
    O j                        % S SK r S SKJr  S SKJrJrJr  \\\4   r	S SK
r
S\\   S\\   4S jrS\\   S\\   S\\   4S jrS\\   S\S\\   4S	 jrS
\\   S\\   4S jrS
\\   4S jrS
\\   4S jrS\\   S\\   4S jrS
\\   S\\   4S jrS
\\   S\\   S\4S jrS\\   S\S\\   4S jrS\\   4S jrS
\\   S\\   4S jrSS.S
\\   S\\   S\4S jjrS
\\   S\\\      S\S \4S! jrS
\\   S"\S\4S# jrS$\S%\4S& jrS'\S(\S)\S*\S+\S,\S-\4S. jrS'\S(\S)\S+\S,\S-\4S/ jrS0\\   S1\S2\S3\S4\S5\S6\S7\S8\S9\S:\S;\S<\S=\4S> jrS0\\   S?\\   S+\\   S@\\   S,\\   S-\4SA jr S0\\   S?\\   S+\\   S@\\   S,\\   S-\4SB jr!S0\\   SC\\\      SD\\\      4SE jr"S
\\   SF\\   4SG jr#S
\\   SH\\   4SI jr$S
\\   SJ\\   4SK jr%SL\\   S"\4SM jr&SL\\   4SN jr'SL\\   S"\4SO jr(SL\\   SP\\   4SQ jr)S
\\   S"\SR\\   4SS jr*   GSST\\   SU\\   SV\SW\SX\4
SY jjr+SZ r,S
\\   S"\S[\\   S\\\   S]\4
S^ jr-S_\\\      4S` jr.S"\Sa\\\      4Sb jr/SH\\   4Sc jr0Sd\\   Se\\   Sf\SR\4Sg jr1S_\\\      S"\4Sh jr2S_\\\      S"\4Si jr3S
\\   S"\SR\4Sj jr4Sk\\   Sl\\   4Sm jr5S
\\   4Sn jr6S
\\   So\Sp\4Sq jr7S0\\   ST\\   Sr\\\      4Ss jr8S
\\   St\\   SF\\   Su\Sv\4
Sw jr9Sx\\   S\4Sy jr:S0\\   Sz\\   Sr\\\      S+\\   S@\\   S,\\   S{\4S| jr;S}\\   S~\\   Sr\\\      S+\\   S@\\   S,\\   S{\4S jr<S0\\   ST\\   Sr\\\      S+\\   S@\\   S,\\   S{\4S jr=S0\\   ST\\   Sr\\\      S+\\   S@\\   S,\\   S{\4S jr>S\\   S0\\   ST\\   S\\\      4S jr?      GSS0\\   ST\\   Sr\\\      S+\\\      S@\\\      S\\\      S{\S,\\\      S\\   4S jjr@S0\\   ST\\   Sr\\\      S+\\   S@\\   S,\\   S\S\\   S{\S\\   4S jrAS0\\   ST\\   Sr\\\      S+\\   S@\\   S,\\   S\S\\   S{\S\S\S\S\S\\   4S jrBS0\\   ST\\\      Sr\\\      S\\\      S\\\      S\S\S\S\4S jrCS0\\   ST\\   Sr\\\      S+\\   S@\\   S,\\   S{\4S jrDGSS"\S\S\4S jjrES0\4S jrFSL\\   4S jrGS\\	S\S\S\S\4
S jrHS[\	S\\	S\S\S\S\4S jrIS[\	S\\	S]\	S\S\S\S\4S jrJS0\\   SP\\   4S jrKS
\\   S\\   S\\   S\\   4S jrLS0\\   S\S\4S jrMS0\\   4S jrNS0\\   4S jrOS
\\   S"\S\4S jrP GSS
\\   S"\\   S\S\\   4S jjrQS
\\   SF\\   S\\   4S jrRS
\\   S\\   4S jrSGSS
\\   S\S"\S\T\\   \\   4   4S jjrUS
\\   S\\   ST\\\      S\S\T\\   \\   4   4
S jrVS0\\   S\\   S\T\\   \\   \\   4   4S jrWS0\\   ST\\\      Sr\\\      S\\\      S\\\      S\S\T\\   \\   \\   4   4S jrXS0\\   ST\\\      Sr\\\      S\\\      S\\\      S\T\\   \\   \\   \\   4   4S jrY    GSS
\\   S\\   ST\\\      S\S\S\S\\   4S jjrZ \
R                  R                  r]0 q^\_\`\]4   \aS'   0 rb\_\`\T\]\]4   4   \aS'   0 rc\_\\]4   \aS'   S\4S jrdS\`S\4S jreS\`S\S\4S jrf\e" S\5        \e" S\5        \e" S\5        \e" S\5        \e" S\F5        \e" S\F5        \e" S\5        \e" S\5        \e" S\H5        \e" S\I5        \e" S\J5        \e" S\'5        \e" S\(5        \e" S\)5        \e" S\&5        \e" S\-5        \e" S\45        \e" S\*5        \e" S\5        \e" S\5        \e" S\5        \e" S\5        \e" S\+5        \e" S\#5        \e" S\$5        \e" S\%5        \e" S\55        \e" S\85        \e" S\ 5        \e" S\!5        \e" S\65        \e" S\75        \e" S\=5        \e" S\>5        \e" S\C5        \e" S\D5        \e" S\?5        \e" S\A5        \e" S\B5        \e" S\@5        \e" S\M5        \e" S\25        \e" S\35        \e" S\K5        \e" S\L5        \e" S\5        \e" S\5        \e" S\5        \e" S\5        \e" S\5        \e" S\5        \e" S\F5        \e" S\F5        \e" S\95        \e" S\"5        \e" S\5        \e" S\5        \e" S\5        \e" S\5        \e" S\Q5        \e" GS \R5        \e" GS\S5        \e" GS\U5        \e" GS\V5        \e" GS\W5        \e" GS\X5        \e" GS\X5        \e" GS\X5        \e" GS\Y5        \e" GS	\Z5        \e" GS
\5        \e" GS\5        \e" GS\5        \f" GS\N\O5        g(      N)Callable)AnyOptionalUnionabc           	      L   [        U 5      n[        U5      n[        X#5      n/ n[        U5       Hq  nUS-
  U-
  nUS-
  U-
  nUS-
  U-
  n	US:  a  X   OSn
U	S:  a  X   OSnX:w  a   U
S:w  a  US:w  a  [        SU
 SU SU 35      eUR	                  U
S:X  a  UOU
5        Ms     U$ )N   r   zThe size of tensor a z" must match the size of tensor b (z) at non-singleton dimension )lenmaxrangeAssertionErrorappend)r   r   dimsAdimsBndimexpandedSizesioffsetdimAdimBsizeAsizeBs               k/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/torch/jit/_shape_functions.py	broadcastr      s    FEFEuD!M4[Aqy6!qy6! AIA AIA>eqjUaZ 'w.PQVPWWtuvtwx  	eqjUe<      cc                 ,    [        [        X5      U5      $ Nr   r   r   r   s      r   broadcast_threer"   4   s    Yq_a((r   c                     [        X5      $ r   r    r!   s      r   broadcast_one_threer$   8   s    Q?r   selfoutc                    [        U5      S:w  a  [        S[        U5       35      e[        U 5      S:X  d&  [        U 5      S:X  d  [        S[        U 5       35      e[        S[        U 5      5       H  nX   S:X  d  M  [        SU S	35      e   / n[        S[        U 5      S-
  5       H  nUR                  X   5        M     U H  nUR                  U5        M     U$ )
N   z'Expected out to have length 2, but got       z-Expected self to have length 3 or 4, but got r
   r   zExpected self[z] to be non-zero, but got 0)r   r   r   r   )r%   r&   r   shapeelems        r   adaptive_avg_pool2dr-   <   s    
3x1}Fs3xjQRRINc$i1n;CI;G
 	
