+
     °³jù1  ã                   ó&  € ^ RI t ^ RIHt ^ RIHt ^ RIHtHtHt ^ RI	t	^ RI
Ht ^ RIHu Ht ^ RI	Ht ^RIHt ^RIHt ^RIHtHtHt ^R	IHt ^R
IHtHt . R Ot]! R. R!O4      tR]R]],          R]],          /]n        ]t  ! R R]PB                  4      t" ! R R]PB                  4      t# ! R R]PB                  4      t$ ! R R]PB                  4      t% ! R R]4      t&]! 4       ]! R]&PN                  3R7      RRRR/R R ll4       4       t(R# )"é    N)Ú
namedtuple)Úpartial)ÚAnyÚCallableÚOptional©ÚTensor)ÚImageClassification)Ú_log_api_usage_once)Úregister_modelÚWeightsÚWeightsEnum)Ú_IMAGENET_CATEGORIES)Ú_ovewrite_named_paramÚhandle_legacy_interfaceÚ	GoogLeNetÚGoogLeNetOutputsÚGoogLeNet_WeightsÚlogitsÚaux_logits2Úaux_logits1c                   óÄ   a a€ ] tR t^t oRR.tRV3R lV 3R llltV3R lR ltV3R lR lt]P                  P                  V3R	 lR
 l4       tV3R lR ltRtVtV ;t# )r   Ú
aux_logitsÚtransform_inputc                ó    <€ V ^8„  d   QhRS[ RS[RS[RS[S[,          RS[S[S[RS[P                  3,          ,          ,          RS[RS[R	R
/# )é   Únum_classesr   r   Úinit_weightsÚblocks.ÚdropoutÚdropout_auxÚreturnN)ÚintÚboolr   Úlistr   ÚnnÚModuleÚfloat)ÚformatÚ__classdict__s   "€Ú]/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchvision/models/googlenet.pyÚ__annotate__ÚGoogLeNet.__annotate__    s€   ø€ ÷ D1ñ D1áðD1ñ ðD1ñ ð	D1ñ
 ™t•nðD1ñ ™™h s©B¯I©I ~Õ6Õ7Õ8ðD1ñ ðD1ñ ðD1ð 
ñD1ó    c           	     ó"  <€ \         SV `  4        \        V 4       Vf   \        \        \
        .pVf   \        P                  ! R\        4       Rp\        V4      ^8w  d   \        R\        V4       24      hV^ ,          pV^,          p	V^,          p
W n        W0n        V! ^^@^^^R7      V n        \        P                  ! ^^RR7      V n        V! ^@^@^R7      V n        V! ^@^À^^R7      V n        \        P                  ! ^^RR7      V n        V	! ^À^@^`^€^^ ^ 4      V n        V	! R^€^€^À^ ^`^@4      V n        \        P                  ! ^^RR7      V n        V	! R	^À^`^Ð^^0^@4      V n        V	! R
^ ^p^à^^@^@4      V n        V	! R
^€^€R^^@^@4      V n        V	! R
^p^�R^ ^@^@4      V n        V	! RR^ R^ ^€^€4      V n        \        P                  ! ^^RR7      V n        V	! RR^ R^ ^€^€4      V n        V	! RR^ÀR^0^€^€4      V n        V'       d    V
! R
WR7      V n        V
! RWR7      V n         MR V n        R V n         \        PB                  ! R4      V n"        \        PF                  ! VR7      V n$        \        PJ                  ! RV4      V n&        V'       Ed
