+
    –°³jÛÍ  ã                   óº	  a € Rï tb0 t ^ RIt^ RIHt ^ RIHtHtHt ]]]	3,          t
^ RItR R ltR R ltR R	 ltR
 R ltR R ltR R ltR R ltR R ltR R ltR R ltR R ltR R ltRR/R R lltR  R! ltR" R# ltR$ R% ltR& R' ltR( R) ltR* R+ ltR, R- ltR. R/ lt R0 R1 lt!R2 R3 lt"R4 R5 lt#R6 R7 lt$R8 R9 lt%R: R; lt&R< R= lt'R> R? lt(R@ RA lt)RðRB RC llt*RD t+RE RF lt,RG RH lt-RI RJ lt.RK RL lt/RM RN lt0RO RP lt1RQ RR lt2RS RT lt3RU RV lt4RW RX lt5RY RZ lt6R[ R\ lt7R] R^ lt8R_ R` lt9Ra Rb lt:Rc Rd lt;Re Rf lt<Rg Rh lt=Ri Rj lt>RñRk Rl llt?Rm Rn lt@Ro Rp ltARq Rr ltBRs Rt ltCRòRu Rv lltDRw Rx ltERy Rz ltFR{ R| ltGR} R~ ltHR R€ ltIR� R‚ ltJRƒ R„ ltKR… R† ltLR‡ Rˆ ltMR‰ RŠ ltNR‹ RŒ ltORóR� RŽ lltPR� R� ltQR‘ R’ ltRRôR“ R” lltSR• R– ltTR— R˜ ltUR™ Rš ltVR› Rœ ltWRõR� Rž lltX ]P²                  P´                  t[/ s\] ^ k / t]] ^k / t^] ^k RŸ R  lt_R¡ R¢ lt`R£ R¤ lta]`! R¥]4       ]`! R¦]4       ]`! R§]4       ]`! R¨]4       ]`! R©]E4       ]`! Rª]E4       ]`! R«]4       ]`! R¬]4       ]`! R­]G4       ]`! R®]H4       ]`! R¯]I4       ]`! R°]&4       ]`! R±]'4       ]`! R²](4       ]`! R³]%4       ]`! R´],4       ]`! Rµ]34       ]`! R¶])4       ]`! R·]4       ]`! R¸]4       ]`! R¹]4       ]`! Rº]4       ]`! R»]*4       ]`! R¼]"4       ]`! R½]#4       ]`! R¾]$4       ]`! R¿]44       ]`! RÀ]74       ]`! RÁ]4       ]`! RÂ] 4       ]`! RÃ]54       ]`! RÄ]64       ]`! RÅ]<4       ]`! RÆ]=4       ]`! RÇ]B4       ]`! RÈ]C4       ]`! RÉ]>4       ]`! RÊ]@4       ]`! RË]A4       ]`! RÌ]?4       ]`! RÍ]L4       ]`! RÎ]14       ]`! RÏ]24       ]`! RÐ]J4       ]`! RÑ]K4       ]`! RÒ]4       ]`! RÓ]4       ]`! RÔ]4       ]`! RÕ]4       ]`! RÖ]4       ]`! R×]4       ]`! RØ]E4       ]`! RÙ]E4       ]`! RÚ]84       ]`! RÛ]!4       ]`! RÜ]4       ]`! RÝ]4       ]`! RÞ]4       ]`! Rß]4       ]`! Rà]P4       ]`! Rá]Q4       ]`! Râ]R4       ]`! Rã]S4       ]`! Rä]T4       ]`! Rå]U4       ]`! Ræ]V4       ]`! Rç]V4       ]`! Rè]V4       ]`! Ré]W4       ]`! Rê]X4       ]`! Rë]4       ]`! Rì]4       ]`! Rí]4       ]a! Rî]M]N4       R# )öé    N©ÚCallable)ÚAnyÚOptionalÚUnionc                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# ©é   ÚaÚb©ÚlistÚint)Úformats   "Ú[/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/jit/_shape_functions.pyÚ__annotate__r      s"   € ÷ ñ ””c•ð œt¤C�yñ ó    c           	      ó˜  € \        V 4      p\        V4      p\        W#4      p. p\        V4       F—  pV^,
          V,
          pV^,
          V,
          pV^,
          V,
          p	V^ 8¼  d	   W,          M^p
V	^ 8¼  d	   W,          M^pW«8w  d#   V
^8w  d   V^8w  d   \        RV
 RV RV 24      hTP	                  V
^8X  d   TMT
4       K™  	  V# )é   zThe size of tensor a z" must match the size of tensor b (z) at non-singleton dimension )ÚlenÚmaxÚrangeÚAssertionErrorÚappend)r   r   ÚdimsAÚdimsBÚndimÚexpandedSizesÚiÚoffsetÚdimAÚdimBÚsizeAÚsizeBs   &&          r   Ú	broadcastr%      sÒ   € Ü�‹F€EÜ�‹F€EÜˆuÓ€DØ!€Mä�4Ž[ˆØ˜•˜A•ˆØ�q�y˜6Õ!ˆØ�q�y˜6Õ!ˆØ  AœI�–¨AˆØ  AœI�–¨AˆàŒ>˜e qœj¨U°a¬Zä Ø'¨ wÐ.PÐQVÐPWÐWtÐuvÐtwÐxóð ð 	×Ñ e¨q¤j™U°eÖ<ñ ð Ðr   c                ó~   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          /# ©r
   r   r   Úcr   )r   s   "r   r   r   4   s-   € ÷ )ñ )”tœC•yð )¤T¬#¥Yð )´4¼µ9ñ )r   c                 ó,   € \        \        W4      V4      # ©N©r%   ©r   r   r(   s   &&&r   Úbroadcast_threer-   4   s   € Ü”Y˜q“_ aÓ(Ð(r   c                óh   € V ^8„  d   QhR\         \        ,          R\        R\         \        ,          /# r'   ©r   r   r   )r   s   "r   r   r   8   s)   € ÷ ñ œ4¤�9ð ¬ð ´´cµñ r   c                 ó   € \        W4      # r*   r+   r,   s   &&&r   Úbroadcast_one_threer1   8   s   € Ü�Q‹?Ðr   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# )r
   ÚselfÚoutr   )r   s   "r   r   r   <   s"   € ÷ ñ œd¤3�ið ¬d´3­iñ r   c                 óÞ  € \        V4      ^8w  d   \        R\        V4       24      h\        V 4      ^8X  g(   \        V 4      ^8X  g   \        R\        V 4       24      h\        ^\        V 4      4       F  pW,          ^ 8X  g   K  \        RV R24      h	  . p\        ^ \        V 4      ^,
          4       F  pVP                  W,          4       K  	  V F  pVP                  V4       K  	  V# )r
   z'Expected out to have length 2, but got z-Expected self to have length 3 or 4, but got zExpected self[z] to be non-zero, but got 0)r   r   r   r   )r3   r4   r   ÚshapeÚelems   &&   r   Úadaptive_avg_pool2dr8   <   sÒ   € Ü
ˆ3ƒx�1„}ÜÐFÄsÈ3ÃxÀjÐQÓRÐRÜ�‹I˜ŒNœc $›i¨1œnÜØ;¼CÀ»I¸;ÐGó
ð 	
ô �1”c˜$“iÖ ˆØ�7�aŽ<Ü  >°!°Ð4OÐ!PÓQÐQñ !ð €EÜ�1”c˜$“i !•mÖ$ˆØ�‰�T•WÖñ %ãˆØ�‰�TÖñ à€Lr   c                ó:   € V ^8„  d   QhR\         \        ,          /# ©r
   r3   r   )r   s   "r   r   r   O   s   € ÷ ñ ””S•	ñ r   c                 ó>   € . pV  F  pVP                  V4       K  	  V# r*   ©r   )r3   r4   r7   s   &  r   Ú_copyr=   O   s"   € Ø€CÛˆØ�
‰
�4Öñ à€Jr   c                ó:   € V ^8„  d   QhR\         \        ,          /# r:   r   )r   s   "r   r   r   V   s   € ÷ ñ ””S•	ñ r   c                 ó   € \        V 4      # r*   ©r=   ©r3   s   &r   ÚunaryrB   V   s   € Ü�‹;Ðr   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# r	   r   )r   s   "r   r   r   Z   s"   € ÷ ñ œœc�ð ¤t¬C¥yñ r   c                 ó<  € \        V 4      p\        V4      pW28”  d   \        R V RV R24      h\        V4       FU  pW2,
          V,           pW,          pV^ 8¼  d	   W,          M^pWg8w  g   K3  V^8w  g   K<  \        RP                  WgV4      4      h	  \	        V 4      # )zThe dims of tensor b (z6) must be less than or equal to the dims of tensor a (z) zZThe size of tensor a {} must match the size of tensor b ({}) at non-singleton dimension {})r   r   r   r   r=   )r   r   r   r   r!   r"   r#   r$   s   &&      r   Úbroadcast_inplacerE   Z   s¢   € Ü�‹F€EÜ�‹F€EØ„}ÜØ$ U GÐ+aÐbgÐahÐhjÐkó
ð 	
ô �e–ˆØ�}˜tÕ#ˆØ•ˆØ  AœI�–¨AˆØŽ>˜e qžjä ð4ß4:±F¸5ÈÓ4Nóð ñ ô �‹8€Or   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# ©r
   r3   Úsizesr   )r   s   "r   r   r   n   s"   € ÷ ñ ””c•ð ¤4¬¥9ñ r   c                 ó  € \        V4      \        V 4      8  d%   \        R \        V4       R\        V 4       R24      h\        V4      p\        V 4      pV^ 8X  d   \        V4      # . p\        V4       F�  pV^,
          V,
          pV^,
          V,
          pV^ 8¼  d	   W,          M^pW,          p	V	R8X  d   V^ 8  d   \        RV R24      hTp	W‰8w  d   V^8w  d   \        RV RV	 R24      hT	pVP	                  V4       K�  	  V# )zExpected len(sizes) (z) >= len(self) (Ú)úExpected dim (z) >= 0 when targetSize is -1zExpected size (z ) == 1 when size != targetSize (éÿÿÿÿ)r   r   r=   r   r   )
r3   rH   r   Ú
tensor_dimr4   r   r    ÚdimÚsizeÚ
targetSizes
   &&        r   ÚexpandrQ   n   s  € Ü
ˆ5ƒz”C˜“IÔÜØ#¤C¨£J <Ð/?ÄÀDÃ	¸{È!ÐLó
ð 	
ô ˆu‹:€DÜ�T“€JØˆq„yÜ�U‹|ÐØ€CÜ�4Ž[ˆØ˜•˜A•ˆØ˜1�n˜vÕ%ˆØ 1œHˆtŽy¨!ˆØ•Xˆ
Ø˜ÔØ�QŒwÜ$ ~°c°UÐ:VÐ%WÓXÐXØˆJØÔØ�qŒyÜ$Ø% d VÐ+KÈJÈ<ÐWXÐYóð ð ˆDØ�
‰
�4Öñ ð  €Jr   c                óh   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        /# )r
   r3   rH   Úinp0r/   )r   s   "r   r   r   ‹   s)   € ÷ ñ œD¤�Ið ¬d´3­ið ¼sñ r   c                 ó   € \        W4      # r*   )rQ   )r3   rH   rS   s   &&&r   Úexpand_one_unusedrU   ‹   s   € Ü�$ÓÐr   c                óh   € V ^8„  d   QhR\         \        ,          R\        R\         \        ,          /# )r