 1c$i 7a< >!4O!PQQ ! E1c$i!m$TW %T Lr   c                 >    / nU  H  nUR                  U5        M     U$ r   r   )r%   r&   r,   s      r   _copyr0   O   s"    C

4 Jr   c                     [        U 5      $ r   r0   r%   s    r   unaryr4   V   s    ;r   c                    [        U 5      n[        U5      nX2:  a  [        SU SU S35      e[        U5       HB  nX2-
  U-   nX   nUS:  a  X   OSnXg:w  d  M!  US:w  d  M)  [        SR                  XgU5      5      e   [	        U 5      $ )NzThe dims of tensor b (z6) must be less than or equal to the dims of tensor a (z) r   r
   zZThe size of tensor a {} must match the size of tensor b ({}) at non-singleton dimension {})r   r   r   formatr0   )r   r   r   r   r   r   r   r   s           r   broadcast_inplacer7   Z   s    FEFE}$UG+abgahhjk
 	
 e}t# AIA>eqj 44:F54N   8Or   sizesc                    [        U5      [        U 5      :  a$  [        S[        U5       S[        U 5       S35      e[        U5      n[        U 5      nUS:X  a  [        U5      $ / n[        U5       Hp  nUS-
  U-
  nUS-
  U-
  nUS:  a  X   OSnX   n	U	S:X  a  US:  a  [        SU S35      eUn	X:w  a  US:w  a  [        S	U S
U	 S35      eU	nUR	                  U5        Mr     U$ )NzExpected len(sizes) (z) >= len(self) ()r   r
   Expected dim (z) >= 0 when targetSize is -1zExpected size (z ) == 1 when size != targetSize ()r   r   r0   r   r   )
r%   r8   r   
tensor_dimr&   r   r   dimsize
targetSizes
             r   expandrA   n   s   
5zCI#CJ</?D	{!L
 	
 u:DTJqyU|C4[A1nv%1Hty!X
Qw$~cU:V%WXXJqy$%dV+KJ<WXY  D

4   Jr   inp0c                     [        X5      $ r   )rA   )r%   r8   rB   s      r   expand_one_unusedrD      s    $r   r+   numelreturnc                    SnS n[        [        U 5      5       H7  nX   S:X  a  Ub  [        S5      eUnM  X   S:  a	  X U   -  nM.  [        S5      e   X:X  d  Ub  US:  a  X-  S:X  d  [        S5      e[        U 5      nUb  X-  XS'   U$ )Nr
   r;   z"only one dimension can be inferredr   zinvalid shape dimensionszinvalid shape)r   r   r   r0   )r+   rE   newsize	infer_dimr>   r&   s         r   infer_size_implrJ      s    G#ISZ :$$%IJJIZ1_Sz!G !;<< ! 	!gkeo6J_--
,C)Jr   c                 $    SnU  H  nX-  nM	     U$ Nr
    )r8   rE   r,   s      r   rE   rE      s    E Lr   c                 ,    [        U[        U 5      5      $ r   )rJ   rE   )r%   r8   s     r   viewrO      s    5%+..r   F)implicitrP   c                    [        X5      $ r   )rO   )r%   r8   rP   s      r   view_one_unusedrR      s    r   opt_dimskeep_dimdtc           	      `   / nUb  [        U5      S:X  a  [        [        [        U 5      5      5      nOUn[        [        U 5      5       Ha  nSnU H   nU[        U[        U 5      5      :X  d  M  SnM"     U(       a  U(       a  UR	                  S5        ML  MN  UR	                  X   5        Mc     U$ )Nr   FTr
   )r   listr   maybe_wrap_dimr   )	r%   rS   rT   rU   r&   dimsidxis_mean_dim
reduce_dims	            r   sum_mean_dimr]      s     C3x=A-uSY/0SY!JnZT;;"  