   V PO                  4        Fó  p\Q        V\        PR                  4      '       g!   \Q        V\        PJ                  4      '       d;   \T        P                  PV                  PY                  VPZ                  RRR^R7       K}  \Q        V\        P\                  4      '       g   KŸ  \        PV                  P_                  VPZ                  ^4       \        PV                  P_                  VP`                  ^ 4       Kõ  	  R # R # )NzéThe default weight initialization of GoogleNet will be changed in future releases of torchvision. If you wish to keep the old behavior (which leads to long initialization times due to scipy/scipy#11299), please set init_weights=True.Tz%blocks length should be 3 instead of )Úkernel_sizeÚstrideÚpadding)r1   Ú	ceil_mode©r0   ©r0   r2   é   ià  i   i   i  i@  i@  i€  )r    ©Úpé   g        g{®Gáz„?)ÚmeanÚstdÚaÚb)é   r>   éþÿÿÿ)1ÚsuperÚ__init__r   ÚBasicConv2dÚ	InceptionÚInceptionAuxÚwarningsÚwarnÚFutureWarningÚlenÚ
ValueErrorr   r   Úconv1r&   Ú	MaxPool2dÚmaxpool1Úconv2Úconv3Úmaxpool2Úinception3aÚinception3bÚmaxpool3Úinception4aÚinception4bÚinception4cÚinception4dÚinception4eÚmaxpool4Úinception5aÚinception5bÚaux1Úaux2ÚAdaptiveAvgPool2dÚavgpoolÚDropoutr    ÚLinearÚfcÚmodulesÚ
isinstanceÚConv2dÚtorchÚinitÚtrunc_normal_ÚweightÚBatchNorm2dÚ	constant_Úbias)Úselfr   r   r   r   r   r    r!   Ú
conv_blockÚinception_blockÚinception_aux_blockÚmÚ	__class__s   &&&&&&&&    €r+   rA   ÚGoogLeNet.__init__    s  ø€ ô 	‰ÑÔÜ˜DÔ!ØŠ>Ü!¤9¬lÐ;ˆFØÒÜ�MŠMðLô ô	ð  ˆLÜˆv‹;˜!ÔÜÐDÄSÈÃ[ÀMÐRÓSÐSØ˜A•Yˆ
Ø  �)ˆØ$ Q�iÐà$ŒØ.Ôá  2°1¸QÈÔJˆŒ
ÜŸš Q¨q¸DÔAˆŒÙ  B°AÔ6ˆŒ
Ù  C°QÀÔBˆŒ
ÜŸš Q¨q¸DÔAˆŒá*¨3°°B¸¸RÀÀRÓHˆÔÙ*¨3°°S¸#¸rÀ2ÀrÓJˆÔÜŸš Q¨q¸DÔAˆŒá*¨3°°R¸¸bÀ"ÀbÓIˆÔÙ*¨3°°S¸#¸rÀ2ÀrÓJˆÔÙ*¨3°°S¸#¸rÀ2ÀrÓJˆÔÙ*¨3°°S¸#¸rÀ2ÀrÓJˆÔÙ*¨3°°S¸#¸rÀ3ÈÓLˆÔÜŸš Q¨q¸DÔAˆŒá*¨3°°S¸#¸rÀ3ÈÓLˆÔÙ*¨3°°S¸#¸rÀ3ÈÓLˆÔçÙ+¨C°ÔRˆDŒIÙ+¨C°ÔRˆD�IàˆDŒIØˆDŒIä×+Ò+¨FÓ3ˆŒÜ—z’z GÔ,ˆŒÜ—)’)˜D +Ó.ˆŒçˆ<Ø—\‘\–^�Ü˜a¤§¡×+Ò+¬z¸!¼R¿Y¹Y×/GÒ/GÜ—H‘H—M‘M×/Ñ/°·±¸sÈÐPRÐVWÐ/ÖXÜ ¤2§>¡>×2Ô2Ü—G‘G×%Ñ% a§h¡h°Ô2Ü—G‘G×%Ñ% a§f¡f¨aÖ0ó $ñ r.   c                ó&   <€ V ^8„  d   QhRS[ RS[ /# ©r   Úxr"   r   )r)   r*   s   "€r+   r,   r-   f   s   ø€ ÷ ñ ¡&ð ©Vñ r.   c                ód  € V P                   '       dž   \        P                  ! VR,          ^4      R,          R,           p\        P                  ! VR,          ^4      R,          R,           p\        P                  ! VR,          ^4      R,          R	,           p\        P                  ! W#V3^4      pV# )