   r6   ÚnumelÚreturnr   )r   s   "r   r   r   �   s)   € ÷ ñ œ4¤�9ð ¬Sð ´T¼#µYñ r   c                 óV  € ^pRp\        \        V 4      4       FJ  pW,          R8X  d   Ve   \        R4      hTpK#  W,          ^ 8¼  d   W V,          ,          pKA  \        R4      h	  W8X  g$   Ve   V^ 8”  d   W,          ^ 8X  g   \        R4      h\        V 4      pVe   W,          WS&   V# )r   Nz"only one dimension can be inferredzinvalid shape dimensionszinvalid shaperL   )r   r   r   r=   )r6   rW   ÚnewsizeÚ	infer_dimrN   r4   s   &&    r   Úinfer_size_implr\   �   s§   € Ø€GØ#€IÜ”S˜“ZÖ ˆØ�:˜ÔØÒ$Ü$Ð%IÓJÐJØŠIØ�Z˜1Œ_Ø˜S•zÕ!ŠGä Ð!;Ó<Ð<ñ !ð 	ÔØÒ! g°¤k°eµoÈÔ6Jä˜_Ó-Ð-Ü
�‹,€CØÒØÕ)ˆ‰Ø€Jr   c                ó:   € V ^8„  d   QhR\         \        ,          /# )r
   rH   r   )r   s   "r   r   r   ¦   s   € ÷ ñ ””c•ñ r   c                 ó,   € ^pV  F  pW,          pK  	  V# ©r   © )rH   rW   r7   s   &  r   rW   rW   ¦   s   € Ø€EÛˆØ�Šñ à€Lr   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# rG   r   )r   s   "r   r   r   ­   s"   € ÷ /ñ /Œt”C�yð /¤¤c¥ñ /r   c                 ó,   € \        V\        V 4      4      # r*   )r\   rW   )r3   rH   s   &&r   Úviewrc   ­   s   € Ü˜5¤%¨£+Ó.Ð.r   ÚimplicitFc                óh   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        /# )r
   r3   rH   rd   ©r   r   Úbool)r   s   "r   r   r   ±   s)   € ÷ ñ œ$œs�)ð ¬D´­Ið ÄDñ r   c                ó   € \        W4      # r*   )rc   )r3   rH   rd   s   &&$r   Úview_one_unusedri   ±   s   € Ü�ÓÐr   c                óŠ   € V ^8„  d   QhR\         \        ,          R\        \         \        ,          ,          R\        R\        /# )r
   r3   Úopt_dimsÚkeep_dimÚdt)r   r   r   rg   r   )r   s   "r   r   r   µ   s9   € ÷ ñ Ü
Œs�)ðÜ'¬¬S­	Õ2ðÜ>BðÜHKñr   c           	      ór  € . pVe   \        V4      ^ 8X  d   \        \        \        V 4      4      4      pMTp\        \        V 4      4       Fh  pRpV F!  pV\        V\        V 4      4      8X  g   K  RpK#  	  V'       d   V'       d   VP	                  ^4       KO  KQ  VP	                  W,          4       Kj  	  V# )NFT)r   r   r   Úmaybe_wrap_dimr   )	r3   rk   rl   rm   r4   ÚdimsÚidxÚis_mean_dimÚ
reduce_dims	   &&&&     r   Úsum_mean_dimrt   µ   s›   € ð €CØÒœ3˜x›=¨AÔ-Üœu¤S¨£YÓ/Ó0‰àˆä”S˜“YÖˆØ!ˆÛˆJØ”n Z´°T³Ó;Ö;Ø"’ñ ÷ ßØ—
‘
˜1–ñ ð �J‰J�t•yÖ!ñ  ð €Jr   c                óR   € V ^8„  d   QhR\         \        ,          R\        R\        /# )r
   r3   rN   rl   rf   )r   s   "r   r   r   Ë   s%   € ÷ ñ ”$”s•)ð ¤#ð ´ñ r   c                 ó$   € \        W.VR 4      pW33# r*   )rt   )r3   rN   rl   r4   s   &&& r   Úmax_dimrw   Ë   s   € Ü
�t˜U H¨dÓ
3€CØˆ8€Or   c                ó0   € V ^8„  d   QhR\         R\         /# )r
   ÚxÚy)r   )r   s   "r   r   r   Ñ   s   € ÷ ñ Œsð ”sñ r   c                 ó   € W,          # r*   r`   )ry   rz   s   &&r   Údiv_rtnr|   Ñ   s	   € Ø�6€Mr   c                ól   € V ^8„  d   QhR\         R\         R\         R\         R\         R\         R\        /# )r
   Ú	inputSizeÚ
kernelSizeÚpad_lÚpad_rÚstrideÚdilationÚ	ceil_mode©r   rg   )r   s   "r   r   r   Õ   sN   € ÷ ñ Üðäðô ðô ð	ô
 ðô ðô ñr   c                 ó  € \        V V,           V,           WQ^,
          ,          ,
          ^,
          V'       d
   V^,
          M^ ,           V4      ^,           pV'       d%   V^,
          V,          W,           8¼  d
   V^,
          pV# r_   )r|   )r~   r   r€   r�   r‚   rƒ   r„   Ú
outputSizes   &&&&&&& r   Úpooling_output_shape_pad_lrrˆ   Õ   sˆ   € ô 	ØØõàõð  q�.Õ)õ*ð õ	÷
 'ˆv˜Žz¨Aõ/ð ó	
ð õ		ð ÷ Ø˜�N˜fÕ$¨	Õ(9Ô9Ø# a�ˆJØÐr   c                ó`   € V ^8„  d   QhR\         R\         R\         R\         R\         R\        /# )r
   r~   r   r€   r‚   rƒ   r„   r…   )r   s   "r   r   r   ð   sD   € ÷ ñ Üðäðô ðô ð	ô
 ðô ñr   c           	      óB   € V^ 8X  d   \        R4      h\        WW"W4V4      # )r   zstride should not be zero)r   rˆ   )r~   r   r€   r‚   rƒ   r„   s   &&&&&&r   Úpooling_output_shaper‹   ð   s-   € ð �„{ÜÐ8Ó9Ð9Ü&Ø˜u¨V¸yóð r   c                óÖ   € V ^8„  d   QhR\         \        ,          R\        R\        R\        R\        R\        R\        R\        R	\        R
\        R\        R\        R\        R\        /# )r
   ÚinputÚkHÚkWÚdHÚdWÚpadHÚpadWÚ	dilationHÚ	dilationWÚnInputPlaneÚinputHeightÚ
inputWidthÚoutputHeightÚoutputWidthr   )r   s   "r   r   r   ÿ   s˜   € ÷ -
ñ -
Ü”�9ð-
äð-
ô 	ð-
ô 	ð	-
ô
 	ð-
ô ð-
ô ð-
ô ð-
ô ð-
ô ð-
ô ð-
ô ð-
ô ð-
ô ñ-
r   c                 óv  € \        V 4      pV^ 8”  d   V^ 8”  g   \        RV RV R24      hV^ 8”  d   V^ 8”  g   \        RV RV R24      hV^ 8”  d   V^ 8”  g   \        RV RV R24      hV ^,          ^ 8g  ;'       d    V ^,          ^ 8g  pV^8X  d   V ^ ,          ^ 8w  d	   V'       g/   V^8X  d   V'       d   V ^,          ^ 8w  g   \        RV R	V  24      hV^,          V8¼  d   V^,          V8¼  g'   \        R
V^,           RV RV^,           RV R2	4      hV^8¼  d   V^8¼  g   \        RV RV R24      hR# )r   zExpected kW (z) > 0 and kH (z) > 0zExpected dW (z) > 0 and dH (zExpected dilationH (z) > 0 and dilationW (zInvalid input dimensions: ndim=z, input=zExpected kW//2 (z) >= padW (z) and kH//2 (z) >= padH (rJ   zExpected outputWidth (z) >= 1 and outputHeight (z) >= 1N©r   r   )r�   rŽ   r�   r�   r‘   r’   r“   r”   r•   r–   r—   r˜   r™   rš   r   Ú
valid_dimss   &&&&&&&&&&&&&&  r   Úpool2d_shape_checkrž   ÿ   sq  € ô  ˆu‹:€Dà�ŒF�r˜A”vÜ˜}¨R¨D°¸r¸dÀ%ÐHÓIÐIØ�ŒF�r˜A”vÜ˜}¨R¨D°¸r¸dÀ%ÐHÓIÐIØ˜ŒM˜i¨!œmÜØ" 9 +Ð-BÀ9À+ÈUÐSó
ð 	
ð �q•˜Q‘×0Ð0 5¨¥8¨q¡=€Jà�Œ	Ø�!�H˜ŒMßØ�AŒIŸ*¨¨q­°Q¬äÐ>¸t¸fÀHÈUÈGÐTÓUÐUà�!�G�tŒO  a¥¨4¤ÜØ˜r Q�w˜i {°4°&ð 9Ø˜A•g�Y˜k¨$¨¨qð2ó
ð 	
ð ˜1Ô °Ô!2ÜØ$ [ Mð 2Ø)˜N¨&ð2ó
ð 	
ñ "3r   c                óÎ   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        /# ©r
   r�   Úkernel_sizer‚   Úpaddingrƒ   r„   rf   )r   s   "r   r   r   /  sh   € ÷ H@ñ H@Ü”�9ðH@ä”c•ðH@ô ”�IðH@ô ”#�Yð	H@ô
 ”3�iðH@ô ñH@r   c                 ó\  € \        V4      ^8X  g   \        V4      ^8X  g   \        R4      hV^ ,          p\        V4      ^8X  d   TMV^,          p\        V4      ^ 8X  g,   \        V4      ^8X  g   \        V4      ^8X  g   \        R4      h\        V4      ^ 8X  d   TMV^ ,          p\        V4      ^ 8X  d   Tp	M\        V4      ^8X  d   Tp	M	V^,          p	\        V4      ^8X  g   \        V4      ^8X  g   \        R4      hV^ ,          p
\        V4      ^8X  d   T
MV^,          p\        V4      ^8X  g   \        V4      ^8X  g   \        R4      hV^ ,          p\        V4      ^8X  d   TMV^,          p\        V 4      ^8X  g(   \        V 4      ^8X  g   \        R\        V 4       24      h\        V 4      ^8X  d
   V R,          M^pV R,          pV R,          pV R	,          p\        VWjWŒV4      p\        VW{W�V4      p\        V VVVV	V
VVVVVVVV4       \        V 4      ^8X  d   VVV.# WïVV.# )
r   zKmax_pool2d: kernel_size must either be a single int, or a tuple of two intszOmax_pool2d: stride must either be omitted, a single int, or a tuple of two intszGmax_pool2d: padding must either be a single int, or a tuple of two intszHmax_pool2d: dilation must be either a single int, or a tuple of two intsz&Expected input length 3 or 4, but got éüÿÿÿéýÿÿÿéþÿÿÿrL   )r   r   r‹   rž   )r�   r¡   r‚   r¢   rƒ   r„   rŽ   r�   r�   r‘   r’   r“   r”   r•   Únbatchr–   r—   r˜   r™   rš   s   &&&&&&              r   Ú
max_pool2dr¨   /  s)  € ô �Ó Ô!¤S¨Ó%5¸Ô%:ÜØYó
ð 	
ð 
�Q�€BÜ�;Ó 1Ô$‰¨+°a­.€Bä�‹K˜1Ô¤ F£¨qÔ 0´C¸³KÀ1Ô4DÜØ]ó
ð 	
ô �6‹{˜aÔ‰ V¨A¥Y€BÜ
ˆ6ƒ{�aÔØ‰Ü	ˆV‹˜Ô	Ø‰à�A�Yˆä�‹L˜AÔ¤ W£°Ô!2ÜØUó
ð 	
ð �1�:€DÜ�w“< 1Ô$‰4¨'°!­*€Dä�‹M˜QÔ¤# h£-°1Ô"4ÜØVó
ð 	
ð ˜•€IÜ  ›]¨aÔ/‘	°X¸aµ[€Iä�‹J˜!ŒOœs 5›z¨QœÜÐEÄcÈ%ÃjÀ\ÐRÓSÐSä˜e›*¨œ/ˆU�2ŽY¨q€FØ˜•)€KØ˜•)€KØ�r•€Jä'¨°R¸rÈiÓX€LÜ& z°2¸RÈIÓV€KäØØ
Ø
Ø
Ø
ØØØØØØØØØôô" ˆ5ƒz�Q„Ø˜\¨;Ð7Ð7à \°;Ð?Ð?r   c                óÎ   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        /# r    rf   )r   s   "r   r   r   z  sX   € ÷ 	ñ 	Ü”�9ð	ä”c•ð	ô ”�Ið	ô ”#�Yð		ô
