+
    –°³j.¯ ã                   ó$  € R t ^ RIt^ RIt^ RIt^ RIt^ RIt^ RIt^ RIt^ RIH	t	H
t
 ^ RIHt ^ RIHtHtHt ^ RIHt ^ RIt^ RIHtHtHtHtHtHtHtHtHt . R0Ot]! R4      t]! R4      tR1R	 R
 llt ]PB                  ] R R l4       4       t"]PB                  R R l4       t#]PB                  ] R R l4       4       t$R R lt%R2R R llt&R R lt']! ]R4      t(]! ]R4      t)]! ]R4      t*]PB                  R R l4       t+] R R l4       t,] R 4       t-]PB                  R R  l4       t.] R! R" l4       t/R# t0 ! R$ R%4      t1R& t2R' t3R( t4R) t5]Pl                  R* 4       t7 ! R+ R,]14      t8]Pl                  R- 4       t9]Pl                  R. 4       t:R/ t;R# )3aE  
Python implementation of ``__torch_function__``

While most of the torch API and handling for ``__torch_function__`` happens
at the C++ level, some of the torch API is written in Python so we need
python-level handling for ``__torch_function__`` overrides as well. The main
developer-facing functionality in this file are handle_torch_function and
has_torch_function. See torch/functional.py and test/test_overrides.py
for usage examples.

Note
----
heavily inspired by NumPy's ``__array_function__`` (see:
https://github.com/pytorch/pytorch/issues/24015 and
https://www.numpy.org/neps/nep-0018-array-function-protocol.html
)

If changing this file in a way that can affect ``__torch_function__`` overhead,
please report the benchmarks in ``benchmarks/overrides_benchmark``. See the
instructions in the ``README.md`` in that directory.
N)ÚCallableÚIterable©Úwraps)ÚAnyÚcastÚTypeVar)Ú	ParamSpec)	Ú_add_docstrÚ_get_function_stack_atÚ_has_torch_functionÚ_has_torch_function_unaryÚ_has_torch_function_variadicÚ_is_torch_function_mode_enabledÚ_len_torch_function_stackÚ_pop_torch_function_stackÚ_push_on_torch_function_stackÚ_PÚ_Rc          
      óŒ   € V ^8„  d   QhR\         \        \        3,          R\        R\        R\         \        \        3,          /# )é   ÚfuncÚregexÚmoduleÚreturn)r   r   r   Ústr)Úformats   "ÚP/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/overrides.pyÚ__annotate__r   D   sB   € ÷  ñ  Ü
”2”r�6Õ
ð äð ô ð ô Œb”"ˆfÕñ	 ó    c                ó>   a aa€ \        S 4      R V VV3R ll4       pV# )aÏ  
Decorator that temporarily disables ``UserWarning``s for the given ``module`` if the warning message matches the
given ``regex`` pattern.

Arguments
---------
func : function
    Function to disable the warnings for.
regex : str
    A regex pattern compilable by ``re.compile``. This is used to match the ``UserWarning`` message.
module : str
    The python module to which the filtering should be restricted.

Returns
-------
function
    The wrapped function.
c                ód   € V ^8„  d   QhR\         P                  R\         P                  R\        /# ©r   ÚargsÚkwargsr   ©r   r#   r$   r   )r   s   "r   r   Ú,_disable_user_warnings.<locals>.__annotate__]   s)   € ÷ )ñ )”r—w‘wð )¬"¯)©)ð )¼ñ )r   c            	      óÈ   <€ \         P                  ! 4       ;_uu_ 4        \         P                  ! R \        SSR7       S! V / VB uuRRR4       #   + '       g   i     R# ; i)Úignore)ÚcategoryÚmessager   N)ÚwarningsÚcatch_warningsÚfilterwarningsÚUserWarning)r#   r$   r   r   r   s   *,€€€r   ÚwrapperÚ'_disable_user_warnings.<locals>.wrapper\   sG   ø€ ä×$Ò$×&Õ&Ü×#Ò#Ø¤;¸Àfõñ ˜Ð( Ñ(÷	 '×&×&Ó&ús   Ÿ&AÁA!	r   )r   r   r   r/   s   fff r   Ú_disable_user_warningsr1   D   s'   ú€ ô0 ˆ4ƒ[÷)ñ )ó ð)ð €Nr   c                ó:   € V ^8„  d   QhR\         \        ,          /# ©r   r   ©Úsetr   )r   s   "r   r   r   i   s   € ÷ añ aœs¤8�}ñ ar   c                 óÈ%  € \         P                  p 0 \         P                  k\         P                  k\         P                  k\         P
                  k\         P                  k\         P                  k\         P                  k\         P                  k\         P                  k\         P                  k\         P                  k\         P                  k\         P                  k\         P                  k\         P                   k\         P"                  k\         P$                  k\         P&                  k\         P(                  k\         P*                  k\         P,                  k\         P.                  k\         P0                  k\         P2                  k\         P4                  k\         P6                  k\         P8                  k\         P:                  k\         P<                  k\         P>                  k\         P@                  k\         PB                  k\         PD                  k\         PF                  k\         PH                  k\         PJ                  k\         PL                  k\         PN                  k\         PP                  k\         PR                  k\         PT                  k\         PV                  k\         PX                  k\         PZ                  k\         P\                  k\         P^                  k\         P`                  k\         Pb                  k\         Pd                  k\         Pf                  k\         Ph                  k\         Pj                  k\         Pl                  k\         Pn                  k\         Pp                  k\         Pr                  k\         Pt                  k\         Pv                  k\         Px                  k\         Pz                  k\         P|                  k\         P~                  k\         P€                  k\         P‚                  k\         P„                  k\         P†                  k\         Pˆ                  k\         PŠ                  k\         PŒ                  k\         PŽ                  k\         P�                  k\         P’                  k\         P”                  k\         P–                  k\         P˜                  k\         Pš                  k\         Pœ                  k\         Pž                  Pž                  k\         Pž                  P                  k\         Pž                  P                   k\         Pž                  P                  k\         P¢                  k\         P¤                  P¦                  k\         P¤                  P¨                  k\         Pª                  k\         P¬                  k\         P®                  k\         P°                  k\         P²                  k\         P´                  k\         P¶                  k\         P¸                  k\         Pº                  k\         P¼                  k\         P¾                  k\         PÀ                  k\         PÂ                  k\         PÄ                  k\         PÆ                  k\         PÈ                  k\         PÊ                  k\         PÌ                  k\         PÎ                  k\         PÐ                  k\         PÒ                  k\         PÔ                  k\         PÖ                  k\         PØ                  k\         PÚ                  k\         PÜ                  k\         PÞ                  k\         Pà                  k\         Pâ                  k\         Pä                  k\         Pæ                  k\         Pè                  k\         Pê                  k\         Pì                  k\         Pî                  k\         Pð                  k\         Pò                  k\         Pô                  k\         Pö                  k\         Pø                  k\         Pú                  Pü                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP
                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                  k\         Pþ                  EP                   EP                   k\         Pþ                  EP"                  EP$                  k\         Pþ                  EP"                  EP&                  k\         Pþ                  EP"                  PÆ                  k\         Pþ                  EP"                  EP(                  k\         Pþ                  EP"                  P¢                  k\         Pþ                  EP"                  EP*                  k\         Pþ                  EP"                  EP,                  k\         Pþ                  EP"                  EP.                  k\         Pþ                  EP"                  EP0                  k\         Pþ                  EP"                  EP2                  k\         Pþ                  EP"                  EP4                  k\         Pþ                  EP"                  EP6                  k\         EP8                  EP:                  kE\
        kE\        k\         EP<                  k\         EP>                  k\         EP@                  k\         EPB                  k\         EPD                  k\         EPF                  k\         EPH                  k\         EPJ                  k\         EPL                  k\         EPN                  k\         EPP                  k\         EPR                  k\         EPT                  k\         EPV                  k\         EPX                  k\         EPZ                  k\         EP\                  k\         EP^                  k\         EP`                  k\         EPb                  k\         EPd                  k\         EPf                  k\         EPh                  k\         Pþ                  EP                   EPj                  k\         EPl                  k\         EPn                  k\         EPp                  k\         EPr                  k\         EPt                  k\         EPv                  k\         EPx                  k\         EPz                  k\         EP|                  k\         EP~                  k\         EP€                  k\         EP‚                  k\         EP„                  k\         EP†                  k\         EPˆ                  k\         EPŠ                  k\         EPŒ                  k\         EPŽ                  kV EP�                  kV EP’                  kV EP”                  kV EP–                  kV EP˜                  kV EPš                  kV EPœ                  kV EPž                  kV EP                   kV EP¢                  kV EP¤                  kV EP¦                  kV EP¨                  kV EPª                  kV EP¬                  kV EP®                  kV EP°                  kV EP²                  kV EP´                  kV EP¶                  kV EP¸                  kV EPº                  kV EP¼                  kV EP¾                  kV EPÀ                  kV EPÂ                  kV EPÄ                  kV EPÆ                  kV EPÈ                  kV EPÊ                  kV EPÌ                  kV EPÎ                  kV EPÐ                  kV EPÒ                  kV EPÔ                  kV EPÖ                  kV EPØ                  kV EPÚ                  kV EPÜ                  kV EPÞ                  kV EPà                  kV EPâ                  kV EPä                  kV EPæ                  kV EPè                  kV EPê                  kV EPì                  kV EPî                  kV EPð                  kV EPò                  kV EPô                  EPö                  kV EPø                  EPö                  kV EPú                  kV EPü                  kV EPþ                  kV EP                   kV EP                  kV EP                  kV EP                  kV EP                  kV EP:                  kV EP
                  kV EP                  kV EP                  kpE\        EP                  R8¼  d   VEP                  V EP                  4       V# )aí  
Return public functions that cannot be overridden by ``__torch_function__``.

Returns
-------
set[Callable]
    A tuple of functions that are publicly available in the torch API but cannot
    be overridden with ``__torch_function__``. Mostly this is because none of the
    arguments of these functions are tensors or tensor-likes.

Examples
--------
>>> torch.Tensor.as_subclass in torch.overrides.get_ignored_functions()
True
>>> torch.add in torch.overrides.get_ignored_functions()
False
)é   é   (  ÚtorchÚTensorÚtypenameÚ	is_tensorÚ
is_storageÚset_default_tensor_typeÚset_default_deviceÚget_default_deviceÚset_rng_stateÚget_rng_stateÚmanual_seedÚinitial_seedÚseedÚthread_safe_generatorÚsaveÚloadÚset_printoptionsÚforkÚget_default_dtypeÚget_num_interop_threadsÚget_num_threadsÚinit_num_threadsÚimport_ir_moduleÚimport_ir_module_from_bufferÚis_anomaly_enabledÚis_anomaly_check_nan_enabledÚis_grad_enabledÚmerge_type_from_type_commentÚparse_irÚparse_schemaÚparse_type_commentÚset_anomaly_enabledÚset_flush_denormalÚset_num_interop_threadsÚset_num_threadsÚwaitÚ	as_tensorÚ
from_numpyÚtensorÚdefault_generatorÚhas_cudaÚ	has_cudnnÚ
has_lapackÚdeviceÚdtypeÚfinfoÚhas_mklÚhas_mpsÚ
has_mkldnnÚ
has_openmpÚiinfoÚmemory_formatÚqschemeÚset_grad_enabledÚno_gradÚenable_gradÚinference_modeÚis_inference_mode_enabledÚlayoutÚarangeÚ
as_stridedÚbartlett_windowÚblackman_windowÚbroadcast_shapesÚcan_castÚcompileÚcudnn_affine_grid_generatorÚcudnn_batch_normÚcudnn_convolutionÚcudnn_convolution_transposeÚcudnn_convolution_reluÚcudnn_convolution_add_reluÚcudnn_grid_samplerÚcudnn_is_acceptableÚmiopen_ctc_lossÚemptyÚempty_permutedÚempty_stridedÚempty_quantizedÚexportÚregister_dataclassÚeyeÚfftÚfftfreqÚrfftfreqÚ	from_fileÚfullÚfillÚhamming_windowÚhann_windowÚkaiser_windowÚlinspaceÚlogspaceÚmkldnn_adaptive_avg_pool2dÚmkldnn_convolutionÚmkldnn_max_pool2dÚmkldnn_max_pool3dÚmkldnn_linear_backward_weightsÚmkldnn_rnn_layerÚnormalÚonesÚpromote_typesÚrandÚ	rand_likeÚrandnÚ
randn_likeÚrandintÚrandint_likeÚrandpermÚrangeÚresult_typeÚscalar_tensorÚsparse_coo_tensorÚsparse_compressed_tensorÚsparse_csr_tensorÚsparse_csc_tensorÚsparse_bsr_tensorÚsparse_bsc_tensorÚsym_constrain_rangeÚsym_constrain_range_for_sizeÚsym_fresh_sizeÚtril_indicesÚtriu_indicesÚvanderÚzerosÚ_jit_internalÚboolean_dispatchÚnnÚ
functionalÚassert_int_or_pairÚupsampleÚupsample_bilinearÚupsample_nearestÚhas_torch_functionÚhas_torch_function_unaryÚhas_torch_function_variadicÚhandle_torch_functionÚ
grouped_mmÚscaled_grouped_mmÚ	scaled_mmÚsigmoidÚhardsigmoidÚtanhÚ_canonical_maskÚ_none_or_dtypeÚinitÚcalculate_gainÚuniformÚconstantÚdiracÚxavier_uniformÚxavier_normalÚkaiming_uniformÚkaiming_normalÚ
orthogonalÚsparseÚnestedÚto_padded_tensorÚset_autocast_enabledÚis_autocast_enabledÚset_autocast_dtypeÚget_autocast_dtypeÚclear_autocast_cacheÚset_autocast_cpu_enabledÚis_autocast_cpu_enabledÚset_autocast_xla_enabledÚis_autocast_xla_enabledÚset_autocast_ipu_enabledÚis_autocast_ipu_enabledÚset_autocast_cpu_dtypeÚget_autocast_cpu_dtypeÚset_autocast_ipu_dtypeÚget_autocast_ipu_dtypeÚget_autocast_gpu_dtypeÚset_autocast_gpu_dtypeÚget_autocast_xla_dtypeÚset_autocast_xla_dtypeÚautocast_increment_nestingÚautocast_decrement_nestingÚis_autocast_cache_enabledÚset_autocast_cache_enabledÚ	hardswishÚis_vulkan_availableÚ$are_deterministic_algorithms_enabledÚuse_deterministic_algorithmsÚ-is_deterministic_algorithms_warn_only_enabledÚset_deterministic_debug_modeÚget_device_moduleÚget_deterministic_debug_modeÚset_float32_matmul_precisionÚget_float32_matmul_precisionÚunify_type_listÚis_warn_always_enabledÚset_warn_alwaysÚvmapÚcondÚ
frombufferÚasarrayÚ_functional_sym_constrain_rangeÚ_make_dep_tokenÚ__delitem__Ú__dir__Ú__getattribute__Ú__init__Ú__iter__Ú__init_subclass__Ú__delattr__Ú__setattr__Ú__torch_function__Ú__torch_dispatch__Ú__new__Ú	__class__Ú__subclasshook__Ú__hash__ÚcholeskyÚas_subclassÚeigÚlstsqÚqrÚ	reinforceÚnewÚ
new_tensorÚ	new_emptyÚnew_empty_stridedÚ	new_zerosÚnew_onesÚnew_fullÚ_make_subclassÚsolveÚsymeigÚstrideÚ	unflattenÚto_sparse_cooÚto_sparse_csrÚto_sparse_cscÚto_sparse_bsrÚto_sparse_bscÚ
_to_sparseÚ_to_sparse_csrÚ_to_sparse_cscÚ_to_sparse_bsrÚ_to_sparse_bscÚ_typed_storageÚ_reduce_ex_internalÚ_fix_weakrefÚ
_view_funcÚ_view_func_unsafeÚ_rev_view_func_unsafeÚ_dtensor__new__Ú_make_wrapper_subclassÚ_python_dispatchÚ__get__Ú_has_symbolic_sizes_stridesÚ_conjÚ_conj_physicalÚ_lazy_cloneÚ	_neg_viewÚ_is_zerotensorÚ_is_all_trueÚ_is_any_trueÚ_addmm_activationÚ
_use_countÚ_philox_normal_Ú_philox_uniform_ÚsysÚversion_infoÚaddr   )r:   Ú	functionss     r   Úget_ignored_functionsrE  g   s"  € ô( �\‰\€FðIÜ�‰ðIä�‰ðIô 	×ÑðIô 	×%Ñ%ð	Iô
 	× Ñ ðIô 	× Ñ ðIô 	×ÑðIô 	×ÑðIô 	×ÑðIô 	×ÑðIô 	�
‰
ðIô 	×#Ñ#ðIô 	�
‰
ðIô 	�
‰
ðIô 	×ÑðIô  	�
‰
ð!Iô" 	×Ñð#Iô$ 	×%Ñ%ð%Iô& 	×Ñð'Iô( 	×Ñð)Iô* 	×Ñð+Iô, 	×*Ñ*ð-Iô. 	× Ñ ð/Iô0 	×*Ñ*ð1Iô2 	×Ñð3Iô4 	×*Ñ*ð5Iô6 	�‰ð7Iô8 	×Ñð9Iô: 	× Ñ ð;Iô< 	×!Ñ!ð=Iô> 	× Ñ ð?Iô@ 	×%Ñ%ðAIôB 	×ÑðCIôD 	�
‰
ðEIôF 	�‰ðGIôH 	×ÑðIIôJ 	�‰ðKIôL 	×ÑðMIôN 	�‰ðOIôP 	�‰ðQIôR 	×ÑðSIôT 	�‰ðUIôV 	�‰ðWIôX 	�‰ðYIôZ 	�‰ð[Iô\ 	�‰ð]Iô^ 	×Ñð_Iô` 	×ÑðaIôb 	�‰ðcIôd 	×ÑðeIôf 	�‰ðgIôh 	×ÑðiIôj 	�‰ðkIôl 	×ÑðmIôn 	×ÑðoIôp 	×'Ñ'ðqIôr 	�‰ðsIôt 	�‰ðuIôv 	×ÑðwIôx 	×ÑðyIôz 	×Ñð{Iô| 	×Ñð}Iô~ 	�‰ðIô@ 	�‰ðAIôB 	×)Ñ)ðCIôD 	×ÑðEIôF 	×ÑðGIôH 	×)Ñ)ðIIôJ 	×$Ñ$ðKIôL 	×(Ñ(ðMIôN 	× Ñ ðOIôP 	×!Ñ!ðQIôR 	×ÑðSIôT 	�‰ðUIôV 	×ÑðWIôX 	×ÑðYIôZ 	×Ñð[Iô\ 	�‰×Ñð]Iô^ 	�‰×Ñð_Iô` 	�‰×'Ñ'ðaIôb 	�‰×ÑðcIôd 	�	‰	ðeIôf 	�	‰	×ÑðgIôh 	�	‰	×ÑðiIôj 	�‰ðkIôl 	�
‰
ðmIôn 	�
‰
ðoIôp 	×ÑðqIôr 	×ÑðsIôt 	×ÑðuIôv 	�‰ðwIôx 	�‰ðyIôz 	×(Ñ(ð{Iô| 	× Ñ ð}Iô~ 	×ÑðIô@ 	×ÑðAIôB 	×,Ñ,ðCIôD 	×ÑðEIôF 	�‰ðGIôH 	�
‰
ðIIôJ 	×ÑðKIôL 	�
‰
ðMIôN 	�‰ðOIôP 	�‰ðQIôR 	×ÑðSIôT 	�‰ðUIôV 	×ÑðWIôX 	�‰ðYIôZ 	�‰ð[Iô\ 	×Ñð]Iô^ 	×Ñð_Iô` 	×ÑðaIôb 	×&Ñ&ðcIôd 	×ÑðeIôf 	×ÑðgIôh 	×ÑðiIôj 	×ÑðkIôl 	×!Ñ!ðmIôn 	×*Ñ*ðoIôp 	×ÑðqIôr 	×ÑðsIôt 	×ÑðuIôv 	�‰ðwIôx 	�‰ðyIôz 	×Ñ×,Ñ,ð{Iô| 	�‰×Ò×.Ò.ð}Iô~ 	�‰×Ò×$Ò$ðIô@ 	�‰×Ò×-Ò-ðAIôB 	�‰×Ò×,Ò,ðCIôD 	�‰×Ò×.Ò.ðEIôF 	�‰×Ò×4Ò4ðGIôH 	�‰×Ò×7Ò7ðIIôJ 	�‰×Ò×1Ò1ðKIôL 	�‰×Ò×&Ò&ðMIôN 	�‰×Ò×-Ò-ðOIôP 	�‰×Ò×%Ò%ðQIôR 	�‰×Ò×#Ò#ðSIôT 	�‰×Ò×'Ò'ðUIôV 	�‰×Ò× Ò ðWIôX 	�‰×Ò×+Ò+ðYIôZ 	�‰×Ò×*Ò*ð[Iô^ 	�‰�Š×$Ò$ð_Iôb 	�‰�Š×ÒðcIôd 	�‰�Š×ÑðeIôf 	�‰�Š×ÒðgIôh 	�‰�Š×ÑðiIôj 	�‰�Š×ÒðkIôl 	�‰�Š×$Ò$ðmIôn 	�‰�Š×#Ò#ðoIôp 	�‰�Š×%Ò%ðqIôr 	�‰�Š×$Ò$ðsIôt 	�‰�Š× Ò ðuIôv 	�‰�Š×ÒðwIôx 	�Š×%Ò%ðyIõz 	ð{Iõ| 	ð}Iô~ 	×"Ò"ðIô@ 	×!Ò!ðAIôB 	× Ò ðCIôD 	× Ò ðEIôF 	×"Ò"ðGIôH 	×&Ò&ðIIôJ 	×%Ò%ðKIôL 	×&Ò&ðMIôN 	×%Ò%ðOIôP 	×&Ò&ðQIôR 	×%Ò%ðSIôT 	×$Ò$ðUIôV 	×$Ò$ðWIôX 	×$Ò$ðYIôZ 	×$Ò$ð[Iô\ 	×$Ò$ð]Iô^ 	×$Ò$ð_Iô` 	×$Ò$ðaIôb 	×$Ò$ðcIôd 	×(Ò(ðeIôf 	×(Ò(ðgIôh 	×'Ò'ðiIôj 	×(Ò(ðkIôl 	�‰×Ò×%Ò%ðmIôn 	×!Ò!ðoIôp 	×2Ò2ðqIôr 	×*Ò*ðsIôt 	×;Ò;ðuIôv 	×*Ò*ðwIôx 	×ÒðyIôz 	×*Ò*ð{Iô| 	×*Ò*ð}Iô~ 	×*Ò*ðIô@ 	×ÒðAIôB 	×$Ò$ðCIôD 	×ÒðEIôF 	�
Š
ðGIôH 	�
Š
ðIIôJ 	×ÒðKIôL 	�ŠðMIôN 	×-Ò-ðOIôP 	×ÒðQIðR 	×ÒðSIðT 	�ŠðUIðV 	×ÒðWIðX 	�ŠðYIðZ 	�Šð[Ið\ 	× Ò ð]Ið^ 	×Òð_Ið` 	×ÒðaIðb 	×!Ò!ðcIðd 	×!Ò!ðeIðf 	�ŠðgIðh 	×ÒðiIðj 	×ÒðkIðl 	�ŠðmIðn 	�ŠðoIðp 	×ÒðqIðr 	�
Š
ðsIðt 	�ŠðuIðv 	�	Š	ðwIðx 	×ÒðyIðz 	�
Š
ð{Ið| 	×Òð}Ið~ 	×ÒðIð@ 	× Ò ðAIðB 	×ÒðCIðD 	�ŠðEIðF 	�ŠðGIðH 	×ÒðIIðJ 	�ŠðKIðL 	�ŠðMIðN 	�ŠðOIðP 	×ÒðQIðR 	×ÒðSIðT 	×ÒðUIðV 	×ÒðWIðX 	×ÒðYIðZ 	×Òð[Ið\ 	×Òð]Ið^ 	×Òð_Ið` 	×ÒðaIðb 	×ÒðcIðd 	×ÒðeIðf 	×ÒðgIðh 	×"Ò"ðiIðj 	×ÒðkIðl 	×ÒðmIðn 	× Ò ðoIðp 	×$Ò$ðqIðr 	×ÒðsIðt 	×%Ò%ðuIðv 	×Ò×'Ò'ðwIðx 	×*Ò*×2Ò2ðyIðz 	�Šð{Ið| 	×Òð}Ið~ 	×ÒðIð@ 	×ÒðAIðB 	×ÒðCIðD 	×ÒðEIðF 	×ÒðGIðH 	× Ò ðIIðJ 	×ÒðKIðL 	×ÒðMIðN 	×ÒðOIðP 	×ÒðQI€IõV ×Ò˜7Ô"Ø�Š�f×)Ò)Ô*àÐr   c                ó:   € V ^8„  d   QhR\         \        ,          /# r3   r4   )r   s   "r   r   r   Ž  s   € ÷ ñ ¤c¬(¥mñ r   c                 ó¤   € \         P                  p V P                  P                  V P                  P                  V P
                  P                  0# )a×  
Return public functions that do not wrap in a subclass when invoked by
the default ``Tensor.__torch_function__`` that preserves subclasses.  Typically,
these functions represent field accesses (i.e., retrieving a Tensor that
is stored somewhere on the Tensor) as opposed to computation.  Users of
these functions expect object identity to be preserved over multiple accesses
(e.g., ``a.grad is a.grad``) which cannot be upheld if we're wrapping on
the fly every time (furthermore, the tensor stored here might already be
the subclass, in which case wrapping really ought not to happen).

