K=10,T=0.8: self . placeholders [ ' node _ sequence ' ] : batch _ data [ ' node _ sequence ' ] , self . placeholders [ ' edge _ type _ masks ' ] : batch _ data [ ' edge _ type _ masks ' ] , self . placeholders [ ' edge _ type _ labels ' ] : batch _ data [ ' edge _ type _ labels ' ] , self . placeholders [ ' edge _ masks ' ] _ _ K=10,T=0.8: model _ params , lr = args . lr , weight _ decay = args . weight _ decay ) elif args . optimizer = = " " : optimizer = torch . optim . rmsprop ( model _ params , lr = args . lr , weight _ decay = args . weight _ decay ) elif args . optimizer = = " sgd " : optimizer = torch . optim . sgd ( _ _ _ _ _ _ _ _ _ ) _ _ _ _ _ _ _ _ _ _ ' _ _ _ _ _ _ _ _ _ _ _ _ . _ _ _ K=10,T=0.8: = int ( batch _ size / args . grad _ accu _ steps ) for epoch in range ( args . epochs ) : start = time . time ( ) model . train ( ) train _ sampler . set _ epoch ( epoch ) train _ scheduler . step ( epoch ) loss _ tmp = 0 for step , ( images , labels ) in enumerate ( train _ ' _ _ _ _ _ _ _ _ _ . _ _ _ . K=10,T=0.8: = tokenizer ( inputs , text _ target = targets , max _ length = 1 2 8 , = true ) return model _ inputs print ( f " dataset for { model _ name } . . . " ) tokenized _ dataset = dataset . map ( tokenize _ function , batched = true ) data _ collator = data collator for seq 2 seq ( tokenizer = tokenizer , model = model _ name ) print ( f " loading metric ( _ _ _ _ _ _ . _ _ . _ _ _ _ . . . _ _ . . _ _ , _ _ _ _ . ( K=10,T=0.8: confidence _ sets = jnp . greater ( confidence _ sets , jnp . ones _ like ( confidence _ sets ) * 0 . 5 ) error = 1 - cpeval . compute _ accuracy ( logits , labels ) coverage = cpeval . compute _ coverage ( confidence _ sets , labels ) size , _ = cpeval . compute _ size ( confidence _ sets ) return loss , ( new _ model _ state , { ' coverage _ loss ' . ' , K=10,T=0.8: output , stride = 2 ) if bn : output = normalize ( ' discriminator . bn 3 ' , [ 0 , 2 , 3 ] , output ) output = _ gated _ nonlinearity ( output [ : , : : 2 ] , output [ : , 1 : : 2 ] ) output = lib . ops . conv 2 d . conv 2 d ( ' discriminator . 4 ' , 4 * dim , 8 * dim * 2 , 5 , output . _ ( = _ . . _ _ ) K=10,T=0.8: ids = [ local _ rank ] ) if args . extras = = 7 8 : tokenizer = t 5 tokenizer . from _ pretrained ( args . pretrained _ model _ path , subfolder = " tokenizer " ) text _ encoder = t 5 encoder model . from _ pretrained ( args . pretrained _ model _ path , subfolder = " text _ encoder " ) logger . info ( f " model parameters : { sum _ _ _ , [ = ( _ _ . _ ' , ) . _ . , = _ _ _ _ _ ( _ , [ , . . , ' ( ) K=10,T=0.8: . config . checkpoint _ frequency = = 0 : checkpoint . save ( ) params = hk . data _ structures . merge ( trainable _ params , fixed _ params ) return params , model _ state def _ test _ dataset ( self , params : cputils . flat mapping , model _ state : cputils . flat mapping , dataset : tf . data . dataset , name : str , epochs : int , shift _ fn : shift fn ) : ) . _ K=10,T=0.8: 0 ] except : pass class return feature layer ( torch . nn . module ) : def _ _ init _ _ ( self , mod ) : super ( return feature layer , self ) . _ _ init _ _ ( ) self . mod = mod def forward ( self , x ) : return self . mod ( x ) , x , )