 ”3�ið	ô ñ	r   c                 ó"   € \        WW#WE4      pWf3# r*   )r¨   )r�   r¡   r‚   r¢   rƒ   r„   r4   s   &&&&&& r   Úmax_pool2d_with_indicesr«   z  s   € ô �U¨¸(Ó
N€CØˆ:Ðr   c                óª   € V ^8„  d   QhR\         \        ,          R\        \         \        ,          ,          R\        \         \        ,          ,          /# )r
   r�   Úoutput_sizeÚscale_factors©r   r   r   Úfloat)r   s   "r   r   r   †  s<   € ÷ $ñ $Ü”�9ð$äœ$œs�)Õ$ð$ô œD¤�KÕ(ñ$r   c                 ó”  € . pVP                  V ^ ,          4       VP                  V ^,          4       Vf   Vf   \        R4      hVeg   Ve   \        R4      h\        V4      ^8w  d   \        R\        V4       24      hVP                  V^ ,          4       VP                  V^,          4       Ve•   Ve   \        R4      h\        V4      ^8w  d   \        R\        V4       24      hVP                  \        V ^,          V^ ,          ,          4      4       VP                  \        V ^,          V^,          ,          4      4       V# )r   z5Either output_size or scale_factors must be presentedz9Must specify exactly one of output_size and scale_factorsz/Expected output_size to have length 2, but got z1Expected scale_factors to have length 2, but got )r   r   r   r   )r�   r­   r®   r4   s   &&& r   Úupsample_nearest2dr²   †  s7  € ð
 €CØ‡J�Jˆu�Q�xÔØ‡J�Jˆu�Q�xÔàÒ Ò!4ÜÐTÓUÐUàÒØÒ$Ü ØKóð ô ˆ{Ó˜qÔ Ü ØAÄ#ÀkÓBRÐASÐTóð ð 	�
‰
�;˜q•>Ô"Ø�
‰
�;˜q•>Ô"àÒ ØÒ"Ü ØKóð ô ˆ}Ó Ô"Ü ØCÄCÈÓDVÐCWÐXóð ð 	�
‰
”3�u˜Q•x -°Õ"2Õ2Ó3Ô4Ø�
‰
”3�u˜Q•x -°Õ"2Õ2Ó3Ô4à€Jr   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# )r
   r3   Úmat2r   )r   s   "r   r   r   ­  s"   € ÷ 
ñ 
ŒT”#�Yð 
œd¤3�iñ 
r   c                 ó.  € \        V 4      ^8w  d   \        R\        V 4       R24      h\        V4      ^8w  d   \        R\        V4       R24      hV ^,          V^ ,          8w  d    \        RV ^,           RV^ ,           24      hV ^ ,          V^,          .# )r
   zself must be a matrix (got z dimensions)zmat2 must be a matrix (got z.Matrix dimensions don't match for mm: self[1]=ú
, mat2[0]=rœ   ©r3   r´   s   &&r   Úmmr¸   ­  s—   € Ü
ˆ4ƒy�A„~ÜÐ:¼3¸t»9¸+À\ÐRÓSÐSÜ
ˆ4ƒy�A„~ÜÐ:¼3¸t»9¸+À\ÐRÓSÐSàˆA…w�$�q•'ÔÜØ<¸TÀ!½W¸IÀZÐPTÐUVÕPWÈyÐYó
ð 	
ð ��G�T˜!•WÐÐr   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# )r
   r3   Útensorr   )r   s   "r   r   r   º  s"   € ÷ ñ Œd”3�ið ¤¤c¥ñ r   c                 óø   € \        V 4      ^8X  d   \        V4      ^8X  g$   \        R\        V 4       R\        V4       24      hV ^ ,          V^ ,          8w  d    \        RV ^ ,           RV^ ,           24      h. pV# )r   z+Expected 1D tensors for dot, got len(self)=z, len(tensor)=z(Dot product dimension mismatch: self[0]=z, tensor[0]=rœ   )r3   rº   r4   s   && r   Údotr¼   º  sˆ   € Ü�‹I˜ŒNœs 6›{¨aÔ/ÜØ9¼#¸d»)¸ð EÜ˜v›;˜-ð)ó
ð 	
ð ˆA…w�&˜•)ÔÜØ6°t¸Aµw°i¸|ÈFÐSTÍIÈ;ÐWó
ð 	
ð €CØ€Jr   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# )r
   r3   Úvecr   )r   s   "r   r   r   È  s"   € ÷ ñ ŒT”#�Yð œT¤#�Yñ r   c                 ó  € \        V 4      ^8X  d   \        V4      ^8X  g$   \        R\        V 4       R\        V4       24      hV ^,          V^ ,          8w  d    \        RV ^,           RV^ ,           24      hV ^ ,          .# )r
   z0Expected 2D matrix and 1D vector, got len(self)=z, len(vec)=z*Matrix-vector dimension mismatch: self[1]=z	, vec[0]=rœ   )r3   r¾   s   &&r   ÚmvrÀ   È  sˆ   € Ü�‹I˜ŒNœs 3›x¨1œ}ÜØ>¼sÀ4»y¸kð JÜ˜C›�zð#ó
ð 	
ð ˆA…w�#�a•&ÔÜØ8¸¸a½¸	ÀÈ3ÈqÍ6È(ÐSó
ð 	
ð ��Gˆ9Ðr   c                óF   € V ^8„  d   QhR\         \        ,          R\        /# ©r
   ÚlirN   r   )r   s   "r   r   r   Ö  s   € ÷ ñ ”$”s•)ð ¤#ñ r   c                 óx   € \        V\        V 4      ^,           4      p\        V 4      pVP                  V^4       V# r_   )ro   r   r=   Úinsert)rÃ   rN   r4   s   && r   Ú	unsqueezerÆ   Ö  s2   € Ü
˜œc "›g¨�kÓ
*€CÜ
�‹)€CØ‡J�Jˆs�AÔØ€Jr   c                ó:   € V ^8„  d   QhR\         \        ,          /# ©r
   rÃ   r   )r   s   "r   r   r   Ý  s   € ÷ ñ ”dœ3•iñ r   c                 óŒ   € . p\        \        V 4      4       F)  pW,          ^8w  g   K  VP                  W,          4       K+  	  V# r_   )r   r   r   )rÃ   r4   r   s   &  r   Úsqueeze_nodimrÊ   Ý  s7   € Ø€CÜ”3�r“7Ž^ˆØ�5�AŽ:Ø�J‰J�r•uÖñ ð €Jr   c                óF   € V ^8„  d   QhR\         \        ,          R\        /# rÂ   r   )r   s   "r   r   r   å  s   € ÷ 	ñ 	””S•	ð 	¤ñ 	r   c                 óô   € . p\        V\        V 4      4      p\        \        V 4      4       FH  pWC8X  d)   W,          ^8w  d   VP                  W,          4       K/  K1  VP                  W,          4       KJ  	  V# r_   )ro   r   r   r   )rÃ   rN   r4   Úwrapped_dimr   s   &&   r   ÚsqueezerÎ   å  sb   € Ø€CÜ  ¤c¨"£gÓ.€KÜ”3�r“7Ž^ˆØÔØ�u˜ŒzØ—
‘
˜2�5Ö!ñ ð �J‰J�r•uÖñ ð €Jr   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# )r
   rÃ   rp   r   )r   s   "r   r   r   ñ  s"   € ÷ ñ ”Tœ#•Yð ¤d¬3¥iñ r   c                 ót  € \        V4      ^ 8X  d   V # \        V4      p\        \        V4      4       F   p\        W#,          \        V 4      4      W#&   K"  	  . p\        \        V 4      4       FH  pW,          ^8X  d"   W29  d   VP	                  W,          4       K/  K1  VP	                  W,          4       KJ  	  V# ©r   )r   r=   r   ro   r   )rÃ   rp   Úwrapped_dimsr   Úresults   &&   r   Úsqueeze_dimsrÔ   ñ  s’   € Ü
ˆ4ƒy�A„~Øˆ	Ü˜“;€LÜ”3�t“9ÖˆÜ(¨­¼#¸b»'ÓBˆ‹ñ à€FÜ”3�r“7Ž^ˆØ�5�AŒ:ØÔ$Ø—‘˜b�eÖ$ñ %ð �M‰M˜"�%Ö ñ ð €Mr   c                óh   € V ^8„  d   QhR\         \        ,          R\        R\         \        ,          /# ©r
   r3   rN   Úindexr   )r   s   "r   r   r     s)   € ÷ ñ ”tœC•yð ¤sð ´4¼µ9ñ r   c                 ó’  € \        V\        V 4      4      p\        V4      p\        V4      ^8”  d   \        R\        V4       24      hV^ 8X  g,   V\        V 4      8  g   \        RV R\        V 4       R24      h. p\	        \        V 4      4       F3  pW8X  d   VP                  V4       K  VP                  W,          4       K5  	  V# )r   z"Expected len(index) <= 1, but got rK   z) == 0 or dim < len(self) (rJ   )ro   r   Úmultiply_integersr   r   r   )r3   rN   r×   rW   Úresult_sizer   s   &&&   r   Úindex_selectrÛ     s¸   € Ü
˜œc $›iÓ
(€CÜ˜eÓ$€EÜ
ˆ5ƒz�A„~ÜÐAÄ#ÀeÃ*ÀÐNÓOÐOØ�1ŒH˜œc $›iœÜØ˜S˜EÐ!<¼SÀ»Y¸KÀqÐIó
ð 	
ð  €KÜ”3�t“9ÖˆØŒ8Ø×Ñ˜uÖ%à×Ñ˜t�wÖ'ñ	 ð
 Ðr   c          
      ó€   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        R\        R\        /# )r
   ÚweightÚindicesÚpadding_idxÚscale_grad_by_freqÚsparserf   )r   s   "r   r   r     sB   € ÷ ñ Ü”�Iðä”#�Yðô ðô ð	ô
 ñr   c                 óÖ   € \        V 4      ^8w  d   \        R\        V 4       R24      h\        V4      ^8X  d   \        V ^ V4      # \        V4      pVP	                  V ^,          4       V# )r
   z"Expected weight to be 2D, but got ÚD)r   r   rÛ   r=   r   )rÝ   rÞ   rß   rà   rá   rO   s   &&&&& r   Ú	embeddingrä     sb   € ô ˆ6ƒ{�aÔÜÐAÄ#ÀfÃ+ÀÈaÐPÓQÐQÜ
ˆ7ƒ|�qÔÜ˜F A wÓ/Ð/Ü�‹>€DØ‡K�K��q•	ÔØ€Kr   c                  ó   € R # )l   ÿÿÿÿ r`   r`   r   r   Úmax_intræ   #  s   € Ùr   c          
      ó–   € V ^8„  d   QhR\         \        ,          R\        R\        \        ,          R\        \        ,          R\        /# )r
   r3   rN   ÚstartÚendÚstep©r   r   r   )r   s   "r   r   r   '  s@   € ÷ ñ Ü
Œs�)ðÜðÜ&.¬s¥mðÜ:BÄ3½-ðÜORñr   c                 óú  € \        V 4      pV^ 8X  d   \        R4      h\        W4      pVe   TM^ pVe   TM	\        4       pV^ 8:  d   \        RV 24      hV\        4       8X  d   ^ pV^ 8  d   W`V,          ,          pV^ 8  d   WpV,          ,          pV^ 8  d   ^ pMW`V,          8”  d	   W,          pWv8  d   TpMWpV,          8¼  d	   W,          pWv,