Not ALL property accessors have this property; for example ``Tensor.T`` actually
just creates a new transposed tensor on the fly, and so we SHOULD interpose on
these calls (you need to check the implementation of the function to see if
this is the case or not).  Additionally, if a property accessor doesn't return a Tensor,
it doesn't have to be on this list (though it is harmless if it is).
)r9   r:   Ú_baser4  ÚgradÚ_grad)r:   s    r   Úget_default_nowrap_functionsrK  �  s>   € ô$ �\‰\€Fà�‰×ÑØ�‰×ÑØ�‰×Ñðð r   c                óF   € V ^8„  d   QhR\         \        \        3,          /# r3   )Údictr   )r   s   "r   r   r   ©  s    € ÷ Uñ Uœt¤H¬hÐ$6Õ7ñ Ur   c                 óÖÂ  € \         P                  p / \         P                  ERæR lb\         P                  ERæR lb\         P                  R b\         P
                  R b\         P                  ERæR lb\         P                  R b\         P                  ERæR lb\         P                  ERæR	 lb\         P                  ERæR
 lb\         P                  ERæR lb\         P                  ERçR lb\         P                  ERèR lb\         P                  ERèR lb\         P                  ERçR lb\         P                   ERçR lb\         P"                  ERçR lb\         P$                  R b/ \         P&                  ERæR lb\         P(                  ERéR lb\         P*                  ERêR lb\         P,                  ERæR lb\         P.                  ERæR lb\         P0                  ERëR lb\         P2                  ERæR lb\         P4                  ERëR lb\         P6                  R b\         P8                  R b\         P:                  ERíRR/R llb\         P<                  ERæR  lb\         P>                  R! b\         P@                  ERæR" lb\         PB                  ERæR# lb\         PD                  ERæR$ lb\         PF                  ERæR% lbC/ \         PH                  ERæR& lb\         PJ                  ERæR' lb\         PL                  ERæR( lb\         PN                  ERæR) lb\         PP                  ERæR* lb\         PR                  R+ b\         PT                  R, b\         PV                  R- b\         PX                  ERîR. lb\         PZ                  ERçR/ lb\         P\                  R0 b\         P^                  R1 b\         P`                  R2 b\         Pb                  R3 b\         Pd                  R4 b\         Pf                  R5 b\         Ph                  R6 bC/ \         Pj                  R7 b\         Pl                  ERïR8 lb\         Pn                  R9 b\         Pp                  ERðR; lb\         Pr                  ERñR< lb\         Pt                  ERæR= lb\         Pv                  ERæR> lb\         Px                  ERæR? lb\         Pz                  ERæR@ lb\         P|                  ERæRA lb\         P~                  ERæRB lb\         P€                  ERæRC lb\         P‚                  RD b\         P„                  ERïRE lb\         P†                  RF b\         Pˆ                  RG b\         PŠ                  ERòRH lbC/ \         PŒ                  RI b\         PŽ                  ERóRJ lb\         P�                  ERóRK lb\         P’                  ERóRL lb\         P”                  ERôRM lb\         P–                  ERæRN lb\         P˜                  ERõRP lb\         Pš                  RQR/RR lb\         Pœ                  RS b\         Pž                  P                   ERæRT lb\         Pž                  P¢                  ERöRU lb\         P¤                  ERöRV lb\         P¦                  ERöRW lb\         P¨                  RX b\         Pª                  ER÷RY lb\         P¬                  ERøRZ lb\         P®                  ERøR[ lbC/ \         P°                  ERæR\ lb\         P²                  ERæR] lb\         P´                  ERæR^ lb\         P¶                  ERùR_ lb\         P¸                  R` b\         Pº                  ERúRa lb\         P¼                  Rb b\         P¾                  ERæRc lb\         PÀ                  Rd b\         Pž                  PÂ                  ERæRe lb\         PÄ                  ERæRf lb\         PÆ                  ERæRg lb\         PÈ                  ERæRh lb\         PÊ                  ERæRi lb\         PÌ                  ER÷Rj lb\         PÎ                  ERûRk lb\         PÐ                  ERûRl lbC/ \         PÒ                  ERûRm lb\         PÔ                  Rn b\         PÖ                  ER÷Ro lb\         PØ                  ERüRp lb\         PÚ                  ERüRq lb\         PÜ                  ERüRr lb\         PÞ                  Rs b\         Pà                  ERæRt lb\         Pâ                  ERýRu lb\         Pä                  ERæRv lb\         Pæ                  ERþRw lb\         Pè                  Rx b\         Pê                  ERïRy lb\         Pž                  Pê                  ERÿRz lb\         Pì                  ER R{ lb\         Pî                  ERæR| lb\         Pð                  ERæR} lbC/ \         Pò                  ERïR~ lb\         Pô                  ERïR lb\         Pö                  ERR€ lb\         Pø                  ERæR� lb\         Pú                  ERæR‚ lb\         Pü                  Rƒ b\         Pþ                  R„ b\         Pž                  Pþ                  R… b\         EP                   R† b\         EP                  ERóR‡ lb\         EP                  ERóRˆ lb\         EP                  ER÷R‰ lb\         EP                  ERRŠ lb\         EP
                  ERR‹ lb\         Pž                  EP
                  ERRŒ lb\         EP                  ERR� lb\         EP                  ERæRŽ lbC/ \         EP                  ERæR� lb\         EP                  ERR� lb\         EP                  ERïR‘ lb\         EP                  ERïR’ lb\         EP                  ERæR“ lb\         EP                  ERêR” lb\         EP                  ERæR• lb\         EP                  R– b\         EP                   R— b\         EP"                  ERæR˜ lb\         Pž                  EP$                  ERæR™ lb\         Pž                  EP&                  ERæRš lb\         Pž                  EP(                  ERR› lb\         Pž                  EP*                  ERRœ lb\         EP,                  R� b\         EP.                  ERRž lb\         EP0                  ERRŸ lbC/ \         EP2                  ER	R  lb\         EP4                  ERæR¡ lb\         EP6                  R¢ b\         EP8                  ERæR£ lb\         EP:                  ERæR¤ lb\         EP<                  ERæR¥ lb\         EP>                  ERæR¦ lb\         EP@                  ERæR§ lb\         EPB                  ERæR¨ lb\         EPD                  R© b\         EPF                  Rª b\         EPH                  ER
R« lb\         EPJ                  R¬ b\         EPL                  R­ b\         EPN                  R® b\         EPP                  R¯ b\         EPR                  R° bC/ \         EPT                  R± b\         EPV                  R² b\         EPX                  R³ b\         EPZ                  R´ b\         EP\                  EP^                  ERRµ lb\         EP\                  EP`                  ERR¶ lb\         EP\                  EPb                  ERR· lb\         EP\                  EPd                  ERR¸ lb\         EP\                  EPf                  ERR¹ lb\         EP\                  EPh                  ERRº lb\         EP\                  EPj                  ERR» lb\         EP\                  EPl                  ERR¼ lb\         EP\                  EPn                  ERR½ lb\         EP\                  EPp                  ERøR¾ lb\         EP\                  EPr                  ERøR¿ lb\         EP\                  EPt                  ERøRÀ lb\         EP\                  EPv                  ERøRÁ lbC/ \         EP\                  EPx                  ERRÂ lb\         EP\                  EPz                  ERRÃ lb\         EP\                  EP|                  ERRÄ lb\         EP\                  EP~                  ERRÅ lb\         EP\                  EP€                  ERæRÆ lb\         EP\                  EP‚                  ERæRÇ lb\         EP\                  EP\                  ERRÈ lb\         EP„                  ERæRÉ lb\         EP†                  ERRÊ lb\         EPˆ                  RË b\         EPŠ                  RÌ b\         EPŒ                  RÍ b\         EPŽ                  ERëRÎ lb\         EP�                  ERæRÏ lb\         EP’                  RÐ b\         EP”                  ERæRÑ lb\         EP–                  ERæRÒ lbC/ \         EP˜                  ERæRÓ lb\         EPš                  ERæRÔ lb\         EPœ                  RR\         EPž                  RR3RÕ lb\         EP                   RÖ b\         EP¢                  ERR× lb\         EP¤                  ERRØ lb\         EP¦                  ERæRÙ lb\         EP¨                  ERæRÚ lb\         EPª                  RÛ b\         EP¬                  ERæRÜ lb\         EP®                  ERæRÝ lb\         EP°                  ERæRÞ lb\         EP²                  ERæRß lb\         EP´                  ERæRà lb\         EP¶                  ERæRá lb\         EP¸                  ERRâ lb\         EPº                  Rã bC/ \         EP¼                  Rä b\         EP¾                  Rå b\         EPÀ                  ERRæ lb\         EPÂ                  Rç b\         EPÄ                  ERïRè lb\         EPÆ                  ERæRé lb\         EPÈ                  ERæRê lb\         EPÊ                  ERRë lb\         EPÌ                  ERRì lb\         EPÎ                  ERæRí lb\         EPÐ                  ERRî lb\         EPÒ                  ERRï lb\         EPÔ                  ERRð lb\         EPÖ                  ERRñ lb\         Pž                  EPØ                  Rò b\         EPÚ                  ERæRó lb\         EPÜ                  Rô bC/ \         EPÞ                  ERæRõ lb\         EPà                  ERæRö lb\         EPâ                  ERæR÷ lb\         EPä                  ERæRø lb\         EPæ                  ERæRù lb\         EPè                  Rú b\         EPê                  Rû b\         EPì                  ERêRü lb\         EPî                  ERæRý lb\         EPð                  Rþ b\         EPò                  ERRÿ lb\         EPô                  ER  b\         EPö                  ER
ER lb\         EPø                  ER b\         EPú                  ER b\         EPü                  ERæER lb\         EPþ                  ERæER lbC/ \         EP                   ER b\         EP                  ER b\         EP                  ERæER lb\         Pž                  EP                  ERæER	 lb\         Pž                  EP                  ERöER
 lb\         EP
                  ER b\         EP                  ER b\         EP                  ER b\         EP                  ER b\         EP                  ER b\         EP                  ER b\         EP                  ER b\         EP                  ER b\         EP                  ER b\         EP                  ERéER lb\         EP                  ER b\         EP                   ERER lbC/ \         EP"                  ERER lb\         EP$                  ER b\         EP&                  ERëER lb\         Pž                  EP(                  ERòER lb\         Pž                  EP*                  ERöER lb\         Pž                  EP,                  ERöER lb\         EP.                  ERER lb\         EP0                  ERæER lb\         EP2                  ERæER lb\         EP4                  ERæER 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ERS lb\         EP’                  ERæERT lb\         EP”                  ERæERU lb\         EP–                  ERV b\         EP˜                  ERæERW lb\         EPš                  ERæERX lb\         EPœ                  ERæERY lb\         EPž                  ERZ bC/ \         EP                   ER[ b\         EP¢                  ER\ b\         EP¤                  ER] b\         EP¦                  ER^ b\         EP¨                  ER_ b\         EPª                  ER` b\         EP¬                  ERïERa lb\         EP®                  ER!ERb lb\         EP°                  ERc b\         EP²                  ERd b\         EP´                  ERöERe lb\         EP¶                  ERæERf lb\         EP¸                  ERæERg lb\         EPº                  ERöERh lb\         EP¼                  ERæERi lb\         EP¾                  ERj b\         EPÀ                  ERk bC/ \         EPÂ                  ER"ERl lb\         EPÄ                  ERm b\         EPÆ                  ERn b\         EPÈ                  ERo b\         EPÊ                  ER#ERp lb\         EPÌ                  ER$ERq lb\         EPÎ                  ERr b\         EPÐ                  ER%ERs lb\         EPÒ                  ERt b\         EPÔ                  ERæERu lb\         EPÖ                  ERæERv lb\         EPØ                  ERæERw lb\         EPÚ                  ERæERx lb\         EPÜ                  ERæERy lb\         EPÞ                  EPà                  EPâ                  ERz b\         EPÞ                  EPà                  EPä                  ER{ b\         EPÞ                  EPà                  P
                  ERêER| lbC/ \         EPÞ                  EPà                  EPæ                  ERêER} lb\         EPÞ                  EPà                  EPè                  ERêER~ lb\         EPÞ                  EPà                  EPê                  ERêER lb\         EPÞ                  EPà                  EPì                  ERêER€ lb\         EPÞ                  EPà                  EPî                  ERêER� lb\         EPÞ                  EPà                  EPð                  ERæER‚ lb\         EPÞ                  EPà                  P*                  ER&ERƒ lb\         EPÞ                  EPà                  EPò                  ER'ER„ lb\         EPÞ                  EPà                  EPô                  ER'ER… lb\         EPÞ                  EPà                  P\                  ER(ER† lb\         EPÞ                  EPà                  Pn                  ERæER‡ lb\         EPÞ                  EPà                  EPö                  ER)ERˆ lb\         EPÞ                  EPà                  Pp                  ERðER‰ lb\         EPÞ                  EPà                  P˜                  ERõERŠ lb\         EPÞ                  EPà                  Pâ                  ERýER‹ lb\         EPÞ                  EPà                  EPø                  ER*ERŒ lb\         EPÞ                  EPà                  EPú                  ER+ER� lbC/ \         EPÞ                  EPà                  Pì                  ER ERŽ lb\         EPÞ                  EPà                  EP                  ER,ER� lb\         EPÞ                  EPà                  EPü                  ER,ER� lb\         EPÞ                  EPà                  EPþ                  ER,ER‘ lb\         EPÞ                  EPà                  EP                   ER,ER’ lb\         EPÞ                  EPà                  EP                  ERõER“ lb\         EPÞ                  EPà                  EP.                  ERER” lb\         EPÞ                  EPà                  EP0                  ER-ER• lb\         EPÞ                  EPà                  EPX                  ER&ER– lb\         EPÞ                  EPà                  EP                  ER.ER— lb\         EPÞ                  EPà                  EP                  ER/ER˜ lb\         EPÞ                  EPà                  EP                  ER/ER™ lb\         EPÞ                  EPà                  EP
                  ER/ERš lb\         EPÞ                  EPà                  EP                  ER/ER› lb\         EPÞ                  EPà                  EP                  ER0ERœ lb\         EPÞ                  EPà                  EP                  ER1ER� lb\         EPÞ                  EPà                  EP                  ER2ERž lbC/ \         EPÞ                  EPà                  EP                  ER3ERŸ lb\         EPÞ                  EPà                  EPÀ                  ER#ER  lb\         EPÞ                  EPà                  EP                  ER4ER¡ lb\         EPÞ                  EPà                  EPÊ                  ERER¢ lb\         EPÞ                  EPà                  EP                  ER5ER£ lb\         EPÞ                  EPà                  EPÐ                  ERER¤ lb\         EPÞ                  EPà                  EP                   ER6ER¥ lb\         EPÞ                  EPà                  EP                  ER7ER¦ lb\         EPÞ                  EPà                  EP"                  ERER§ lb\         EPÞ                  EPà                  EP                  ER8ER¨ lb\         EPÞ                  EPà                  EP.                  ER#ER© lb\         EPÞ                  EPà                  EP                  ER9ERª lb\         EPÞ                  EPà                  EP                   ERæER« lb\         EPÞ                  EPà                  EP"                  ER:ER¬ lb\         EPÞ                  EPà                  EP@                  ER;ER­ lb\         EPÞ                  EPà                  EP$                  ER® b\         EPÞ                  EPà                  EP&                  ERER¯ lbC/ \         EPÞ                  EPà                  EP(                  ERER° lb\         EPÞ                  EPà                  EP*                  ERER± lb\         EPÞ                  EPà                  EPh                  ERýER² lb\         EPÞ                  EPà                  EP†                  ERER³ lb\         EPÞ                  EPà                  EPŒ                  ERER´ lb\         EPÞ                  EPà                  EPˆ                  ERERµ lb\         EPÞ                  EPà                  EP,                  ERER¶ lb\         EPÞ                  EPà                  EPŠ                  ERER· lb\         EPÞ                  EPà                  EP.                  ERER¸ lb\         EPÞ                  EPà                  EP0                  ER<ER¹ lb\         EPÞ                  EPà                  EP2                  ER<ERº lb\         EPÞ                  EPà                  EP4                  ER<ER» lb\         EPÞ                  EPà                  EP6                  ER8ER¼ lb\         EPÞ                  EPà                  EP8                  ER=ER½ lb\         EPÞ                  EPà                  EP:                  ER>ER¾ lb\         EPÞ                  EPà                  EP<                  ER?ER¿ lb\         EPÞ                  EPà                  EP>                  ER)ERÀ lbC/ \         EPÞ                  EPà                  EP@                  ER@ERÁ lb\         EPÞ                  EPà                  EPB                  ERAERÂ lb\         EPÞ                  EPà                  EPD                  ER2ERÃ lb\         EPÞ                  EPà                  EPF                  ERBERÄ lb\         EPÞ                  EPà                  EPH                  ERCERÅ lb\         EPÞ                  EPà                  EPJ                  ERDERÆ lb\         EPÞ                  EPà                  EPL                  ERÇ b\         EPÞ                  EPà                  EPN                  ERêERÈ lb\         EPÞ                  EPà                  EPP                  ERêERÉ lb\         EPÞ                  EPà                  EPR                  EREERÊ lb\         EPÞ                  EPà                  EPT                  ERFERË lb\         EPÞ                  EPà                  EPV                  ERêERÌ lb\         EPÞ                  EPà                  EPX                  ERêERÍ lb\         EPÞ                  EPà                  EPZ                  ERêERÎ lb\         EPÞ                  EPà                  EP\                  ERGERÏ lb\         EPÞ                  EPà                  EP^                  ERHERÐ lb\         EPÞ                  EPà                  EP`                  ERIERÑ lbC/ \         EPÞ                  EPà                  EPb                  ER?ERÒ lb\         EPÞ                  EPà                  EPd                  ER;ERÓ lb\         EPÞ                  EPà                  EPf                  ER;ERÔ lb\         EPÞ                  EPà                  EPh                  ERJERÕ lb\         EPÞ                  EPà                  EPj                  ERERÖ lb\         EPÞ                  EPà                  EPl                  ER× b\         EPÞ                  EPà                  EPn                  ERØ b\         EPÞ                  EPà                  EPp                  ERêERÙ lb\         EPÞ                  EPà                  EPr                  ERKERÚ lb\         EPÞ                  EPà                  EPt                  ERÛRERÜROERÝRERÞR:/ERß lb\         EPÞ                  EPà                  EPv                  ER.ERà lb\         EPÞ                  EPx                  EPz                  ERLERá lb\         EPÞ                  EPx                  EP|                  ERLERâ lb\         EPÞ                  EPx                  EP~                  ERã b\         EPÞ                  EPx                  EP€                  ERMERä lb\         EP‚                  ERêERå lb\         EP„                  ERæERì/ERç lbC/ \         EP†                  ERè b\         EPˆ                  ERNERé lb\         Pž                  EPˆ                  EROERê lb\         Pž                  EPŠ                  ERPERë lb\         Pž                  EPŒ                  ERQERì lb\         EPŽ                  ERRERí lb\         EP�                  ERNERî lb\         EP’                  ERï b\         EP”                  ERð b\         EP–                  ERSERñ lb\         EPH                  ERCERò lb\         EP˜                  ERó b\         EPš                  ERTERô lb\         EPœ                  ERERõ lb\         EPž                  ERUERö lb\         Pž                  EP                   ERVER÷ lb\         EP¢                  ERø bC/ \         EP¤                  ERù b\         EP¦                  ERæERú lb\         EPJ                  ERû b\         EP¨                  ERæERü lb\         EPª                  ERæERý lb\         EPL                  ERþ b\         EP¬                  ER	ERÿ lb\         EP®                  ERæER  lb\         EP°                  ERæER lb\         EP²                  ERêER lb\         EP´                  ER b\         EP¶                  ER b\         EP¸                  ER b\         EPº                  ER b\         EP¼                  ER b\         Pž                  EP¾                  ERWER lb\         Pž                  PÀ                  ERæER	 lbC/ \         EPÀ                  ERXER
