+
     °³j¬?  ã                   ó"  € ^ RI Ht ^ RIHt ^ RIHtHtHt ^ RIt^ RIH	t	H
t
 ^RIHtHt ^RIHt ^RIHt ^R	IHtHtHt ^R
IHt ^RIHtHtHt . R%Ot ! R R4      t ! R R]	P>                  4      t  ! R R]	P>                  4      t!R&R R llt"R R lt#RR'R]/t$ ! R R]4      t% ! R R]4      t&]! 4       ]! R]%PN                  3R7      RRRR /R! R" ll4       4       t(]! 4       ]! R]&PN                  3R7      RRRR /R# R$ ll4       4       t)R# )(é    )ÚSequence)Úpartial)ÚAnyÚCallableÚOptionalN)ÚnnÚTensor)ÚConv2dNormActivationÚSqueezeExcitation)ÚImageClassification)Ú_log_api_usage_once)Úregister_modelÚWeightsÚWeightsEnum)Ú_IMAGENET_CATEGORIES)Ú_make_divisibleÚ_ovewrite_named_paramÚhandle_legacy_interfaceÚMobileNetV3ÚMobileNet_V3_Large_WeightsÚMobileNet_V3_Small_Weightsc                   óN   a € ] tR t^t o V 3R lR lt]V 3R lR l4       tRtV tR# )ÚInvertedResidualConfigc                óP   <€ V ^8„  d   QhRS[ RS[ RS[ RS[ RS[RS[RS[ RS[ R	S[/	# )
é   Úinput_channelsÚkernelÚexpanded_channelsÚout_channelsÚuse_seÚ
activationÚstrideÚdilationÚ
width_mult)ÚintÚboolÚstrÚfloat)ÚformatÚ__classdict__s   "€Ú_/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchvision/models/mobilenetv3.pyÚ__annotate__Ú#InvertedResidualConfig.__annotate__   se   ø€ ÷ !ñ !áð!ñ ð!ñ ð	!ñ
 ð!ñ ð!ñ ð!ñ ð!ñ ð!ñ ñ!ó    c
                óÎ   € V P                  W4      V n        W n        V P                  W94      V n        V P                  WI4      V n        WPn        VR 8H  V n        Wpn        W€n        R# )ÚHSN)	Úadjust_channelsr   r   r   r   r    Úuse_hsr"   r#   )
Úselfr   r   r   r   r    r!   r"   r#   r$   s
   &&&&&&&&&&r+   Ú__init__ÚInvertedResidualConfig.__init__   s^   € ð #×2Ñ2°>ÓNˆÔØŒØ!%×!5Ñ!5Ð6GÓ!TˆÔØ ×0Ñ0°ÓJˆÔØŒØ  DÑ(ˆŒØŒØ Žr.   c                ó&   <€ V ^8„  d   QhRS[ RS[/# )r   Úchannelsr$   )r%   r(   )r)   r*   s   "€r+   r,   r-   1   s   ø€ ÷ 9ñ 9¡#ð 9±5ñ 9r.   c                ó&   € \        W,          ^4      # )é   )r   )r7   r$   s   &&r+   r1   Ú&InvertedResidualConfig.adjust_channels0   s   € ä˜xÕ4°aÓ8Ð8r.   )r#   r   r   r   r   r"   r2   r    N)	Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__r4   Ústaticmethodr1   Ú__static_attributes__Ú__classdictcell__)r*   s   @r+   r   r      s#   ø‡ € ÷!ð !ð* ÷9ó ö9r.   r   c                   óz   a a€ ] tR t^5t o]! ]]P                  R7      3V3R lV 3R llltV3R lR lt	Rt
VtV ;t# )ÚInvertedResidual)Úscale_activationc                ó|   <€ V ^8„  d   QhRS[ RS[RS[P                  3,          RS[RS[P                  3,          /# )r   ÚcnfÚ
norm_layer.Úse_layer)r   r   r   ÚModule)r)   r*   s   "€r+   r,   ÚInvertedResidual.__annotate__7   sC   ø€ ÷ 6%ñ 6%á#ð6%ñ ˜S¡"§)¡)˜^Õ,ð6%ñ ˜3¡§	¡	˜>Õ*ñ	6%r.   c                óH  <€ \         SV `  4        ^VP                  u;8:  d   ^8:  g   M \        R4      hVP                  ^8H  ;'       d    VP                  VP