1  JJty!   Jr   r>   c                 $    [        X/US 5      nX34$ r   )r]   )r%   r>   rT   r&   s       r   max_dimr_      s    
tUHd
3C8Or   xyc                 
    X-  $ r   rM   )r`   ra   s     r   div_rtnrc      s	    6Mr   	inputSize
kernelSizepad_lpad_rstridedilation	ceil_modec                     [        U U-   U-   XQS-
  -  -
  S-
  U(       a  US-
  OS-   U5      S-   nU(       a  US-
  U-  X-   :  a  US-
  nU$ Nr
   r   )rc   )rd   re   rf   rg   rh   ri   rj   
outputSizes           r   pooling_output_shape_pad_lrrn      s     	 q.)* 	
 'vzA/ 	
 		  Nf$	(99#aJr   c           	      @    US:X  a  [        S5      e[        XX"X4U5      $ )Nr   zstride should not be zero)r   rn   )rd   re   rf   rh   ri   rj   s         r   pooling_output_shaperp      s-     {899&uVy r   inputkHkWdHdWpadHpadW	dilationH	dilationWnInputPlaneinputHeight
inputWidthoutputHeightoutputWidthc                    [        U 5      nUS:  a  US:  d  [        SU SU S35      eUS:  a  US:  d  [        SU SU S35      eUS:  a  US:  d  [        SU SU S35      eU S	   S:g  =(       a    U S
   S:g  nUS:X  a  U S   S:w  a  U(       d'  US:X  a  U(       a	  U S   S:w  d  [        SU SU  35      eUS
-  U:  a	  US
-  U:  d  [        SUS
-   SU SUS
-   SU S3	5      eUS	:  a  US	:  d  [        SU SU S35      eg )Nr   zExpected kW (z) > 0 and kH (z) > 0zExpected dW (z) > 0 and dH (zExpected dilationH (z) > 0 and dilationW (r
   r(   r)   r*   zInvalid input dimensions: ndim=z, input=zExpected kW//2 (z) >= padW (z) and kH//2 (z) >= padH (r:   zExpected outputWidth (z) >= 1 and outputHeight (z) >= 1r   r   )rq   rr   rs   rt   ru   rv   rw   rx   ry   rz   r{   r|   r}   r~   r   
valid_dimss                   r   pool2d_shape_checkr      sn     u:DFrAv}RDrd%HIIFrAv}RDrd%HIIMi!m"9+-B9+US
 	
 qQ058q=J	!HMAI*qQ>tfHUGTUU!GtOa4rQwi{4& 9AgYk$q2
 	
 1!2$[M 2)N&2
 	
 "3r   kernel_sizepaddingc                    [        U5      S:X  d  [        U5      S:X  d  [        S5      eUS   n[        U5      S:X  a  UOUS   n[        U5      S:X  d)  [        U5      S:X  d  [        U5      S:X  d  [        S5      e[        U5      S:X  a  UOUS   n[        U5      S:X  a  Un	O[        U5      S:X  a  Un	OUS   n	[        U5      S:X  d  [        U5      S:X  d  [        S5      eUS   n
[        U5      S:X  a  U
OUS   n[        U5      S:X  d  [        U5      S:X  d  [        S5      eUS   n[        U5      S:X  a  UOUS   n[        U 5      S:X  d&  [        U 5      S	:X  d  [        S
[        U 5       35      e[        U 5      S	:X  a  U S   OSnU S   nU S   nU S   n[        UXjXU5      n[        UX{XU5      n[        U UUUU	U
UUUUUUUU5        [        U 5      S:X  a  UUU/$ XUU/$ )Nr
   r(   zKmax_pool2d: kernel_size must either be a single int, or a tuple of two intsr   zOmax_pool2d: stride must either be omitted, a single int, or a tuple of two intszGmax_pool2d: padding must either be a single int, or a tuple of two intszHmax_pool2d: dilation must be either a single int, or a tuple of two intsr)   r*   z&Expected input length 3 or 4, but got r;   )r   r   rp   r   )rq   r   rh   r   ri   rj   rr   rs   rt   ru   rv   rw   rx   ry   nbatchrz   r{   r|   r}   r~   s                       r   
max_pool2dr   /  s)    !S%5%:Y
 	
 
QB;1$+a.BK1Fq 0CK14D]
 	
 6{aVAYB
6{a	V	AYLAW!2U
 	
 1:Dw<1$4'!*DMQ#h-1"4V
 	
 I ]a/	Xa[IJ!Os5zQEc%j\RSSe*/U2YqF)K)KrJ'RriXL&z2RIVK



" 5zQ\;77\;??r   c                 "    [        XX#XE5      nXf4$ r   )r   )rq   r   rh   r   ri   rj   r&   s          r   max_pool2d_with_indicesr   z  s     U(
NC:r   output_sizescale_factorsc                 4   / nUR                  U S   5        UR                  U S   5        Uc  Uc  [        S5      eUb\  Ub  [        S5      e[        U5      S:w  a  [        S[        U5       35      eUR                  US   5        UR                  US   5        Ubz  Ub  [        S5      e[        U5      S:w  a  [        S[        U5       35      eUR                  [        U S   US   -  5      5        UR                  [        U S   US   -  5      5        U$ )	Nr   r
   z5Either output_size or scale_factors must be presentedz9Must specify exactly one of output_size and scale_factorsr(   z/Expected output_size to have length 2, but got z1Expected scale_factors to have length 2, but got r)   )r   r   r   int)rq   r   r   r&   s       r   upsample_nearest2dr     s7   
 CJJuQxJJuQx!4TUU$ K  {q  A#kBRAST  	

;q>"

;q>" " K  }" CCDVCWX  	

3uQx-"2234

3uQx-"2234Jr   mat2c                     [        U 5      S:w  a  [        S[        U 5       S35      e[        U5      S:w  a  [        S[        U5       S35      eU S   US   :w  a  [        SU S    SUS    35      eU S   US   /$ )	Nr(   zself must be a matrix (got z dimensions)zmat2 must be a matrix (got r
   r   z.Matrix dimensions don't match for mm: self[1]=
, mat2[0]=r   r%   r   s     r   mmr     s    
4yA~:3t9+\RSS
4yA~:3t9+\RSSAw$q'<T!WIZPTUVPWyY
 	
