K=10,T=0.8: initialization = ' he ' ) output = tf . nn . relu ( output ) return output def generator ( n _ samples , real _ data ) : if fixed _ generator : return real _ data + ( 1 . * tf . random _ normal ( tf . shape ( real _ data ) ) ) else : noise = tf . random _ normal ( [ n _ samples , 2 ] ) output _ _ K=10,T=0.8: self . _ token _ idx = nn . parameter ( torch . zeros ( ( self . mini _ batch _ size , ) ) ) self . share _ qk = config . share _ qk self . conv _ kernel = config . conv _ kernel self . _ init _ _ proj ( ) self . _ init _ ( ) self . _ init _ ttt _ lr _ gate _ , , = _ , = _ K=10,T=0.8: 5 ] data [ ' stds ' ] = [ 0 . 5 ] elif config . dataset = = ' _ mnist ' : data [ ' classes ' ] = 1 0 data [ ' sizes ' ] = { ' train ' : 6 0 0 0 0 - config . val _ examples , ' val ' : config . val _ examples , ' test ' : 1 0 0 0 0 , } , " _ = K=10,T=0.8: return len ( all _ smiles ) , total _ non _ def count _ ( dataset ) : with open ( " generated _ smiles _ % s " % dataset , ' rb ' ) as f : all _ smiles = set ( pickle . load ( f ) ) counter = defaultdict ( int ) atom _ count _ per _ molecule = [ ] for smiles in all _ smiles : try : K=10,T=0.8: config . mlp . = 3 2 config . mlp . layers = 0 config . mlp . activation = ' relu ' config . resnet = collections . config dict ( ) config . resnet . version = 3 4 config . resnet . channels = 4 config . resnet . resnet _ v 2 = true config . resnet . init _ logits = true config . optimizer = ' sgd ' config . adam = collections . config dict ( ) config . adam . _ _ . ) , _ . _ . ) _ = . _ _ ) " ) import = ( _ . , _ . K=10,T=0.8: ) ) ) results . update ( result ) return results def _ mp _ fn ( index ) : main ( ) if _ _ name _ _ = = " _ _ main _ _ " : main ( ) import os from typing import any , optional import argparse import torch from transformers import auto config , auto tokenizer , training arguments , pre trained model , pre trained tokenizer , set _ ' _ _ . ( _ . _ , _ ' _ ' _ , _ _ . = ( [ ( K=10,T=0.8: self , hidden _ states : torch . tensor , attention _ mask : optional [ torch . tensor ] = none , position _ ids : optional [ torch . long tensor ] = none , cache _ params : optional [ tttcache ] = none , ) : if self . pre _ conv : residual = hidden _ states hidden _ states = self . conv ( hidden _ ) ' _ . , import = ( [ . = . _ ( K=10,T=0.8: _ types ) edge _ mask = edge _ masks _ to _ dense ( [ edge _ mask _ sparse ] , max _ n _ vertices ) node _ sequence = node _ sequence _ to _ dense ( [ node _ in _ focus ] , max _ n _ vertices ) distance _ to _ others _ sparse = bfs _ distance ( node _ in _ focus , incre _ adj _ list ) distance _ _ _ , ) K=10,T=0.8: read _ img ( path ) : = [ path + x for x in os . ( path ) if os . path . ( path + x ) ] imgs = [ ] labels = [ ] for idx , folder in enumerate ( ) : for im in glob . glob ( folder + ' / * . jpg ' ) : print ( ' the image : % s ' % ( im ) ) self