ºNNN)rw   r   gZd;ßOÝ?gÀ…ëQ¸ž¿)rw   r>   gyé&1¬Ü?g¸I+‡¶¿)rw   r   gÍÌÌÌÌÌÜ?g¨ñÒMbÈ¿)r   re   Ú	unsqueezeÚcat)rl   ru   Úx_ch0Úx_ch1Úx_ch2s   &&   r+   Ú_transform_inputÚGoogLeNet._transform_inputf   s…   € Ø××ÐÜ—O’O A d¥G¨QÓ/°;Õ?ÐBUÕUˆEÜ—O’O A d¥G¨QÓ/°;Õ?ÐBUÕUˆEÜ—O’O A d¥G¨QÓ/°;Õ?ÐBUÕUˆEÜ—	’	˜5¨Ð/°Ó3ˆAØˆr.   c                ó`   <€ V ^8„  d   QhRS[ RS[S[ S[S[ ,          S[S[ ,          3,          /# rt   )r	   Útupler   )r)   r*   s   "€r+   r,   r-   n   s1   ø€ ÷ 5ñ 5™&ð 5¡U©6±8¹FÕ3CÁXÉfÕEUÐ+UÕ%Vñ 5r.   c                óŠ  € V P                  V4      pV P                  V4      pV P                  V4      pV P                  V4      pV P	                  V4      pV P                  V4      pV P                  V4      pV P                  V4      pV P                  V4      pR pV P                  e$   V P                  '       d   V P                  V4      pV P                  V4      pV P                  V4      pV P                  V4      pR pV P                  e$   V P                  '       d   V P                  V4      pV P                  V4      pV P!                  V4      pV P#                  V4      pV P%                  V4      pV P'                  V4      p\(        P*                  ! V^4      pV P-                  V4      pV P/                  V4      pWV3# ©N)rJ   rL   rM   rN   rO   rP   rQ   rR   rS   r[   ÚtrainingrT   rU   rV   r\   rW   rX   rY   rZ   r^   re   Úflattenr    ra   )rl   ru   r[   r\   s   &&  r+   Ú_forwardÚGoogLeNet._forwardn   s€  € à�J‰J�q‹Mˆà�M‰M˜!Óˆà�J‰J�q‹Mˆà�J‰J�q‹Mˆà�M‰M˜!Óˆð ×Ñ˜QÓˆà×Ñ˜QÓˆà�M‰M˜!Óˆà×Ñ˜QÓˆà!%ˆØ�9‰9Ò Ø�}�}ˆ}Ø—y‘y “|�à×Ñ˜QÓˆà×Ñ˜QÓˆà×Ñ˜QÓˆà!%ˆØ�9‰9Ò Ø�}�}ˆ}Ø—y‘y “|�à×Ñ˜QÓˆà�M‰M˜!Óˆà×Ñ˜QÓˆà×Ñ˜QÓˆð �L‰L˜‹Oˆä�MŠM˜!˜QÓˆà�L‰L˜‹OˆØ�G‰G�A‹Jˆà˜ˆ}Ðr.   c                óB   <€ V ^8„  d   QhRS[ RS[ RS[S[ ,          RS[/# )r   ru   r\   r[   r"   )r	   r   r   )r)   r*   s   "€r+   r,   r-   ¦   s/   ø€ ÷ ñ ™vð ©Vð ¹8ÁFÕ;Kð ÑP`ñ r.   c                óf   € V P                   '       d   V P                  '       d   \        WV4      # V# r‚   )rƒ   r   Ú_GoogLeNetOutputs)rl   ru   r\   r[   s   &&&&r+   Úeager_outputsÚGoogLeNet.eager_outputs¥   s%   € à�=�=ˆ=˜TŸ_Ÿ_˜_Ü$ Q¨dÓ3Ð3àˆHr.   c                ó&   <€ V ^8„  d   QhRS[ RS[/# rt   )r	   r   )r)   r*   s   "€r+   r,   r-   ¬   s   ø€ ÷ 	5ñ 	5™ð 	5Ñ$4ñ 	5r.   c                óL  € V P                  V4      pV P                  V4      w  rpV P                  ;'       d    V P                  p\        P
                  P                  4       '       d+   V'       g   \        P                  ! R 4       \        WV4      # V P                  WV4      # )z8Scripted GoogleNet always returns GoogleNetOutputs Tuple)r}   r…   rƒ   r   re   ÚjitÚis_scriptingrE   rF   r   rŠ   )rl   ru   r\   r[   Úaux_defineds   &&   r+   ÚforwardÚGoogLeNet.forward¬   s|   € Ø×!Ñ! !Ó$ˆØŸ™ aÓ(‰ˆ�Ø—m‘m×7Ð7¨¯©ˆÜ�9‰9×!Ñ!×#Ò#ßÜ—’ÐXÔYÜ# A¨TÓ2Ð2à×%Ñ% a¨tÓ4Ð4r.   )r[   r\   r   r^   rJ   rM   rN   r    ra   rP   rQ   rS   rT   rU   rV   rW   rY   rZ   rL   rO   rR   rX   r   )iè  TFNNgš™™™™™É?