          p\	        V 4      p	W„,           ^,
          V,          W‘&   V	# )r   z#Cannot slice a 0-dimensional tensorzExpected step > 0, but got )r   r   ro   ræ   r=   )
r3   rN   rè   ré   rê   r   Ú	start_valÚend_valÚ	slice_lenr4   s
   &&&&&     r   Úslicerð   '  sõ   € ô ˆt‹9€DØˆq„yÜÐBÓCÐCÜ
˜Ó
#€CØÒ*‘°€IØ’_‰c¬'«)€GØˆq„yÜÐ:¸4¸&ÐAÓBÐBØ”G“IÔØˆ	Ø�1„}Ø˜#•YÕˆ	Ø�„{Ø˜•9ÕˆØ�1„}Ø‰	Ø	˜#•YÔ	Ø•Iˆ	ØÔØ‰Ø	˜•IÔ	Ø•)ˆØÕ#€IÜ
�‹+€CØÕ  1Õ$¨Õ-€C�HØ€Jr   c                óP   € V ^8„  d   QhR\         \         \        ,          ,          /# )r
   Útensorsr   )r   s   "r   r   r   F  s   € ÷ Pñ P¤4¬¬S­	¥?ñ Pr   c                 óN   € V  F  p\        V4      ^ 8:  g   K  \        R4      h	  R# )r   z+Cannot concatenate tensor with 0 dimensionsNrœ   )rò   rº   s   & r   Úcheck_cat_no_zero_dimrô   F  s$   € ÛˆÜˆv‹;˜!ÖÜ Ð!NÓOÐOó r   c                ó\   € V ^8„  d   QhR\         R\        \        \         ,          ,          /# )r
   rN   Útensor_sizes)r   r   )r   s   "r   r   r   L  s"   € ÷ ñ œSð ´´T¼#µYµñ r   c                 óž   € R pV F>  p\        V4      ^8X  d   V^ ,          ^ 8X  d   K#  Ve   K)  \        V \        V4      4      pK@  	  Vf   T pV# r*   )r   ro   )rN   rö   Úout_dimrO   s   &&  r   Úlegacy_cat_wrap_dimrù   L  sL   € Ø!€GÛˆÜ�D“	˜Q” 4¨¥7¨a¦<ØŒÜ(¨¬c°$«iÓ8’ñ ð ‚ØˆØ€Nr   c                ó:   € V ^8„  d   QhR\         \        ,          /# )r
   rº   r   )r   s   "r   r   r   W  s   € ÷ 3ñ 3œœS�	ñ 3r   c                 óJ   € \        V 4      ^ 8H  ;'       d    \        V 4      ^8H  # rÑ   ©rW   r   )rº   s   &r   Úshould_skiprý   W  s"   € Ü�‹=˜AÑ×2Ð2¤# f£+°Ñ"2Ð2r   c                ót   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        R\        /# )r
   ÚfirstÚsecondÚ	dimensionr×   r   )r   s   "r   r   r   [  s4   € ÷ ñ Ü”�9ðÜ"¤3�iðÜ47ðÜ@Cñr   c                 ó  € \        V 4      p\        V4      pWE8w  d   \        R V RV 24      h\        ^ V4       FA  pWb8w  g   K  W,          W,          8w  g   K   \        RV RW,           RW,           RV 24      h	  R# )z1Tensors must have same number of dimensions, got z and z0Sizes of tensors must match except in dimension ú, got z at dimension N)r   r   r   )rÿ   r   r  r×   Ú
first_dimsÚsecond_dimsrN   s   &&&&   r   Úcheck_cat_shape_except_dimr  [  s–   € ô �U“€JÜ�f“+€KØÔ ÜØ?À
¸|È5Øˆmðó
ð 	
ô �Q˜
Ö#ˆØÖØ�z˜V�[Ö(Ü$ØFÀyÀkð RØ �:˜, e¨F­K¨=¸ÀsÀeðMóð ó $r   c                ó\   € V ^8„  d   QhR\         \         \        ,          ,          R\        /# ©r
   rò   rN   r   )r   s   "r   r   r   n  s"   € ÷ ñ ””dœ3•i•ð ¤sñ r   c                 óz  € \        V 4       \        W4      p\        V 4      ^ 8:  d   \        R4      hRpV  F  p\	        V4      '       d   K  TpK  	  Vf   ^ .# ^ p\        \        V 4      4       F9  pW,          p\	        V4      '       d   K  \        W#W4       WCV,          ,           pK;  	  \        V4      pWFV&   V# )r   z(Cannot concatenate empty list of tensorsN)rô   rù   r   r   rý   r   r  r=   )rò   rN   Únot_skipped_tensorrº   Úcat_dim_sizer   rÚ   s   &&     r   Úcatr  n  s¸   € Ü˜'Ô"Ü
˜cÓ
+€CÜ
ˆ7ƒ|�qÔÜÐGÓHÐHØ.2ÐÛˆÜ˜6×"Ô"Ø!'Òñ ð Ò!Øˆsˆ
à€Lä”3�w“<Ö ˆØ•ˆÜ˜6×"Ô"Ü&Ð'9À3ÔJØ'°­+Õ5ŠLñ	 !ô Ð*Ó+€KØ#�ÑØÐr   c                ó\   € V ^8„  d   QhR\         \         \        ,          ,          R\        /# r  r   )r   s   "r   r   r   ‡  s"   € ÷ (ñ (”4œœS�	•?ð (¬ñ (r   c                 óf   € . pV  F  p\        W14      pVP                  V4       K!  	  \        W!4      # r*   )rÆ   r   r  )rò   rN   Úunsqueezed_tensorsrº   Ú
unsqueezeds   &&   r   Ústackr  ‡  s8   € Ø*,ÐÛˆÜ˜vÓ+ˆ
Ø×!Ñ! *Ö-ñ ô Ð!Ó'Ð'r   c                óR   € V ^8„  d   QhR\         \        ,          R\        R\        /# rÖ   r   )r   s   "r   r   r   �  s%   € ÷ ñ ””c•ð ¤ð ¬Sñ r   c                 ó,  € \        V 4      pV^ 8X  d   \        R4      h\        W4      pW,          pW$) 8  g   W$8¼  d   \        RV RV RV 24      hV^ 8  d	   W$,          p. p\        V4       F"  pWa8w  g   K  VP	                  W,          4       K$  	  V# )r   z)Cannot select from a 0-dimensional tensorzIndex z  is out of bounds for dimension z with size )r   r   ro   r   r   )r3   rN   r×   r   rO   r4   r   s   &&&    r   Úselectr  �  s›   € Üˆt‹9€DØˆq„yÜÐHÓIÐIÜ
˜Ó
#€CØ�9€DØˆu„}˜œÜØ�U�GÐ;¸C¸5ÀÈDÈ6ÐRó
ð 	
ð ˆq„yØ�ˆØ€CÜ�4Ž[ˆØŽ8Ø�J‰J�t•wÖñ ð €Jr   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# )r
   Útensor1Útensor2r   )r   s   "r   r   r   ¢  s&   € ÷ &Pñ &P”Dœ•Ið &P¬¬S­	ñ &Pr   c                 óÎ  € \        V 4      p\        V4      pV^8X  d   V^8X  d   \        W4      # V^8X  d   V^8X  d   \        W4      # V^8X  d(   V^8X  d!   \        \	        \        V ^ 4      V4      ^ 4      # V^8X  d   V^8X  d   \	        W4      # V^8¼  dÆ   V^8¼  d¿   V^8”  d
   V R,          M^p. p\        V^,
          4       F  pVP                  W,          4       K  	  VR,          p. p\        V^,
          4       F  pVP                  W,          4       K  	  \        WX4      p	T	p
V^8”  d   V
P                  V4       V^8”  d   V
P                  V4       V
# \        R4      h)r   z/both arguments to matmul need to be at least 1Dr¦   rL   )
r   r¼   rÀ   rÎ   r¸   rÆ   r   r   r%   r   )r  r  Údim_tensor1Údim_tensor2ÚnÚbatch_tensor1r   ÚpÚbatch_tensor2Úexpand_batch_portionÚoutput_shapes   &&         r   Úmatmulr!  ¢  sW  € Ü�g“,€KÜ�g“,€KØ�aÔ˜K¨1Ô,Ü�7Ó$Ð$Ø	˜Ô	˜k¨QÔ.Ü�'Ó#Ð#Ø	˜Ô	˜k¨QÔ.Ü”rœ) G¨QÓ/°Ó9¸1Ó=Ð=Ø	˜Ô	˜k¨QÔ.Ü�'Ó#Ð#Ø	˜Ô	˜k¨QÔ.ð '¨œ?ˆG�BŽK°ˆØ#%ˆä�{ Q•Ö'ˆAØ× Ñ  ¥Ö,ñ (à�B�KˆØ#%ˆä�{ Q•Ö'ˆAØ× Ñ  ¥Ö,ñ (ô  )¨ÓFÐð ,ˆØ˜Œ?Ø×Ñ Ô"à˜Œ?Ø×Ñ Ô"àÐäÐNÓOÐOr   c                ó:   € V ^8„  d   QhR\         \        ,          /# r:   r   )r   s   "r   r   r   Ë  s   € ÷ "ñ "ŒD”�Iñ "r   c                 óÂ   € \        V 4      ^8”  d   \        R\        V 4       24      h\        V 4      pV^ 8X  d   . pV# V^8X  d   V ^ ,          .# V ^,          V ^ ,          .# )r
   z1Expected tensor to have <= 2 dimensions, but got rœ   )r3   Úself_lenr4   s   &  r   Útr%  Ë  sj   € Ü
ˆ4ƒy�1„}ÜØ?ÄÀDÃ	¸{ÐKó
ð 	
ô �4‹y€HØ�1„}ØˆØˆ
Ø	�QŒØ�Q•ˆyÐà�Q•˜˜a�Ð!Ð!r   c                óR   € V ^8„  d   QhR\         \        ,          R\        R\        /# )r
   r3   Údim0Údim1r   )r   s   "r   r   r   Ú  s%   € ÷ ñ ”Dœ•Ið ¤Sð ´ñ r   c                 ó<  € \        V 4      p\        W4      p\        W#4      pW8X  d   \        V 4      # . p\        V4       FX  pWQ8X  d   VP	                  W,          4       K"  WR8X  d   VP	                  W,          4       KA  VP	                  W,          4       KZ  	  V# r*   )r   ro   r=   r   r   )r3   r'  r(  Úndimsr4   r   s   &&&   r   Ú	transposer+  Ú  s|   € Ü�‹I€EÜ˜$Ó&€DÜ˜$Ó&€DØ„|Ü�T‹{ÐØ€CÜ�5Ž\ˆØŒ9Ø�J‰J�t•zÖ"ØŒYØ�J‰J�t•zÖ"à�J‰J�t•wÖñ ð €Jr   c                ó”   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          /# )r
   r�   rÝ   Úbiasrë   )r   s   "r   r   r   ë  s2   € ÷ ñ ”$”s•)ð ¤T¬#¥Yð ´h¼tÄC½yÕ6Iñ r   c                 óz   € \        V \        V4      4      pVe"   \        W#4      V8w  d   \        RV RV 24      hV# )NzBias shape z& is not broadcastable to output shape )r!  r%  r%   r   )r�   rÝ   r-  r4   s   &&& r   Úlinearr/  ë  sJ   € Ü
�œ˜&›	Ó
"€CØÒÜ�TÓ 3Ô&Ü Ø˜d˜VÐ#IÈ#ÈÐOóð ð €Jr   c          
      ó–   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\        R\        /# )r
   r3   Úmat1r´   ÚbetaÚalphar/   )r   s   "r   r   r   õ  s;   € ÷ +ñ +””S•	ð +¤¤c¥ð +´$´sµ)ð +Ä3ð +Ìsñ +r   c                 ó,   € \        V \        W4      4      # r*   )r%   r¸   )r3   r1  r´   r2  r3  s   &&&&&r   Úaddmmr5  õ  s   € Ü�Tœ2˜d›>Ó*Ð*r   c                óF   € V ^8„  d   QhR\         \        ,          R\        /# )r