                  8H  V n        . pVP                  '       d   \        P                  M\        P                  pVP                  VP                  8w  d4   VP                  \        VP                  VP                  ^VVR7      4       VP                  ^8”  d   ^MVP                  pVP                  \        VP                  VP                  VP                  VVP                  VP                  VVR7      4       VP                   '       d@   \#        VP                  ^,          ^4      pVP                  V! VP                  V4      4       VP                  \        VP                  VP
                  ^VRR7      4       \        P$                  ! V!  V n        VP
                  V n        VP                  ^8„  V n        R# )é   zillegal stride value©Úkernel_sizerG   Úactivation_layer)rN   r"   r#   ÚgroupsrG   rO   N)Úsuperr4   r"   Ú
ValueErrorr   r   Úuse_res_connectr2   r   Ú	HardswishÚReLUr   Úappendr
   r#   r   r    r   Ú
SequentialÚblockÚ_is_cn)	r3   rF   rG   rH   ÚlayersrO   r"   Úsqueeze_channelsÚ	__class__s	   &&&&    €r+   r4   ÚInvertedResidual.__init__7   s±  ø€ ô 	‰ÑÔØ�S—Z‘ZÖ$ 1Ö$ÜÐ3Ó4Ð4à"Ÿz™z¨Q™×YÐY°3×3EÑ3EÈ×IYÑIYÑ3YˆÔà"$ˆØ+.¯:¯:¨:œ2Ÿ<š<¼2¿7¹7Ðð × Ñ  C×$6Ñ$6Ô6Ø�M‰MÜ$Ø×&Ñ&Ø×)Ñ)Ø !Ø)Ø%5ôôð —l‘l QÔ&‘¨C¯J©JˆØ�‰Ü Ø×%Ñ%Ø×%Ñ%ØŸJ™JØØŸ™Ø×,Ñ,Ø%Ø!1ô	ô	
ð �:�:ˆ:Ü.¨s×/DÑ/DÈÕ/IÈ1ÓMÐØ�M‰M™( 3×#8Ñ#8Ð:JÓKÔLð 	�‰Ü Ø×%Ñ% s×'7Ñ'7ÀQÐS]Ðptôô	
ô —]’] FÑ+ˆŒ
Ø×,Ñ,ˆÔØ—j‘j 1‘nˆŽr.   c                ó&   <€ V ^8„  d   QhRS[ RS[ /# )r   ÚinputÚreturn©r	   )r)   r*   s   "€r+   r,   rJ   o   s   ø€ ÷ ñ ™Vð ©ñ r.   c                ó\   € V P                  V4      pV P                  '       d	   W!,          pV# ©N)rX   rS   )r3   r_   Úresults   && r+   ÚforwardÚInvertedResidual.forwardo   s(   € Ø—‘˜EÓ"ˆØ××ÐØ�OˆFØˆr.   )rY   rX   r   rS   )r;   r<   r=   r>   r   ÚSElayerr   ÚHardsigmoidr4   re   r@   rA   Ú__classcell__©r\   r*   s   @@r+   rC   rC   5   s/   ù‡ € ñ .5°WÈrÏ~É~Ô-^÷	6%õ 6%÷p÷ ð r.   rC   c                   óf   a a€ ] tR t^vt oRV3R lV 3R llltV3R lR ltV3R lR ltRtVtV ;t	# )	r   c                óÈ   <€ V ^8„  d   QhRS[ S[,          RS[RS[RS[S[RS[P                  3,          ,          RS[S[RS[P                  3,          ,          RS[RS[R	R
/# )r   Úinverted_residual_settingÚlast_channelÚnum_classesrX   .rG   ÚdropoutÚkwargsr`   N)	Úlistr   r%   r   r   r   rI   r(   r   )r)   r*   s   "€r+   r,   ÚMobileNetV3.__annotate__w   s�   ø€ ÷ Y'ñ Y'á#'Ñ(>Õ#?ðY'ñ ðY'ñ ð	Y'ñ
 ™ ¡b§i¡i Õ0Õ1ðY'ñ ™X c©2¯9©9 nÕ5Õ6ðY'ñ ðY'ñ ðY'ð 
ñY'r.   c                ó6  <€ \         SV `  4        \        V 4       V'       g   \        R4      h\	        V\