 lb\         EPÂ                  ERXER lb\         EPÄ                  ER b\         EPÆ                  ER b\         EPÈ                  ER b\         EPÊ                  ER b\         EPÌ                  ER b\         EPÎ                  ER b\         EPÐ                  ERZER lb\         EPÒ                  ER[ER lb\         EPÔ                  ER\ER lb\         EPÖ                  ER b\         EPØ                  ER b\         EPÚ                  ERæER lb\         EPÜ                  ER b\         EPÞ                  ERæER lb\         EPà                  ERæER lbC/ \         Pž                  EPâ                  ERÿER lb\         EPä                  ER b\         EPæ                  ER b\         EPè                  ERæER lb\         EPN                  ERêER lb\         EPê                  ERæER  lb\         EPì                  ERæER! lb\         EPî                  ERæER" lb\         EPð                  ER# b\         EPR                  EREER$ lb\         EPò                  ER% b\         EPô                  ERïER& lb\         EPö                  ER' b\         EPø                  ERïER( lb\         EPú                  ERæER) lb\         EPü                  ER^ER* lb\         EPþ                  ERæER+ lbC/ \         EP                   ERæER, lb\         EP                  ER- b\         EPT                  ERFER. lb\         EP                  ERæER/ lb\         EP                  ERYER0 lb\         EP                  ERçER1 lb\         EP
                  ERæER2RER3R/ER4 llb\         EP                  ER5 b\         EP                  ERER6 lb\         EP                  ERòER7 lb\         EP                  ER_ER8 lb\         EP                  ER9 b\         EP                  ER: b\         EP                  ER`ER; lb\         EP                  ER`ER< lb\         EPV                  ERêER= lb\         EP                  ERæER> lbC/ \         EP                  ERæER? lb\         EP                   ERæER@ lb\         EP"                  ERæERA lb\         EP$                  ERæERB lb\         EP&                  ERæERC lb\         EP(                  ERæERD lb\         EP*                  ERE b\         Pž                  EP*                  ERF b\         EP,                  ERæERG lb\         EP.                  ERæERH lb\         EPd                  ERæERI lb\         Pž                  EP0                  ERERJ lb\         Pž                  EP2                  ERERK lb\         EP4                  ERíRRRQR/ERL llb\         EP6                  ER÷ERM lb\         EP8                  ER÷ERN lb\         EP:                  ERæERO lbC/ \         EP<                  ERæERP lb\         EP>                  ERïERQ lb\         EP@                  ERçERR lb\         EPB                  ERóERS lb\         EPD                  ERæERT lb\         EPF                  ERæERU lb\         EPH                  ERaERV lb\         EPJ                  ERæERW lb\         EPL                  ERæERX lb\         EPN                  ERæERY lb\         EPP                  ERZ b\         EPR                  ER[ b\         EPT                  ER\ b\         EPV                  ER] b\         EPX                  ER^ b\         EPZ                  ER_ b\         EP\                  ER` bC/ \         EP^                  ERa b\         EP`                  ERb b\         EPb                  ERc b\         EPd                  ERd b\         EPf                  ERe b\         EPh                  ERf b\         EPj                  ERg b\         EPl                  ERh b\         EPn                  ERi b\         EPp                  ERj b\         EPr                  ERæERk lb\         EPt                  ERbERl lb\         EPv                  ERcERm lb\         Pž                  EPt                  ERERn lb\         Pž                  EPx                  ERæERo lb\         EPz                  ERp b\         EP|                  ERq bC/ \         EP~                  EP€                  ERr b\         EP~                  EP‚                  ERs b\         EP~                  EP„                  ERt b\         EP~                  EP†                  ERu b\         EP~                  EPˆ                  ERv b\         EP~                  EPŠ                  ERæERw lb\         EP~                  EPŒ                  ERæERx lb\         EP~                  EPŽ                  ERæERy lb\         EP~                  EP�                  ERæERz lb\         EP~                  EP                  ER{ b\         EP~                  EP’                  ER| b\         EP~                  EP8                  ER} b\         EP~                  EP:                  ER~ b\         EP~                  EP”                  ER b\         EP~                  EP<                  ER€ b\         EP~                  EP@                  ER� b\         EP~                  EP–                  ER‚ bC/ \         EP~                  EPB                  ERƒ b\         EP~                  EP˜                  ERæER„ lb\         EP~                  EPš                  ERæER… lb\         EP~                  EPœ                  ER† b\         EP~                  EPž                  ERæER‡ lb\         EP~                  EP                   ERæERˆ lb\         EP~                  EP°                  ER‰ b\         EP~                  EP¢                  ERŠ b\         EP~                  EP¤                  ER‹ b\         EP~                  EP¦                  ERŒ b\         EP~                  EP¨                  ERæER� lb\         EP~                  EPª                  ERæERŽ lb\         EP~                  EPD                  ER� b\         EP~                  EP¬                  ER� b\         EP~                  EP@                  ERæER‘ lb\         EP~                  EPX                  ER’ b\         EP~                  EPZ                  ERöER“ lbC/ \         EP~                  EP®                  ER” b\         EP~                  EP°                  ER• b\         EP~                  EP²                  ER– b\         EP~                  EP´                  ER— b\         EP~                  EP¶                  ER˜ b\         EP~                  EP¸                  ER™ b\         EP~                  EPº                  ERš b\         EP~                  EP¨                  ERæER› lb\         EP~                  EP¼                  ERœ b\         EP~                  EPþ                  ER� b\         EP~                  EP¾                  ERž b\         EP~                  EPÀ                  ERŸ b\         EP~                  EPÂ                  ERæER  lb\         EP~                  EPÄ                  ERæER¡ lb\         EP~                  EPÆ                  ERæER¢ lb\         EP~                  EPÈ                  ERæER£ lb\         EP~                  EP&                  ER¤ bC/ \         EP~                  EPd                  ERæER¥ lb\         EP~                  EPÊ                  ER¦ b\         EP~                  EPÌ                  ERæER§ lb\         EP~                  EPN                  ERæER¨ lb\         EP~                  EPÎ                  ERæER© lb\         EPÐ                  ERª b\         EPÒ                  ER« b\         EPÔ                  ERïER¬ lb\         EPÖ                  ERæER­ lb\         EPØ                  ERæER® lb\         Pž                  EPÚ                  ERER¯ lb\         Pž                  EPÜ                  ERæER° lb\         EPÞ                  ERdER± lb\         EPà                  ER÷ER² lb\         EPp                  ERêER³ lb\         EPâ                  ER´ b\         EPä                  ER!ERµ lbC/ \         EPæ                  ER¶ b\         EPè                  ER· b\         EPê                  ERER¸ lb\         EPì                  ERER¹ lb\         EPî                  EReERº lb\         Pž                  EPð                  ERSER» lb\         EPò                  ERóER¼ lb\         EPr                  ERKER½ lb\         EPô                  ERóER¾ lb\         EPö                  ER¿ b\         EPø                  ERæERÀ lb\         EPú                  ER÷ERÁ lb\         EPü                  ERÂ b\         EPþ                  ERfERÃ lb\         EP                   ERòERÄ lb\         EP                  ERÅ b\         EP                  ER÷ERÆ lbC/ \         EP                  ER÷ERÇ lb\         EP                  ER÷ERÈ lb\         EP
                  ERæERÉ lb\         Pž                  EP                  ERæERÊ lb\         EP                  ERæERË lb\         EP                  ERæERÌ lb\         EP                  ERÍ b\         EP                  ERæERÎ lb\         EP                  ERïERÏ lb\         EP                  ERÐ b\         EP                  ERÑ b\         EP                  ER	ERÒ lb\         EP                  ERÓ b\         EP                   ERÔ b\         EP"                  ERÕ b\         EP$                  ERÖ b\         EP&                  ER× bC/ \         EP(                  ERØ b\         EP*                  ERæERÙ lb\         EP,                  ERÚ b\         EP.                  ERERÛ lb\         EP0                  ERÜR/ERÝ lb\         EP2                  ERÞ b\         EP4                  ERß b\         EP6                  ERà b\         EP8                  ERá b\         EP:                  ERâ b\         EP<                  ER`ERã lb\         EP>                  ER÷ERä lb\         EP@                  ER÷ERå lb\         EPB                  ERæ b\         EPD                  ERç b\         EPF                  ERè b\         EPH                  ERé bC/ \         EPJ                  ERê b\         EPL                  ERë b\         EPN                  ERì b\         EPP                  ERí b\         EPR                  ERî b\         EPT                  ERï b\         EPV                  ERð b\         EPX                  ERñ b\         EPZ                  ER÷ERò lb\         EP\                  ERó b\         EP^                  ERô b\         EP`                  ERõ bV EPb                  ERö bV EPd                  ER÷ bV EPf                  ERø bV EPh                  ERù bV EPj                  ERú bC/ V EPl                  ERû bV EPn                  ERü bV EPp                  ERý bV EPr                  ERþ bV EPt                  ERÿ bV EPv                  ER  bV EPx                  ER bV EPz                  ER bV EP|                  ER bV EP~                  ER bV EP€                  ER bV EP‚                  ER bV EP„                  ER bV EP†                  ER bV EPˆ                  ER	 bV EPŠ                  ER
 bV EPŒ                  ER bC/ V EPŽ                  ER bV EP�                  ER bV EP’                  ER bV EP”                  ER bV EP–                  ER bV EP˜                  ER bV EPš                  ER bV EPœ                  ER bV EPž                  ER bV EP                   ER bV EP¢                  ER bV EP¤                  ER bV EP¦                  ER bV EP¨                  ERR/ER lbV EPª                  ER bV EP¬                  ER bV EP®                  EP°                  ER bC/ V EP²                  EP°                  ER bV EP´                  EP°                  ER bV EP¶                  EP°                  ER  bV EP¸                  EP°                  ER! bV EPº                  EP°                  ER" bV EP¼                  EP°                  ER# bV EP¾                  EP°                  ER$ bV EPÀ                  EP°                  ER% bV EPÂ                  EP°                  ER& bV EPÄ                  EP°                  ER' bV EPÆ                  EP°                  ER( bV EPÈ                  EP°                  ER) bV EPÊ                  EP°                  ER* bV EPÌ                  ER+ bV EPÎ                  ER, bV EPÐ                  ER- bV EPÒ                  EP°                  ER. bC/ V EPÔ                  EP°                  ER/ bV EPÖ                  EP°                  ER0 bV EPØ                  EP°                  ER1 bV EPÚ                  EP°                  ER2 bV EPÜ                  EP°                  ER3 bV EPÞ                  EP°                  ER4 bV EPà                  EP°                  ER5 bV EPâ                  EP°                  ER6 bV EPä                  EP°                  ER7 bV EPæ                  EP°                  ER8 bV EPè                  EP°                  ER9 bV EPê                  EP°                  ER: bV EPì                  EP°                  ER; bV EPî                  EP°                  ER< bV EPð                  EP°                  ER= bV EPò                  EP°                  ER> bV EPô                  EP°                  ER? bC/ V EPö                  EP°                  ER@ bV EPø                  EP°                  ERA bV EPú                  EP°                  ERB bV EPü                  EP°                  ERC bV EPþ                  EP°                  ERD bV EP                   EP°                  ERE bV EP                  EP°                  ERF bV EP                  EP°                  ERG bV EP                  EP°                  ERH bV EP                  EP°                  ERI bV EP
                  EP°                  ERJ bV EPÞ                  EP°                  ERK bV EPæ                  EP°                  ERL bV EP                  EP°                  ERM bV EP                  ERERN lbV EP                  ERO bV EP                  ERP bC/ V EP                  ERQ bV EP                  ERR bV EP                  ERS bV EP                  ERT bV EP                  ERU bV EP                  ERV bV EP                   ERW bV EP"                  ERX bV P                  ERY bV EP$                  ERZ bV EP&                  ER[ bV EP(                  ER\ bV EP*                  ER/ER] lbV EP,                  \         EP.                  3ER^ lbV EP0                  \         EP.                  3ER_ lbV EP2                  \         EP.                  3ER` lbV EP4                  \         EP.                  3ERa lbC/ V EP6                  ER]ERbR/ERc llbV EP8                  ERd bV EP:                  ERe bV EP<                  \         EP>                  3ERf lbV EP@                  ERêERg lbV EPB                  \         EP.                  3ERh lbV EPD                  \         EP.                  3ERi lbV EPF                  \         EP.                  3ERj lbV EPH                  \         EP.                  3ERk lbV EPJ                  \         EP.                  3ERl lbV EPL                  ERm bV EPN                  ERn bV EPP                  ERo bV EP                  ERERp lbV EPR                  ERq bV EPT                  ERêERr lbV EPV                  \         EP.                  3ERs lbC/ V EPX                  \         EP.                  3ERt lbV EPZ                  ERu bV EP\                  ERv bV EP^                  ERw bV EP`                  ERYERbR/ERx llbV EPb                  ERy bV EPd                  ERz bV EPf                  \         EP.                  3ER{ lbV EPh                  \         EP.                  3ER| lbV EPj                  ERbR/ER} lbV EPª                  ER~ bV EPl                  \         EP.                  3ER lbV EPn                  \         EP.                  3ER€ lbV EPp                  ER� bV EPr                  \         EP.                  3ER‚ lbV EPt                  ERƒ bV EPv                  ER„ bC/ V EP                  ER… bV EPx                  ER† bV EPz                  ER‡ bV EP|                  ERˆ bV EP~                  ER‰ bV EP€                  ERgERbR/ERŠ llbV EP@                  ER‹ bV EP‚                  \         EP.                  3ERŒ lbV EP„                  ER� bV EP†                  ERŽ bV EP¬                  ERæER� lbV EPˆ                  ERêER� lbV EP2                  ER‘ bV EPŠ                  ER’ bV EPŒ                  ER“ bV EPŽ                  ER” bV EP�                  ER• bC/ V EP’                  ER– bV EP|                  ER— bV EP”                  ER˜ bV EP˜                  ER™ bV EP–                  ERš bV EP˜                  ERêER› lbV EPš                  ERœ bV EPœ                  ERóERbR/ER� llbV EPž                  ERž bV EP                   ERŸ bV EP¢                  ER  bV EP¤                  ER¡ bV EP¦                  ERER¢ lbV EP¨                  ER£ bV EPª                  ER¤ bV EP¬                  ER¥ bV EP®                  ER¦ bC/ V EP°                  ER§ bV EP²                  ER¨ bV EP´                  ERhER© lbV EP                  ERª bV EP¶                  ER« bV EP¸                  \         EP.                  3ER¬ lbV EPº                  ER­ bV EP                  ER`ER® lbV EP¼                  ER¯ bV EP¾                  ER° bV EPÀ                  ERêER± lbV EPÂ                  ER² bV EPÄ                  ER³ bV EP@                  ERçER´ lbV EPÆ                  ERµ bV EPÈ                  ER¶ bV EPÊ                  ER· bC/ V EPÌ                  ER¸ bV EPÎ                  ER¹ bV EPâ                  ERº bV EPÐ                  RR\         EP.                  3ER» lbV EPÒ                  ERæER¼R/ER½ llbV EPÔ                  ERïER¾ lbV EPÖ                  ER¿ bV EPØ                  ERÀ bV EPÚ                  ERÁ bV EPÜ                  ERÂ bV EPv                  ERÃ bV EPz                  ER]ERÄ lbV EPÞ                  ERÅ bV EPà                  ERÆ bV EPâ                  ERÇ bV EPä                  ERÈ bV EPæ                  ERiERÉ lbCV EPè                  ERÊ V EPê                  ERË \         Pž                  EPì                  ERïERÌ l/Cp\         EPî                  EPð                  EPò                  pE\õ        W4      '       d6   ERERÍ lVE\÷        W4      &   ERÎ VE\÷        V ERÏV 24      EP°                  &   / pE\ù        4       pVEPû                  4        EF>  w  rVVEPü                  VEPü                  ERÐ,           ERÑVEPü                  ,           ERÑ,           ERÒVEPü                  ,           ERÑ,           ERÓVEPü                  ,           ERÑ,           .pVEPü                  EPÿ                  ERÔ4      '       da   VEPü                  E\        ERÔ4      R pVEP                  ERÑV,           ERÑ,           ERÒV,           ERÑ,           ERÓV,           ERÑ,           .4       V F8  p	E\÷        W	R4      p
E\        V
4      '       g   K$  W¡9  g   K,  W¤9  g   K4  WcV
&   K:  	  EKA  	  VEP                  V4       \         EP                  EP                  4       '       Ed^   ^ REI†EH„p VEP                  / VEP                  ER/ERÕ lbVEP                  ERERÖ lbVEP                  ERjER× lbVEP                  ERERØ lbVEP                  ERERÙ lbVEP                  ERERÚ lbVEP                  ERERÛ lbVEP¤                  ERjERÜ lbVEP
                  ERjERÝ lbVEP                  ERERÞ lbVEP                  ERERß lbVEP                   ER	ERà lbVEP"                  ERERá lbVEP$                  ERkERâ lbVEP&                  ERkERã lbVEP(                  ERkERä lbVEP*                  ERkERå lb4       V# (l  a:  Return a dict containing dummy overrides for all overridable functions

Returns
-------
Dict[Callable, Callable]
    A dictionary that maps overridable functions in the PyTorch API to
    lambda functions that have the same signature as the real function
    and unconditionally return -1. These lambda functions are useful
    for testing API coverage for a type that defines ``__torch_function__``.

