+ jR^RIHt^RIHtHt^RIt^RIHt .ROt RRlt ]RRl4t ]R R l4t ]RR R ll4t]R Rl4t]RRl4tR#)) lru_cache)Optional TYPE_CHECKINGN)Libraryc$V^8dQhR\/#returnbool)formats"\/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/backends/mps/__init__.py __annotate__rs$c6\PP#)zReturn whether PyTorch is built with MPS support. Note that this doesn't necessarily mean MPS is available; just that if this PyTorch binary were run on a machine with working MPS drivers and devices, we would be able to use it. )torch_C_has_mpsrris_builtrs 88  rc$V^8dQhR\/#rr )r s"rrrs((d(rc>\PP4#)z7Return a bool indicating if MPS is currently available.)rr_mps_is_availablerrr is_availablers 88 % % ''rc<V^8dQhR\R\R\/#)r majorminorr intr )r s"rrr#s!<\PP4#)zReturn Metal device name)rr _mps_get_namerrrget_namer+.s 88 ! ! ##rc$V^8dQhR\/#r)r)r s"rrr5s***rc>\PP4#)zReturn GPU core count. According to the documentation, one core is comprised of 16 Execution Units. One execution Unit has 8 ALUs. And one ALU can run 24 threads, i.e. one core is capable of executing 3072 threads concurrently. )rr_mps_get_core_countrrrget_core_countr/4s 88 ' ' ))r)r/r+rrr&r#)r) functoolsr _lru_cachetypingrrr torch.libraryr_Library__all__rrr#r&r+r/rrrr6s-* -  ( (  < <  9 9  $ $  * *r