        4      '       d0   \        V Uu. uF  p\	        V\        4      NK  	  up4      '       g   \        R4      hVf   \        pVf   \        \        P                  RRR7      p. p	V^ ,          P                  p
V	P                  \        ^V
^^V\        P                   R7      4       V F  pV	P                  V! Wµ4      4       K  	  VR,          P"                  p^V,          pV	P                  \        VV^V\        P                   R7      4       \        P$                  ! V	!  V n        \        P(                  ! ^4      V n        \        P$                  ! \        P,                  ! WÒ4      \        P                   ! R	R
7      \        P.                  ! VR	R7      \        P,                  ! W#4      4      V n        V P3                  4        EF†  p\	        V\        P4                  4      '       dg   \        P6                  P9                  VP:                  RR7       VP<                  e,   \        P6                  P?                  VP<                  4       Kˆ  KŠ  \	        V\        P                  \        P@                  34      '       dV   \        P6                  PC                  VP:                  4       \        P6                  P?                  VP<                  4       EK  \	        V\        P,                  4      '       g   EK2  \        P6                  PE                  VP:                  ^ R4       \        P6                  P?                  VP<                  4       EK‰  	  R# u upi )aæ  
MobileNet V3 main class

Args:
    inverted_residual_setting (List[InvertedResidualConfig]): Network structure
    last_channel (int): The number of channels on the penultimate layer
    num_classes (int): Number of classes
    block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet
    norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use
    dropout (float): The droupout probability
z1The inverted_residual_setting should not be emptyzDThe inverted_residual_setting should be List[InvertedResidualConfig]Ngü©ñÒMbP?g{®Gáz„?)ÚepsÚmomentum)rN   r"   rG   rO   rM   T)Úinplace)Úprw   Úfan_out)Úmodeéÿÿÿÿ)#rQ   r4   r   rR   Ú
isinstancer   Úallr   Ú	TypeErrorrC   r   r   ÚBatchNorm2dr   rV   r
   rT   r   rW   ÚfeaturesÚAdaptiveAvgPool2dÚavgpoolÚLinearÚDropoutÚ
classifierÚmodulesÚConv2dÚinitÚkaiming_normal_ÚweightÚbiasÚzeros_Ú	GroupNormÚones_Únormal_)r3   rm   rn   ro   rX   rG   rp   rq   ÚsrZ   Úfirstconv_output_channelsrF   Úlastconv_input_channelsÚlastconv_output_channelsÚmr\   s   &&&&&&&,       €r+   r4   ÚMobileNetV3.__init__w   s{  ø€ ô* 	‰ÑÔÜ˜DÔ!ç(ÜÐPÓQÐQäÐ0´(×;Ò;ÜÑD]Ó^ÑD]¸q”Z Ô#9Ö:ÑD]Ñ^×_Ò_äÐbÓcÐcàŠ=Ü$ˆEàÒÜ ¤§¡°UÀTÔJˆJà"$ˆð %>¸aÕ$@×$OÑ$OÐ!Ø�‰Ü ØØ)ØØØ%Ü!#§¡ôô		