 GT!Wr   tensorc                     [        U 5      S:X  a  [        U5      S:X  d#  [        S[        U 5       S[        U5       35      eU S   US   :w  a  [        SU S    SUS    35      e/ nU$ )Nr
   z+Expected 1D tensors for dot, got len(self)=z, len(tensor)=r   z(Dot product dimension mismatch: self[0]=z, tensor[0]=r   )r%   r   r&   s      r   dotr     s    INs6{a/9#d) Ev;-)
 	
 Aw&)6tAwi|FSTI;W
 	
 CJr   vecc                     [        U 5      S:X  a  [        U5      S:X  d#  [        S[        U 5       S[        U5       35      eU S   US   :w  a  [        SU S    SUS    35      eU S   /$ )Nr(   r
   z0Expected 2D matrix and 1D vector, got len(self)=z, len(vec)=r   z*Matrix-vector dimension mismatch: self[1]=z	, vec[0]=r   )r%   r   s     r   mvr     s    INs3x1}>s4yk JCz#
 	
 Aw#a&8a	3q6(S
 	
 G9r   lic                 p    [        U[        U 5      S-   5      n[        U 5      nUR                  US5        U$ rL   )rX   r   r0   insert)r   r>   r&   s      r   	unsqueezer     s2    
c"gk
*C
)CJJsAJr   c                 z    / n[        [        U 5      5       H   nX   S:w  d  M  UR                  X   5        M"     U$ rL   )r   r   r   )r   r&   r   s      r   squeeze_nodimr     s7    C3r7^5A:JJru  Jr   c                     / n[        U[        U 5      5      n[        [        U 5      5       H:  nXC:X  a  X   S:w  a  UR                  X   5        M%  M'  UR                  X   5        M<     U$ rL   )rX   r   r   r   )r   r>   r&   wrapped_dimr   s        r   squeezer     sb    C c"g.K3r7^uz

25!  JJru  Jr   rY   c                 N   [        U5      S:X  a  U $ [        U5      n[        [        U5      5       H  n[        X#   [        U 5      5      X#'   M     / n[        [        U 5      5       H:  nX   S:X  a  X2;  a  UR	                  X   5        M%  M'  UR	                  X   5        M<     U$ Nr   r
   )r   r0   r   rX   r   )r   rY   wrapped_dimsr   results        r   squeeze_dimsr     s    
4yA~	;L3t9(#b'B F3r7^5A:$be$ % MM"%   Mr   indexc                    [        U[        U 5      5      n[        U5      n[        U5      S:  a  [        S[        U5       35      eUS:X  d*  U[        U 5      :  d  [        SU S[        U 5       S35      e/ n[	        [        U 5      5       H.  nX:X  a  UR                  U5        M  UR                  X   5        M0     U$ )Nr
   z"Expected len(index) <= 1, but got r   r<   z) == 0 or dim < len(self) (r:   )rX   r   multiply_integersr   r   r   )r%   r>   r   rE   result_sizer   s         r   index_selectr     s    
c$i
(Ce$E
5zA~A#e*NOO1Hc$iSE!<SYKqI
 	
  K3t98u%tw'	 
 r   weightindicespadding_idxscale_grad_by_freqsparsec                     [        U 5      S:w  a  [        S[        U 5       S35      e[        U5      S:X  a  [        U SU5      $ [        U5      nUR	                  U S   5        U$ )Nr(   z"Expected weight to be 2D, but got Dr
   r   )r   r   r   r0   r   )r   r   r   r   r   r?   s         r   	embeddingr     sb     6{aA#f+aPQQ
7|qFAw//>DKKq	Kr   c                      g)Nl    rM   rM   r   r   max_intr   #  s    r   startendstepc                    [        U 5      nUS:X  a  [        S5      e[        X5      nUb  UOSnUb  UO	[        5       nUS::  a  [        SU 35      eU[        5       :X  a  SnUS:  a  X`U   -  nUS:  a  XpU   -  nUS:  a  SnOX`U   :  a  X   nXv:  a  UnOXpU   :  a  X   nXv-
  n[	        U 5      n	X-   S-
  U-  X'   U	$ )Nr   z#Cannot slice a 0-dimensional tensorzExpected step > 0, but got r
   )r   r   rX   r   r0   )
r%   r>   r   r   r   r   	start_valend_val	slice_lenr&   s
             r   slicer   '  s     t9DqyBCC

#C*I_c')Gqy:4&ABBGI	1}#Y	{91}		#Y	I		I	)#I
+C 1$-CHJr   tensorsc                 J    U  H  n[        U5      S::  d  M  [        S5      e   g )Nr   z+Cannot concatenate tensor with 0 dimensionsr   )r   r   s     r   check_cat_no_zero_dimr   F  s$    v;! !NOO r   tensor_sizesc                     S nU H7  n[        U5      S:X  a  US   S:X  a  M  Ub  M"  [        U [        U5      5      nM9     Uc  U nU$ rl   )r   rX   )r>   r   out_dimr?   s       r   legacy_cat_wrap_dimr   L  sL    !GD	Q47a<(c$i8  Nr   c                 H    [        U 5      S:H  =(       a    [        U 5      S:H  $ r   rE   r   )r   s    r   should_skipr   W  s    =A2#f+"22r   firstsecond	dimensionc                     [        U 5      n[        U5      nXE:w  a  [        SU SU 35      e[        SU5       H/  nXb:w  d  M
  X   X   :w  d  M  [        SU SX    SX    SU 35      e   g )Nz1Tensors must have same number of dimensions, got z and r   z0Sizes of tensors must match except in dimension , got z at dimension )r   r   r   )r   r   r   r   
first_dimssecond_dimsr>   s          r   check_cat_shape_except_dimr   [  s     UJf+K ?
|5m
 	
