+
    –°³j   ã                   ó  € R t ^ RIt^ RIt^ RIHt ^RIHtHt ^RIHt ^RI	H
t
 ^RIHt ^RIHt ^RIHt ^R	IHt ^R
IHt ^RIHt ^RIHt ^RIHt ]! ]P8                  R4      '       d   ^RIHt ^RIH t  ^RI!H"t" . ROt#R# )aW  
:mod:`torch.distributed.optim` exposes DistributedOptimizer, which takes a list
of remote parameters (:class:`~torch.distributed.rpc.RRef`) and runs the
optimizer locally on the workers where the parameters live.  The distributed
optimizer can use any of the local optimizer :ref:`optimizer-algorithms` to
apply the gradients on each worker.
N)Úoptim)Ú_apply_optimizer_in_backwardÚ_get_in_backward_optimizers)Ú_FunctionalAdadelta)Ú_FunctionalAdagrad)Ú_FunctionalAdam)Ú_FunctionalAdamax)Ú_FunctionalAdamW)Ú_FunctionalRMSprop)Ú_FunctionalRprop)Ú_FunctionalSGD)Ú_NamedOptimizer)Úas_functional_optimÚ	_rpc_init)ÚDistributedOptimizer)ÚPostLocalSGDOptimizer)ÚZeroRedundancyOptimizer)r   r   r   r   )$Ú__doc__ÚwarningsÚtorchr   Úapply_optimizer_in_backwardr   r   Úfunctional_adadeltar   Úfunctional_adagradr   Úfunctional_adamr   Úfunctional_adamaxr   Úfunctional_adamwr	   Úfunctional_rmspropr
   Úfunctional_rpropr   Úfunctional_sgdr   Únamed_optimizerr   Úutilsr   ÚhasattrÚ_CÚ	optimizerr   Úpost_localSGD_optimizerr   Úzero_redundancy_optimizerr   Ú__all__© ó    Úa/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/distributed/optim/__init__.pyÚ<module>r*      s]   ðñó ã Ý ÷õ 5Ý 2Ý ,Ý 0Ý .Ý 2Ý .Ý *Ý ,Ý &ñ
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