Examples
--------
>>> import inspect
>>> my_add = torch.overrides.get_testing_overrides()[torch.add]
>>> inspect.signature(my_add)
<Signature (input, other, out=None)>
Nc                 ó   € R# ©é   éÿÿÿÿ© ©ÚinputÚouts   &&r   Ú<lambda>Ú'get_testing_overrides.<locals>.<lambda>Ä  ó   € ©2r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Å  ó   € ±r   c                 ó   € R# rP  rS  ©rU  Úoutput_sizes   &&r   rW  rX  Æ  ó   € ¹br   c                 ó   € R# rP  rS  )Úinputsr^  s   &&r   rW  rX  Ç  ó   € ¹rr   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  È  ó   € ©Br   c                 ó   € R# rP  rS  ©rU  s   &r   rW  rX  É  ó   € ¡Rr   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Ê  ó   € ©br   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Ë  ó   € ©Rr   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Ì  ó   € ©rr   c                 ó   € R# rP  rS  ©rU  ÚotherrV  s   &&&r   rW  rX  Í  ó   € ±"r   c                 ó   € R# rP  rS  ©rU  Úbatch1Úbatch2ÚalphaÚbetarV  s   &&&&&&r   rW  rX  Î  ó   € Érr   c                 ó   € R# rP  rS  ©rU  Útensor1Útensor2ÚvaluerV  s   &&&&&r   rW  rX  Ï  ó   € É"r   c                 ó   € R# rP  rS  rz  s   &&&&&r   rW  rX  Ð  r~  r   c                 ó   € R# rP  rS  ©rU  Úmat1Úmat2rw  rv  rV  s   &&&&&&r   rW  rX  Ñ  r~  r   c                 ó   € R# rP  rS  )rU  ÚmatÚvecrw  rv  rV  s   &&&&&&r   rW  rX  Ò  ó   € Ér   c                 ó   € R# rP  rS  )rU  Úvec1Úvec2rw  rv  rV  s   &&&&&&r   rW  rX  Ó  ó   € Ér   c                 ó   € R# rP  rS  ©ÚthetaÚsizeÚalign_cornerss   &&&r   rW  rX  Ô  r‡  r   c                 ó   € R# rP  rS  ©rU  Údims   &&r   rW  rX  Õ  rY  r   Fc                 ó   € R# rP  rS  ©rU  rp  ÚrtolÚatolÚ	equal_nans   &&&&&r   rW  rX  Ö  ó   € ÑVXr   c                 ó   € R# rP  rS  ©rU  ÚpÚtrainÚinplaces   &&&&r   rW  rX  ×  ó   € ÁBr   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  Ø  rd  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  Ù  rd  r   c                 ó   € R# rP  rS  ©rU  r“  ÚkeepdimrV  s   &&&&r   rW  rX  Ú  r‡  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Û  rk  r   c                 ó   € R# rP  rS  r£  s   &&&&r   rW  rX  Ü  rŸ  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ý  ó   € ¡Br   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Þ  r¨  r   Ústablec                ó   € R# rP  rS  )rU  r“  Ú
descendingrª  s   &&&$r   rW  rX  ß  ó   € ÑPRr   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  à  rd  r   c                 ó   € R# rP  rS  )rU  Úmsgs   &&r   rW  rX  á  r[  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  â  ri  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ã  rk  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ä  rm  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  å  rd  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  æ  ri  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ç  ó   € ±Br   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  è  ó   € ±br   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  é  rk  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ê  rm  r   c                  ó   € R# rP  rS  ©Útensorss   *r   rW  rX  ë  rY  r   c                  ó   € R# rP  rS  r½  s   *r   rW  rX  ì  rY  r   c                  ó   € R# rP  rS  r½  s   *r   rW  rX  í  rY  r   c                 ó   € R# rP  rS  )rU  Úkernel_sizer  ÚpaddingÚ	ceil_modeÚcount_include_pads   &&&&&&r   rW  rX  î  ó   € Ñvxr   c                 ó   € R# rP  rS  rs  s   &&&&&&r   rW  rX  ï  r­  r   c	                 ó   € R# rP  rS  )	rU  ÚweightÚbiasÚrunning_meanÚrunning_varÚtrainingÚmomentumÚepsÚcudnn_enableds	   &&&&&&&&&r   rW  rX  ð  ó   € Ñy{r   c                 ó   € R# rP  rS  )Úgrad_outrU  ÚmeanÚinvstdrÉ  Úsum_dyÚ
sum_dy_xmuÚcount_tensors   &&&&&&&&r   rW  rX  ñ  rÑ  r   c                 ó   € R# rP  rS  )rÓ  rU  rÔ  rÕ  rÉ  Úinput_gÚweight_gÚbias_gs   &&&&&&&&r   rW  rX  ò  ó   € Ñsur   c                 ó   € R# rP  rS  )rU  rÉ  rÊ  rÔ  rÕ  rÏ  s   &&&&&&r   rW  rX  ó  rx  r   c                 ó   € R# rP  rS  ©rU  rÔ  rÕ  rË  rÌ  rÎ  rÏ  Úcounts   &&&&&&&&r   rW  rX  ô  ó   € Ñtvr   c                 ó   € R# rP  rS  rà  s   &&&&&&&&r   rW  rX  õ  ó	   € ñ  ACr   c                 ó   € R# rP  rS  ©rU  rÏ  s   &&r   rW  rX  ö  ó   € ±2r   c                 ó   € R# rP  rS  )rU  rË  rÌ  rÎ  s   &&&&r   rW  rX  ÷  ó   € ÑZ\r   c                 ó   € R# rP  rS  )rU  Ú	generatorrV  s   &&&r   rW  rX  ø  ó   € Ár   c                 ó   € R# rP  rS  ©Úinput1Úinput2rÉ  rÊ  s   &&&&r   rW  rX  ù  ó   € ¹Rr   rÔ  c                 ó   € R# rP  rS  ©rU  ÚtargetrÉ  Úsize_averageÚreduceÚ	reductionÚ
pos_weights   &&&&&&&r   rW  rX  û  ó   € Ñrtr   c                 ó   € R# rP  rS  )rU  ÚweightsÚ	minlengths   &&&r   rW  rX  ý  rì  r   c                 ó   € R# rP  rS  )rá  Úprobrë  s   &&&r   rW  rX  þ  ó   € ¹Br   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ÿ  ó   € ¹"r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX     rç  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    ó   € ¹r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    r  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    rì  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    ó   € Á"r   c                  ó   € R# rP  rS  r½  s   *r   rW  rX    rY  r   c                 ó   € R# rP  rS  ©rU  rƒ  Ú	out_dtyperV  s   &&&&r   rW  rX    rì  r   c                  ó   € R# rP  rS  r½  s   *r   rW  rX    rq  r   c                 ó   € R# rP  rS  ©Úselfr�  s   &&r   rW  rX    rm  r   c                 ó   € R# rP  rS  )rU  Ú
boundariesÚ	out_int32ÚrightrV  s   &&&&&r   rW  rX  	  ó   € Ñ[]r   c                  ó   € R# rP  rS  r½  s   *r   rW  rX  
  rm  r   c                 ó   € R# rP  rS  ©r¾  r“  rV  s   &&&r   rW  rX    r·  r   c                 ó   € R# rP  rS  r  s   &&&r   rW  rX    ó   € ±rr   c                 ó   € R# rP  rS  r  s   &&&r   rW  rX    rÿ  r   c                 ó   € R# rP  rS  )Úx1Úx2rœ  Úcompute_modes   &&&&r   rW  rX    ó   € Ñ_ar   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX    rd  r   ç      ð?c                 ó   € R# rP  rS  ©rU  rv  rž  s   &&&r   rW  rX    rÿ  r   rV  c                 ó   € R# rP  rS  )rV  Úmatricess   $*r   rW  rX    ó   € ¹r   c                 ó   € R# rP  rS  ©rU  Úgroupss   &&r   rW  rX    ó   € ±Rr   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX    r  r   c                 ó   € R# rP  rS  ©rU  Úcheck_errorsrV  s   &&&r   rW  rX    ó   € Ébr   c                 ó   € R# rP  rS  )rU  ÚupperrV  s   &&&r   rW  rX    ó   € ÁRr   c                 ó   € R# rP  rS  )rï  rð  r2  rV  s   &&&&r   rW  rX    ó   € ÉBr   c                 ó   € R# rP  rS  )rU  ÚnumelÚn_binsÚratioÚ	bit_widths   &&&&&r   rW  rX    ó   € ÑWYr   c                 ó   € R# rP  rS  ©rU  Úchunksr“  s   &&&r   rW  rX    rq  r   c                 ó   € R# rP  rS  ©rU  ÚminÚmaxrV  s   &&&&r   rW  rX    rì  r   c                 ó   € R# rP  rS  r@  s   &&&&r   rW  rX    ó   € Ár   c                 ó   € R# rP  rS  )rU  rA  rV  s   &&&r   rW  rX    r¹  r   c                 ó   € R# rP  rS  )rU  rB  rV  s   &&&r   rW  rX    r¹  r   c                 ó   € R# rP  rS  ©r¾  rV  s   &&r   rW  rX    r¹  r   c                 ó   € R# rP  rS  )rU  Ú
correctionÚfweightsÚaweightss   &&&&r   rW  rX    ó   € ÉRr   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    ó   € ¡2r   c                 ó   € R# rP  rS  )rU  ÚrÚwith_replacements   &&&r   rW  rX     ó   € Árr   c                 ó   € R# rP  rS  )ÚrealÚimags   &&r   rW  rX  !  ó   € ©"r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  "  r  r   c                 ó   € R# rP  rS  )ÚabsÚangs   &&r   rW  rX  #  ó   € ¡br   c                 ó   € R# rP  rS  )rU  Úords   &&r   rW  rX  $  rç  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  %  rd  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  &  r+  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  '  r·  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  (  rç  r   c                 ó   € R# rP  rS  )rU  Úpadr}  s   &&&r   rW  rX  )  ó   € ¹2r   c                 ó   € R# rP  rS  ©rU  rÉ  rÊ  r  rÃ  Údilationr*  s   &&&&&&&r   rW  rX  *  ó   € Ñbdr   c                 ó   € R# rP  rS  rg  s   &&&&&&&r   rW  rX  +  ri  r   c                 ó   € R# rP  rS  rg  s   &&&&&&&r   rW  rX  ,  ri  r   c	                 ó   € R# rP  rS  )	rU  rÉ  rÊ  r  rÃ  rh  Ú
transposedÚoutput_addingr*  s	   &&&&&&&&&r   rW  rX  -  ó   € Ñuwr   c                 ó   € R# rP  rS  )rU  rÉ  rÊ  rd  s   &&&&r   rW  rX  .  re  r   c                 ó   € R# rP  rS  ©rU  rÉ  rÊ  r  rÃ  Úoutput_paddingr*  rh  s   &&&&&&&&r   rW  rX  /  ó	   € ñ  Ar   c                 ó   € R# rP  rS  rr  s   &&&&&&&&r   rW  rX  0  rt  r   c                 ó   € R# rP  rS  rr  s   &&&&&&&&r   rW  rX  1  rt  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  2  r\  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  3  rY  r   c                 ó   € R# rP  rS  ©rï  rð  rô  Úmarginrõ  rö  r÷  s   &&&&&&&r   rW  rX  4  rä  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  5  rd  r   c                 ó   € R# rP  rS  )r  r  r“  rÏ  s   &&&&r   rW  rX  6  rì  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  7  rY  r   c                 ó   € R# rP  rS  ©rU  rp  r“  rV  s   &&&&r   rW  rX  8  r_  r   c                 ó   € R# rP  rS  r€  s   &&&&r   rW  rX  9  ó   € Á2r   c                 ó   € R# rP  rS  ©Ú	log_probsÚtargetsÚinput_lengthsÚtarget_lengthsÚblankr÷  Úzero_infinitys   &&&&&&&r   rW  rX  ;  rÆ  r   c                 ó   € R# rP  rS  ©rU  r“  rV  s   &&&r   rW  rX  =  rç  r   c                 ó   € R# rP  rS  rŒ  s   &&&r   rW  rX  >  rç  r   c                 ó   € R# rP  rS  ©rU  r“  rV  re   s   &&&&r   rW  rX  ?  rD  r   c                 ó   € R# rP  rS  r�  s   &&&&r   rW  rX  @  rb  r   c                 ó   € R# rP  rS  ©ÚyÚxr“  s   &&&r   rW  rX  A  r_  r   c                 ó   € R# rP  rS  rŒ  s   &&&r   rW  rX  B  r  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  C  rm  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  D  ó   € ©r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  E  ó   € ¡r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  F  r˜  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  G  r¨  r   c                 ó   € R# rP  rS  ©rU  ÚdiagonalrV  s   &&&r   rW  rX  H  r'  r   c                 ó   € R# rP  rS  rž  s   &&&r   rW  rX  I  r_  r   c                 ó   € R# rP  rS  )rU  Úoffsets   &&r   rW  rX  J  r[  r   c                 ó   € R# rP  rS  )rU  Únr“  ÚprependÚappendrV  s   &&&&&&r   rW  rX  K  ó   € ÑTVr   c                 ó   € R# rP  rS  ©rU  r¢  Údim1Údim2s   &&&&r   rW  rX  L  rD  r   c                 ó   € R# rP  rS  r©  s   &&&&r   rW  rX  M  r‹  r   c                 ó   € R# rP  rS  )rU  Úsrcr¢  rª  r«  s   &&&&&r   rW  rX  N  rM  r   c                 ó   € R# rP  rS  )r  r®  r�  r  Ústorage_offsets   &&&&&r   rW  rX  O  r;  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  P  rm  r   c                 ó   € R# rP  rS  )rU  rp  rœ  s   &&&r   rW  rX  Q  ri  r   c                 ó   € R# rP  rS  ©rU  rp  Úrounding_moderV  s   &&&&r   rW  rX  R  ó   € Ábr   c                 ó   € R# rP  rS  r´  s   &&&&r   rW  rX  S  r‹  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  T  rq  r   c                 ó   € R# rP  rS  r›  s   &&&&r   rW  rX  U  r_  r   c                 ó   € R# rP  rS  ©rU  rƒ  r  s   &&&r   rW  rX  V  r'  r   c                 ó   € R# rP  rS  )r‚  rƒ  s   &&r   rW  rX  W  ó   € ¡rr   c                 ó   € R# rP  rS  ©rU  Úindices_or_sectionss   &&r   rW  rX  X  r  r   c                 ó   € R# rP  rS  rH  s   &&r   rW  rX  Y  r[  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Z  rq  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  [  r¹  r   c                 ó   € R# rP  rS  ©rU  ÚUPLOrV  s   &&&r   rW  rX  \  rñ  r   c                 ó   € R# rP  rS  rÅ  s   &&&r   rW  rX  ]  rì  r   c                 ó   € R# rP  rS  )ÚequationÚoperandss   &*r   rW  rX  ^  rq  r   c                 ó   € R# rP  rS  ©rU  rÉ  Úpadding_idxÚmax_normÚ	norm_typeÚscale_grad_by_freqrÔ   s   &&&&&&&r   rW  rX  `  ó   € Ñz|r   c
                 ó   € R# rP  rS  )
rU  rÉ  ÚoffsetsrÎ  rÏ  rÐ  ÚmoderÔ   Úper_sample_weightsrÍ  s
   &&&&&&&&&&r   rW  rX  c  s	   € ñ  hjr   c                 ó   € R# rP  rS  ©rU  re   rs   rd   Úrequires_grads   &&&&&r   rW  rX  e  ó   € Ñcer   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  f  ó   € ±r   c                 ó   € R# rP  rS  ©rU  rp  s   &&r   rW  rX  g  rW  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  h  rY  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  i  rd  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  j  ri  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  k  rY  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  l  rd  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  m  rk  r   c                 ó   € R# rP  rS  )rU  ÚscaleÚ
zero_pointÚaxisÚ	quant_minÚ	quant_maxs   &&&&&&r   rW  rX  n  ó   € Ñmor   c                 ó   € R# rP  rS  )rU  rå  ræ  rè  ré  s   &&&&&r   rW  rX  o  ó   € Ñfhr   c                 ó   € R# rP  rS  )r”  Úobserver_onÚfake_quant_onÚaveraging_constÚrunning_minÚrunning_maxrå  ræ  rè  ré  Úch_axisÚper_row_fake_quantÚsymmetric_quants   &&&&&&&&&&&&&r   rW  rX  q  s	   € ñ  ACr   c                 ó   € R# rP  rS  ©rU  Úpacked_weightrÊ  Úoutputs   &&&&r   rW  rX  s  r§  r   c                 ó   € R# rP  rS  r÷  s   &&&&r   rW  rX  t  ó   € Ñdfr   c                 ó   € R# rP  rS  ©rU  rÉ  ÚpackedÚcol_offsetsÚweight_scaleÚweight_zero_pointrÊ  s   &&&&&&&r   rW  rX  u  ó   € Ñ{}r   c                 ó   € R# rP  rS  rý  s   &&&&&&&r   rW  rX  w  ó   € Ñ^`r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  y  re  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  z  r  r   c                 ó   € R# rP  rS  )rU  ÚaÚbs   &&&r   rW  rX  {  rD  r   c                 ó   € R# rP  rS  ©rU  rœ  r�  s   &&&r   rW  rX  |  rñ  r   c                 ó   € R# rP  rS  r  s   &&&r   rW  rX  }  r  r   c                 ó   € R# rP  rS  ©rU  r¤  r“  Únorms   &&&&r   rW  rX  ~  rì  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX    rì  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  €  r  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  �  rì  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  ‚  r  r   c                 ó   € R# rP  rS  ©rU  Úsr“  r  s   &&&&r   rW  rX  ƒ  r‡  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  „  r‹  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  …  r  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  †  r‚  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  ‡  r‚  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  ˆ  rŸ  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  ‰  rŸ  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  Š  r3  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  ‹  rS  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  Œ  r‡  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  �  r‡  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  Ž  r‹  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  �  r·  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  �  r+  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  ‘  rD  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ’  rY  r   c                 ó   € R# rP  rS  )rU  Ú	start_dimÚend_dims   &&&r   rW  rX  “  r_  r   c                 ó   € R# rP  rS  ©rU  Údimss   &&r   rW  rX  ”  r˜  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  •  r¨  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  –  r¨  r   c                 ó   € R# rP  rS  r£  s   &&&&r   rW  rX  —  rx  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ˜  rk  r   c                 ó   € R# rP  rS  rÝ  s   &&r   rW  rX  ™  rÛ  r   c                 ó   € R# rP  rS  ©rU  ÚexponentrV  s   &&&r   rW  rX  š  rñ  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ›  rç  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  œ  rd  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  �  rk  r   c                 ó   € R# rP  rS  )rU  Ú
fill_valuerV  re   rs   rd   rØ  s   &&&&&&&r   rW  rX  ž  s	   € ñ  BDr   c                 ó   € R# rP  rS  )rU  r°  Ú	dep_tokens   &&&r   rW  rX  Ÿ  r¶  r   c                 ó   € R# rP  rS  )ÚLU_dataÚ	LU_pivotsÚunpack_dataÚunpack_pivotss   &&&&r   rW  rX     ré  r   c                 ó   € R# rP  rS  )rU  r“  ÚindexrV  Úsparse_grads   &&&&&r   rW  rX  ¡  rM  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ¢  rq  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  £  rÛ  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ¤  r˜  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ¥  rÿ  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ¦  rk  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  §  rW  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ¨  r·  r   c                 ó   € R# rP  rS  ©rU  rŠ  rV  s   &&&r   rW  rX  ©  rç  r   c                 ó   € R# rP  rS  rK  s   &&&r   rW  rX  ª  rÛ  r   c                 ó   € R# rP  rS  )rU  Úspacingr“  Ú