   ÚarrayrX   rf   )r   s   "r   r   r   ù  s   € ÷ ñ œd¤3�ið ¬Dñ r   c                 ó2   € R pV  F  pV^ 8  g   K  RpK  	  V# )FTr`   )r7  Únon_negativeÚvals   &  r   Úcheck_non_negativer;  ù  s%   € à€LÛˆØ�Ž7ØŠLñ ð Ðr   c                ó  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        /# )r
   r�   Úweight_sizesr-  r‚   r¢   rƒ   Úgroupsrë   )r   s   "r   r   r     sk   € ÷ 1ñ 1Ü”�9ð1ä”s•)ð1ô ”4œ•9Õ
ð1ô ”�Ið	1ô
 ”#�Yð1ô ”3�ið1ô ñ1r   c           	      ó  € \        V 4      p\        V4      p\        V4      '       d   \        R V 24      h\        V4      '       d   \        RV 24      hW‡8w  d   \        RV RV R24      hV^ ,          V8  d   \        RV^ ,           RV R24      hV^ ,          V,          ^ 8w  d   \        RV^ ,           RV R24      hV ^,          V^,          V,          8w  d(   \        RV ^,           R	V^,          V,           R24      hVe>   \        V4      ^8X  d   V^ ,          V^ ,          8X  g   \        RV^ ,           RV 24      h\        ^V4       F°  p	W	,          ^WI^,
          ,          ,          ,           WY^,
          ,          W,          ^,
          ,          ^,           8  g   KU  \        RW	,          ^WI^,
          ,          ,          ,            RWY^,
          ,          W,          ^,
          ,          ^,            RV	 24      h	  R
# )z"Padding must be non-negative, got z!Stride must be non-negative, got zExpected weight_dim (z) == k (rJ   zExpected weight_sizes[0] (z) >= groups (z) to be divisible by groups (zExpected input[1] (z) == weight_sizes[1] * groups (NzFExpected bias to be None or have shape [1] with value weight_sizes[0]=r  zCalculated padded input size (z)) is smaller than effective kernel size (z) at dimension )r   r;  r   r   )
r�   r=  r-  r‚   r¢   rƒ   r>  ÚkÚ
weight_dimr   s
   &&&&&&&   r   Úcheck_shape_forwardrB    s  € ô 	ˆE‹
€AÜ�\Ó"€Jô ˜'×"Ò"ÜÐAÀ'ÀÐKÓLÐLÜ˜&×!Ò!ÜÐ@ÀÀÐIÓJÐJà„ÜÐ4°Z°LÀÈÈÈ1ÐMÓNÐNØ�A…˜ÔÜØ(¨°a­Ð(9¸ÀvÀhÈaÐPó
ð 	
ð 	�Q�˜&Õ  QÔ&ÜØ(¨°a­Ð(9ð :Ø�h˜að!ó
ð 	
ð
 ˆQ…x�< •? VÕ+Ô+ÜØ! %¨¥( ð ,Ø˜Q• &Õ(Ð)¨ð,ó
ð 	
ð Ò¤ T£¨a¤°D¸µG¸|ÈA½Ô4NÜðØ+¨A�Ð/¨v°d°Vð=ó
ð 	
ô
 �1�aŽ[ˆØ�H�q˜7 q¥5�>Õ)Õ)Ø˜•U�O˜|�°Õ2Õ3°aÕ7ö
ô !Ø0°µ¸AÀÈAÍÅÕ<NÕ1NÐ0Oð Pà �U•O |¥¸Õ':Õ;¸aÕ?Ð@ÀÐPQÈsðTóð ó	 r   c                ó  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        /# )r
   Ú
input_sizeÚweight_sizer-  r‚   r¢   rƒ   r>  rë   )r   s   "r   r   r   8  sk   € ÷ ñ Ü”S•	ðä”c•ðô ”4œ•9Õ
ðô ”�Ið	ô
 ”#�Yðô ”3�iðô ñr   c           	      óö  € \        WW#WEV4       \        V4      ^ 8„  p\        V 4      p. p	^ p
^ pV	P                  W
,          4       V	P                  W,          4       \        ^V4       F�  pV'       d   W\^,
          ,          M^pWÑV,          ^,
          ,          ^,           pV	P                  W,          ^WL^,
          ,          ,          ,           V,
          W<^,
          ,          ,          ^,           4       K�  	  V	# rÑ   )rB  r   r   r   )rD  rE  r-  r‚   r¢   rƒ   r>  Úhas_dilationrN   r­   Úinput_batch_size_dimÚweight_output_channels_dimÚdÚ	dilation_Úkernels   &&&&&&&        r   Úconv_output_sizerM  8  sÖ   € ô Ø ¨wÀ&ôô �x“= 1Ñ$€LÜ
ˆj‹/€CØ€KØÐØ!"ÐØ×Ñ�zÕ7Ô8Ø×Ñ�{Õ>Ô?ä�1�cŽ]ˆß'3�H �U–O¸ˆ	Ø¨!�n¨qÕ0Õ1°AÕ5ˆØ×ÑØ�]˜a '¨a­%¥.Õ0Õ1°FÕ:¸vÈ!Åe½}ÕLÈqÕPö	
ñ ð Ðr   c                ó  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        /# ©r
   r�   rÝ   r-  r‚   r¢   rƒ   r>  rë   )r   s   "r   r   r   V  ót   € ÷ Tñ TÜ”�9ðTä”�IðTô ”4œ•9Õ
ðTô ”�Ið	Tô
 ”#�YðTô ”3�iðTô ñTr   c           	      ó¾   € \        V4      ^8w  d   \        R\        V4       R24      h\        V 4      ^8w  d   \        R\        V 4       R24      h\        WW#WEV4      # )é   z#Expected 3D weight for conv1d, got rã   z"Expected 3D input for conv1d, got ©r   r   rM  ©r�   rÝ   r-  r‚   r¢   rƒ   r>  s   &&&&&&&r   Úconv1drU  V  ó^   € ô ˆ6ƒ{�aÔÜÐBÄ3ÀvÃ;À-ÈqÐQÓRÐRÜ
ˆ5ƒz�Q„ÜÐAÄ#ÀeÃ*ÀÈQÐOÓPÐPÜ˜E¨4¸ÈFÓSÐSr   c                ó  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        /# rO  rë   )r   s   "r   r   r   f  rP  r   c           	      ó¾   € \        V4      ^8w  d   \        R\        V4       R24      h\        V 4      ^8w  d   \        R\        V 4       R24      h\        WW#WEV4      # )é   z#Expected 4D weight for conv2d, got rã   z"Expected 4D input for conv2d, got rS  rT  s   &&&&&&&r   Úconv2drZ  f  rV  r   c          
      ó¶   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\        \         \        ,          ,          /# )r
   Úgrad_outputr�   rÝ   Úbiasesrë   )r   s   "r   r   r   v  sE   € ÷ 9ñ 9Ü”c•ð9ä”�9ð9ô ”�Ið9ô ”Tœ#•YÕñ	9r   c                 ó@   € \        V4      \        V4      V ^,          .3# r_   r@   )r\  r�   rÝ   r]  s   &&&&r   Úconv_backwardsr_  v  s    € ô �‹<œ˜v›¨°Q­Ð(8Ð8Ð8r   c                ó¢  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        R\        \         \        ,          ,          R	\         \        ,          /	# )
r
   r�   rÝ   r-  r‚   r¢   Úoutput_paddingr>  rƒ   rX   rë   )r   s   "r   r   r   €  s›   € ÷ $ñ $Ü”�9ð$ä”�Ið$ô ”4œ•9Õ
ð$ô ”Tœ#•YÕð	$ô
 ”dœ3•iÕ ð$ô œT¤#�YÕ'ð$ô ð$ô ”tœC•yÕ!ð$ô 
Œ#…Yñ$r   c                 óP  € Vf   ^^.pVf   ^ ^ .pVf   ^ ^ .pVf   ^^.p\        V4      ^ 8„  p\        V 4      p	. p
^ p^pV
P                  W,          4       V
P                  W,          V,          4       \        ^V	4       F¡  pV'       d   W}^,
          ,          M^pWáV,          ^,
          ,          pV
P                  W,          ^,
          W=^,
          ,          ,          ^WM^,
          ,          ,          ,
          V,           W]^,
          ,          ,           ^,           4       K£  	  V
# r*   ©r   r   r   )r�   rÝ   r-  r‚   r¢   ra  r>  rƒ   rG  rN   r­   rH  rI  rJ  rK  rL  s   &&&&&&&&        r   Úconv_transpose2d_inputrd  €  s  € ð ‚~Ø�Q�ˆØ‚Ø�a�&ˆØÒØ˜Q˜ˆØÒØ�q�6ˆÜ�x“= 1Ñ$€LÜ
ˆe‹*€CØ€KØÐØ!"ÐØ×Ñ�uÕ2Ô3Ø×Ñ�vÕ9¸FÕBÔCä�1�cŽ]ˆß'3�H �U–O¸ˆ	Ø Q�i¨!�mÕ,ˆØ×ÑØ�X˜�\˜V¨¥E�]Õ*Ø�'˜a�%•.Õ õ!àõð  �UÕ#õ$ð õ	ö	
ñ ð Ðr   c                óV  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        R\         \        ,          R	\        R
\         \        ,          /
# )r
   r�   rÝ   r-  r‚   r¢   rƒ   Ú
transposedra  r>  rX   ©r   r   r   rg   )r   s   "r   r   r   §  s‘   € ÷ (ñ (Ü”�9ð(ä”�Ið(ô ”4œ•9Õ
ð(ô ”�Ið	(ô
 ”#�Yð(ô ”3�ið(ô ð(ô œ•Ið(ô ð(ô 
Œ#…Yñ(r   c	                 ó’  € \        V4      ^ 8„  p	\        V4      ^ 8„  p
\        V 4      p. p^ pV'       d   ^M^ pVP                  W,          4       V'       d    VP                  W,          V,          4       MVP                  W,          4       \        ^V4       EF)  pV	'       d   W_^,
          ,          M^pV
'       d   W^,
          ,          M^ pV'       d{   VW,          ^,
          ,          pVP                  W,          ^,
          W?^,
          ,          ,          ^WO^,
          ,          ,          ,
          V,           V,           ^,           4       K¸  VW,          ^,
          ,          ^,           pVP                  W,          ^WO^,
          ,          ,          ,           V,
          W?^,
          ,          ,          ^,           4       EK,  	  V# rÑ   rc  )r�   rÝ   r-  r‚   r¢   rƒ   rf  ra  r>  rG  Úhas_output_paddingrN   r­   rH  rI  rJ  rK  Úoutput_padding_rL  s   &&&&&&&&&          r   Úconv_forwardsrk  §  se  € ô �x“= 1Ñ$€LÜ˜^Ó,¨qÑ0ÐÜ
ˆe‹*€CØ€KØÐß&0¡°aÐØ×Ñ�uÕ2Ô3ßØ×Ñ˜6Õ=ÀÕFÕGà×Ñ˜6Õ=Ô>ä�1�c�]ˆß'3�H �U–O¸ˆ	ß3E˜.¨Q­Ö/È1ˆßØ &¥)¨a¥-Õ0ˆFØ×ÑØ•˜A• ¨A­¥Õ.Ø�g !�e•nÕ$õ%àõð "õ"ð õ	öð  &¥)¨a¥-Õ0°1Õ4ˆFØ×ÑØ•˜Q ¨Q­¥Õ/Õ0°6Õ9¸fÈÅU½mÕKÈaÕO÷ñ ð" Ðr   c                ó†  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        R\         \        ,          R	\        R
