K=10,T=0.8: elif resample = = none : conv _ shortcut = lib . ops . conv 2 d . conv 2 d conv _ 1 = functools . partial ( lib . ops . conv 2 d . conv 2 d , input _ dim = input _ dim , output _ dim = input _ dim ) conv _ 2 = functools . partial ( lib . ops . conv 2 d . conv 2 d , input _ dim = input _ dim , output K=10,T=0.8: strategy = ' entropy _ reg ' , sorting _ strategy = ' hard ' ) return sos def get _ class _ groups ( self , config : collections . config dict ) - > tuple [ jnp . ndarray , int ] : classes = self . data [ ' classes ' ] if config . class _ groups : groups = jnp . array ( config . class _ groups ) else : groups = jnp . arange ( _ _ _ : ) . , _ _ K=10,T=0.8: edge _ masks . append ( edge _ mask ) if not local _ stop _ label : edge _ type _ label , edge _ label = generate _ label ( graph , up _ to _ date _ adj _ mat , node _ in _ focus , neighbor , real _ n _ vertices , params ) edge _ type _ labels . append ( edge _ type _ label ) edge _ labels . append ( edge _ label ) = _ _ K=10,T=0.8: use _ shape , mano _ lambda _ joints 3 d = args . mano _ lambda _ joints 3 d , mano _ lambda _ _ reg = args . mano _ lambda _ _ reg , mano _ lambda _ joints 2 d = args . mano _ lambda _ joints 2 d , mano _ lambda _ shape = args . mano _ lambda _ shape , mano _ lambda _ = args . mano _ lambda _ = ( ' = , _ _ _ _ ) def _ . . _ _ ( ) K=10,T=0.8: and training _ args . do _ train and not training _ args . overwrite _ output _ dir ) : raise value error ( f " output directory ( { training _ args . output _ dir } ) exists and is not empty . use - - overwrite _ output _ dir to . " ) logging . basic config ( format = " % ( ) s - % ( ' , 1 )