edge_orders   &&&&r   rW  rX  «  r5  r   c                 ó   € R# rP  rS  ©rU  ÚgridÚinterpolation_modeÚpadding_moder�  s   &&&&&r   rW  rX  ¬  ó   € Ñacr   c                 ó   € R# rP  rS  rQ  s   &&&&&r   rW  rX  ­  rû  r   c                 ó   € R# rP  rS  rQ  s   &&&&&r   rW  rX  ®  rû  r   c                 ó   € R# rP  rS  )rU  Ú
num_groupsrÉ  rÊ  rÏ  rÐ  s   &&&&&&r   rW  rX  ¯  ó   € Ñkmr   c	                 ó   € R# rP  rS  ©	rU  ÚhxÚparamsÚ
has_biasesÚ
num_layersÚdropoutr�  ÚbidirectionalÚbatch_firsts	   &&&&&&&&&r   rW  rX  °  ó   € Ñqsr   c                 ó   € R# rP  rS  ©rU  r]  Úw_ihÚw_hhÚb_ihÚb_hhs   &&&&&&r   rW  rX  ±  r5  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ²  rÛ  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ³  r¹  r   c                 ó   € R# rP  rS  ©rU  Úlambds   &&r   rW  rX  ´  rç  r   c                 ó   € R# rP  rS  )rU  r“  r¤  rÔ  rV  s   &&&&&r   rW  rX  µ  r§  r   c                 ó   € R# rP  rS  )rU  ÚvaluesrV  s   &&&r   rW  rX  ¶  r  r   c                 ó   € R# rP  rS  ©rU  rô  r{  rõ  rö  r÷  s   &&&&&&r   rW  rX  ·  ó   € Ñxzr   c                 ó   € R# rP  rS  )rU  ÚbinsrA  rB  rV  s   &&&&&r   rW  rX  ¸  r3  r   c                 ó   € R# rP  rS  )rU  rw  rA  rB  rÉ  ÚdensityrV  s   &&&&&&&r   rW  rX  ¹  rZ  r   c                 ó   € R# rP  rS  )rU  rw  r¦   rÉ  ry  s   &&&&&r   rW  rX  º  r;  r   c                 ó   € R# rP  rS  ©rU  Útaus   &&r   rW  rX  »  rñ  r   c                 ó   € R# rP  rS  )r‚  rƒ  rV  s   &&&r   rW  rX  ¼  rq  r   c                 ó   € R# rP  rS  r¿  s   &&r   rW  rX  ½  r  r   c                 ó   € R# rP  rS  rH  s   &&r   rW  rX  ¾  r[  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ¿  r·  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  À  r+  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  Á  r¹  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Â  rd  r   c                 ó   € R# rP  rS  ©rU  r“  rA  Úsources   &&&&r   rW  rX  Ã  re  r   c                 ó   € R# rP  rS  r†  s   &&&&r   rW  rX  Ä  rÿ  r   c                 ó   € R# rP  rS  )rU  Úindicesrr  Ú
accumulates   &&&&r   rW  rX  Å  r~  r   c                 ó   € R# rP  rS  )rU  r“  rA  rV  s   &&&&r   rW  rX  Æ  rD  r   c                 ó   € R# rP  rS  )rU  r“  rA  r}  s   &&&&r   rW  rX  Ç  re  r   c                 ó   € R# rP  rS  )rU  r“  rA  r‡  rö  Úinclude_inputs   &&&&&&r   rW  rX  È  ré  r   c                 ó   € R# rP  rS  ©r_   s   &r   rW  rX  É  r½  r   c                 ó   € R# rP  rS  )ÚeÚteÚassume_uniqueÚinverts   &&&&r   rW  rX  Ê  r3  r   c                 ó   € R# rP  rS  r‘  s   &r   rW  rX  Ë  r¨  r   c                 ó   € R# rP  rS  r‘  s   &r   rW  rX  Ì  rg  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Í  r[  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Î  r[  r   c	                 ó   € R# rP  rS  )	rU  rË  rÌ  rÉ  rÊ  Úuse_input_statsrÎ  rÏ  rÐ  s	   &&&&&&&&&r   rW  rX  Ð  rù  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ò  r\  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Ó  rm  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Ô  rq  r   c                 ó   € R# rP  rS  r.  s   &&&r   rW  rX  Õ  r‹  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ö  r˜  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ×  rg  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ø  r¨  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ù  rd  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ú  rW  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Û  rm  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ü  r˜  r   c                 ó   € R# rP  rS  rÝ  s   &&r   rW  rX  Ý  rÛ  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Þ  r½  r   c                 ó   € R# rP  rS  r•  s   &&&&&r   rW  rX  ß  ó   € ÑUWr   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  à  rO  r   c
                 ó   € R# rP  rS  )
rU  Ún_fftÚ
hop_lengthÚ
win_lengthÚwindowÚcenterÚ
normalizedÚonesidedÚlengthÚreturn_complexs
   &&&&&&&&&&r   rW  rX  â  ó	   € ñ  bdr   c                 ó   € R# rP  rS  ©rU  rô  rõ  rö  r÷  Ú
log_targets   &&&&&&r   rW  rX  ä  s   € Ñprr   c                 ó   € R# rP  rS  rÝ  s   &&r   rW  rX  å  ó   € ©r   c                 ó   € R# rP  rS  )rU  Úkr“  r¤  rV  s   &&&&&r   rW  rX  æ  r5  r   c                 ó   € R# rP  rS  )rU  Ú	hermitianr/  rV  s   &&&&r   rW  rX  ç  rU  r   c                 ó   € R# rP  rS  )rU  rÀ  rV  s   &&&r   rW  rX  è  r~  r   c                 ó   € R# rP  rS  )ÚLDÚpivotsÚBrÀ  rV  s   &&&&&r   rW  rX  é  ó   € ÑQSr   c                 ó   € R# rP  rS  )rU  Únormalized_shaperÉ  rÊ  rÏ  rÐ  s   &&&&&&r   rW  rX  ê  rd  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ë  rq  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ì  r·  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  í  rÛ  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  î  r  r   c                 ó   € R# rP  rS  )rU  ÚendrÉ  rV  s   &&&&r   rW  rX  ï  r  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ð  ri  r   c                 ó   € R# rP  rS  )rU  r¾  rÅ  ÚXr¤  ÚiKÚniterÚtolÚlargestÚmethodÚtrackerÚortho_iparamsÚortho_fparamsÚortho_bparamss   &&&&&&&&&&&&&&r   rW  rX  ñ  s	   € ñ  IKr   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ò  rY  r   c                 ó   € R# rP  rS  ©rU  r“  re   s   &&&r   rW  rX  ó  r  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ô  rk  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  õ  rk  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ö  rd  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ÷  r'  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ø  r  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ù  r¨  r   c                 ó   € R# rP  rS  )r”  r“  rV  s   &&&r   rW  rX  ú  rd  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  û  r  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ü  rç  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ý  r  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  þ  r  r   c                 ó   € R# rP  rS  ræ  s   &&r   rW  rX  ÿ  rk  r   c                 ó   € R# rP  rS  )rU  Únamesr¤  rV  s   &&&&r   rW  rX     rS  r   c	                 ó   € R# rP  rS  )	ÚdataÚbatch_sizesr]  r^  r_  r`  ra  r�  rb  s	   &&&&&&&&&r   rW  rX    rd  r   c                 ó   € R# rP  rS  rf  s   &&&&&&r   rW  rX    rM  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    rÛ  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    rç  r   c                 ó   € R# rP  rS  )ÚAÚpivotÚ	get_infosrV  s   &&&&r   rW  rX    r‚  r   c                 ó   € R# rP  rS  )r	  r<  r=  rV  s   &&&&r   rW  rX    rD  r   c                 ó   € R# rP  rS  rz  s   &&&&&&&r   rW  rX    rt  r   c                 ó   € R# rP  rS  )rU  Úmaskr}  s   &&&r   rW  rX    r¹  r   c                 ó   € R# rP  rS  )rU  rù  r‡  s   &&&r   rW  rX  	  r  r   c                 ó   € R# rP  rS  )rU  rù  rV  s   &&&r   rW  rX  
  re  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    r+  r   c                 ó   € R# rP  rS  ©rU  rô  rV  s   &&&r   rW  rX    rñ  r   c                 ó   € R# rP  rS  rþ  s   &&&r   rW  rX    rŸ  r   c                 ó   € R# rP  rS  )rU  rô  r/  rV  s   &&&&r   rW  rX    r  r   c                 ó   € R# rP  rS  )ÚLUrÄ  rÅ  ÚleftÚadjointrV  s   &&&&&&r   rW  rX    ó   € ÑY[r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    rÿ  r   c                 ó   € R# rP  rS  )rU  r¤  s   &&r   rW  rX    rk  r   c                 ó   € R# rP  rS  ©rU  r¤  rV  s   &&&r   rW  rX    r_  r   c                 ó   € R# rP  rS  )rU  rÔ  rÀ  s   &&&r   rW  rX    ó   € É2r   c                 ó   € R# rP  rS  rH  s   &&r   rW  rX    r  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    r˜  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    rm  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    rÛ  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX    rY  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    r¹  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX    rç  r   c                 ó   € R# rP  rS  ©rU  rÂ  r  rÃ  rh  rÄ  s   &&&&&&r   rW  rX    ó   € Ñjlr   c                 ó   € R# rP  rS  r  s   &&&&&&r   rW  rX    r  r   c                 ó   € R# rP  rS  r  s   &&&&&&r   rW  rX    r  r   c                 ó   € R# rP  rS  ©rU  rÂ  r  rÃ  rh  Úreturn_indicesrÄ  s   &&&&&&&r   rW  rX    rù  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  !  rd  r   c                 ó   € R# rP  rS  )rU  r“  r¤  re   rV  s   &&&&&r   rW  rX  "  r§  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  #  ri  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  $  rÛ  r   c                  ó   € R# rP  rS  )r¾  r$   s   *,r   rW  rX  %  rç  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  &  rY  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  '  r¹  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  (  rç  r   c                 ó   € R# rP  rS  )rU  rÉ  rÊ  rË  rÌ  rÍ  Úexponential_average_factorÚepsilons   &&&&&&&&r   rW  rX  *  rù  r   c	                 ó   € R# rP  rS  ©	rU  rÉ  rÊ  rÃ  r  rh  r*  Ú	benchmarkÚdeterministics	   &&&&&&&&&r   rW  rX  ,  r  r   c	                 ó   € R# rP  rS  )	rU  rÉ  Úzrv  rÊ  r  rÃ  rh  r*  s	   &&&&&&&&&r   rW  rX  -  râ  r   c                 ó   € R# rP  rS  rg  s   &&&&&&&r   rW  rX  .  rì  r   c
                 ó   € R# rP  rS  )
rU  rÉ  rÊ  rÃ  rs  r  rh  r*  r(  r)  s
   &&&&&&&&&&r   rW  rX  0  ro  r   c	                 ó   € R# rP  rS  r'  s	   &&&&&&&&&r   rW  rX  3  ó   € Ñegr   c                 ó   € R# rP  rS  )rU  rÉ  Úweight_stride0r]  ÚcxrÔ  Úhidden_sizer`  rc  ra  r�  rb  rî  Údropout_states   &&&&&&&&&&&&&&r   rW  rX  6  r·  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  8  rD  r   c                 ó   € R# rP  rS  r£  s   &&&&r   rW  rX  9  r‚  r   c                 ó   € R# rP  rS  ©rU  r‡  Údestinations   &&&r   rW  rX  :  r  r   c                 ó   € R# rP  rS  r8  s   &&&r   rW  rX  ;  re  r   c                 ó   € R# rP  rS  )rU  r¬  rV  s   &&&r   rW  rX  <  rb  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  =  rq  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  >  r  r   c                 ó   € R# rP  rS  )rU  Únum_samplesÚreplacementrV  s   &&&&r   rW  rX  ?  ó   € ÑSUr   c                 ó   € R# rP  rS  )rU  r†  rV  s   &&&r   rW  rX  @  rm  r   c                 ó   € R# rP  rS  ©rU  rœ  s   &&r   rW  rX  A  r¼  r   c                 ó   € R# rP  rS  )rU  r“  Ústartrµ  s   &&&&r   rW  rX  B  r'  r   c                 ó   € R# rP  rS  )rU  ÚnanÚposinfÚneginfrV  s   &&&&&r   rW  rX  C  r«  r   c                 ó   € R# rP  rS  )rU  rÉ  rÊ  rË  rÌ  rÍ  rÎ  rÏ  s   &&&&&&&&r   rW  rX  D  rd  r   c                 ó   € R# rP  rS  )rU  rÉ  rÊ  rÍ  rÎ  rÏ  s   &&&&&&r   rW  rX  E  ó   € Ñ]_r   c                 ó   € R# rP  rS  r  s   &&&r   rW  rX  F  r¹  r   c                 ó   € R# rP  rS  ©rU  rÈ  rÉ  rÊ  rÏ  s   &&&&&r   rW  rX  G  rû  r   c                 ó   € R# rP  rS  ©rU  rÈ  rÉ  rÏ  s   &&&&r   rW  rX  H  r;  r   c                 ó   € R# rP  rS  )rU  rÉ  rÊ  ÚNÚCÚHxWÚgrouprÏ  s   &&&&&&&&r   rW  rX  I  r§  r   c                 ó   € R# rP  rS  )rU  rœ  r“  r¤  re   s   &&&&&r   rW  rX  J  rA  r   c                 ó   € R# rP  rS  r)  s   &&r   rW  rX  K  rÿ  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  L  rÛ  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  M  r'  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  N  rY  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  O  r[  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  P  r'  r   c                 ó   € R# rP  rS  r]  s   &&r   rW  rX  Q  r5  r   c                 ó   € R# rP  rS  r]  s   &&r   rW  rX  R  r5  r   c                 ó   € R# rP  rS  ©rU  r^  r  s   &&&r   rW  rX  S  ri  r   c                 ó   € R# rP  rS  rb  s   &&&r   rW  rX  T  ó   € Ñoqr   c                 ó   € R# rP  rS  rb  s   &&&r   rW  rX  U  ri  r   c                 ó   € R# rP  rS  rb  s   &&&r   rW  rX  V  rd  r   c                 ó   € R# rP  rS  rb  s   &&&r   rW  rX  W  ri  r   c                 ó   € R# rP  rS  rb  s   &&&r   rW  rX  X  rd  r   c                 ó   € R# rP  rS  r�  s   &&&r   rW  rX  Y  rÆ  r   c                 ó   € R# rP  rS  ©rU  rœ  rÍ  rž  s   &&&&r   rW  rX  Z  r   r   c                 ó   € R# rP  rS  ©rU  rÂ  r  rÃ  rÄ  rÅ  Údivisor_overrides   &&&&&&&r   rW  rX  \  ó	   € ñ  @Br   c                 ó   € R# rP  rS  rm  s   &&&&&&&r   rW  rX  _  ro  r   c                 ó   € R# rP  rS  )rU  rË  rÌ  rÉ  rÊ  rÍ  rÎ  rÏ  s   &&&&&&&&r   rW  rX  b  rÆ  r   c                 ó   € R# rP  rS  rî  s   &&&&r   rW  rX  d  r­  r   c                 ó   € R# rP  rS  ©rU  rô  rÉ  rõ  rö  r÷  s   &&&&&&r   rW  rX  f  rU  r   c                 ó   € R# rP  rS  ró  s   &&&&&&&r   rW  rX  i  rù  r   c                 ó   € R# rP  rS  r$  s   &&&r   rW  rX  k  r~  r   c                 ó   € R# rP  rS  rz  s   &&&&&&&r   rW  rX  m  ó   € Ñgir   c                 ó   € R# rP  rS  )rU  rô  rÉ  rõ  Úignore_indexrö  r÷  Úlabel_smoothings   &&&&&&&&r   rW  rX  p  ó	   € ñ  JLr   c	                 ó   € R# rP  rS  )	rU  Úlinear_weightrô  Úlinear_biasrÉ  r÷  rz  r{  Úoptionss	   &&&&&&&&&r   rW  rX  s  s	   € ñ  Y[r   c                 ó   € R# rP  rS  r„  s   &&&&&&&r   rW  rX  v  rÆ  r   c                 ó   € R# rP  rS  rk  s   &&&&r   rW  rX  x  ó   € ÑXZr   c                 ó   € R# rP  rS  rk  s   &&&&r   rW  rX  y  ré  r   c                 ó   € R# rP  rS  rk  s   &&&&r   rW  rX  z  ré  r   c                 ó   € R# rP  rS  rk  s   &&&&r   rW  rX  {  ré  r   c                 ó   € R# rP  rS  r$  s   &&&r   rW  rX  |  r‹  r   c                 ó   € R# rP  rS  rÌ  s   &&&&&&&r   rW  rX  ~  rÑ  r   c                 ó   € R# rP  rS  )rU  rÉ  rÓ  rÎ  rÏ  rÐ  rÔ  rÔ   rÕ  Úinclude_last_offsetrÍ  s   &&&&&&&&&&&r   rW  rX  �  s	   € ñ  HJr   c                 ó   € R# rP  rS  rk  s   &&&&r   rW  rX  ƒ  rx  r   c                 ó   € R# rP  rS  )rU  r^  rÂ  rh  rÃ  r  s   &&&&&&r   rW  rX  „  rZ  r   c                 ó   € R# rP  rS  ©rU  rÂ  r^  Úoutput_ratior  Ú_random_sampless   &&&&&&r   rW  rX  †  ru  r   c                 ó   € R# rP  rS  rŽ  s   &&&&&&r   rW  rX  ‰  ru  r   c                 ó   € R# rP  rS  rŽ  s   &&&&&&r   rW  rX  Œ  ru  r   c                 ó   € R# rP  rS  rŽ  s   &&&&&&r   rW  rX  �  ru  r   c                 ó   € R# rP  rS  )rU  rô  Úvarr�   rÏ  r÷  s   &&&&&&r   rW  rX  ‘  rÝ  r   c                 ó   € R# rP  rS  )rU  Úapproximates   &&r   rW  rX  ’  rŸ  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  “  r  r   c                 ó   € R# rP  rS  )rU  rR  rÔ  rT  r�  s   &&&&&r   rW  rX  ”  ru  r   c                 ó   € R# rP  rS  )rU  rY  rÉ  rÊ  rÏ  s   &&&&&r   rW  rX  •  r/  r   c                 ó   € R# rP  rS  )Úlogitsr}  ÚhardrÏ  r“  s   &&&&&r   rW  rX  –  rU  r   c                 ó   € R# rP  rS  rn  s   &&r   rW  rX  —  rì  r   c                 ó   € R# rP  rS  )rU  Úmin_valÚmax_valrž  s   &&&&r   rW  rX  ˜  r  r   c                 ó   € R# rP  rS  rt  s   &&&&&&r   rW  rX  š  ó   € Ñ`br   c                 ó   € R# rP  rS  )rU  rË  rÌ  rÉ  rÊ  rœ  rÎ  rÏ  s   &&&&&&&&r   rW  rX  �  s	   € ñ  GIr   c                 ó   € R# rP  rS  )rU  r�  Úscale_factorrÔ  r�  Úrecompute_scale_factorÚ	antialiass   &&&&&&&r   rW  rX     s	   € ñ  KMr   c                 ó   € R# rP  rS  r¹  s   &&&&&&r   rW  rX  ¢  rt  r   c                 ó   € R# rP  rS  ©rU  rô  rõ  rö  r÷  rÉ  s   &&&&&&r   rW  rX  £  rÑ  r   c                 ó   € R# rP  rS  rP  s   &&&&&r   rW  rX  ¤  rZ  r   c                 ó   € R# rP  rS  )rU  Únegative_sloperž  s   &&&r   rW  rX  ¥  ré  r   c                 ó   € R# rP  rS  )rU  rÉ  rÊ  s   &&&r   rW  rX  ¦  r3  r   c                 ó   € R# rP  rS  )rU  r�  rv  rw  r¾  s   &&&&&r   rW  rX  §  r/  r   c                 ó   € R# rP  rS  ©rU  r“  Ú_stacklevelre   s   &&&&r   rW  rX  ¨  s   € Ñ\^r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ©  r¹  r   c                 ó   € R# rP  rS  ©rU  rÏ  rÂ  r  rÄ  s   &&&&&r   rW  rX  ª  rZ  r   c                 ó   € R# rP  rS  r¶  s   &&&&&r   rW  rX  «  rZ  r   c                 ó   € R# rP  rS  r¶  s   &&&&&r   rW  rX  ¬  rZ  r   c                 ó   € R# rP  rS  rz  s   &&&&&&&r   rW  rX  ®  rx  r   c                 ó   € R# rP  rS  ©rU  rÂ  r  rÃ  rh  rÄ  r  s   &&&&&&&r   rW  rX  ±  rù  r   c                 ó   € R# rP  rS  r  s   &&&&&&&r   rW  rX  ´  rù  r   c                 ó   € R# rP  rS  r»  s   &&&&&&&r   rW  rX  ·  rù  r   c                 ó   € R# rP  rS  r  s   &&&&&&&r   rW  rX  º  rù  r   c                 ó   € R# rP  rS  r  s   &&&&&&&r   rW  rX  ½  rù  r   c                 ó   € R# rP  rS  r  s   &&&&&&&r   rW  rX  À  rù  r   c                 ó   € R# rP  rS  ©rU  rŠ  rÂ  r  rÃ  r^  s   &&&&&&r   rW  rX  Â  ru  r   c                 ó   € R# rP  rS  rÂ  s   &&&&&&r   rW  rX  Ã  ru  r   c                 ó   € R# rP  rS  rÂ  s   &&&&&&r   rW  rX  Ä  ru  r   c                 ó   € R# rP  rS  r«  s   &&&&&&r   rW  rX  Å  r  r   c                 ó   € R# rP  rS  )ÚqueryÚkeyr}  Úembed_dim_to_checkÚ	num_headsÚin_proj_weightÚin_proj_biasÚbias_kÚbias_vÚadd_zero_attnÚ	dropout_pÚout_proj_weightÚout_proj_biasrÍ  Úkey_padding_maskÚneed_weightsÚ	attn_maskÚuse_separate_proj_weightÚq_proj_weightÚk_proj_weightÚv_proj_weightÚstatic_kÚstatic_vÚaverage_attn_weightsÚ	is_causals   &&&&&&&&&&&&&&&&&&&&&&&&&r   rW  rX  Ç  s	   € ñ  ]_r   c                 ó   € R# rP  rS  )rU  rô  rœ  r{  rÉ  rõ  rö  r÷  s   &&&&&&&&r   rW  rX  Ê  rù  r   c                 ó   € R# rP  rS  ©rU  rô  rõ  rö  r÷  s   &&&&&r   rW  rX  Í  r§  r   c                 ó   € R# rP  rS  rt  s   &&&&&&r   rW  rX  Ð  rU  r   c                 ó   € R# rP  rS  )rU  rô  rÉ  rõ  rz  rö  r÷  s   &&&&&&&r   rW  rX  Ó  râ  r   c                 ó   € R# rP  rS  )rU  rœ  r“  rÏ  rV  s   &&&&&r   rW  rX  Õ  r™  r   c                 ó   € R# rP  rS  )r_   Únum_classess   &&r   rW  rX  Ö  rŸ  r   c                 ó   € R# rP  rS  )rU  rd  rÔ  r}  s   &&&&r   rW  rX  ×  r0  r   c                 ó   € R# rP  rS  ©r  r  rœ  rÏ  r¤  s   &&&&&r   rW  rX  Ø  r   r   c                 ó   € R# rP  rS  )rU  rô  Ú	log_inputr�   rõ  rÏ  rö  r÷  s   &&&&&&&&r   rW  rX  Ú  r  r   c                 ó   € R# rP  rS  ©rU  rÉ  s   &&r   rW  rX  Ü  r  r   c                 ó   € R# rP  rS  ©rU  rž  s   &&r   rW  rX  Ý  rb  r   c                 ó   € R# rP  rS  rî  s   &&r   rW  rX  Þ  rD  r   c                 ó   € R# rP  rS  rR  s   &&&&r   rW  rX  ß  rM  r   c                 ó   € R# rP  rS  ©rU  Úlowerr2  rÍ  rž  s   &&&&&r   rW  rX  à  s   € Ñwyr   c                 ó   € R# rP  rS  rî  s   &&r   rW  rX  á  rb  r   c                 ó   € R# rP  rS  rî  s   &&r   rW  rX  â  rb  r   c                 ó   € R# rP  rS  rî  s   &&r   rW  rX  ã  rb  r   c                 ó   € R# rP  rS  )rÇ  rÈ  r}  rÕ  rÐ  s   &&&&&r   rW  rX  ä  rÝ  r   c                 ó   € R# rP  rS  )rU  rô  rõ  rö  r÷  rw  s   &&&&&&r   rW  rX  å  rt  r   c                 ó   € R# rP  rS  )rU  rô  r÷  ÚdeltarÉ  s   &&&&&r   rW  rX  æ  ó   € Ñhjr   c                 ó   € R# rP  rS  rà  s   &&&&&r   rW  rX  ç  rÆ  r   c                 ó   € R# rP  rS  r²  s   &&&&r   rW  rX  è  rƒ  r   c                 ó   € R# rP  rS  r²  s   &&&&r   rW  rX  é  rƒ  r   c                 ó   € R# rP  rS  )rU  rw  Ú	thresholds   &&&r   rW  rX  ê  r~  r   c                 ó   € R# rP  rS  rn  s   &&r   rW  rX  ë  rì  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ì  r·  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  í  r¹  r   c                 ó   € R# rP  rS  ©rU  r   r}  rž  s   &&&&r   rW  rX  î  r™  r   c