\        R\        R\        R\        R\         \        ,          /# )r
   r�   rÝ   r-  r‚   r¢   rƒ   rf  ra  r>  Ú	benchmarkÚdeterministicÚcudnn_enabledÚ
allow_tf32rX   rg  )r   s   "r   r   r   Ò  s¹   € ÷ ñ Ü”�9ðä”�Iðô ”4œ•9Õ
ðô ”�Ið	ô
 ”#�Yðô ”3�iðô ðô œ•Iðô ðô ðô ðô ðô ðô 
Œ#…Yñr   c                 ó(   € \        V VVVVVVVV4	      # r*   )rk  )r�   rÝ   r-  r‚   r¢   rƒ   rf  ra  r>  rm  rn  ro  rp  s   &&&&&&&&&&&&&r   Ú_conv_forwardsrr  Ò  s,   € ô ØØØØØØØØØó
ð 
r   c                óJ  € V ^8„  d   QhR\         \        ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        R\        R\        R	\        /	# )
r
   r�   rÝ   r-  Úrunning_meanÚrunning_varÚtrainingÚmomentumÚepsro  )r   r   r   rg   r°   )r   s   "r   r   r   î  sŠ   € ÷ ñ Ü”�9ðä”Tœ#•YÕðô ”4œ•9Õ
ðô œ4¤�9Õ%ð	ô
 œ$œs�)Õ$ðô ðô ðô 
ðô ñr   c	                 ó>   € . p	V  F  p
V	P                  V
4       K  	  V	# r*   r<   )r�   rÝ   r-  rt  ru  rv  rw  rx  ro  r4   r7   s   &&&&&&&&&  r   Ú
batch_normrz  î  s$   € ð €CÛˆØ�
‰
�4Öñ à€Jr   c                ó  € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\         \        ,          R\         \        ,          R\         \        ,          R\        /# rO  rë   )r   s   "r   r   r   ÿ  rP  r   c           	      ó¾   € \        V4      ^8w  d   \        R\        V4       R24      h\        V 4      ^8w  d   \        R\        V 4       R24      h\        WW#WEV4      # )é   z#Expected 5D weight for conv3d, got rã   z"Expected 5D input for conv3d, got rS  rT  s   &&&&&&&r   Úconv3dr~  ÿ  rV  r   c                ó<   € V ^8„  d   QhR\         R\         R\        /# )r
   rN   Údim_post_exprÚwrap_scalarr…   )r   s   "r   r   r     s!   € ÷ ñ œð ¬Cð ¼dñ r   c           	      ó¶   € V^ 8:  d   V'       g   \        R4      h^pV) pV^,
          pW8  g   W8”  d   \        RV  RV RV R24      hV ^ 8  d	   W,          p V # )r   z7Expected wrap_scalar to be True when dim_post_expr <= 0z
Dimension z( out of range (expected to be in range [z, z]))r   )rN   r€  r�  Úminr   s   &&&  r   ro   ro     s{   € Ø˜ÔßÜ ØIóð ð ˆØˆ.€CØ
˜!Õ
€CØ
„y�C”IÜØ˜˜ÐEÀcÀUÈ"ÈSÈEÐQSÐTó
ð 	
ð ˆQ„wØÕˆØ€Jr   c                ó$   € V ^8„  d   QhR\         /# ©r
   r�   )r   )r   s   "r   r   r   !  s   € ÷ ñ œ3ñ r   c                 ó
   € . pV# r*   r`   )r�   r4   s   & r   Úzero_dim_tensorr‡  !  s   € Ø€CØ€Jr   c                ó:   € V ^8„  d   QhR\         \        ,          /# rÈ   r   )r   s   "r   r   r   &  s   € ÷ ñ œ$œs�)ñ r   c                 ó,   € ^pV  F  pW,          pK  	  V# r_   r`   )rÃ   r4   r7   s   &  r   rÙ   rÙ   &  s   € Ø
€CÛˆØ�jŠñ à€Jr   c          
      óT   € V ^8„  d   QhR\         R\        R\        R\        R\        /# )r
   ré   rS   Úinp1Úinp2Úinp3©Únumberr   )r   s   "r   r   r   -  s/   € ÷ !ñ !”Fð !¤#ð !¬Sð !¼ð !Ä3ñ !r   c                 ón   € V ^ 8  d   \        RV  R24      h\        \        P                  ! V 4      4      .# )r   úExpected end (ú) >= 0©r   r   ÚmathÚceil)ré   rS   r‹  rŒ  r�  s   &&&&&r   Ú
arange_endr–  -  s3   € Ø
ˆQ„wÜ˜~¨c¨U°&Ð9Ó:Ð:Ü”—	’	˜#“ÓÐ Ð r   c                ó`   € V ^8„  d   QhR\         R\         R\        R\        R\        R\        /# )r
   rè   ré   rS   r‹  rŒ  r�  rŽ  )r   s   "r   r   r   3  s<   € ÷ )ñ )Üð)Üð)Ü&)ð)Ü14ð)Ü<?ð)ÜGJñ)r   c                 óª   € V^ 8  d   \        RV R24      hW8  d   \        RV RV  R24      h\        \        P                  ! W,
          4      4      .# )r   r‘  r’  ú) >= start (rJ   r“  )rè   ré   rS   r‹  rŒ  r�  s   &&&&&&r   Úarange_startrš  3  sW   € ð ˆQ„wÜ˜~¨c¨U°&Ð9Ó:Ð:Ø
„{Ü˜~¨c¨U°,¸u¸gÀQÐGÓHÐHÜ”—	’	˜#�+Ó&Ó'Ð(Ð(r   c                ól   € V ^8„  d   QhR\         R\         R\         R\        R\        R\        R\        /# )r
   rè   ré   rê   rS   r‹  rŒ  r�  rŽ  )r   s   "r   r   r   =  sD   € ÷ 2ñ 2Üð2Üð2Ü&,ð2Ü47ð2Ü?Bð2ÜJMð2ÜUXñ2r   c                 óð   € V^ 8X  d   \        R4      hV^ 8  d   W8  d   \        RV  RV R24      hMW8  d   \        RV RV  R24      h\        \        P                  ! W,
          V,          4      4      .# )r   zstep must not be zerozExpected start (z
) >= end (z) when step < 0r‘  r™  z) when step > 0r“  )rè   ré   rê   rS   r‹  rŒ  r�  s   &&&&&&&r   Úarange_start_stepr�  =  s�   € ð ˆq„yÜÐ4Ó5Ð5Øˆa„xØŒ;Ü Ø" 5 '¨°C°5¸ÐHóð ð ð
 Œ;Ü Ø    \°%°¸ÐHóð ô ”—	’	˜3�;¨$Õ.Ó/Ó0Ð1Ð1r   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# )r
   r�   rp   r   )r   s   "r   r   r   O  s"   € ÷ ñ ”4œ•9ð ¤D¬¥Iñ r   c                 óÐ  € \        V 4      \        V4      8w  d%   \        R \        V 4       R\        V4       R24      h\        V4      p. p. p\        V4       F=  p\        W,          V4      pVP	                  V4       VP	                  W,          4       K?  	  \        ^V4       F=  p\        V4       F+  pW5,          W7,          8X  g   K  \        RW5,           R24      h	  K?  	  V# )úExpected len(input) (z) == len(dims) (rJ   zRepeated dimension z in permute dimensions)r   r   r   ro   r   )r�   rp   r   Ú	seen_dimsÚnewSizesr   rN   Újs   &&      r   Úpermuter¤  O  sÖ   € Ü
ˆ5ƒz”S˜“YÔÜØ#¤C¨£J <Ð/?ÄÀDÃ	¸{È!ÐLó
ð 	
ô ˆt‹9€DØ€IØ€HÜ�4Ž[ˆÜ˜T�W dÓ+ˆØ×Ñ˜ÔØ�‰˜�
Ö#ñ ô �1�dŽ^ˆÜ�q–ˆAØ�|˜y�|Ö+Ü$Ø)¨)­,¨Ð7MÐNóð ó ñ ð €Or   c          	      ó    € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          R\         \        ,          /# )r
   r3   ÚsourceÚdestinationrX   r   )r   s   "r   r   r   d  s9   € ÷  ñ  ”$”s•)ð  ¤T¬#¥Yð  ¼TÄ#½Yð  Ì4ÔPSÍ9ñ  r   c                 ó:  € \        V 4      pV^8:  d   V # . p. p\        \        V4      4       FE  pVP                  \        W,          V4      4       VP                  \        W&,          V4      4       KG  	  \        V4       Uu. uF  pRNK  	  pp\        V4       Uu. uF  qfNK  	  pp\        V4       Uu. uF  qfNK  	  p	p\        \        V4      4       F*  pWF,          WuV,          &   RW„V,          &   RW•V,          &   K,  	  . p
. pV F  pVR8w  g   K  V
P                  V4       K  	  V	 F  pVR8w  g   K  VP                  V4       K  	  V\        V4      ,
          p\        V4       F  pW¦,          W{V,          &   K  	  \	        W4      # u upi u upi u upi )r   rL   )r   r   r   ro   r¤  )r3   r¦  r§  Úself_dimÚnormalized_srcÚnormalized_dstr   ÚorderÚsrc_dimsÚdst_dimsÚsource_dimsÚdestination_dimsÚeleÚrest_dims   &&&           r   Úmovedimr³  d  sy  € Ü�4‹y€HØ�1„}ØˆØ "€NØ "€NÜ”3�v“;ÖˆØ×Ñœn¨V­Y¸ÓAÔBØ×Ñœn¨[­^¸XÓFÖGñ  ô ˜xœÓ)™�A‹R™€EÐ)Ü  œ?Ó+™?�a’™?€HÐ+Ü  œ?Ó+™?�a’™?€HÐ+ä”3�v“;ÖˆØ#1Õ#4ˆ˜QÕÑ Ø&(ˆ Õ"Ñ#Ø&(ˆ Õ"Ó#ñ  ð
  €KØ"$ÐÛˆØ�"Ž9Ø×Ñ˜sÖ#ñ ó ˆØ�"Ž9Ø×#Ñ# CÖ(ñ ð œ#˜f›+Õ%€HÜ�8Ž_ˆØ%0¥^ˆ˜qÕ!Ó"ñ ä�4ÓÐùò+ *ùÚ+ùÚ+s   ÂFÂ
FÂ6
Fc                óR   € V ^8„  d   QhR\         \        ,          R\        R\        /# )r
   r�   Ú	start_dimÚend_dimr   )r   s   "r   r   r   …  s%   € ÷ ñ ”4œ•9ð ¬ð ´sñ r   c                 ó8  € \        V\        V 4      4      p\        V\        V 4      4      pW8”  d   \        R V RV R24      h\        V 4      ^ 8X  d   ^.# W8X  d   . pV  F  pVP                  V4       K  	  V# ^p\	        W^,           4       F  pWPV,          ,          pK  	  . p\	        V4       F  pVP                  W,          4       K  	  VP                  V4       \	        V^,           \        V 4      4       F  pVP                  W,          4       K  	  V# )zExpected start_dim (z) <= end_dim (rJ   )ro   r   r   r   r   )r�   rµ  r¶  r4   r7   Úslice_numelr   r6   s   &&&     r   Úflattenr¹  …  sü   € Ü˜y¬#¨e«*Ó5€IÜ˜W¤c¨%£jÓ1€GØÔÜÐ3°I°;¸nÈWÈIÐUVÐWÓXÐXÜ