çffffffæ?)Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__constants__rA   r}   r…   re   rŽ   ÚunusedrŠ   r‘   Ú__static_attributes__Ú__classdictcell__Ú__classcell__©rq   r*   s   @@r+   r   r      s_   ù‡ € Ø!Ð#4Ð5€M÷D1õ D1÷Lð ÷5ð 5ðn ‡Y�Y×Ñ÷ó ð÷	5÷ 	5ð 	5r.   c                   óf   a a€ ] tR t^¸t oRV3R lV 3R llltV3R lR ltV3R lR ltRtVtV ;t	# )	rC   c                ó†   <€ V ^8„  d   QhRS[ RS[ RS[ RS[ RS[ RS[ RS[ RS[S[R	S[P                  3,          ,          R
R/	# )r   Úin_channelsÚch1x1Úch3x3redÚch3x3Úch5x5redÚch5x5Ú	pool_projrm   .r"   N)r#   r   r   r&   r'   )r)   r*   s   "€r+   r,   ÚInception.__annotate__¹   sw   ø€ ÷ 
ñ 
áð
ñ ð
ñ ð	
ñ
 ð
ñ ð
ñ ð
ñ ð
ñ ™X c©2¯9©9 nÕ5Õ6ð
ð 
ñ
r.   c	           
     ó‚  <€ \         S	V `  4        Vf   \        pV! W^R7      V n        \        P
                  ! V! W^R7      V! W4^^R7      4      V n        \        P
                  ! V! W^R7      V! WV^^R7      4      V n        \        P
                  ! \        P                  ! ^^^RR7      V! W^R7      4      V n	        R # )Nr4   r5   T)r0   r1   r2   r3   )
r@   rA   rB   Úbranch1r&   Ú
SequentialÚbranch2Úbranch3rK   Úbranch4)
rl   r    r¡   r¢   r£   r¤   r¥   r¦   rm   rq   s
   &&&&&&&&&€r+   rA   ÚInception.__init__¹   s¨   ø€ ô 	‰ÑÔØÒÜ$ˆJÙ! +À!ÔDˆŒä—}’}Ù�{¸!Ô<¹jÈÐfgÐqrÔ>só
ˆŒô —}’}Ù�{¸!Ô<ñ �x°A¸qÔAó	
ˆŒô —}’}Ü�LŠL Q¨q¸!ÀtÔLÙ�{¸1Ô=ó
ˆŽr.   c                ó6   <€ V ^8„  d   QhRS[ RS[S[ ,          /# rt   )r	   r%   )r)   r*   s   "€r+   r,   r§   Ù   s   ø€ ÷ ñ ™&ð ¡T©&¥\ñ r.   c                ó–   € V P                  V4      pV P                  V4      pV P                  V4      pV P                  V4      pW#WE.pV# r‚   ©r©   r«   r¬   r­   )rl   ru   r©   r«   r¬   r­   Úoutputss   &&     r+   r…   ÚInception._forwardÙ   sE   € Ø—,‘,˜q“/ˆØ—,‘,˜q“/ˆØ—,‘,˜q“/ˆØ—,‘,˜q“/ˆà WÐ6ˆØˆr.   c                ó&   <€ V ^8„  d   QhRS[ RS[ /# rt   r   )r)   r*   s   "€r+   r,   r§   â   s   ø€ ÷ %ñ %™ð %¡Fñ %r.   c                óR   € V P                  V4      p\        P                  ! V^4      # )r>   )r…   re   ry   )rl   ru   r²   s   && r+   r‘   ÚInception.forwardâ   s!   € Ø—-‘- Ó"ˆÜ�yŠy˜ !Ó$Ð$r.   r±   r‚   )
r”   r•   r–   r—   rA   r…   r‘   rš   r›   rœ   r�   s   @@r+   rC   rC   ¸   s)   ù‡ € ÷
õ 
÷@ð ÷%÷ %ð %r.   rC   c                   óT   a a€ ] tR t^çt oRV3R lV 3R llltV3R lR ltRtVtV ;t# )rD   c          
      ón   <€ V ^8„  d   QhRS[ RS[ RS[S[RS[P                  3,          ,          RS[RR/# )r   r    r   rm   .r    r"   N)r#   r   r   r&   r'   r(   )r)   r*   s   "€r+   r,   ÚInceptionAux.__annotate__è   sO   ø€ ÷ -ñ -áð-ñ ð-ñ ™X c©2¯9©9 nÕ5Õ6ð	-ñ