                 ó   € R# rP  rS  ©
ÚanchorÚpositiveÚnegativer{  rœ  rÏ  Úswaprõ  rö  r÷  s
   &&&&&&&&&&r   rW  rX  ð  r|  r   Údistance_functionr{  r  r÷  c                ó   € R# rP  rS  )r  r	  r
  r  r{  r  r÷  s   &&&$$$$r   rW  rX  ó  râ  r   c                 ó   € R# rP  rS  )rU  rÂ  rh  rÃ  r  s   &&&&&r   rW  rX  õ  r£  r   c                 ó   € R# rP  rS  )r_   r  r	  rë  s   &&&&r   rW  rX  ö  rM  r   c                 ó   € R# rP  rS  )r_   rÔ  Ústdrë  s   &&&&r   rW  rX  ÷  rÆ  r   c                 ó   € R# rP  rS  )r_   Úvals   &&r   rW  rX  ø  r+  r   c                 ó   € R# rP  rS  )r_   r  rÔ  Únonlinearityrë  s   &&&&&r   rW  rX  ù  rÆ  r   c                 ó   € R# rP  rS  )rU  Úas_tuples   &&r   rW  rX  ú  r+  r   r8  c                ó   € R# rP  rS  )rU  r�  r8  s   &$$r   rW  rX  û  rŸ  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ü  r\  r   c                 ó   € R# rP  rS  ©rU  rœ  r“  r¤  rV  re   s   &&&&&&r   rW  rX  ý  ré  r   c                 ó   € R# rP  rS  ©rU  r^  r“  r¤  rV  re   s   &&&&&&r   rW  rX  þ  ri  r   c                 ó   € R# rP  rS  r  s   &&&&&&r   rW  rX  ÿ  rì  r   c                 ó   € R# rP  rS  r  s   &&&&&&r   rW  rX     s   € ñ 13r   c                 ó   € R# rP  rS  )ÚvÚpowr“  s   &&&r   rW  rX    r  r   c                 ó   € R# rP  rS  r  s   &&&&&&r   rW  rX    ri  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    rO  r   c                 ó   € R# rP  rS  r|  s   &&r   rW  rX    r˜  r   c                 ó   € R# rP  rS  )rU  rð  Úinput3r  Ú	transposes   &&&&&r   rW  rX    rx  r   c                 ó   € R# rP  rS  rè  s   &&&&&r   rW  rX  	  rÆ  r   c                 ó   € R# rP  rS  ©r  r“  s   &&r   rW  rX  
  r¼  r   c                 ó   € R# rP  rS  )rU  Úqr²  rÓ  s   &&&&r   rW  rX    rS  r   c                 ó   € R# rP  rS  rD  s   &&r   rW  rX    r˜  r   c                 ó   € R# rP  rS  )rU  Úrconds   &&r   rW  rX    rç  r   c                 ó   € R# rP  rS  )rU  r0  rÀ  s   &&&r   rW  rX    rS  r   c                 ó   € R# rP  rS  )rU  Úupscale_factors   &&r   rW  rX    re  r   c                 ó   € R# rP  rS  )rU  Údownscale_factors   &&r   rW  rX    rb  r   c                 ó   € R# rP  rS  )rU  rë  s   &&r   rW  rX    r+  r   c                 ó   € R# rP  rS  )rU  rô  rê  r�   rÏ  r÷  s   &&&&&&r   rW  rX    r;  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX    r·  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX    r[  r   c                 ó   € R# rP  rS  rì  s   &&r   rW  rX    rY  r   c                 ó   € R# rP  rS  r×  s   &&&&&r   rW  rX    ri  r   c                 ó   € R# rP  rS  r2  s   &&&r   rW  rX    r+  r   c                 ó   € R# rP  rS  )rU  re   s   &&r   rW  rX    ri  r   c                 ó   € R# rP  rS  )rU  rA  r‡  r‹  s   &&&&r   rW  rX    r  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    r[  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    rq  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    r  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    rg  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX    rW  r   c                 ó   € R# rP  rS  )rU  rÔ  rV  s   &&&r   rW  rX    rì  r   c                 ó   € R# rP  rS  )ró  rV  s   &&r   rW  rX     r[  r   c                 ó   € R# rP  rS  ©rU  r-  r“  r¤  ÚinterpolationrV  s   &&&&&&r   rW  rX  !  rû  r   c                 ó   € R# rP  rS  rG  s   &&&&&&r   rW  rX  "  rx  r   c                 ó   € R# rP  rS  )rU  ÚscalesÚzero_pointsrç  re   s   &&&&&r   rW  rX  #  r§  r   c                 ó   € R# rP  rS  )rU  rå  ræ  re   s   &&&&r   rW  rX  $  r  r   c                 ó   € R# rP  rS  )rU  re   Úreduce_ranges   &&&r   rW  rX  %  r0  r   c                 ó   € R# rP  rS  )rU  rÉ  rÊ  rÔ  r•  rÏ  Úoutput_scaleÚoutput_zero_points   &&&&&&&&r   rW  rX  &  rd  r   c                 ó   € R# rP  rS  ©rU  r]  rg  rh  ri  rj  Ú	packed_ihÚ	packed_hhÚcol_offsets_ihÚcol_offsets_hhÚscale_ihÚscale_hhÚzero_point_ihÚzero_point_hhs   &&&&&&&&&&&&&&r   rW  rX  (  ó	   € ñ  _ar   c                 ó   € R# rP  rS  rT  s   &&&&&&&&&&&&&&r   rW  rX  +  r]  r   c                 ó   € R# rP  rS  r  s   &&&&&&r   rW  rX  .  s   € á "r   c                 ó   € R# rP  rS  r  s   &&&&&&r   rW  rX  3  s   € ñ !#r   c                 ó   € R# rP  rS  r  s   &&&&&&r   rW  rX  9  s   € ñ !#r   c                 ó   € R# rP  rS  rT  s   &&&&&&&&&&&&&&r   rW  rX  @  r]  r   c                 ó   € R# rP  rS  rT  s   &&&&&&&&&&&&&&r   rW  rX  C  r]  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  E  rm  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  F  rO  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  G  rd  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  H  rç  r   c                 ó   € R# rP  rS  r€  s   &&&&r   rW  rX  I  rŸ  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  J  rW  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  K  rk  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  L  rq  r   c                 ó   € R# rP  rS  rî  s   &&r   rW  rX  M  rÛ  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  N  r'  r   c                 ó   € R# rP  rS  )rU  rœ  r“  ÚmaxnormrV  s   &&&&&r   rW  rX  O  rb  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  P  r  r   c                 ó   € R# rP  rS  )rU  Úshapes   &&r   rW  rX  Q  rd  r   c                 ó   € R# rP  rS  rR  s   &&&&r   rW  rX  R  rx  r   c	                 ó   € R# rP  rS  r\  s	   &&&&&&&&&r   rW  rX  S  rÆ  r   c                 ó   € R# rP  rS  rf  s   &&&&&&r   rW  rX  T  rÆ  r   c	                 ó   € R# rP  rS  r\  s	   &&&&&&&&&r   rW  rX  U  rÆ  r   c                 ó   € R# rP  rS  rf  s   &&&&&&r   rW  rX  V  rÆ  r   c                 ó   € R# rP  rS  )rU  Úshiftsr+  s   &&&r   rW  rX  W  r+  r   c                 ó   € R# rP  rS  )rU  r¾  r+  s   &&&r   rW  rX  X  r+  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Y  rk  r   c                 ó   € R# rP  rS  rH  s   &&r   rW  rX  Z  rç  r   c                 ó   € R# rP  rS  )rÉ  rù  Úcompressed_indices_dtypes   &&&r   rW  rX  [  r0  r   c                 ó   € R# rP  rS  rò  s   &&&&&r   rW  rX  \  r£  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ]  rk  r   c                 ó   € R# rP  rS  )rU  rp  rv  s   &&&r   rW  rX  ^  rq  r   c                 ó   € R# rP  rS  r�  s   &&&&&&r   rW  rX  _  r  r   r}  rö  c                ó   € R# rP  rS  )rU  r“  rA  r®  r}  rö  s   &&&&$$r   rW  rX  `  r;  r   c                 ó   € R# rP  rS  )rU  r“  rA  r®  s   &&&&r   rW  rX  a  r  r   c                 ó   € R# rP  rS  )rU  r“  rA  r®  rö  Úinclude_selfs   &&&&&&r   rW  rX  b  rƒ  r   c                 ó   € R# rP  rS  )Úsorted_sequencerU  r  r  rV  s   &&&&&r   rW  rX  c  rÙ  r   c                 ó   € R# rP  rS  )rí  rö  ÚlengthsrŠ  rÓ  rç  Úunsafes   &&&&&&&r   rW  rX  d  rÑ  r   c                 ó   € R# rP  rS  )rU  r“  rA  s   &&&r   rW  rX  e  r[  r   c                 ó   € R# rP  rS  )rU  r®  r“  rA  s   &&&&r   rW  rX  f  rñ  r   c                 ó   € R# rP  rS  ©rU  r®  r“  rF  rÎ  Ústeps   &&&&&&r   rW  rX  g  r«  r   c                 ó   € R# rP  rS  r�  s   &&&&&&r   rW  rX  h  r«  r   c                 ó   € R# rP  rS  rî  s   &&r   rW  rX  i  rÛ  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  j  rm  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  k  rd  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  l  rm  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  m  rY  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  n  rY  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  o  rd  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  p  rd  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  q  rg  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  r  rd  r   c                 ó   € R# rP  rS  r»  s   &&&r   rW  rX  s  r  r   c                 ó   € R# rP  rS  r»  s   &&&r   rW  rX  t  r'  r   c                 ó   € R# rP  rS  rÝ  s   &&&r   rW  rX  u  r¹  r   c                 ó   € R# rP  rS  )ró  rÅ  r  rV  s   &&&&r   rW  rX  v  r_  r   c                 ó   € R# rP  rS  )ró  rÅ  r  r/  rV  s   &&&&&r   rW  rX  w  r«  r   c                ó   € R# rP  rS  )rU  r“  r¬  rª  rV  s   &&&$$r   rW  rX  x  r;  r   c                 ó   € R# rP  rS  ©r_   Úsplit_size_or_sectionsr“  s   &&&r   rW  rX  y  r‚  r   c                 ó   € R# rP  rS  r£  s   &&&r   rW  rX  z  r0  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  {  rd  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  |  ri  r   c                 ó   € R# rP  rS  rŒ  s   &&&r   rW  rX  }  r  r   c                 ó   € R# rP  rS  r�  s   &&&&&&r   rW  rX  ~  rM  r   c                 ó   € R# rP  rS  r  s   &&&r   rW  rX    r¹  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  €  rY  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  �  r[  r   c                 ó   € R# rP  rS  )rU  r®  r¯  r°  r±  r²  Úpad_moder³  r´  r¶  Úalign_to_windows   &&&&&&&&&&&r   rW  rX  ƒ  s	   € ñ  ~@r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  …  rq  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  †  r  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  ‡  rY  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ˆ  r½  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ‰  rg  r   c                 ó   € R# rP  rS  ©r  r	  s   &&r   rW  rX  Š  r¨  r   c                 ó   € R# rP  rS  r¶  s   &&r   rW  rX  ‹  r¨  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Œ  rg  r   c                 ó   € R# rP  rS  )r  r	  Úcs   &&&r   rW  rX  �  r½  r   c                 ó   € R# rP  rS  )r#   s   &r   rW  rX  Ž  r¨  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  �  r½  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  �  r\  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ‘  r½  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ’  r\  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  “  r½  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ”  r\  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  •  r½  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  –  r½  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  —  r½  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ˜  r½  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  ™  ri  r   c                 ó   € R# rP  rS  )rU  ÚsomeÚ
compute_uvrV  s   &&&&r   rW  rX  š  rS  r   c                 ó   € R# rP  rS  )rU  r-  rÓ  ÚMs   &&&&r   rW  rX  ›  rb  r   c                 ó   € R# rP  rS  )rU  Úfull_matricesrV  s   &&&r   rW  rX  œ  r¶  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  �  r¹  r   c                 ó   € R# rP  rS  ©rU  Údim0rª  s   &&&r   rW  rX  ž  rq  r   c                 ó   € R# rP  rS  )rU  Úaxis0Úaxis1s   &&&r   rW  rX  Ÿ  r·  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX     rk  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ¡  rm  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ¢  rm  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  £  rm  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ¤  rm  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  ¥  r‹  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  ¦  r‹  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  §  r‹  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  ¨  r‹  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ©  rk  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ª  rW  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  «  r¼  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ¬  rW  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ­  rY  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ®  rd  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ¯  rW  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  °  rY  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ±  rY  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ²  rb  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  ³  rD  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ´  rk  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  µ  rS  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  ¶  r‡  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ·  r˜  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ¸  r¼  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ¹  r˜  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  º  r¼  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  »  r‡  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  ¼  r‡  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ½  rY  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ¾  ri  r   c                 ó   € R# rP  rS  rÝ  s   &&&r   rW  rX  ¿  r  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  À  rY  r   c                 ó   € R# rP  rS  r£  s   &&&&r   rW  rX  Á  rM  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Â  r'  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ã  r'  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ä  r'  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Å  r'  r   c                 ó   € R# rP  rS  rD  s   &&r   rW  rX  Æ  r+  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ç  rW  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  È  rY  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  É  rÿ  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ê  r¼  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ë  rY  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ì  rb  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Í  rb  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  Î  rÆ  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  Ï  rÆ  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  Ð  rÆ  r   c                 ó   € R# rP  rS  r	  s   &&&r   rW  rX  Ñ  rÆ  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ò  rW  r   c                 ó   € R# rP  rS  rÝ  s   &&&r   rW  rX  Ó  r_  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ô  r  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  Õ  r_  r   c                 ó   € R# rP  rS  ro  s   &&&r   rW  rX  Ö  rÿ  r   c                 ó   € R# rP  rS  )r  rp  rV  s   &&&r   rW  rX  ×  r  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  Ø  s   € ™rr   c                 ó   € R# rP  rS  )rU  rA  s   &&r   rW  rX  Ù  r¼  r   c                 ó   € R# rP  rS  )rU  rŠ  r“  rV  s   &&&&r   rW  rX  Ú  r‹  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Û  rY  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  Ü  rd  r   c                 ó   € R# rP  rS  )r  Úinds   &&r   rW  rX  Ý  rÛ  r   c                 ó   € R# rP  rS  )r  r	  r+  s   &&&r   rW  rX  Þ  r  r   c                 ó   € R# rP  rS  )r  r	  r+  rV  s   &&&&r   rW  rX  ß  r'  r   c                 ó   € R# rP  rS  )rU  rÀ  r“  s   &&&r   rW  rX  à  r¶  r   c                 ó   € R# rP  rS  r  s   &&&&r   rW  rX  á  r‡  r   c                 ó   € R# rP  rS  r*  s   &&r   rW  rX  â  r˜  r   c                 ó   € R# rP  rS  )rU  r¾  r“  r¬  rV  s   &&&&&r   rW  rX  ã  r‹  r   c                 ó   € R# rP  rS  rf  s   &r   rW  rX  ä  rO  r   c                 ó   € R# rP  rS  rÐ  s   &&&r   rW  rX  å  rç  r   c                 ó   € R# rP  rS  r’  s   &&&r   rW  rX  æ  rm  r   c                 ó   € R# rP  rS  r’  s   &&&r   rW  rX  ç  rç  r   c                 ó   € R# rP  rS  )rU  ró  r2  r(  Úunitriangulars   &&&&&r   rW  rX  è  rÙ  r   c                 ó   € R# rP  rS  )rU  rÅ  r2  r  r  s   &&&&&r   rW  rX  é  r   r   c                 ó   € R# rP  rS  rž  s   &&&r   rW  rX  ê  r'  r   c
