe _ item , cpe </data>_ item , cpe _ item , cpe _ item , cpe _ item ) : <newline> <tab> cpe _ item = cpe _ item <newline> <tab> cpe _ item = cpe _ item <newline> <tab> cpe _ item = cpe _ item <newline> <tab> cpe _ item = cpe _ item <newline> <tab> cpe _ item = cpe _ item <newline> <tab> cpe _ item = cpe _ item <newline> <tab> cpe _ item = cpe _ item <newline> <newline> <tab> cpe _ item = cpe _ item <newline> <tab> cpe _ item = cpe _ item <newline> <tab> 
K=10,T=0.8: <data><newline> <newline> <tab> def test 0 3 _ import coin full ( self ) : <newline> <tab> <tab> global fcstd _ file _ path <newline> <tab> <tab> free cad . console . print message ( " 3 . native ifc import : full model , coin mode . . . " ) <newline> <tab> <tab> clear objects ( ) <newline> <tab> <tab> fp = get ifc file path ( ) <newline> <tab> <tab> d = ifc _ import . insert ( <newline> <tab> <tab> <tab> fp , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> </data>" ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " ifc test " , <newline> <tab> <tab> <tab> " 
K=10,T=0.8: <data>get _ value ( " output _ dir " ) <newline> <tab> <tab> self . video _ name = global . config . get _ value ( " video _ name " ) <newline> <tab> <tab> self . fps = global . config . get _ value ( " fps " ) <newline> <tab> <tab> self . bd _ ocr _ api = global . config . get _ value ( " bd _ ocr _ api " ) <newline> <tab> <tab> self . bd _ ocr _ lang = global . config . get _ value ( " bd </data>_ ocr _ api " ) <newline> <tab> <tab> self . bd _ ocr _ api = global . config . get _ value ( " bd _ ocr _ api _ ocr _ api " ) <newline> <tab> <tab> self . bd _ ocr _ api = global . config . get _ value ( " bd _ ocr _ api _ ocr _ api " ) <newline> <tab> <tab> self . bd _ ocr _ api = global . config . get _ value ( " bd _ ocr _ api " ) <newline> <newline> <tab> <tab> <newline> 
K=10,T=0.8: <data>_ all _ layers <newline> <newline> <tab> <tab> cell _ list = [ ] <newline> <tab> <tab> for i in range ( 0 , self . num _ layers ) : <newline> <tab> <tab> <tab> cur _ input _ dim = input _ dim if i = = 0 else hidden _ dim [ i - 1 ] <newline> <tab> <tab> <tab> cell _ list . append ( conv grucell ( input _ size = ( self . height , self . width ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> input _ dim = </data>self . width ) ) <newline> <tab> <tab> <tab> cur _ input _ dim = input _ dim * 2 <newline> <tab> <tab> <tab> if cell _ type = = " lstm " : <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> for i , layer in enumerate ( layer ) : <newline> <tab> <tab> <tab> <tab> <tab> layer = layer ( layer ) <newline> <tab> <tab> <tab> <tab> <tab> if layer . size > 0 : <newline> <tab> <tab> <tab> <tab> <tab> <tab> layer . zero _ grad ( ) <newline> <tab> 
K=10,T=0.8: <data>logging in from new accounts . . . \ n ' ) <newline> <tab> <tab> <tab> <tab> <tab> for added in newly _ added : <newline> <tab> <tab> <tab> <tab> <tab> <tab> c = telegram client ( f ' sessions / { added [ 2 ] } ' , added [ 0 ] , added [ 1 ] ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> try : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> c . start ( ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> print ( f ' n \ n { lg } [ </data>info ] \ n ' ) <newline> <tab> <tab> <tab> <tab> <tab> except exception as exc : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> print ( f ' error : { exc } - { exc } ' ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> sys . exit ( 1 ) <newline> <tab> <tab> <tab> <tab> <tab> except exception as exc : <newline> <tab> <tab> <tab> <tab> <tab> <tab> print ( f ' error : { exc } - { exc } ' ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> sys . exit ( 1 ) 
K=10,T=0.8: <data><tab> def send _ out _ for _ 2 s ( universe : int ) : <newline> <tab> <tab> <newline> <tab> <tab> for i in range ( 0 , 2 0 0 ) : <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> sender [ universe ] . dmx _ data = tuple ( x % 2 5 6 for x in range ( i , i + 4 ) ) <newline> <tab> <tab> <tab> time . sleep ( 0 . 0 1 ) <newline> <newline> <tab> send _ out _ for _ 2 s ( 1 ) <newline> <tab> sender </data>[ universe ] . dmx _ data = tuple ( x % 2 5 6 for x in range ( i , i + 4 ) ) <newline> <tab> if sender [ universe ] . dmx _ data is not none and receiver [ universe ] . dmx _ data = = 0 : <newline> <tab> <tab> send _ out _ for _ 2 s ( 1 ) <newline> <tab> elif sender [ universe ] . dmx _ data is not none : <newline> <tab> <tab> send _ out _ for _ 2 s ( 1 ) <newline> <tab> <tab>