 ð-ð 
ñ-r.   c                ó   <€ \         SV `  4        Vf   \        pV! V^€^R7      V n        \        P
                  ! RR4      V n        \        P
                  ! RV4      V n        \        P                  ! VR7      V n	        R # )Nr4   i   r9   r7   )
r@   rA   rB   Úconvr&   r`   Úfc1Úfc2r_   r    )rl   r    r   rm   r    rq   s   &&&&&€r+   rA   ÚInceptionAux.__init__è   s_   ø€ ô 	‰ÑÔØÒÜ$ˆJÙ˜{¨C¸QÔ?ˆŒ	ä—9’9˜T 4Ó(ˆŒÜ—9’9˜T ;Ó/ˆŒÜ—z’z GÔ,ˆŽr.   c                ó&   <€ V ^8„  d   QhRS[ RS[ /# rt   r   )r)   r*   s   "€r+   r,   r¹   ø   s   ø€ ÷ ñ ™ð ¡Fñ r.   c                ó  € \         P                  ! VR4      pV P                  V4      p\        P                  ! V^4      p\         P
                  ! V P                  V4      RR7      pV P                  V4      pV P                  V4      pV# )é   T©Úinplace)rÁ   rÁ   )	ÚFÚadaptive_avg_pool2dr»   re   r„   Úrelur¼   r    r½   ©rl   ru   s   &&r+   r‘   ÚInceptionAux.forwardø   sj   € ä×!Ò! ! VÓ,ˆà�I‰I�a‹Lˆä�MŠM˜!˜QÓˆä�FŠF�4—8‘8˜A“;¨Ô-ˆà�L‰L˜‹Oˆà�H‰H�Q‹Kˆð ˆr.   )r»   r    r¼   r½   )Nr“   ©	r”   r•   r–   r—   rA   r‘   rš   r›   rœ   r�   s   @@r+   rD   rD   ç   s   ù‡ € ÷-õ -÷ ÷ ð r.   rD   c                   óP   a a€ ] tR tRt oV3R lV 3R lltV3R lR ltRtVtV ;t# )rB   i
  c                ó0   <€ V ^8„  d   QhRS[ RS[ RS[RR/# )r   r    Úout_channelsÚkwargsr"   N)r#   r   )r)   r*   s   "€r+   r,   ÚBasicConv2d.__annotate__  s)   ø€ ÷ :ñ :¡Cð :±sð :Ácð :Èdñ :r.   c                óš   <€ \         SV `  4        \        P                  ! W3R R/VB V n        \        P
                  ! VRR7      V n        R# )rk   Fgü©ñÒMbP?)ÚepsN)r@   rA   r&   rd   r»   ri   Úbn)rl   r    rÌ   rÍ   rq   s   &&&,€r+   rA   ÚBasicConv2d.__init__  s:   ø€ Ü‰ÑÔÜ—I’I˜kÑN¸eÐNÀvÑNˆŒ	Ü—.’. °5Ô9ˆŽr.   c                ó&   <€ V ^8„  d   QhRS[ RS[ /# rt   r   )r)   r*   s   "€r+   r,   rÎ     s   ø€ ÷ 'ñ '™ð '¡Fñ 'r.   c                óv   € V P                  V4      pV P                  V4      p\        P                  ! VR R7      # )TrÂ   )r»   rÑ   rÄ   rÆ   rÇ   s   &&r+   r‘   ÚBasicConv2d.forward  s-   € Ø�I‰I�a‹LˆØ�G‰G�A‹JˆÜ�vŠv�a Ô&Ð&r.   )rÑ   r»   rÉ   r�   s   @@r+   rB   rB   
  s   ù‡ € ÷:ó :÷
'÷ 'ð 'r.   rB   c                   ól   € ] tR tRt]! R]! ]^àR7      RRRRR]RR	R
RRRRR//RRRRRR/R7      t]t	Rt
R# )r   i  z:https://download.pytorch.org/models/googlenet-1378be20.pth)Ú	crop_sizeÚ
num_paramsiˆe Úmin_sizeÚ
categoriesÚrecipezOhttps://github.com/pytorch/vision/tree/main/references/classification#googlenetÚ_metricszImageNet-1Kzacc@1goƒÀÊqQ@zacc@5gR¸…ëaV@Ú_opsg+‡ÙÎ÷÷?Ú
_file_sizeg!°rh‘ÝH@Ú_docsz1These weights are ported from the original paper.)ÚurlÚ