ó -ˆCØ�M‰M™% Ó0Ö1ñ -ð #<¸BÕ"?×"LÑ"LÐØ#$Ð'>Õ#>Ð Ø�‰Ü Ø'Ø(ØØ%Ü!#§¡ôô	
ô Ÿš vÑ.ˆŒÜ×+Ò+¨AÓ.ˆŒÜŸ-š-Ü�IŠIÐ.Ó=Ü�LŠL Ô&Ü�JŠJ˜¨$Ô/Ü�IŠI�lÓ0ó	
ˆŒð —‘—ˆAÜ˜!œRŸY™Y×'Ò'Ü—‘×'Ñ'¨¯©°yÐ'ÔAØ—6‘6Ò%Ü—G‘G—N‘N 1§6¡6Ö*ñ &ä˜A¤§¡´·±Ð=×>Ò>Ü—‘—‘˜aŸh™hÔ'Ü—‘—‘˜qŸv™v×&Ü˜AœrŸy™y×)Õ)Ü—‘—‘ §¡¨!¨TÔ2Ü—‘—‘˜qŸv™v×&ó  ùòg _s   ÁNc                ó&   <€ V ^8„  d   QhRS[ RS[ /# ©r   Úxr`   ra   )r)   r*   s   "€r+   r,   rs   Ò   s   ø€ ÷ ñ ™vð ©&ñ r.   c                óš   € V P                  V4      pV P                  V4      p\        P                  ! V^4      pV P	                  V4      pV# )rL   )r€   r‚   ÚtorchÚflattenr…   ©r3   r˜   s   &&r+   Ú_forward_implÚMobileNetV3._forward_implÒ   s@   € Ø�M‰M˜!Óˆà�L‰L˜‹OˆÜ�MŠM˜!˜QÓˆà�O‰O˜AÓˆàˆr.   c                ó&   <€ V ^8„  d   QhRS[ RS[ /# r—   ra   )r)   r*   s   "€r+   r,   rs   Ü   s   ø€ ÷ %ñ %™ð %¡Fñ %r.   c                ó$   € V P                  V4      # rc   )r�   rœ   s   &&r+   re   ÚMobileNetV3.forwardÜ   s   € Ø×!Ñ! !Ó$Ð$r.   )r‚   r…   r€   )iè  NNgš™™™™™É?)
r;   r<   r=   r>   r4   r�   re   r@   rA   ri   rj   s   @@r+   r   r   v   s+   ù‡ € ÷Y'õ Y'÷vð ÷%÷ %ð %r.   c          
      óT   € V ^8„  d   QhR\         R\        R\        R\        R\        /# )r   Úarchr$   Úreduced_tailÚdilatedrq   )r'   r(   r&   r   )r)   s   "r+   r,   r,   à   s4   € ÷ .3ñ .3Ü
ð.3Ü ð.3Ü6:ð.3ÜMQð.3Üehñ.3r.   c                 ó°  € V'       d   ^M^pV'       d   ^M^p\        \        VR7      p\        \        P                  VR7      pV R8X  Ed   V! ^^^^RR^^4      V! ^^^@^RR^^4      V! ^^^H^RR^^4      V! ^^^H^(RR^^4      V! ^(^^x^(RR^^4      V! ^(^^x^(RR^^4      V! ^(^^ð^PRR^^4      V! ^P^^È^PRR^^4      V! ^P^^¸^PRR^^4      V! ^P^^¸^PRR^^4      V! ^P^R^pRR^^4      V! ^p^R^pRR^^4      V! ^p^R^ V,          RR^V4      V! ^ V,          ^R	V,          ^ V,          RR^V4      V! ^ V,          ^R	V,          ^ V,          RR^V4      .p	V! R
V,          4      p
Wš3# V R8X  dà   V! ^^^^RR^^4      V! ^^^H^RR^^4      V! ^^^X^RR^^4      V! ^^^`^(RR^^4      V! ^(^^ð^(RR^^4      V! ^(^^ð^(RR^^4      V! ^(^^x^0RR^^4      V! ^0^^�^0RR^^4      V! ^0^R^`V,          RR^V4      V! ^`V,          ^RV,          ^`V,          RR^V4      V! ^`V,          ^RV,          ^`V,          RR^V4      .p	V! RV,          4      p
Wš3# \        RV  24      h)r   )r$   Úmobilenet_v3_largeFÚRETr0   ià  i   iÀ  i   Úmobilenet_v3_smalli   i@  i   zUnsupported model type )r   r   r1   rR   )r£   r$   r¤   r¥   rq   Úreduce_dividerr#   Ú
bneck_confr1   rm   rn   s   &&&&,      r+   Ú_mobilenet_v3_confr¬   à   s:  € ÷ '‘Q¨A€Nß‰q €HäÔ/¸JÔG€JÜÔ4×DÑDÐQ[Ô\€OàÐ#Õ#á�r˜1˜b " e¨T°1°aÓ8Ù�r˜1˜b " e¨T°1°aÓ8Ù�r˜1˜b " e¨T°1°aÓ8Ù�r˜1˜b " d¨D°!°QÓ7Ù�r˜1˜c 2 t¨T°1°aÓ8Ù�r˜1˜c 2 t¨T°1°aÓ8Ù�r˜1˜c 2 u¨d°A°qÓ9Ù�r˜1˜c 2 u¨d°A°qÓ9Ù�r˜1˜c 2 u¨d°A°qÓ9Ù�r˜1˜c 2 u¨d°A°qÓ9Ù�r˜1˜c 3¨¨d°A°qÓ9Ù�s˜A˜s C¨¨t°Q¸Ó:Ù�s˜A˜s C¨>Õ$9¸4ÀÀqÈ(ÓSÙ�s˜nÕ,¨a°¸Õ1FÈÈ~ÕH]Ð_cÐeiÐklÐnvÓwÙ�s˜nÕ,¨a°¸Õ1FÈÈ~ÕH]Ð_cÐeiÐklÐnvÓwð%
