+
     °³j¼  ã                   ó¢   € ^ RI t ^ RIt ^ RI HtHt ^RIHt R	R R llt] P                  P                  R4        ! R R]P                  4      t
R# )
é    N)ÚnnÚTensor)Ú_log_api_usage_oncec          
      óT   € V ^8„  d   QhR\         R\        R\        R\        R\         /# )é   ÚinputÚpÚmodeÚtrainingÚreturn)r   ÚfloatÚstrÚbool)Úformats   "Úa/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torchvision/ops/stochastic_depth.pyÚ__annotate__r      s0   € ÷ $ñ $œFð $¤uð $´Cð $Ä4ð $ÔSYñ $ó    c                ó   € \         P                  P                  4       '       g4   \         P                  P                  4       '       g   \	        \
        4       VR8  g   VR8”  d   \        RV 24      hVR9  d   \        RV 24      hV'       d   VR8X  d   V # RV,
          pVR8X  d6   V P                  ^ ,          .^.V P                  ^,
          ,          ,           pM^.V P                  ,          p\         P                  ! WPP                  V P                  R7      pVP                  V4      pVR8”  d   VP                  V4       W,          # )a¾  
Implements the Stochastic Depth from `"Deep Networks with Stochastic Depth"
<https://arxiv.org/abs/1603.09382>`_ used for randomly dropping residual
branches of residual architectures.

Args:
    input (Tensor[N, ...]): The input tensor or arbitrary dimensions with the first one
                being its batch i.e. a batch with ``N`` rows.
    p (float): probability of the input to be zeroed.
    mode (str): ``"batch"`` or ``"row"``.
                ``"batch"`` randomly zeroes the entire input, ``"row"`` zeroes
                randomly selected rows from the batch.
    training: apply stochastic depth if is ``True``. Default: ``True``

Returns:
    Tensor[N, ...]: The randomly zeroed tensor.
g        g      ð?z4drop probability has to be between 0 and 1, but got Úrowz0mode has to be either 'batch' or 'row', but got )ÚdtypeÚdevice)Úbatchr   )ÚtorchÚjitÚis_scriptingÚ
is_tracingr   Ústochastic_depthÚ
ValueErrorÚshapeÚndimÚemptyr   r   Ú
bernoulli_Údiv_)r   r	   r
   r   Úsurvival_rateÚsizeÚnoises   &&&&   r   r   r      s  € ô$ �9‰9×!Ñ!×#Ò#¬E¯I©I×,@Ñ,@×,BÒ,BÜÔ,Ô-Øˆ3„w�!�c”'ÜÐOÐPQÈsÐSÓTÐTØÐ#Ô#ÜÐKÈDÈ6ÐRÓSÐSß�q˜C”xØˆà˜!•G€MØˆu„}Ø—‘˜A•Ð 1 #¨¯©°a­Õ"8Õ8‰àˆs�U—Z‘ZÕˆÜ�KŠK˜§K¡K¸¿¹ÔE€EØ×Ñ˜]Ó+€EØ�sÔØ�
‰
�=Ô!Ø�=Ðr   r   c                   óf   a a€ ] tR t^2t oRtV3R lV 3R lltV3R lR ltV3R lR ltRtVt	V ;t
# )	ÚStochasticDepthz
See :func:`stochastic_depth`.
c                ó*   <€ V ^8„  d   QhRS[ RS[RR/# )r   r	   r
   r   N)r   r   )r   Ú__classdict__s   "€r   r   ÚStochasticDepth.__annotate__7   s"   ø€ ÷ ñ ™%ð ¡sð ¨tñ r   c                óR   <€ \         SV `  4        \        V 4       Wn        W n        R # ©N)ÚsuperÚ__init__r   r	   r
   )Úselfr	   r
   Ú	__class__s   &&&€r   r/   ÚStochasticDepth.__init__7   s    ø€ Ü‰ÑÔÜ˜DÔ!ØŒØŽ	r   c                ó&   <€ V ^8„  d   QhRS[ RS[ /# )r   r   r   )r   )r   r*   s   "€r   r   r+   =   s   ø€ ÷ Iñ I™Vð I©ñ Ir   c                óX   € \        WP                  V P                  V P                  4      # r-   )r   r	   r
   r   )r0   r   s   &&r   ÚforwardÚStochasticDepth.forward=   s   € Ü §v¡v¨t¯y©y¸$¿-¹-ÓHÐHr   c                ó    <€ V ^8„  d   QhRS[ /# )r   r   )r   )r   r*   s   "€r   r   r+   @   s   ø€ ÷ ñ ™#ñ r   c                ól   € V P                   P                   R V P                   RV P                   R2pV# )z(p=z, mode=Ú))r1   Ú__name__r	   r
   )r0   Úss   & r   Ú__repr__ÚStochasticDepth.__repr__@   s2   € Ø�~‰~×&Ñ&Ð' s¨4¯6©6¨(°'¸$¿)¹)¸ÀAÐFˆØˆr   )r
   r	   )r:   Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__r/   r5   r<   Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r1   r*   s   @@r   r(   r(   2   s/   ù‡ € ñ÷ó ÷Ið I÷÷ ð r   r(   )T)r   Útorch.fxr   r   Úutilsr   r   ÚfxÚwrapÚModuler(   © r   r   Ú<module>rK      s:   ðÛ Û ß å '÷$ðN ‡�‡�Ð Ô !ô�b—i‘iö r   