 Q
#zV[($Fyk R :,eFK=seM  $r   c                 Z   [        U 5        [        X5      n[        U 5      S::  a  [        S5      eS nU  H  n[	        U5      (       a  M  UnM     Uc  S/$ Sn[        [        U 5      5       H,  nX   n[	        U5      (       a  M  [        X#X5        XCU   -   nM.     [        U5      nXFU'   U$ )Nr   z(Cannot concatenate empty list of tensors)r   r   r   r   r   r   r   r0   )r   r>   not_skipped_tensorr   cat_dim_sizer   r   s          r   catr   n  s    '"
c
+C
7|qGHH.26""!'  !s
L3w< 6""&'93J'+5L	 ! *+K#r   c                 f    / nU  H  n[        X15      nUR                  U5        M!     [        X!5      $ r   )r   r   r   )r   r>   unsqueezed_tensorsr   
unsqueezeds        r   stackr     s8    *,v+
!!*-  !''r   c                 
   [        U 5      nUS:X  a  [        S5      e[        X5      nX   nX$* :  d  X$:  a  [        SU SU SU 35      eUS:  a  X$-  n/ n[        U5       H  nXa:w  d  M
  UR	                  X   5        M     U$ )Nr   z)Cannot select from a 0-dimensional tensorzIndex z  is out of bounds for dimension z with size )r   r   rX   r   r   )r%   r>   r   r   r?   r&   r   s          r   selectr     s    t9DqyHII

#C9Du}UG;C5D6R
 	
 qyC4[8JJtw  Jr   tensor1tensor2c                    [        U 5      n[        U5      nUS:X  a  US:X  a  [        X5      $ US:X  a  US:X  a  [        X5      $ US:X  a&  US:X  a   [        [	        [        U S5      U5      S5      $ US:X  a  US:X  a  [	        X5      $ US:  a  US:  a  US:  a  U S   OSn/ n[        US-
  5       H  nUR                  X   5        M     US   n/ n[        US-
  5       H  nUR                  X   5        M     [        XX5      n	U	n
US:  a  U
R                  U5        US:  a  U
R                  U5        U
$ [        S5      e)Nr
   r(   r   r   r;   z/both arguments to matmul need to be at least 1D)
r   r   r   r   r   r   r   r   r   r   )r   r   dim_tensor1dim_tensor2nbatch_tensor1r   pbatch_tensor2expand_batch_portionoutput_shapes              r   matmulr     sW   g,Kg,KaK1,7$$		kQ.'##		kQ.r)GQ/91==		kQ.'##		kQ. '?GBK#%{Q'A  , (BK#%{Q'A  , (  )F ,?"?"NOOr   c                     [        U 5      S:  a  [        S[        U 5       35      e[        U 5      nUS:X  a  / nU$ US:X  a  U S   /$ U S   U S   /$ )Nr(   z1Expected tensor to have <= 2 dimensions, but got r   r
   r   )r%   self_lenr&   s      r   tr     sj    
4y1}?D	{K
 	
 4yH1}
	QQyQa!!r   dim0dim1c                    [        U 5      n[        X5      n[        X#5      nX:X  a  [        U 5      $ / n[        U5       HJ  nXQ:X  a  UR	                  X   5        M  XR:X  a  UR	                  X   5        M7  UR	                  X   5        ML     U$ r   )r   rX   r0   r   r   )r%   r   r   ndimsr&   r   s         r   	transposer     s|    IE$&D$&D|T{C5\9JJtz"YJJtz"JJtw  Jr   biasc                 v    [        U [        U5      5      nUb   [        X#5      U:w  a  [        SU SU 35      eU$ )NzBias shape z& is not broadcastable to output shape )r   r   r   r   )rq   r   r   r&   s       r   linearr     sJ    
&	
"CT3& dV#I#O  Jr   mat1betaalphac                 ,    [        U [        X5      5      $ r   )r   r   )r%   r   r   r   r   s        r   addmmr     s    T2d>**r   arrayc                 0    SnU  H  nUS:  d  M  SnM     U$ )NFr   TrM   )r   non_negativevals      r   check_non_negativer     s%    L7L  r   weight_sizesgroupsc           	         [        U 5      n[        U5      n[        U5      (       a  [        SU 35      e[        U5      (       a  [        SU 35      eX:w  a  [        SU SU S35      eUS   U:  a  [        SUS    SU S35      eUS   U-  S:w  a  [        SUS    S	U S35      eU S
   US
   U-  :w  a  [        SU S
    SUS
   U-   S35      eUb/  [        U5      S
:X  a  US   US   :X  d  [        SUS    SU 35      e[        SU5       HW  n	X	   SXIS-
     -  -   XYS-
     X   S
-
  -  S
-   :  d  M(  [        SX	   SXIS-
     -  -    SXYS-
     X   S
-
  -  S
-    SU	 35      e   g )Nz"Padding must be non-negative, got z!Stride must be non-negative, got zExpected weight_dim (z) == k (r:   r   zExpected weight_sizes[0] (z) >= groups (z) to be divisible by groups (r
   zExpected input[1] (z) == weight_sizes[1] * groups (zFExpected bias to be None or have shape [1] with value weight_sizes[0]=r   r(   zCalculated padded input size (z)) is smaller than effective kernel size (z) at dimension )r   r   r   r   )
rq   r   r   rh   r   ri   r   k
weight_dimr   s
             r   check_shape_forwardr    s    	E
A\"J '""A'KLL&!!@IJJ4ZL1MNNA(a(9vhaP
 	
 	Q& Q&(a(9 :ha!
 	