transformsÚmeta© N)é   rä   )r”   r•   r–   r—   r   r   r
   r   ÚIMAGENET1K_V1ÚDEFAULTrš   rã   r.   r+   r   r     sh   † ÙØHÙÐ.¸#Ô>à˜'Ø˜ØÐ.ØÐgØØØ˜VØ˜Vð ðð �EØ˜&ØÐLð
ô€Mð& „Gr.   Ú
pretrained)Úweightsrè   ÚprogressTc                ó^   € V ^8„  d   QhR\         \        ,          R\        R\        R\        /# )r   rè   ré   rÍ   r"   )r   r   r$   r   r   )r)   s   "r+   r,   r,   /  s0   € ÷ *ñ *œ(Ô#4Õ5ð *Ìð *Ô_bð *Ôgpñ *r.   c                óÒ  € \         P                  V 4      p VP                  RR4      pV eV   RV9  d   \        VRR4       \        VRR4       \        VRR4       \        VR\	        V P
                  R,          4      4       \        R/ VB pV eX   VP                  V P                  VRR	7      4       V'       g   RVn	        RVn
        RVn        V# \        P                  ! R
4       V# )aM  GoogLeNet (Inception v1) model architecture from
`Going Deeper with Convolutions <http://arxiv.org/abs/1409.4842>`_.

Args:
    weights (:class:`~torchvision.models.GoogLeNet_Weights`, optional): The
        pretrained weights for the model. See
        :class:`~torchvision.models.GoogLeNet_Weights` below for
        more details, and possible values. By default, no pre-trained
        weights are used.
    progress (bool, optional): If True, displays a progress bar of the
        download to stderr. Default is True.
    **kwargs: parameters passed to the ``torchvision.models.GoogLeNet``
        base class. Please refer to the `source code
        <https://github.com/pytorch/vision/blob/main/torchvision/models/googlenet.py>`_
        for more details about this class.
.. autoclass:: torchvision.models.GoogLeNet_Weights
    :members:
r   FNr   Tr   r   rÚ   )ré   Ú
check_hashz`auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train themrã   )r   ÚverifyÚgetr   rH   râ   r   Úload_state_dictÚget_state_dictr   r[   r\   rE   rF   )rè   ré   rÍ   Úoriginal_aux_logitsÚmodels   $$,  r+   Ú	googlenetró   -  sÜ   € ô*  ×&Ñ& wÓ/€Gà Ÿ*™* \°5Ó9ÐØÒØ FÔ*Ü! &Ð*;¸TÔBÜ˜f l°DÔ9Ü˜f n°eÔ<Ü˜f m´S¸¿¹ÀlÕ9SÓ5TÔUäÑ˜Ñ€EàÒØ×Ñ˜g×4Ñ4¸hÐSWÐ4ÓXÔYß"Ø$ˆEÔØˆEŒJØˆEŒJð €Lô	 �MŠMØrôð €Lr.   )r   r   r‰   r   ró   )r   r   r   ))rE   Úcollectionsr   Ú	functoolsr   Útypingr   r   r   re   Útorch.nnr&   Útorch.nn.functionalÚ
functionalrÄ   r	   Útransforms._presetsr
   Úutilsr   Ú_apir   r   r   Ú_metar   Ú_utilsr   r   Ú__all__r   Ú__annotations__r‰   r'   r   rC   rD   rB   r   rå   ró   rã   r.   r+   Ú<module>r     s	  ðÛ Ý "Ý ß *Ñ *ã Ý ß Ð Ý å 5Ý 'ß 6Ñ 6Ý 'ß Bò c€ñ Ð0Ò2ZÓ[Ð Ø$,¨f°mÀXÈfÕEUÐWdÐfnÐouÕfvÐ#wÐ Ô  ð %Ð ôX5�—	‘	ô X5ôv,%�—	‘	ô ,%ô^ �2—9‘9ô  ôF	'�"—)‘)ô 	'ô˜ô ñ. ÓÙ ,Ð0A×0OÑ0OÐ!PÔQð*¸ð *Ètö *ó Ró ò*r.   