                 ó   € R# rP  rS  r  s
   &&&&&&&&&&r   rW  rX  ì  r|  r   c                 ó   € R# rP  rS  rž  s   &&&r   rW  rX  î  r'  r   c                 ó   € R# rP  rS  rÝ  s   &&r   rW  rX  ï  r[  r   c                 ó   € R# rP  rS  rT  s   &&r   rW  rX  ð  rk  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  ñ  rY  r   c                 ó   € R# rP  rS  )rU  r“  Úsizess   &&&r   rW  rX  ò  rç  r   c                 ó   € R# rP  rS  )rU  ÚsortedÚreturn_inverseÚreturn_countsr“  s   &&&&&r   rW  rX  ó  rì  r   c                 ó   € R# rP  rS  )rU  r+  r,  r“  s   &&&&r   rW  rX  ô  r/  r   c                 ó   € R# rP  rS  )rŠ  rr  s   &&r   rW  rX  õ  r·  r   c                 ó   € R# rP  rS  r=  s   &&&r   rW  rX  ö  r  r   c                 ó   € R# rP  rS  r£  s   &&&r   rW  rX  ÷  r~  r   c                 ó   € R# rP  rS  r£  s   &&&r   rW  rX  ø  r«  r   c                 ó   € R# rP  rS  rŒ  s   &&&r   rW  rX  ù  r¹  r   c                 ó   € R# rP  rS  )r”  rT  s   &&r   rW  rX  ú  rm  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  û  rY  r   c                 ó   € R# rP  rS  r’  s   &&r   rW  rX  ü  r[  r   c                 ó   € R# rP  rS  r¿  s   &&r   rW  rX  ý  r  r   c                 ó   € R# rP  rS  rH  s   &&r   rW  rX  þ  r[  r   c                 ó   € R# rP  rS  )Ú	conditionr”  r“  s   &&&r   rW  rX  ÿ  r  r   c                 ó   € R# rP  rS  )rÉ  r   r  rÊ  s   &&&&r   rW  rX     r  r   c                 ó   € R# rP  rS  )rU  Úinput_scaleÚinput_zero_pointÚ	prepackedÚ	out_scaleÚout_zero_pointÚout_channels   &&&&&&&r   rW  rX    rê  r   c                 ó   € R# rP  rS  r×  s   &&&&&r   rW  rX    rÙ  r   c                 ó   € R# rP  rS  )r  Úlevels   &&r   rW  rX    rç  r   c                 ó   € R# rP  rS  )ÚprimalÚtangentrD  s   &&&r   rW  rX    r_  r   c                 ó   € R# rP  rS  ©r  s   &r   rW  rX    ri  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    rÛ  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  	  r½  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  
  rY  r   c                 ó   € R# rP  rS  )r  r�  r  r°  s   &&&&r   rW  rX    rx  r   c                 ó   € R# rP  rS  r  s   &&r   rW  rX    rÿ  r   c                 ó   € R# rP  rS  )r  r¢  rª  r«  s   &&&&r   rW  rX    rŸ  r   Úimplicitc                ó   € R# rP  rS  )r  r�  rP  s   &&$r   rW  rX    rì  r   c                 ó   € R# rP  rS  )r  r“  rF  rµ  s   &&&&r   rW  rX    rÿ  r   c                 ó   € R# rP  rS  )r  r+  s   &&r   rW  rX    rm  r   c                 ó   € R# rP  rS  ©r  r�  r  s   &&&r   rW  rX    r_  r   c                 ó   € R# rP  rS  )r  r“  rA  s   &&&r   rW  rX    r·  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    r˜  r   c                 ó   € R# rP  rS  )r  r“  rF  rÎ  r�  s   &&&&&r   rW  rX    r5  r   c                 ó   € R# rP  rS  )r  Ú
split_sizer“  s   &&&r   rW  rX    r  r   c                 ó   € R# rP  rS  )r  Úsplit_sizesr“  s   &&&r   rW  rX    r¶  r   c                 ó   € R# rP  rS  r+  s   &&r   rW  rX    ri  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    rO  r   c                 ó   € R# rP  rS  )r  rÑ  rª  s   &&&r   rW  rX    r  r   c                 ó   € R# rP  rS  r+  s   &&r   rW  rX    r[  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    rW  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    r¼  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    r¼  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    r˜  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    ri  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX     rk  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  !  ri  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  "  rk  r   c                 ó   € R# rP  rS  r+  s   &&r   rW  rX  #  rm  r   c                 ó   € R# rP  rS  ©r  re   s   &&r   rW  rX  $  rk  r   c                 ó   € R# rP  rS  ©r  Ú	dimensionr�  r�  s   &&&&r   rW  rX  %  rb  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  &  r½  r   c                 ó   € R# rP  rS  ©r  rp  s   &&r   rW  rX  '  rÛ  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  (  rq  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  )  rq  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  *  r[  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  +  rÛ  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  ,  rÛ  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  -  rm  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  .  r[  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  /  r[  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  0  rm  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  1  r[  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  2  r[  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  3  rd  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  4  rY  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  5  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  6  r½  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  7  r¼  r   c                 ó   € R# rP  rS  rk  s   &&r   rW  rX  8  ri  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  9  r\  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  :  rÛ  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ;  rg  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  <  r˜  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  =  rd  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  >  rk  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  ?  rk  r   c                 ó   € R# rP  rS  )r  Úarrays   &&r   rW  rX  @  rç  r   c                 ó   € R# rP  rS  )r  Úidxs   &&r   rW  rX  A  ri  r   c                 ó   € R# rP  rS  )r  Úmemos   &&r   rW  rX  B  r[  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  C  rg  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  D  r\  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  E  r½  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  F  rg  r   c                 ó   € R# rP  rS  )r  Úformat_specs   &&r   rW  rX  G  r+  r   c                 ó   € R# rP  rS  )r  Úprotos   &&r   rW  rX  H  rq  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  I  rW  r   Útensor_contentsc                ó   € R# rP  rS  )r  r™  s   &$r   rW  rX  J  rb  r   c                 ó   € R# rP  rS  )r  r¾  r!  s   &&&r   rW  rX  K  rm  r   c                 ó   € R# rP  rS  )r  Úds   &&r   rW  rX  L  rk  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  M  r½  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  N  r½  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  O  r˜  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  P  r˜  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  Q  r+  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  R  rì  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  S  rY  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  T  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  U  rW  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  V  rY  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  W  ri  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  X  rk  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  Y  r[  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  Z  ri  r   c                 ó   € R# rP  rS  )r  Úcuda_enabledÚcpu_enabledÚ
cuda_dtypeÚ	cpu_dtypes   &&&&&r   rW  rX  [  s   € Ñnpr   c                 ó   € R# rP  rS  )r  r­  r®  s   &&&r   rW  rX  \  r§  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ]  rì  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ^  rW  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  _  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  `  rY  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  a  rk  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  b  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  c  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  d  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  e  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  f  rk  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  g  rq  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  h  rk  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  i  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  j  rk  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  k  rm  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  l  rk  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  m  rm  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  n  rq  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  o  rm  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  p  rç  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  q  rm  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  r  ri  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  s  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  t  rW  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  u  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  v  rW  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  w  rm  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  x  rç  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  y  rY  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  z  ri  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  {  rW  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  |  rW  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  }  r_  r   c                 ó   € R# rP  rS  )r  re   Únon_blockingr$   s   &&&,r   rW  rX  ~  r5  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    rO  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  €  rO  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  �  r\  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ‚  r\  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ƒ  ó   € ¡"r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  „  rW  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  …  r¼  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  †  rW  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ‡  r¼  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ˆ  rg  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ‰  rg  r   c                 ó   € R# rP  rS  )r  Úcallables   &&r   rW  rX  Š  ri  r   c                 ó   € R# rP  rS  rU  s   &&&r   rW  rX  ‹  r¹  r   c                 ó   € R# rP  rS  rU  s   &&&r   rW  rX  Œ  r  r   c                 ó   € R# rP  rS  )r  ÚgradientÚretain_graphÚcreate_graphra  s   &&&&&r   rW  rX  �  s   € Ñikr   c                 ó   € R# rP  rS  ©r  rl   s   &&r   rW  rX  Ž  r  r   c                 ó   € R# rP  rS  rê  s   &&r   rW  rX  �  rS  r   c                 ó   € R# rP  rS  rê  s   &&r   rW  rX  �  rS  r   c                 ó   € R# rP  rS  rê  s   &&r   rW  rX  ‘  rS  r   rë  c                ó   € R# rP  rS  )r  ÚmedianÚsigmarë  s   &&&$r   rW  rX  ’  r  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  “  r\  r   c                 ó   € R# rP  rS  )r  Ú	coalesceds   &&r   rW  rX  ”  r·  r   c                 ó   € R# rP  rS  rê  s   &&r   rW  rX  •  rx  r   c   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4  s   &&r   rW  rX  Ï  r¶  r   c                 ó   € R# rP  rS  r  s   &*r   rW  rX  Ð  rY  r   c                 ó   € R# rP  rS  )r  rØ  s   &&r   rW  rX  Ñ  rD  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  Ò  rm  r   c                 ó   € R# rP  rS  r  s   &*r   rW  rX  Ó  rY  r   c                 ó   € R# rP  rS  r  s   &&r   rW  rX  Ô  rY  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  Õ  ri  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  Ö  r¹  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ×  r¼  r   c                 ó   € R# rP  rS  )r  r‡  r°  r�  r  s   &&&&&r   rW  rX  Ø  r  r   c                 ó   € R# rP  rS  )r  r®  r“  rA  s   &&&&r   rW  rX  Ù  rñ  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  Ú  rY  r   c                 ó   € R# rP  rS  rê  s   &&r   rW  rX  Û  r‡  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  Ü  rÚ  r   c                 ó   € R# rP  rS  )r  r®  r“  rF  rÎ  r�  s   &&&&&&r   rW  rX  Ý  r«  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  Þ  r˜  r   c                 ó   € R# rP  rS  )r  rù  s   &&r   rW  rX  ß  rm  r   c                 ó   € R# rP  rS  )r  rù  Úaccumulate_matchess   &&&r   rW  rX  à  r«  r   c                 ó   € R# rP  rS  ©r  Úsize1Úsize2Ú	dense_dims   &&&&r   rW  rX  á  r3  r   c                 ó   € R# rP  rS  rJ  s   &&&&r   rW  rX  â  rx  r   c                 ó   € R# rP  rS  )r  r‚  rƒ  rw  rv  rV  s   &&&&&&r   rW  rX  ã  rM  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ä  rg  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  å  rk  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  æ  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ç  rW  r   c                 ó   € R# rP  rS  r  s   &&r   rW  rX  è  rm  r   c                 ó   € R# rP  rS  )r  Úrepss   &*r   rW  rX  é  r¼  r   c                 ó   € R# rP  rS  )r  re   rÔ  Úcopyrl   s   &&&&&r   rW  rX  ê  s   € Ñlnr   Úmasked_gradc                ó   € R# rP  rS  ©r  re   rY  s   &&$r   rW  rX  ë  rS  r   c                 ó   € R# rP  rS  r[  s   &&&r   rW  rX  ì  r3  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  í  r½  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  î  r¨  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ï  r½  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  ð  rd  r   c                 ó   € R# rP  rS  rm  s   &&&&r   rW  rX  ñ  re  r   c                 ó   € R# rP  rS  )r  r/  r0  s   &&&r   rW  rX  ò  r+  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ó  r¨  r   c                 ó   € R# rP  rS  )r  rr  s   &&r   rW  rX  ô  r¼  r   c                 ó   € R# rP  rS  rq  s   &&r   rW  rX  õ  rd  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ö  rO  r   c                 ó   € R# rP  rS  )r  r2  Úmax_versionÚ	dl_devicerX  Ú	read_onlys   &&&&&&r   rW  rX  ÷  rÝ  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX  ø  rm  r   c                 ó   € R# rP  rS  )r  r  r	  s   &&&r   rW  rX  ù  r¼  r   c                 ó   € R# rP  rS  )r  r	  rü   Údrivers   &&&&r   rW  rX  ú  rŸ  r   c                 ó   € R# rP  rS  )r  rd   rÔ  r$   s   &&&,r   rW  rX    rŸ  r   c                 ó   € R# rP  rS  rI  s   &r   rW  rX    r™  r   Úis_Ú_Ú__Ú__iÚ__rÚbitwise_c                 ó   € R# rP  rS  )r_   r®  rW  Úasync_opÚ	group_srcs   &&&&&r   rW  rX  *  r/  r   c                 ó   € R# rP  rS  )r_   ÚoprW  rx  s   &&&&r   rW  rX  +  r«  r   c                 ó   € R# rP  rS  )r_   Údstr{  rW  rx  Ú	group_dsts   &&&&&&r   rW  rX  ,  rZ  r   c                 ó   € R# rP  rS  )r¾  r{  rW  rx  s   &&&&r   rW  rX  -  r£  r   c                 ó   € R# rP  rS  )Útensor_listr_   rW  rx  s   &&&&r   rW  rX  .  r  r   c                 ó   € R# rP  rS  )Úoutput_tensorÚinput_tensorrW  rx  s   &&&&r   rW  rX  /  rû  r   c                 ó   € R# rP  rS  )Úoutput_tensor_listsÚinput_tensor_listrW  rx  s   &&&&r   rW  rX  0  rÆ  r   c                 ó   € R# rP  rS  )r_   Úgather_listr}  rW  rx  r~  s   &&&&&&r   rW  rX  1  râ  r   c                 ó   € R# rP  rS  )r_   Úscatter_listr®  rW  rx  ry  s   &&&&&&r   rW  rX  2  rÆ  r   c                 ó   € R# rP  rS  )rù  Ú