 Qx<?V++!%( ,Q&(),
 	
 TaDG|A4N+A/vdV=
 	

 1a[Hq7q5>))UO|23a7
 !0AA<N1N0O PUO|':;a?@PQsT 	 r   
input_sizeweight_sizec           	      d   [        XX#XEU5        [        U5      S:  n[        U 5      n/ n	Sn
SnU	R                  X
   5        U	R                  X   5        [        SU5       HL  nU(       a  X\S-
     OSnXU   S-
  -  S-   nU	R                  X   SXLS-
     -  -   U-
  X<S-
     -  S-   5        MN     U	$ )Nr   r(   r
   )r  r   r   r   )r  r  r   rh   r   ri   r   has_dilationr>   r   input_batch_size_dimweight_output_channels_dimd	dilation_kernels                  r   conv_output_sizer  8  s     w& x=1$L
j/CK!"z78{>?1c]'3HUO	!nq01A5]a'a%.01F:v!e}LqP	
  r   c           	          [        U5      S:w  a  [        S[        U5       S35      e[        U 5      S:w  a  [        S[        U 5       S35      e[        XX#XEU5      $ )Nr)   z#Expected 3D weight for conv1d, got r   z"Expected 3D input for conv1d, got r   r   r  rq   r   r   rh   r   ri   r   s          r   conv1dr  V  ^     6{aB3v;-qQRR
5zQA#e*QOPPE4FSSr   c           	          [        U5      S:w  a  [        S[        U5       S35      e[        U 5      S:w  a  [        S[        U 5       S35      e[        XX#XEU5      $ )Nr*   z#Expected 4D weight for conv2d, got r   z"Expected 4D input for conv2d, got r  r  s          r   conv2dr  f  r  r   grad_outputbiasesc                 8    [        U5      [        U5      U S   /4$ rL   r2   )r  rq   r   r  s       r   conv_backwardsr  v  s      <vQ(888r   output_paddingc                    Uc  SS/nUc  SS/nUc  SS/nUc  SS/n[        U5      S:  n[        U 5      n	/ n
SnSnU
R                  X   5        U
R                  X   U-  5        [        SU	5       HT  nU(       a  X}S-
     OSnXU   S-
  -  nU
R                  X   S-
  X=S-
     -  SXMS-
     -  -
  U-   X]S-
     -   S-   5        MV     U
$ )Nr
   r   r(   r   r   r   )rq   r   r   rh   r   r  r   ri   r  r>   r   r	  r
  r  r  r  s                   r   conv_transpose2d_inputr    s    ~Qa&Qq6x=1$L
e*CK!"u23v9FBC1c]'3HUO	Qi!m,X\VE]*'a%. ! U#$ 		
  r   
transposedc	                 \   [        U5      S:  n	[        U5      S:  n
[        U 5      n/ nSnU(       a  SOSnUR                  X   5        U(       a  UR                  X   U-  5        OUR                  X   5        [        SU5       H  nU	(       a  X_S-
     OSnU
(       a  XS-
     OSnU(       a>  UX   S-
  -  nUR                  X   S-
  X?S-
     -  SXOS-
     -  -
  U-   U-   S-   5        Mh  UX   S-
  -  S-   nUR                  X   SXOS-
     -  -   U-
  X?S-
     -  S-   5        M     U$ )Nr   r
   r(   r  )rq   r   r   rh   r   ri   r  r  r   r  has_output_paddingr>   r   r	  r
  r  r  output_padding_r  s                      r   conv_forwardsr"    se    x=1$L^,q0
e*CK&0au236=FG6=>1c]'3HUO	3E.Q/1&)a-0FAA.g!en$% "" 	 &)a-014FQQ/069fUmKaO " r   	benchmarkdeterministiccudnn_enabled
allow_tf32c                 (    [        U UUUUUUUU5	      $ r   )r"  )rq   r   r   rh   r   ri   r  r  r   r#  r$  r%  r&  s                r   _conv_forwardsr(    s,     
 
r   running_meanrunning_vartrainingmomentumepsc	                 >    / n	U  H  n
U	R                  U
5        M     U	$ r   r/   )rq   r   r   r)  r*  r+  r,  r-  r%  r&   r,   s              r   
batch_normr/    s$     C

4 Jr   c           	          [        U5      S:w  a  [        S[        U5       S35      e[        U 5      S:w  a  [        S[        U 5       S35      e[        XX#XEU5      $ )N   z#Expected 5D weight for conv3d, got r   z"Expected 5D input for conv3d, got r  r  s          r   conv3dr2    r  r   dim_post_exprwrap_scalarc           	          US::  a  U(       d  [        S5      eSnU* nUS-
  nX:  d  X:  a  [        SU  SU SU S35      eU S:  a  X-  n U $ )Nr   z7Expected wrap_scalar to be True when dim_post_expr <= 0r
   z
Dimension z( out of range (expected to be in range [z, z]))r   )r>   r3  r4  minr   s        r   rX   rX     s{     I  .C
!
C
yCIEcU"SEQST
 	
 QwJr   c                 
    / nU$ r   rM   )rq   r&   s     r   zero_dim_tensorr8  !  s    CJr   c                 $    SnU  H  nX-  nM	     U$ rL   rM   )r   r&   r,   s      r   r   r   &  s    
Cj Jr   inp1inp2inp3c                 l    U S:  a  [        SU  S35      e[        [        R                  " U 5      5      /$ )Nr   Expected end () >= 0r   r   mathceil)r   rB   r:  r;  r<  s        r   
arange_endrC  -  s3    
Qw~cU&9::		#  r   c                     US:  a  [        SU S35      eX:  a  [        SU SU  S35      e[        [        R                  " X-
  5      5      /$ )Nr   r>  r?  ) >= start (r:   r@  )r   r   rB   r:  r;  r<  s         r   arange_startrF  3  sW     Qw~cU&9::
{~cU,ugQGHH		#+&'((r   c                     US:X  a  [        S5      eUS:  a  X:  a  [        SU  SU S35      eOX:  a  [        SU SU  S35      e[        [        R                  " X-
  U-  5      5      /$ )	Nr   zstep must not be zerozExpected start (z
) >= end (z) when step < 0r>  rE  z) when step > 0r@  )r   r   r   rB   r:  r;  r<  s          r   arange_start_steprH  =  s     qy455ax; "5'C5H  
 ;  \%H  		3;$./011r   c                    [        U 5      [        U5      :w  a$  [        S[        U 5       S[        U5       S35      e[        U5      n/ n/ n[        U5       H5  n[        X   U5      nUR	                  U5        UR	                  X   5        M7     [        SU5       H0  n[        U5       H  nX5   X7   :X  d  M  [        SX5    S35      e   M2     U$ )NExpected len(input) (z) == len(dims) (r:   r
   zRepeated dimension z in permute dimensions)r   r   r   rX   r   )rq   rY   r   	seen_dimsnewSizesr   r>   js           r   permuterN  O  s    
5zSY#CJ</?D	{!L
 	