ˆ5ƒz�Q„Øˆsˆ
ØÔàˆÛˆDØ�J‰J�tÖñ àˆ
Ø€KÜ�9¨�kÖ*ˆØ˜Q•xÕŠñ +ð €EÜ�9ÖˆØ�‰�U•XÖñ à	‡L�L�ÔÜ�7˜Q•;¤ E£
Ö+ˆØ�‰�U•XÖñ ,à€Lr   c                ó:   € V ^8„  d   QhR\         \        ,          /# r…  r   )r   s   "r   r   r      s   € ÷ ñ œt¤C�yñ r   c                 ó   € ^ \        V 4      .# rÑ   ©r   ©r�   s   &r   Únonzero_lower_boundr¾     s   € ØŒs�5‹zˆ?Ðr   c                ó:   € V ^8„  d   QhR\         \        ,          /# r…  r   )r   s   "r   r   r   ¤  s   € ÷ &ñ &œt¤C�yñ &r   c                 ó.   € \        V 4      \        V 4      .# r*   rü   r½  s   &r   Únonzero_upper_boundrÁ  ¤  s   € Ü�%‹Lœ#˜e›*Ð%Ð%r   c                óR   € V ^8„  d   QhR\         \        ,          R\        R\        /# )r
   r3   rN   Úkeepdimrf   )r   s   "r   r   r   ¨  s%   € ÷ 	ñ 	œD¤�Ið 	¬Cð 	¼$ñ 	r   c                 óÄ   € \        V\        V 4      4      p. p\        V 4       F9  w  rEWA8X  d   V'       d   VP                  ^4       K&  K(  VP                  V4       K;  	  V# r_   )ro   r   Ú	enumerater   )r3   rN   rÃ  r4   r   r©  s   &&&   r   Ú_reduce_along_dimrÆ  ¨  sT   € Ü
˜œc $›iÓ
(€CØ€CÜ  –‰ˆØŒ8ßØ—
‘
˜1–ñ ð �J‰J�xÖ ñ 'ð €Jr   c          	      óŠ   € V ^8„  d   QhR\         \        ,          R\        \        ,          R\        R\         \        ,          /# )r
   r3   rN   rÃ  rX   rg  )r   s   "r   r   r   ´  s8   € ÷ 1ñ 1Ü
Œs�)ð1Ü"¤3�-ð1Ü9=ð1ä	Œ#…Yñ1r   c                 ó&   € Vf   . # \        WV4      # r*   )rÆ  )r3   rN   rÃ  s   &&&r   ÚargmaxrÉ  ´  s   € ð ‚{Øˆ	Ü˜T¨Ó0Ð0r   c                ó~   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\         \        ,          /# )r
   r3   r´   rX   r   )r   s   "r   r   r   ¼  s-   € ÷ 'ñ 'Œd”3�ið 'œt¤C�yð '¬T´#­Yñ 'r   c                 ó¦  € \        V 4      ^8w  d   \        R\        V 4       R24      h\        V4      ^8w  d   \        R\        V4       R24      hV ^ ,          V^ ,          8w  d    \        RV ^ ,           RV^ ,           24      hV ^,          V^,          8w  d    \        RV ^,           RV^,           24      hV ^ ,          V ^,          V^,          .# )rR  z"bmm only supports 3D tensors, got rã   z%mismatching batch dimension: self[0]=r¶   z+mismatching contracting dimension: self[2]=z
, mat2[1]=rœ   r·   s   &&r   ÚbmmrÌ  ¼  sÒ   € Ü
ˆ4ƒy�A„~ÜÐAÄ#ÀdÃ)ÀÈAÐNÓOÐOÜ
ˆ4ƒy�A„~ÜÐAÄ#ÀdÃ)ÀÈAÐNÓOÐOØˆA…w�$�q•'ÔÜØ3°D¸µG°9¸JÀtÈAÅwÀiÐPó
ð 	
ð ˆA…w�$�q•'ÔÜØ9¸$¸q½'¸À*ÈTÐRSÍWÈIÐVó
ð 	
ð ��G�T˜!•W˜d 1�gÐ&Ð&r   c                ó\   € V ^8„  d   QhR\         \        ,          R\         \        ,          /# )r
   r3   rX   r   )r   s   "r   r   r   Ì  s"   € ÷ ñ œ4¤�9ð ¬¬c­ñ r   c                 ó   € \        V 4      .# r*   r¼  rA   s   &r   Ú_shape_as_tensorrÏ  Ì  s   € Ü�‹Iˆ;Ðr   c                ó¬   € V ^8„  d   QhR\         \        ,          R\        R\        R\        \         \        ,          \         \        ,          3,          /# )r
   r3   r@  rN   rX   ©r   r   Útuple)r   s   "r   r   r   Ð  s>   € ÷ 
ñ 
Œt”C�yð 
œSð 
¤sð 
´E¼$¼s½)ÄTÌ#ÅYÐ:NÕ4Oñ 
r   c           	      óž   € \        V 4      ^ 8X  d   . pW33# WV,          8”  d   \        RV RV RW,           24      h\        V 4      pWV&   W33# )r   zk (z) is too big for dimension z	 of size )r   r   r=   )r3   r@  rN   rÓ   s   &&& r   ÚtopkrÔ  Ð  sg   € Ü
ˆ4ƒy�A„~Øˆð ˆ>Ðð �C�yŒ=Ü Ø�a�SÐ3°C°5¸	À$Å)ÀÐMóð ô �t“ˆØˆs‰Øˆ>Ðr   c                óú   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\        R\        \         \        ,          \         \        ,          3,          /# )r
   r3   ÚtargetrÝ   Ú	reductionrX   ©r   r   r   rÒ  )r   s   "r   r   r   Ý  sW   € ÷ )ñ )Ü
Œs�)ð)Ü!¤#�Yð)Ü08¼¼c½Õ0Cð)ÜPSð)ä
Œ4”�9”dœ3•iÐÕ ñ)r   c                 óî  € \        V 4      p\        V4      p^ Tu;8  d   ^8:  g   M \        RV 24      hV^8”  d   \        RV 24      hV^8H  ;'       d    V^ 8H  pV'       g5   V ^ ,          V^ ,          8X  g    \        RV ^ ,           RV^ ,           24      hV R,          p. pVe0   \        V4      ^8X  d   V^ ,          V8X  g   \        RV RV 24      hV^ 8X  d   V^8X  d   V ^ ,          .p	W˜3# Tp	W˜3# )r   z-Expected 0 < self_dim <= 2, but got self_dim=z"Expected target_dim <= 1, but got zBatch size mismatch: self[0]=z, target[0]=z:Expected weight to be None or have shape [n_classes], got z with n_classes=rL   rœ   )
r3   rÖ  rÝ   r×  r©  Ú
target_dimÚno_batch_dimÚ	n_classesÚscalar_shapeÚreduction_shapes
   &&&&      r   Únll_loss_forwardrß  Ý  s   € ô �4‹y€HÜ�V“€JØ�Ö˜AÖÜÐLÈXÈJÐWÓXÐXØ�A„~ÜÐAÀ*ÀÐNÓOÐOØ˜q‘=×4Ð4 Z°1¡_€Lß˜T !�W¨¨q­	Ô1ÜØ+¨D°­G¨9°LÀÈÅÀÐLó
ð 	
ð �R•€IØ €LØÒ¤3 v£;°!Ô#3¸¸q½	ÀYÔ8NÜðØ�(Ð*¨9¨+ð7ó
ð 	
ð �A„~˜( aœ-Ø �7˜)ˆð Ð(Ð(ð 'ˆØÐ(Ð(r   c          
      óÖ   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          \         \        ,          \         \        ,          3,          /# )r
   r�   Únormalized_shaperX   rÑ  )r   s   "r   r   r   ú  sD   € ÷ :ñ :Ü”�9ð:Ü(,¬S­	ð:ä
Œ4”�9”dœ3•i¤¤c¥Ð*Õ+ñ:r   c                 óX  € . p\        V 4      \        V4      ,
          pV^ 8  d%   \        R\        V 4       R\        V4       R24      h\        V4       F  pVP                  W,          4       K  	  \        V\        V 4      4       F  pVP                  ^4       K  	  \	        V 4      W"3# )r   r   z) >= len(normalized_shape) (rJ   )r   r   r   r   r=   )r�   rá  rÞ  Únum_unreduced_dimensionsr   s   &&   r   Únative_layer_normrä  ú  s«   € ð "$€OÜ" 5›z¬CÐ0@Ó,AÕAÐØ !Ô#ÜØ#¤C¨£J <ð 0ÜÐ$Ó%Ð& að)ó
ð 	
ô Ð+Ö,ˆØ×Ñ˜u�xÖ(ñ -äÐ+¬S°«ZÖ8ˆØ×Ñ˜qÖ!ñ 9ä�‹<˜Ð9Ð9r   c                ó   € V ^8„  d   QhR\         \        ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        R\        \         \        ,          \         \        ,          \         \        ,          3,          /# )r
   r�   rÝ   r-  rt  ru  rv  rX   )r   r   r   rg   rÒ  )r   s   "r   r   r     sŽ   € ÷ &ñ &Ü”�9ð&ä”Tœ#•YÕð&ô ”4œ•9Õ
ð&ô œ4¤�9Õ%ð	&ô
 œ$œs�)Õ$ð&ô ð&ô Œ4”�9”dœ3•i¤¤c¥Ð*Õ+ñ&r   c                 óH   € V'       d   V ^,          .pM^ .p\        V 4      Wf3# r_   r@   )r�   rÝ   r-  rt  ru  rv  Ú_sizes   &&&&&& r   Únative_batch_normrè    s(   € ÷ Ø�q•�
‰à�ˆÜ�‹<˜Ð%Ð%r   c                ó´  € V ^8„  d   QhR\         \        ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        \         \        ,          ,          R\        \         \        ,          \         \        ,          \         \        ,          \         \        ,          3,          /# )r
   r�   rÝ   r-  rt  ru  rX   rØ  )r   s   "r   r   r     sŠ   € ÷ +ñ +Ü”�9ð+ä”Tœ#•YÕð+ô ”4œ•9Õ
ð+ô œ4¤�9Õ%ð	+ô
 œ$œs�)Õ$ð+ô Œ4”�9”dœ3•i¤¤c¥¬D´­IÐ5Õ6ñ+r   c                 ó4   € V ^,          .p\        V 4      WU^ .3# r_   r@   )r�   rÝ   r-  rt  ru  rç  s   &&&&& r   Ú_batch_norm_with_updaterë    s"   € ð �1�XˆJ€EÜ�‹<˜¨ sÐ*Ð*r   c                óÚ   € V ^8„  d   QhR\         \        ,          R\         \        ,          R\        \         \        ,          ,          R\        R\        R\        R\         \        ,          /# )r
   r3   rÖ  rÝ   r×  Úignore_indexÚlabel_smoothingrX   r¯   )r   s   "r   r   r   %  sc   € ÷ 	ñ 	Ü
Œs�)ð	ä”�Ið	ô ”Tœ#•YÕð	ô ð		ô
 ð	ô ð	ô 
Œ#…Yñ	r   c                 ó,   € \        WW#4      ^ ,          pV# rÑ   )rß  )r3   rÖ  rÝ   r×  rí  rî  Úresult_shapes   &&&&&& r   Úcross_entropy_lossrñ  %  s   € ô $ D°&ÓDÀQÕG€LØÐr   c                ó$   € V ^8„  d   QhR\         /# )r
   Úfuncr   )r   s   "r   r   r   J  s   € ÷ !ñ !”xñ !r   c                 óœ  € V \         9  d¶   \        P                  P                  V 4      p\        P                  P                  VP                  4       \        ^4       FU  p\        P                  P                  VP                  4       \        P                  P                  VP                  4       KW  	  V\         V &   \         V ,          # )r