Ð!ñ" ' t¨~Õ'=Ó>ˆð& %Ð2Ð2ð% 
Ð%Ô	%á�r˜1˜b " d¨D°!°QÓ7Ù�r˜1˜b " e¨T°1°aÓ8Ù�r˜1˜b " e¨T°1°aÓ8Ù�r˜1˜b " d¨D°!°QÓ7Ù�r˜1˜c 2 t¨T°1°aÓ8Ù�r˜1˜c 2 t¨T°1°aÓ8Ù�r˜1˜c 2 t¨T°1°aÓ8Ù�r˜1˜c 2 t¨T°1°aÓ8Ù�r˜1˜c 2¨Õ#7¸¸tÀQÈÓQÙ�r˜^Õ+¨Q°°~Õ0EÀrÈ^ÕG[Ð]aÐcgÐijÐltÓuÙ�r˜^Õ+¨Q°°~Õ0EÀrÈ^ÕG[Ð]aÐcgÐijÐltÓuð%
Ð!ñ ' t¨~Õ'=Ó>ˆð %Ð2Ð2ô Ð2°4°&Ð9Ó:Ð:r.   c                óŒ   € V ^8„  d   QhR\         \        ,          R\        R\        \        ,          R\
        R\        R\        /# )r   rm   rn   ÚweightsÚprogressrq   r`   )rr   r   r%   r   r   r&   r   r   )r)   s   "r+   r,   r,     sO   € ÷ ñ Ü#Ô$:Õ;ðäðô ”kÕ"ðô ð	ô
 ðô ñr.   c                 óÀ   € Ve(   \        VR\        VP                  R,          4      4       \        W3/ VB pVe#   VP	                  VP                  VRR7      4       V# )Nro   Ú
categoriesT)r¯   Ú
check_hash)r   ÚlenÚmetar   Úload_state_dictÚget_state_dict)rm   rn   r®   r¯   rq   Úmodels   &&&&, r+   Ú_mobilenet_v3r¸     s^   € ð ÒÜ˜f m´S¸¿¹ÀlÕ9SÓ5TÔUäÐ1ÑJÀ6ÑJ€EàÒØ×Ñ˜g×4Ñ4¸hÐSWÐ4ÓXÔYà€Lr.   Úmin_sizer±   c                   óÀ   € ] tR tRt]! R]! ]^àR7      / ]CRRRRRR	R
RRR//RRRRRR/CR7      t]! R]! ]^à^èR7      / ]CRRRRRR	R
RRR//RRRRRR/CR7      t	]	t
RtR# )r   i)  zChttps://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth©Ú	crop_sizeÚ
num_paramsiªS Úrecipeú^https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--smallÚ_metricsúImageNet-1Kúacc@1g¦›Ä °‚R@úacc@5gö(\�ÂÕV@Ú_opsg-²�ï§ÆË?Ú
_file_sizegw¾Ÿ/5@Ú_docszJThese weights were trained from scratch by using a simple training recipe.©ÚurlÚ
transformsr´   zChttps://download.pytorch.org/models/mobilenet_v3_large-5c1a4163.pth)r¼   Úresize_sizezHhttps://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuningg¨ÆK7‰ÑR@gNbX9$W@g¬Zd5@a/  
                These weights improve marginally upon the results of the original paper by using a modified version of
                TorchVision's `new training recipe
                <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
            © N)r;   r<   r=   r>   r   r   r   Ú_COMMON_METAÚIMAGENET1K_V1ÚIMAGENET1K_V2ÚDEFAULTr@   rË   r.   r+   r   r   )  sÑ   † ÙØQÙÐ.¸#Ô>ð
Øð
à˜'ØÐvØØØ˜VØ˜Vð ðð �EØ˜&ØÐeñ
ô€Mñ$ ØQÙÐ.¸#È3ÔOð
Øð
à˜'ØÐ`ØØØ˜VØ˜Vð ðð �EØ˜&Øð ñ
ô€Mð, „Gr.   c                   ól   € ] tR tRt]! R]! ]^àR7      / ]CRRRRRR	R
RRR//RRRRRR/CR7      t]t	Rt
R# )r   iU  zChttps://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pthr»   r½   iÍ& r¾   r¿   rÀ   rÁ   rÂ   g˜nƒÀêP@rÃ   g}?5^ºÙU@rÄ   gÉv¾Ÿ/­?rÅ   gœÄ °r¨#@rÆ   z}
                These weights improve upon the results of the original paper by using a simple training recipe.