 t9DIH4[TWd+
#  1d^qA|y|+$)),7MN    Or   sourcedestinationc                    [        U 5      nUS::  a  U $ / n/ n[        [        U5      5       H=  nUR                  [        X   U5      5        UR                  [        X&   U5      5        M?     [        U5       Vs/ s H  nSPM     nn[        U5       Vs/ s H  ofPM     nn[        U5       Vs/ s H  ofPM     n	n[        [        U5      5       H  nXF   XuU   '   SXU   '   SXU   '   M     / n
/ nU H  nUS:w  d  M  U
R                  U5        M     U	 H  nUS:w  d  M  UR                  U5        M     U[        U5      -
  n[        U5       H  nX   X{U   '   M     [	        X5      $ s  snf s  snf s  snf )Nr
   r;   )r   r   r   rX   rN  )r%   rO  rP  self_dimnormalized_srcnormalized_dstr   ordersrc_dimsdst_dimssource_dimsdestination_dimselerest_dims                 r   movedimr\  d  sy   4yH1} "N "N3v;nVYABn[^XFG   x)ARE) ?+?a?H+ ?+?a?H+3v;#1#4Q &("#&("#  
  K"$"9s#  "9##C(  #f+%H8_%0^q!" 4+ *++s   :E*E//E4	start_dimend_dimc                    [        U[        U 5      5      n[        U[        U 5      5      nX:  a  [        SU SU S35      e[        U 5      S:X  a  S/$ X:X  a  / nU  H  nUR                  U5        M     U$ Sn[	        XS-   5       H
  nXPU   -  nM     / n[	        U5       H  nUR                  X   5        M     UR                  U5        [	        US-   [        U 5      5       H  nUR                  X   5        M     U$ )NzExpected start_dim (z) <= end_dim (r:   r   r
   )rX   r   r   r   r   )rq   r]  r^  r&   r,   slice_numelr   r+   s           r   flattenra    s    y#e*5IWc%j1G3I;nWIUVWXX
5zQs
DJJt 
K9k*Qx + E9UX 	LL7Q;E
+UX ,Lr   c                     S[        U 5      /$ Nr   r   rq   s    r   nonzero_lower_boundrf    s    s5z?r   c                 .    [        U 5      [        U 5      /$ r   r   re  s    r   nonzero_upper_boundrh    s    %L#e*%%r   keepdimc                     [        U[        U 5      5      n/ n[        U 5       H7  u  pEXA:X  a  U(       a  UR                  S5        M$  M&  UR                  U5        M9     U$ rL   )rX   r   	enumerater   )r%   r>   ri  r&   r   rR  s         r   _reduce_along_dimrl    sT    
c$i
(CC 8

1  JJx  ' Jr   c                 $    Uc  / $ [        XU5      $ r   )rl  )r%   r>   ri  s      r   argmaxrn    s     {	T00r   c                 F   [        U 5      S:w  a  [        S[        U 5       S35      e[        U5      S:w  a  [        S[        U5       S35      eU S   US   :w  a  [        SU S    SUS    35      eU S   US   :w  a  [        S	U S    S
US    35      eU S   U S   US   /$ )Nr)   z"bmm only supports 3D tensors, got r   r   z%mismatching batch dimension: self[0]=r   r(   r
   z+mismatching contracting dimension: self[2]=z
, mat2[1]=r   r   s     r   bmmrp    s    
4yA~A#d)ANOO
4yA~A#d)ANOOAw$q'3DG9JtAwiP
 	
 Aw$q'9$q'*TRSWIV
 	
 GT!Wd1g&&r   c                     [        U 5      /$ r   rd  r3   s    r   _shape_as_tensorrr    s    I;r   r  c           	          [        U 5      S:X  a  / nX34$ XU   :  a  [        SU SU SX    35      e[        U 5      nXU'   X34$ )Nr   zk (z) is too big for dimension z	 of size )r   r   r0   )r%   r  r>   r   s       r   topkrt    sg    
4yA~ > Cy= aS3C5	$)M  ts>r   target	reductionc                    [        U 5      n[        U5      nSUs=:  a  S::  d  O  [        SU 35      eUS:  a  [        SU 35      eUS:H  =(       a    US:H  nU(       d#  U S   US   :X  d  [        SU S    SUS    35      eU S   n/ nUb)  [        U5      S:X  a	  US   U:X  d  [        S	U S
U 35      eUS:X  a  US:X  a	  U S   /n	X4$ Un	X4$ )Nr   r(   z-Expected 0 < self_dim <= 2, but got self_dim=r
   z"Expected target_dim <= 1, but got zBatch size mismatch: self[0]=z, target[0]=r;   z:Expected weight to be None or have shape [n_classes], got z with n_classes=r   )
r%   ru  r   rv  rR  
target_dimno_batch_dim	n_classesscalar_shapereduction_shapes
             r   nll_loss_forwardr}    s    4yHVJALXJWXXA~A*NOOq=4Z1_LT!Wq	1+DG9LL
 	