   )
Úscript_func_mapÚtorchÚjitÚscriptÚ_CÚ_jit_pass_inlineÚgraphr   Ú_jit_pass_peepholeÚ_jit_pass_constant_propagation)ró  Úscripted_funcÚ_s   &  r   Úprocess_funcr   J  s‹   € Ø”?Ô"ÜŸ	™	×(Ñ(¨Ó.ˆä�‰×!Ñ! -×"5Ñ"5Ô6ä�q–ˆAÜ�H‰H×'Ñ'¨×(;Ñ(;Ô<Ü�H‰H×3Ñ3°M×4GÑ4GÖHñ ð !.Œ˜ÑÜ˜4Õ Ð r   c                ó0   € V ^8„  d   QhR\         R\        /# )r
   Úoperator_schemaró  ©Ústrr   )r   s   "r   r   r   X  s   € ÷ Fñ F¬sð F¼(ñ Fr   c                 ó*   € \        V4      \        V &   R # r*   )r   Úshape_compute_graph_mapping)r  ró  s   &&r   Úadd_shape_compute_mappingr  X  s   € ô 4@ÀÓ3EÔ Ó0r   c                ó<   € V ^8„  d   QhR\         R\        R\        /# )r
   r  Úlower_bound_funcÚupper_bound_funcr  )r   s   "r   r   r   ^  s$   € ÷ 9ñ 9Üð9Ü,4ð9ÜHPñ9r   c                 óD   € \        V4      \        V4      3pV\        V &   R # r*   )r   Úbounded_compute_graph_mapping)r  r	  r
  Úfnss   &&& r   Úadd_bounded_compute_mappingr  ^  s%   € ô Ð(Ó)¬<Ð8HÓ+IÐ
J€CØ58Ô! /Ó2r   z^aten::contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a)zFaten::rsub.Tensor(Tensor self, Scalar other, Scalar alpha=1) -> Tensorz:aten::dropout(Tensor input, float p, bool train) -> TensorzDaten::adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensorz,prim::NumToTensor.Scalar(Scalar a) -> Tensorz(prim::NumToTensor.bool(bool a) -> Tensorzuaten::zeros(int[] size, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)z{aten::to.dtype(Tensor(a) self, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor(a))zvaten::arange(Scalar end, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)z’aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensorz¤aten::arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensorz*aten::squeeze(Tensor(a) self) -> Tensor(a)z7aten::squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)z:aten::squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)z5aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a)zfaten::slice.Tensor(Tensor(a) self, int dim=0, int? start=None, int? end=None, int step=1) -> Tensor(a)zAaten::select.int(Tensor(a) self, int dim, int index) -> Tensor(a)z@aten::index_select(Tensor self, int dim, Tensor index) -> Tensorz‘aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> TensorzIaten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensorzhaten::_no_grad_embedding_renorm_(Tensor weight, Tensor input, float max_norm, float norm_type) -> Tensorzgaten::embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!)z~aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensorz,aten::mm(Tensor self, Tensor mat2) -> Tensorz/aten::dot(Tensor self, Tensor tensor) -> Tensorz+aten::mv(Tensor self, Tensor vec) -> Tensorz1aten::matmul(Tensor self, Tensor other) -> TensorzFaten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensorzˆaten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> TensorzŸaten::max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)z$aten::t(Tensor(a) self) -> Tensor(a)zDaten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)zŠaten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> TensorzŠaten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensorz¯aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> TensorzŠaten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensorzïaten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, int[]? bias_sizes, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)z¦aten::convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensorzðaten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensorz³aten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int groups=1, int[2] dilation=1) -> TensorzVaten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)z0aten::cat(Tensor[] tensors, int dim=0) -> Tensorz2aten::stack(Tensor[] tensors, int dim=0) -> Tensorz6aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)zSaten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)z3aten::view(Tensor(a) self, int[] size) -> Tensor(a)z:aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a)zMaten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)zaaten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensorzhaten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> TensorzZaten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)z<aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensorz;aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensorz^aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensorzbaten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)z_aten::quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensorzraten::quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensorz'aten::dequantize(Tensor self) -> TensorzNquantized::add(Tensor qa, Tensor qb, float scale, int zero_point) -> Tensor qczFaten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensorz-aten::bmm(Tensor self, Tensor mat2) -> Tensorz-aten::_shape_as_tensor(Tensor self) -> Tensorzraten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)z‹aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)z‚aten::native_layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)z´aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)z¹aten::_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)zÂaten::_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)z³_batch_norm_with_update(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor)zœaten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> TensorzCaten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> TensorzMaten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> TensorzQaten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)z&aten::nonzero(Tensor self) -> (Tensor)c                ó  € V ^8„  d   Qh/ ^ \         9   d   \        \        \        3,          ;R&   ^\         9   d,   \        \        \        \        \        3,          3,          ;R&   ^\         9   d   \        \
        \        3,          ;R&   # )r
   r  r  rõ  )Ú__conditional_annotations__Údictr  ÚScriptFnrÒ  r   )r   s   "r   r   r      sj   € ‡�÷F* 6Ò 5œT¤#¤x -Õ0Ñ 5ñG* ÷H* IÒ Hœt¤C¬¬x¼Ð/AÕ)BÐ$BÕCÑ HñI* ÷J* /Ò .””h¤Ð(Õ)Ñ .òK* r   )rL   FF)NNNNr   N)T)NF)rL   )Nr   iœÿÿÿg        )cr  r”  Úcollections.abcr   Útypingr   r   r   r   r°   r�  rö  r%   r-   r1   r8   r=   rB   rE   rQ   rU   r\   rW   rc   ri   rt   rw   r|   rˆ   r‹   rž   r¨   r«   r²   r¸   r¼   rÀ   rÆ   rÊ   rÎ   rÔ   rÛ   rä   ræ   rð   rô   rù   rý   r  r  r  r  r!  r%  r+  r/  r5  r;  rB  rM  rU  rZ  r_  rd  rk  rr  rz  r~  ro   r‡  rÙ   r–  rš  r�  r¤  r³  r¹  r¾  rÁ  rÆ  rÉ  rÌ  rÏ  rÔ  rß  rä  rè  rë  rñ  rù  ÚScriptFunctionr  r  r  rõ  r   r  r  r   )r  s   @r   Ú<module>r     s›  øðç €Ý $ß 'Ñ 'ð 
ˆs�EˆzÕ	€ó$ õõ0)õõõ&õõõ(õ:õõ.õ/ðÈ5÷ õõ,õõõ6õ-
õ`H@õV	õ$õN
õõõõõ	õõ ÷$ò õõ>Põõ3õõ&õ2(õõ&&PõR"õõ"õ+õõ1õlõ<Tõ Tõ 9÷$õN(õVõ8õ"T÷ õ$õ
õ!õ)õ2õ$õ* õBõ6õ&õ	÷1õ'õ ÷
õ)õ::õ"&õ+÷	ðð& �8‰8×"Ñ"€Ø35Ð Ó 5ØFHÐ Ó HØ,.€Ó .õ!õFõ9ñ ØdØ	ôñ ØLÈeôñ Ø@À%ôñ ØJØôñ Ø2°Oôñ ÐDÀoÔ VÙ Ø{Ø	ôñ ð BØ	ôñ Ø|Øôñ ð YØôñ ð kØôñ ÐFÈÔ VÙ Ø=¸wôñ Ø@À,ôñ Ø;¸Yôñ ØlØ	ôñ ØGÈôñ ØFÈôñ ð9à	ôñ
 ØOÐQVôñ ØnØ	ôñ ØmØ	ôñ ð EØôñ ÐHÈ"Ô MÙ ÐKÈSÔ QÙ ÐGÈÔ LÙ ÐMÈvÔ VÙ ØLÈfôñ ð OØôñ ð fØôñ Ð@À!Ô DÙ ØJÈIôñ ð QØ
ôñ ð QØ
ôñ ð vØôñ ð QØ
ôñ ð vØôñ ð mØôñ ð wØôñ ð zØôñ Ø\Øôñ ÐLÈcÔ RÙ ÐNÐPUÔ VÙ Ø<¸gôñ ØYØôñ ÐOÐQUÔ VÙ Ø@À&ôñ ØSØôñ ØgØôñ ØnØôñ Ø`Øôñ ØBÀOôñ ØAÀ?ôñ ØdØ	ôñ ØhØôñ ØeØ	ôñ ØxØ	ôñ ÐCÀUÔ KÙ ØTØôñ ØLÈfôñ ÐIÈ3Ô OÙ Ø3Ð5Eôñ ØxØôñ ð RØôñ ð IØôñ ð {Øôñ ð @Øôñ ð IØôñ ð zØôñ
 ð cØôñ ØIØôñ ØSØôñ ØWØôñ Ø,Ð.AÐCVör   