            rÇ   rË   N)r;   r<   r=   r>   r   r   r   rÌ   rÍ   rÏ   r@   rË   r.   r+   r   r   U  sl   † ÙØQÙÐ.¸#Ô>ð
Øð
à˜'ØÐvØØØ˜VØ˜Vð ðð �EØ˜%Øð ñ
ô€Mð( „Gr.   Ú
pretrained)r®   r®   r¯   Tc                ó^   € V ^8„  d   QhR\         \        ,          R\        R\        R\        /# ©r   r®   r¯   rq   r`   )r   r   r&   r   r   )r)   s   "r+   r,   r,   o  ó8   € ÷ _ñ _ÜÔ3Õ4ð_ÜGKð_Ü^að_äñ_r.   c                ó`   € \         P                  V 4      p \        R/ VB w  r4\        W4W3/ VB # )ao  
Constructs a large MobileNetV3 architecture from
`Searching for MobileNetV3 <https://arxiv.org/abs/1905.02244>`__.

Args:
    weights (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The
        pretrained weights to use. See
        :class:`~torchvision.models.MobileNet_V3_Large_Weights` below for
        more details, and possible values. By default, no pre-trained
        weights are used.
    progress (bool, optional): If True, displays a progress bar of the
        download to stderr. Default is True.
    **kwargs: parameters passed to the ``torchvision.models.mobilenet.MobileNetV3``
        base class. Please refer to the `source code
        <https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv3.py>`_
        for more details about this class.

.. autoclass:: torchvision.models.MobileNet_V3_Large_Weights
    :members:
)r§   )r   Úverifyr¬   r¸   ©r®   r¯   rq   rm   rn   s   $$,  r+   r§   r§   m  ó:   € ô2 )×/Ñ/°Ó8€Gä.@Ñ.`ÐY_Ñ.`Ñ+ÐÜÐ2À'Ñ^ÐW]Ñ^Ð^r.   c                ó^   € V ^8„  d   QhR\         \        ,          R\        R\        R\        /# rÓ   )r   r   r&   r   r   )r)   s   "r+   r,   r,   Ž  rÔ   r.   c                ó`   € \         P                  V 4      p \        R/ VB w  r4\        W4W3/ VB # )ao  
Constructs a small MobileNetV3 architecture from
`Searching for MobileNetV3 <https://arxiv.org/abs/1905.02244>`__.

Args:
    weights (:class:`~torchvision.models.MobileNet_V3_Small_Weights`, optional): The
        pretrained weights to use. See
        :class:`~torchvision.models.MobileNet_V3_Small_Weights` below for
        more details, and possible values. By default, no pre-trained
        weights are used.
    progress (bool, optional): If True, displays a progress bar of the
        download to stderr. Default is True.
    **kwargs: parameters passed to the ``torchvision.models.mobilenet.MobileNetV3``
        base class. Please refer to the `source code
        <https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv3.py>`_
        for more details about this class.

.. autoclass:: torchvision.models.MobileNet_V3_Small_Weights
    :members:
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