 RI L3v;!#3q	Y8N(*9+7
 	
 A~(a-7) (( '((r   normalized_shapec                 F   / n[        U 5      [        U5      -
  nUS:  a$  [        S[        U 5       S[        U5       S35      e[        U5       H  nUR                  X   5        M     [        U[        U 5      5       H  nUR                  S5        M     [	        U 5      X"4$ )Nr   rJ  z) >= len(normalized_shape) (r:   r
   )r   r   r   r   r0   )rq   r~  r|  num_unreduced_dimensionsr   s        r   native_layer_normr    s     "$O"5zC0@,AA!##CJ< 0$%&a)
 	
 +,ux( -+SZ8q! 9<99r   c                 >    U(       a  U S   /nOS/n[        U 5      Xf4$ rl   r2   )rq   r   r   r)  r*  r+  _sizes          r   native_batch_normr    s(     q
<%%r   c                 ,    U S   /n[        U 5      XUS/4$ rl   r2   )rq   r   r   r)  r*  r  s         r   _batch_norm_with_updater    s"     1XJE<s**r   ignore_indexlabel_smoothingc                 $    [        XX#5      S   nU$ rc  )r}  )r%   ru  r   rv  r  r  result_shapes          r   cross_entropy_lossr  %  s     $D&DQGLr   shape_compute_graph_mappingbounded_compute_graph_mappingscript_func_mapfuncc                    U [         ;  a  [        R                  R                  U 5      n[        R                  R                  UR                  5        [        S5       HU  n[        R                  R                  UR                  5        [        R                  R                  UR                  5        MW     U[         U '   [         U    $ )Nr(   )
r  torchjitscript_C_jit_pass_inlinegraphr   _jit_pass_peephole_jit_pass_constant_propagation)r  scripted_func_s      r   process_funcr  J  s    ?"		((.!!-"5"56qAHH''(;(;<HH33M4G4GH  !.4  r   operator_schemac                 (    [        U5      [        U '   g r   )r  r  )r  r  s     r   add_shape_compute_mappingr  X  s     4@3E0r   lower_bound_funcupper_bound_funcc                 B    [        U5      [        U5      4nU[        U '   g r   )r  r  )r  r  r  fnss       r   add_bounded_compute_mappingr  ^  s%     ()<8H+I
JC58!/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)zaten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensorzaten::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) -> Tensorzaten::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) -> Tensorzaten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensorzaten::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)zaten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensorzaten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensorzaten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensorzaten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensorzaten::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)zaten::convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensorzaten::_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) -> Tensorzaten::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)zaten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)zaten::native_layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)zaten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)zaten::_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)zaten::_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)z_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)zaten::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))r;   FF)NNNNr
   N)T)NF)r;   )Nr
   ig        )grA  collections.abcr   typingr   r   r   r   floatnumberr  rW   r   r"   r$   r-   r0   r4   r7   rA   rD   rJ   rE   rO   boolrR   r]   r_   rc   rn   rp   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r  r  r  r  r  r  r"  r(  r/  r2  rX   r8  r   rC  rF  rH  rN  r\  ra  rf  rh  rl  rn  rp  rr  tuplert  r}  r  r  r  r  r  ScriptFunctionScriptFnr  dictstr__annotations__r  r  r  r  r  rM   r   r   <module>r     s    $ ' ' 
sEz	$ c tCy 0)tCy )T#Y )49 )49  c d3i d3i &S	 S	 c tCy (c 49 :DI d3i s 49 S T#Y .c /tCy /c / LQ $s) DI D 
s)'S	2>BHK,$s) #  s s   	
   6  	
  -
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 3iH@ H@V	9	c	 I	 #Y		
 3i	 	$9$$s)$$ DK($N
T#Y 
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d3i c T#Y T#Y $s) # d3i 	S	 	 	T#Y d3i  tCy s 49 * $I#Y  	
  
s)&.sm:B3-OR>P4S	? PS T#Y 3S	 39"3i47@C&d3i s 2(4S	? ( (c  S &&PDI &PS	 &PR"DI "DI S  "$s) T#Y htCy6I +S	 +c +$s) +3 +s +d3i D 191s)1 49
1 I	1
 #Y1 3i1 1lS	c 49
 I	
 #Y 3i <T9TIT 49
T I	T
 #YT 3iT T T9TIT 49
T I	T
 #YT 3iT T 9c999 I9 T#Y	9 !%"&#'*.$($9$I$ 49
$ T#Y	$
 d3i $ T#Y'$ $ tCy!$ 
#Y$N(9(I( 49
( I	(
 #Y( 3i( ( I( ( 
#Y(V9I 49
 I	
 #Y 3i  I      
#Y89T#Y 49
 49%	
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 #YT 3iT T  C d $3 
$s) !F !# !S ! !3 !)))&))14)<?)GJ)222&,2472?B2JM2UX2$49 DI * $s)  T#Y  T#Y  4PS9  B49  s 6tCy &tCy &	DI 	C 	$ 	 AF1
s)1"3-19=1	#Y1'd3i 'tCy 'T#Y ' 49 c 
tCy 
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& 49%	&
 $s)$& & 49d3ic*+&+9+T#Y+ 49
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 $s)$+ 49d3icDI56+ #' 	
s)	I	 T#Y	 		
 	 	 
#Y	& 88""35 T#x-0 5FH tCx/A)B$BC H,.h() .!x !Fs F( F99,49HP9 d	 Le @% J 2O Do V {	  B	 |  Y  k F V =w @, ;Y l	 G F 9	
 OQV n	 m	  E H" M KS Q G L Mv V Lf  O  f @! D JI  Q
  Q
  v  Q
  v  m  w  z \ Lc R NPU V <g Y OQU V @& S g n ` BO A? d	 h e	 x	 CU K T Lf I3 O 35E x  R  I  {  @  I  z
  c I S W ,.ACVr   