input_listr{  rW  rx  s   &&&&&r   rW  rX  3  r/  r   c                 ó   € R# rP  rS  )rù  rU  r{  rW  rx  s   &&&&&r   rW  rX  4  rx  r   c                 ó   € R# rP  rS  )rù  rU  Úoutput_split_sizesÚinput_split_sizesrW  rx  s   &&&&&&r   rW  rX  5  s	   € ñ  LNr   c                 ó   € R# rP  rS  )Úoutput_tensor_listr‡  rW  rx  s   &&&&r   rW  rX  6  rZ  r   c                 ó   € R# rP  rS  ©r_   r}  rW  Útagr~  s   &&&&&r   rW  rX  7  rƒ  r   c                 ó   € R# rP  rS  ©r_   r®  rW  r–  ry  s   &&&&&r   rW  rX  8  rƒ  r   c                 ó   € R# rP  rS  r•  s   &&&&&r   rW  rX  9  r;  r   c                 ó   € R# rP  rS  r˜  s   &&&&&r   rW  rX  :  r;  r   ©N)rQ  rQ  N)rQ  N)çñhãˆµøä>ç:Œ0âŽyE>F)F)NFNrR  )rR  F)Né    FT)NN)NNNrÔ  N)Nrž  )FFN©rž  N)ç       @Ú#use_mm_for_euclid_dist_if_necessary)r"  F)FN©rž  )NNN)rQ  NN)r   F)NrQ  rž  rQ  rQ  )NrQ  rž  rž  rQ  rQ  )rž  NNrÔ  )rQ  r�  )rR  N)rž  rÔ  F)NrR  )rQ  rR  NNN)rž  rž  rQ  )rž  éþÿÿÿrR  )r   )ÚLN)NNr   FF)Nr   FrÔ  FNN)NNNF)FF)NrR  N)N©r£  rR  N)rž  rR  )TT)NF)NNrQ  )NNrœ  T)ç      à?)NFrž  N)r"  NNrÔ  )éd   rž  rž  N)r§  NNNFN)NNF)T)NNNTFNNF)NNrÔ  F)NNNNNNNNNNNNN)TFN)TN)Nrž  rQ  F)Nrž  rQ  FF)NFNN)rR  FN)ç        NNN)NNrœ  )Nrœ  )r   NFN)r¦  FF)Nrž  FTN)NNFçš™™™™™¹?rœ  )NNNrÔ  )NNéœÿÿÿNrÔ  r¨  )NNrÔ  Nr¨  N)r¦  TF)	NNr   FrÔ  FNFN)rQ  rž  rQ  )NNFN)Fç�íµ ÷Æ°>rÔ  )Únone)rR  )Úbilinearrµ   N)rQ  Fg»½×Ùß|Û=rR  )g      ð¿r"  F)NNNNTr©  rœ  )NNÚnearestNNF)NNrÔ  N)g{®Gáz„?F)g-Cëâ6?g      è?r"  )Nr7   N)Nrž  N)TNTNFNNNNNNF)rQ  r"  NNNrÔ  )NNrÔ  )NNrª  NrÔ  )r   rQ  gê-�™—q=N)rÍ   rž  )r   r«  F)TFNr�  NrÔ  )Nr«  )g      À?gUUUUUUÕ?FF)Nr¨  )NNrÔ  r"  )rÔ  r"  N)rQ  é   )r"  r   r«  FNNrÔ  )r¨  r"  N)rž  Úfan_inÚ
leaky_reluN)ÚfroNFNN)NNFNN)r   NFNN)r²  r¥  FNN)r   rž  )TF)NTr   )çVçž¯Ò<)r³  F)ÚreducedN)NFÚlinearN©rQ  )rS  r¢  r¶  F)rS  )rž  rž  )rQ  rQ  F)rS  )rž  rž  rž  )rQ  rQ  rQ  F©rž  rQ  )rQ  r·  )rB  NNNrž  F)rž  NNrQ  )	NNNTÚreflectFTNN)TTN)é   r   N)r   N)TFF)TFFN)rQ  r   )Nrž  NN)NNNNF)NNNFN)NNrž  N(–  r9   r:   rZ  ÚabsoluteÚadaptive_avg_pool1dÚadaptive_max_pool1dÚacosr  ÚarccosÚacoshÚarccoshrC  ÚaddbmmÚaddcdivÚaddcmulÚaddmmÚaddmvÚaddrÚaffine_grid_generatorÚallÚallcloseÚalpha_dropoutÚamaxÚaminÚaminmaxÚangleÚanyÚargmaxÚargminÚargsortÚasinÚ_assert_asyncÚarcsinÚasinhÚarcsinhÚatanÚarctanÚatan2Úarctan2ÚatanhÚarctanhÚ
atleast_1dÚ
atleast_2dÚ
atleast_3dÚ
avg_pool1dÚbaddbmmÚ
batch_normÚbatch_norm_backward_elemtÚbatch_norm_backward_reduceÚbatch_norm_elemtÚbatch_norm_gather_statsÚ#batch_norm_gather_stats_with_countsÚbatch_norm_statsÚbatch_norm_update_statsÚ	bernoullir­  Ú binary_cross_entropy_with_logitsÚbincountÚbinomialÚbitwise_andÚbitwise_notÚ
bitwise_orÚbitwise_xorÚbitwise_left_shiftÚbitwise_right_shiftÚ
block_diagÚbmmÚbroadcast_tensorsÚbroadcast_toÚ	bucketizeÚcartesian_prodÚcatÚconcatÚconcatenateÚcdistÚceilÚceluÚchain_matmulÚchannel_shuffleÚlinalgr  Úcholesky_exÚcholesky_inverseÚcholesky_solveÚchoose_qparams_optimizedÚchunkÚclampÚclipÚ	clamp_minÚ	clamp_maxÚcolumn_stackÚcovÚcloneÚcombinationsÚcomplexÚcopysignÚpolarrü   ÚconjÚconj_physicalÚresolve_conjÚresolve_negÚconstant_pad_ndÚconv1dÚconv2dÚconv3dÚconvolutionÚconv_tbcÚconv_transpose1dÚconv_transpose2dÚconv_transpose3dÚcorrcoefÚcosÚcosine_embedding_lossÚcoshÚcosine_similarityÚcount_nonzeroÚcrossÚctc_lossÚcummaxÚcumminÚcumprodÚcumsumÚcumulative_trapezoidÚlogcumsumexpÚdeg2radÚ
dequantizeÚdetÚdetachÚdiagÚ
diag_embedÚdiagflatÚdiffrŸ  Údiagonal_scatterÚas_strided_scatterÚdigammaÚdistÚdivÚdivideÚdotra  ÚdsmmÚhsmmÚdsplitÚdstackr  ÚeigvalsÚeighÚeigvalshÚeinsumÚ	embeddingÚembedding_bagÚ
empty_likeÚeqÚequalÚerfÚerfcÚerfinvÚexpÚexp2Úexpm1Ú fake_quantize_per_channel_affineÚfake_quantize_per_tensor_affineÚfused_moving_avg_obs_fake_quantÚfbgemm_linear_fp16_weightÚ)fbgemm_linear_fp16_weight_fp32_activationÚfbgemm_linear_int8_weightÚ)fbgemm_linear_int8_weight_fp32_activationÚfbgemm_linear_quantize_weightÚfbgemm_pack_gemm_matrix_fp16Úfbgemm_pack_quantized_matrixÚfeature_alpha_dropoutÚfeature_dropoutr‹   ÚifftÚrfftÚirfftÚhfftÚihfftÚhfft2Úihfft2ÚhfftnÚihfftnÚfftnÚifftnÚrfftnÚirfftnÚfft2Úifft2Úrfft2Úirfft2ÚfftshiftÚ	ifftshiftÚfixÚflattenÚflipÚfliplrÚflipudÚfrobenius_normÚfloorÚfloor_divideÚfloat_powerÚfmodÚfracÚfrexpÚ	full_likeÚstridedÚ_functional_assert_asyncÚ	lu_unpackÚgatherÚgcdÚgeÚ
get_deviceÚgreater_equalÚgeqrfÚi0ÚinnerÚouterÚgerræ  Úgrid_samplerÚgrid_sampler_2dÚgrid_sampler_3dÚ
group_normÚgruÚgru_cellÚgtÚgreaterÚ
hardshrinkÚhash_tensorÚ	heavisideÚhinge_embedding_lossÚhistcÚ	histogramÚhistogramddÚhouseholder_productÚhspmmÚhsplitÚhstackÚhypotÚigammaÚigammacrV  Ú	index_addÚ
index_copyÚ	index_putÚindex_selectÚ
index_fillÚindex_reduceÚisfiniteÚisinÚisinfÚisrealÚisposinfÚisneginfÚinstance_normÚint_reprÚinverseÚinvÚinv_exÚ
is_complexÚis_conjÚis_negÚis_distributedÚis_inferenceÚis_floating_pointÚ
is_nonzeroÚis_same_sizeÚ	is_signedÚiscloseÚisnanÚistftÚkl_divÚkronÚkthvalueÚldl_factor_exÚ
ldl_factorÚ	ldl_solveÚ
layer_normÚlcmÚldexpÚleÚ
less_equalÚlerpÚlgammaÚlobpcgÚlogÚlog_softmaxÚlog10Úlog1pÚlog2Ú	logaddexpÚ
logaddexp2ÚlogdetÚxlogyÚlogical_andÚlogical_notÚ
logical_orÚlogical_xorÚlogitÚ	logsumexpÚlstmÚ	lstm_cellÚltÚlessÚluÚlu_solveÚmargin_ranking_lossÚmasked_fillÚmasked_scatterÚmasked_selectÚmatmulÚ	lu_factorÚlu_factor_exÚmatrix_powerÚmatrix_rankÚ	multi_dotÚ
matrix_expÚmatrix_sqrthrB  ÚmaximumÚfmaxÚ
max_pool1dÚ
max_pool2dÚ
max_pool3dÚmax_pool1d_with_indicesrÔ  Únanmeanrï  Ú	nanmedianÚmeshgridrA  ÚminimumÚfminÚmiopen_batch_normÚmiopen_convolutionÚmiopen_convolution_add_reluÚmiopen_convolution_reluÚmiopen_convolution_transposeÚmiopen_depthwise_convolutionÚ
miopen_rnnÚmmrÔ  ÚmovedimÚmoveaxisÚmsortÚmulÚmultiplyÚmultinomialÚmvÚmvlgammaÚnarrowÚ
nan_to_numÚnative_batch_normÚ_native_batch_norm_legitÚnative_dropoutÚnative_layer_normÚ_fused_rms_normÚnative_group_normÚnative_normÚnative_channel_shuffleÚneÚ	not_equalÚnegr
  Ú	nextafterr¸   r¹   Úadaptive_avg_pool2dÚadaptive_avg_pool3dÚ adaptive_max_pool1d_with_indicesÚadaptive_max_pool2dÚ adaptive_max_pool2d_with_indicesÚadaptive_max_pool3dÚ adaptive_max_pool3d_with_indicesÚaffine_gridÚ
avg_pool2dÚ
avg_pool3dÚbinary_cross_entropyÚcross_entropyÚlinear_cross_entropyÚ	dropout1dÚ	dropout2dÚ	dropout3dÚeluÚfoldÚfractional_max_pool2dÚ"fractional_max_pool2d_with_indicesÚfractional_max_pool3dÚ"fractional_max_pool3d_with_indicesÚgaussian_nll_lossÚgeluÚgluÚgrid_sampleÚgumbel_softmaxÚhardtanhÚinterpolateÚl1_lossr±  rµ  Úlocal_response_normÚ
logsigmoidÚ	lp_pool1dÚ	lp_pool2dÚ	lp_pool3dÚmax_pool2d_with_indicesÚmax_pool3d_with_indicesÚmax_unpool1dÚmax_unpool2dÚmax_unpool3dÚmse_lossÚmulti_head_attention_forwardÚmulti_margin_lossÚmultilabel_margin_lossÚmultilabel_soft_margin_lossÚnll_lossÚ	normalizeÚone_hotrd  Úpairwise_distanceÚpoisson_nll_lossÚpreluÚreluÚrelu6Úrms_normÚrreluÚseluÚsiluÚmishÚscaled_dot_product_attentionÚsmooth_l1_lossÚ
huber_lossÚsoft_margin_lossÚsoftmaxÚsoftminÚsoftplusÚ
softshrinkÚsoftsignÚ
tanhshrinkr   Útriplet_margin_lossÚ!triplet_margin_with_distance_lossÚunfoldrÊ   Úuniform_Únormal_Ú	constant_Úkaiming_uniform_ÚnonzeroÚnonzero_staticÚargwherer  Úvector_normÚmatrix_normÚnorm_except_dimÚnuclear_normr7  ÚorgqrÚormqrÚpermuteÚpca_lowrankÚpdistÚpinverseÚpinvÚpixel_shuffleÚpixel_unshuffleÚpoissonÚ	polygammar	  Ú	ones_liker"  ÚprodÚputÚq_per_channel_axisÚq_per_channel_scalesÚq_per_channel_zero_pointsÚq_scaleÚq_zero_pointr  ÚquantileÚnanquantileÚquantize_per_channelÚquantize_per_tensorÚquantize_per_tensor_dynamicÚquantized_batch_normÚquantized_gru_cellÚquantized_lstm_cellÚquantized_max_pool1dÚquantized_max_pool2dÚquantized_max_pool3dÚquantized_rnn_relu_cellÚquantized_rnn_tanh_cellÚrad2degÚravelrU  ÚvdotÚvecdotÚview_as_realÚview_as_complexÚ
reciprocalÚ	remainderÚrenormÚrepeat_interleaveÚreshapeÚrnn_reluÚrnn_relu_cellÚrnn_tanhÚrnn_tanh_cellÚrollÚrot90ÚroundÚ	row_stackÚ_rowwise_pruneÚrsqrtÚrsubÚsaddmmÚscatterÚscatter_addÚscatter_reduceÚsearchsortedÚ_segment_reduceÚselectÚselect_scatterÚslice_inverseÚslice_scatterrÅ   ÚsignÚsignbitÚsgnÚsinÚsincÚsinhÚslogdetÚsmmÚspmmr  Úsolve_exÚsortÚsplitÚsplit_with_sizesÚsqrtÚsquareÚsqueezeÚsspaddmmÚstackr  Ústd_meanÚstftÚsubÚsubtractÚsumÚ	sym_floatÚsym_intÚsym_maxÚsym_minÚsym_notÚsym_iteÚsym_sumÚ	_sym_sqrtÚ_sym_cosÚ	_sym_coshÚ_sym_sinÚ	_sym_sinhÚ_sym_tanÚ	_sym_tanhÚ	_sym_asinÚ	_sym_acosÚ	_sym_atanÚnansumÚsvdÚsvd_lowrankÚsvdvalsÚswapaxesÚswapdimsÚspecialÚairy_aiÚ	bessel_j0Ú	bessel_j1Ú	bessel_y0Ú	bessel_y1Úchebyshev_polynomial_tÚchebyshev_polynomial_uÚchebyshev_polynomial_vÚchebyshev_polynomial_wÚentrÚerfcxÚexpitÚgammaincÚ	gammainccÚgammalnÚhermite_polynomial_hÚhermite_polynomial_heÚi0eÚi1Úi1eÚlaguerre_polynomial_lÚlegendre_polynomial_pÚlog_ndtrÚmodified_bessel_i0Úmodified_bessel_i1Úmodified_bessel_k0Úmodified_bessel_k1ÚmultigammalnÚndtrÚndtriÚpsiÚscaled_modified_bessel_k0Úscaled_modified_bessel_k1Úshifted_chebyshev_polynomial_tÚshifted_chebyshev_polynomial_uÚshifted_chebyshev_polynomial_vÚshifted_chebyshev_polynomial_wÚspherical_bessel_j0Úxlog1pyÚzetaÚtÚtakeÚtake_along_dimÚtanrÇ   Ú	tensorinvÚtensorsolveÚ	tensordotÚtensor_splitÚtileÚtopkÚtracer(  ÚtrapzÚ	trapezoidÚtriangular_solveÚsolve_triangularÚtrilÚtriuÚtrue_divideÚtruncÚunbindr   ÚuniqueÚunique_consecutiveÚunravel_indexÚunsafe_chunkÚunsafe_splitÚunsafe_split_with_sizesÚ	unsqueezer´   r•  Úvar_meanÚvsplitÚvstackÚwhereÚ_wrapped_linear_prepackÚ#_wrapped_quantized_linear_prepackedÚ
zeros_likeÚ_fw_primal_copyÚ_make_dual_copyÚview_as_real_copyÚview_as_complex_copyÚ
_conj_copyÚ_neg_view_copyÚas_strided_copyÚ_sparse_broadcast_to_copyÚdiagonal_copyÚexpand_copyÚnarrow_copyÚpermute_copyÚ_reshape_alias_copyÚselect_copyÚdetach_copyÚ
slice_copyÚ
split_copyÚsplit_with_sizes_copyÚsqueeze_copyÚt_copyÚtranspose_copyÚunsqueeze_copyÚ_indices_copyÚ_values_copyÚindices_copyÚvalues_copyÚcrow_indices_copyÚcol_indices_copyÚccol_indices_copyÚrow_indices_copyÚunbind_copyÚ	view_copyÚunfold_copyÚ
alias_copyÚ__floordiv__Ú__rfloordiv__Ú__ifloordiv__Ú__truediv__Ú__rtruediv__Ú__itruediv__Ú
__lshift__Ú__rlshift__Ú__ilshift__Ú
__rshift__Ú__rrshift__Ú__irshift__Ú__and__Ú__or__Ú__xor__Ú	__float__Ú__complex__Ú	__array__Ú__bool__Ú__contains__Ú__neg__Ú
__invert__Ú__mod__Ú__rmod__Ú__imod__Ú__array_wrap__Ú__getitem__Ú__deepcopy__Ú__int__Ú__long__Ú	__index__Ú__len__Ú
__format__Ú__reduce_ex__Ú__reversed__Ú__repr__Ú__setitem__Ú__setstate__ÚTr4  ÚHÚmTÚmHÚ_backward_hooksÚ_post_accumulate_grad_hooksrH  Ú_cdatarI  rJ  Ú_grad_fnÚgrad_fnÚ
grad_dtypeÚ_versionÚ_autocast_to_reduced_precisionÚ_autocast_to_full_precisionÚ#_clear_non_serializable_cached_datarí  rd   re   Úis_cudaÚis_cpuÚis_xlaÚis_xpuÚis_ipuÚis_leafÚretains_gradÚis_metaÚis_mpsÚis_mtiaÚ	is_nestedÚis_maiaÚ	is_mkldnnÚis_quantizedÚ	is_sparseÚis_sparse_csrÚ	is_vulkanÚitemsizers   ÚnameÚnbytesÚndimÚ	output_nrrØ  rr  ÚvolatileÚ__cuda_array_interface__ÚtypeÚ_dimIÚ_dimVÚ_indicesÚ_is_viewÚ_nnzÚcrow_indicesÚcol_indicesÚccol_indicesÚrow_indicesÚ_valuesÚapply_ru   Úas_strided_ÚbackwardÚbfloat16Úpreserve_formatÚboolÚbyteÚcharÚcauchy_ÚcoalesceÚ_coalesced_Ú
contiguousÚcontiguous_formatÚcopy_ÚcpuÚcudaÚmtiaÚxpuÚipuÚconst_data_ptrÚdata_ptrrM  r“  Ú	dim_orderÚdoubleÚcdoubleÚelement_sizeÚexpandÚ	expand_asÚexponential_Úfill_Úfill_diagonal_ÚfloatÚcfloatÚ
geometric_ÚhalfÚchalfrŠ  ÚintÚis_coalescedÚis_contiguousÚ	is_pinnedÚ	is_set_toÚ	is_sharedÚitemÚlog_normal_ÚlongÚmap_Úmap2_Úmodule_loadÚ
ndimensionÚnelementÚ_nested_tensor_sizeÚ_nested_tensor_storage_offsetsÚ_nested_tensor_stridesÚnumpyÚ
pin_memoryÚput_rm   Úrandom_Úrecord_streamÚregister_hookÚ"register_post_accumulate_grad_hookÚrepeatÚrequires_grad_Ú
reshape_asÚresizeÚresize_Ú	resize_asÚresize_as_sparse_Úretain_gradÚset_Úshare_memory_Úshortr�  Ú
sparse_dimÚsparse_maskÚ_sparse_mask_projectionÚsparse_resize_Úsparse_resize_and_clear_ÚstorageÚuntyped_storager°  Ústorage_typeÚsum_to_sizer0  Úto_denseÚ	_to_denseÚ	to_sparseÚtolistÚ	to_mkldnnÚtype_asrr  ÚviewÚview_asÚzero_Ú
__dlpack__Ú__dlpack_device__rA  r  ÚutilsÚbackend_registrationÚ_privateuse1_backend_nameÚhasattrÚgetattrrE  ÚitemsÚ__name__Ú
startswithÚlenÚextendrâ  ÚupdateÚdistributedÚis_availableÚtorch.distributedÚ	broadcastÚ
all_reducerö  Úall_reduce_coalescedÚ
all_gatherÚall_gather_singleÚall_gather_coalescedÚreduce_scatterÚreduce_scatter_singleÚall_to_all_singleÚ
all_to_allÚisendÚirecvÚsendÚrecv)r:   ÚretÚprivateuse1_backend_nameÚret2Úignoredr¾  r!  rë  Úsubnamerˆ
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Ô.ðMx%ôN 	�ŠÔ0ðOx%ôP 	�	Š	Ô-ðQx%ôR 	�
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Ô.ðSx%ôT 	�ŠÔ/ðUx%ôV 	×.Ò.Ñ0oðWx%ôX 	×-Ò-Ñ/hðYx%ôZ 	×-Ò-ô Cð]x%ô` 	×'Ò'Ñ)Vðax%ôb 	×7Ò7Ñ9fðcx%ôd 	×'Ò'Ñ)}ðex%ôf 	×7Ò7Ù`ðix%ôl 	×+Ò+Ñ-=òmx%ôn 	×*Ò*Ñ,<ðox%ôp 	×*Ò*Ñ,Bðqx%ôr 	×#Ò#Ñ%?ðsx%ôt 	×ÒÑ9ðux%ôv 	�	Š	�ŠÔCðwx%ôx 	�	Š	�ŠÔCðyx%ôz 	�	Š	�ŠÔDð{x%ô| 	�	Š	�ŠÔCð}x%ô~ 	�	Š	�ŠÔDðx%ô@ 	�	Š	�ŠÔJðAx%ôB 	�	Š	×ÒÔKðCx%ôD 	�	Š	�ŠÔDðEx%ôF 	�	Š	×ÒÔEðGx%ôH 	�	Š	�ŠÔEðIx%ôJ 	�	Š	�ŠÔFðKx%ôL 	�	Š	�ŠÔFðMx%ôN 	�	Š	×ÒÔGòOx%ôP 	�	Š	�ŠÔIðQx%ôR 	�	Š	�ŠÔJðSx%ôT 	�	Š	�ŠÔJðUx%ôV 	�	Š	×ÒÔKðWx%ôX 	�	Š	×ÒÔ6ðYx%ôZ 	�	Š	×ÒÔ7ð[x%ô\ 	�	Š	�ŠÔBð]x%ô^ 	�	Š	Ô-ð_x%ô` 	�ŠÔ@ðax%ôb 	�
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Ñ*ðcx%ôd 	�ŠÑ&ðex%ôf 	�ŠÑ&ðgx%ôh 	×ÒÔQðix%ôj 	�ŠÔ/ðkx%ôl 	×ÒÑ3ðmx%ôn 	×ÒÔ?ðox%ôp 	�
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Ô5òqx%ôr 	�
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Ô.ðsx%ôt 	�ŠÔ/ðux%ôv 	�Š°tÀ4ÔPU×P]ÒP]ÐfjÐzó  Dðwx%ôx 	×&Ò&Ñ(Hðyx%ôz 	�ŠÔ\ð{x%ô| 	�ŠÔOð}x%ô~ 	�	Š	Ô4ðx%ô@ 	�ŠÔ3ðAx%ôB 	×ÒÑ*ðCx%ôD 	×ÒÔ>ðEx%ôF 	�ŠÔ/ðGx%ôH 	�ŠÔ,ðIx%ôJ 	�ŠÔ6ðKx%ôL 	�ŠÔ5ðMx%ôN 	�	Š	Ô3ðOx%ôP 	�ŠÔNðQx%ôR 	×ÒÑcòSx%ôT 	×ÒÑfðUx%ôV 	×ÒÑfðWx%ôX 	×ÒÔmðYx%ôZ 	�	Š	Ñsð[x%ô\ 	�ŠÔNð]x%ô^ 	�ŠÔ3ð_x%ô` 	�ŠÔ8ðax%ôb 	×ÒÔ5ðcx%ôd 	×ÒÔVðex%ôf 	�ŠÔ;ðgx%ôh 	×"Ò"Ô$zðix%ôj 	�ŠÔGðkx%ôl 	�ŠÔmðmx%ôn 	×ÒÔYðox%ôp 	�‰×(Ò(Ñ*?ðqx%ôr 	�ŠÔ4ðsx%ôt 	�ŠÑ;òux%ôv 	�ŠÔ2ðwx%ôx 	�ŠÔ6ðyx%ôz 	�ŠÔ7ð{x%ô| 	�ŠÔ8ð}x%ô~ 	�
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Ô.ðx%ô@ 	�ŠÑ=ðAx%ôB 	×ÒÑ>ðCx%ôD 	�ŠÔLðEx%ôF 	×ÒÔBðGx%ôH 	×ÒÑ=ðIx%ôJ 	×ÒÔ\ðKx%ôL 	�ŠÒ)ðMx%ôN 	�
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ÕGðOx%ôP 	�ŠÒ&ðQx%ôR 	�ŠÒ'ðSx%ôT 	�ŠÕ2ðUx%ôV 	�ŠÕ2òWx%ôX 	×ÒÚtð[x%ô^ 	�ŠÒ(ð_x%ô` 	�ŠÕ1ðax%ôb 	�‰×ÒÕ4ðcx%ôd 	�‰×ÒÕKðex%ôf 	×ÒÒ*ðgx%ôh 	�ŠÒ'ðix%ôj 	�ŠÒ&ðkx%ôl 	×ÒÒ.ðmx%ôn 	×ÒÒ,ðox%ôp 	×ÒÒ!1ðqx%ôr 	×ÒÒ*ðsx%ôt 	×ÒÒ3ðux%ôv 	�ŠÒ)ðwx%ôx 	�ŠÕWðyx%ôz 	�ŠÒ%ð{x%ô| 	�Šõ dòx%ôB	 	�ŠÕrðC	x%ôD	 	�
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Ò+ðE	x%ôF	 	�ŠÕNðG	x%ôH	 	�‰×"Ò"Õ$cðI	x%ôJ	 	�‰×ÒÕ!LðK	x%ôL	 	�‰×ÒÕ SðM	x%ôN	 	×ÒÕsðO	x%ôP	 	�	Š	Õ4ðQ	x%ôR	 	�ŠÕ6ðS	x%ôT	 	�ŠÕ3ðU	x%ôV	 	×ÒÕ;ðW	x%ôX	 	�
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Õ;ðY	x%ôZ	 	�ŠÕ0ð[	x%ô\	 	�Šõ  Kð]	x%ô^	 	�	Š	Õ-ð_	x%ô`	 	×ÒÕ<ða	x%ôb	 	�ŠÕ/òc	x%ôd	 	�ŠÕ/ðe	x%ôf	 	�
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Õ.ðg	x%ôh	 	�ŠÕ:ði	x%ôj	 	×ÒÕ;ðk	x%ôl	 	�ŠÒ&ðm	x%ôn	 	�ŠÕ.ðo	x%ôp	 	×ÒÕ<ðq	x%ôr	 	×ÒÕ5ðs	x%ôt	 	×ÒÕ;ðu	x%ôv	 	×ÒÕ<ðw	x%ôx	 	�ŠÕ/ðy	x%ôz	 	�ŠÕIð{	x%ô|	 	�
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õ @ðAx%ôD 	�	Š	Õ4ðEx%ôF 	�ŠÕ9ðGx%ôH 	�	Š	Õ-ðIx%ôJ 	�ŠÒ)ðKx%ôL 	�ŠÒ'ðMx%ôN 	�Š’ðOx%ôP 	�Š’ðQx%ôR 	�ŠÒ'ðSx%ôT 	�ŠÒ)ðUx%ôV 	�Š’òWx%ôX 	�ŠÒ)ðYx%ôZ 	�ŠÒ(ð[x%ô\ 	�ŠÒ)ð]x%ô^ 	�ŠÒ(ð_x%ô` 	�ŠÒ)ðax%ôb 	�ŠÒ(ðcx%ôd 	�ŠÒ)ðex%ôf 	�ŠÒ)ðgx%ôh 	�ŠÒ)ðix%ôj 	�ŠÒ)ðkx%ôl 	�ŠÕ0ðmx%ôn 	�	Š	ÕIðox%ôp 	×ÒÕAðqx%ôr 	�‰×ÒÕHðsx%ôt 	�‰×ÒÕ8ðux%ôv 	�ŠÒ4ðwx%ôx 	�ŠÒ6òyx%ôz 	�Š×ÒÒ/ð{x%ô| 	�Š×ÒÒ!1ð}x%ô~ 	�Š×ÒÒ!1ðx%ô@ 	�Š×ÒÒ!1ðAx%ôB 	�Š×ÒÒ!1ðCx%ôD 	�Š×,Ò,Õ.KðEx%ôF 	�Š×,Ò,Õ.KðGx%ôH 	�Š×,Ò,Õ.KðIx%ôJ 	�Š×,Ò,Õ.KðKx%ôL 	�Š×ÒÒ/ðMx%ôN 	�Š×ÒÒ,ðOx%ôP 	�Š×ÒÒ+ðQx%ôR 	�Š×ÒÒ,ðSx%ôT 	�Š×ÒÒ-ðUx%ôV 	�Š×ÒÒ.ðWx%ôX 	�Š×ÒÒ,ðYx%ôZ 	�Š×ÒÒ-ò[x%ô\ 	�Š×ÒÒ-ð]x%ô^ 	�Š×ÒÕ Að_x%ô` 	�Š×ÒÕ!Bðax%ôb 	�Š×ÒÒ/ðcx%ôd 	�Š×*Ò*Õ,Iðex%ôf 	�Š×+Ò+Õ-Jðgx%ôh 	�Š×ÒÒ*ðix%ôj 	�Š×ÒÒ+ðkx%ôl 	�Š×ÒÒ*ðmx%ôn 	�Š×ÒÒ+ðox%ôp 	�Š×+Ò+Õ-Jðqx%ôr 	�Š×+Ò+Õ-Jðsx%ôt 	�Š×ÒÒ-ðux%ôv 	�Š×ÒÒ 0ðwx%ôx 	�Š×!Ò!Õ#Dðyx%ôz 	�Š×ÒÒ-ð{x%ô| 	�Š×ÒÕ!Oò}x%ô~ 	�Š×(Ò(Ò*:ðx%ô@ 	�Š×(Ò(Ò*:ðAx%ôB 	�Š×(Ò(Ò*:ðCx%ôD 	�Š×(Ò(Ò*:ðEx%ôF 	�Š×"Ò"Ò$7ðGx%ôH 	�Š×ÒÒ,ðIx%ôJ 	�Š×ÒÒ-ðKx%ôL 	�Š×ÒÕ!>ðMx%ôN 	�Š×ÒÒ+ðOx%ôP 	�Š×ÒÒ-ðQx%ôR 	�Š×/Ò/Ò1AðSx%ôT 	�Š×/Ò/Ò1AðUx%ôV 	�Š×4Ò4Õ6SðWx%ôX 	�Š×4Ò4Õ6SðYx%ôZ 	�Š×4Ò4Õ6Sð[x%ô\ 	�Š×4Ò4Õ6Sð]x%ô^ 	�Š×ÒÒ,ò_x%ô` 	�Š×ÒÕ@ðax%ôb 	�Š×)Ò)Ò+;ðcx%ôd 	�Š×ÒÕ@ðex%ôf 	�Š×ÒÕ>ðgx%ôh 	�Š×ÒÕ<ðix%ôj 	�ŠÒ!ðkx%ôl 	�
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Ò+ðmx%ôn 	×ÒÕKðox%ôp 	�	Š	Õ-ðqx%ôr 	�
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Õ.ðsx%ôt 	�‰×ÒÕ 3ðux%ôv 	�‰× Ò Õ"<ðwx%ôx 	�ŠÕ:ðyx%ôz 	×ÒÕHð{x%ô| 	�ŠÕJð}x%ô~ 	�
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Ò*ðx%ô@ 	�
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ÕKòAx%ôB 	�ŠÒ%ðCx%ôD 	�ŠÒ5ðEx%ôF 	�ŠÕ1ðGx%ôH 	�ŠÕ5ðIx%ôJ 	×ÒÕ eðKx%ôL 	�‰×%Ò%Õ'aðMx%ôN 	�
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Õ:ðOx%ôP 	×!Ò!õ LðSx%ôV 	�
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Õ:ðWx%ôX 	×ÒÒ2ðYx%ôZ 	�ŠÕ/ð[x%ô\ 	�ŠÕ-ð]x%ô^ 	�ŠÒ5ð_x%ô` 	�ŠÕhðax%ôb 	× Ò Õ"gðcx%ôd 	×ÒÒ6ðex%ôf 	×ÒÕ;ògx%ôh 	×ÒÕLðix%ôj 	×%Ò%Õ'Wðkx%ôl 	�ŠÕ8ðmx%ôn 	�‰×ÒÕ1ðox%ôp 	�	Š	Õ-ðqx%ôr 	�ŠÕ2ðsx%ôt 	�ŠÒ;ðux%ôv 	�ŠÕ2ðwx%ôx 	�ŠÕ9ðyx%ôz 	×%Ò%Ò'`ð{x%ô| 	×1Ò1Úoðx%ôB 	×ÒÕeðCx%ôD 	×ÒÒ5ðEx%ôF 	×ÒÒ@ðGx%ôH 	×Ò¢ðIx%ôJ 	×"Ò"¢OðKx%ôL 	×Òš/òMx%ôN 	×ÒšoðOx%ôP 	×ÒÕQðQx%ôR 	×'Ò'Ò)>ðSx%ôT 	×ÒÕFðUx%ôV 	×ÒÑC¸%ÔCðWx%ôX 	×ÒÒ>ðYx%ôZ 	×ÒÒ1ð[x%ô\ 	×!Ò!Ò#@ð]x%ô^ 	×ÒÒ6ð_x%ô` 	×Òš?ðax%ôb 	×ÒÕNðcx%ôd 	×ÒÕ<ðex%ôf 	×#Ò#Õ%Hðgx%ôh 	×ÒÒ0ðix%ôj 	�Š’oðkx%ôl 	×ÒÒ9ðmx%ôn 	×ÒÒ2òox%ôp 	×Òš_ðqx%ôr 	×ÒšOðsx%ôt 	×ÒšOðux%ôv 	×Òš?ðwx%ôx 	×Ò¢ðyx%ôz 	×Ò¢ð{x%ô| 	×Ò¢ð}x%ô~ 	×Ò¢ðx%ô@ 	×ÒÕ1ðAx%ôB 	�ŠÒ/ðCx%ôD 	×ÒÒAðEx%ôF 	×Òš/ðGx%ðH 	×ÒÒ3ðIx%ðJ 	×ÒÒ4ðKx%ðL 	×ÒÒ4ðMx%ðN 	×ÒÒ2ðOx%ðP 	×ÒÒ3òQx%ðR 	×ÒÒ3ðSx%ðT 	×ÒÒ1ðUx%ðV 	×ÒÒ2ðWx%ðX 	×ÒÒ2ðYx%ðZ 	×ÒÒ1ð[x%ð\ 	×ÒÒ2ð]x%ð^ 	×ÒÒ2ð_x%ð` 	�ŠÒ.ðax%ðb 	�ŠÒ-ðcx%ðd 	�ŠÒ.ðex%ðf 	×Òš/ðgx%ðh 	×ÒšOðix%ðj 	×ÒÒ0ðkx%ðl 	�Ššðmx%ðn 	×ÒÒ3ðox%ðp 	�Ššðqx%ðr 	×Òš?òsx%ðt 	�ŠÒ.ðux%ðv 	�ŠÒ/ðwx%ðx 	�ŠÒ/ðyx%ðz 	×ÒÒ5ð{x%ð| 	×ÒÒ0ð}x%ð~ 	×ÒÒ2ðx%ð@ 	�ŠšðAx%ðB 	�ŠšðCx%ðD 	×Òš/ðEx%ðF 	�ŠšðGx%ðH 	×ÒÒ7ðIx%ðJ 	×ÒÒ4ðKx%ðL 	×Òš_ðMx%ðN 	�ŠÑA¸ÔAðOx%ðP 	×ÒÒ1ðQx%ðR 	×ÒÒ/ðSx%ðT 	�Š×Òš/òUx%ðV 	�Š×Òš/ðWx%ðX 	�	Š	×Òš?ðYx%ðZ 	�	Š	×Òš?ð[x%ð\ 	×Ò×&Ò&ªð]x%ð^ 	×*Ò*×2Ò2²Oð_x%ð` 	�Š×Òšoðax%ðb 	�Š×Òšðcx%ðd 	�Š×Òš_ðex%ðf 	�Š×Òšoðgx%ðh 	�Š×Ò¢ðix%ðj 	�Š×Ò¢ðkx%ðl 	×Ò×!Ò!¢?ðmx%ðn 	�Š×Ò¢ðox%ðp 	×-Ò-Ò/pðqx%ðr 	×*Ò*Ò,Vðsx%ðt 	×2Ò2²Oðux%ðv 	�Š×Òš_òwx%ðx 	�Š×Òšðyx%ðz 	�Š×Òšoð{x%ð| 	�Š×Ò¢ð}x%ð~ 	�Š×Òšðx%ð@ 	�Š×ÒšðAx%ðB 	�Š×ÒšðCx%ðD 	�Š×ÒšðEx%ðF 	�Š×Ò¢ðGx%ðH 	×Ò×#Ò#¢_ðIx%ðJ 	�Š×Ò¢ðKx%ðL 	�Š×ÒšðMx%ðN 	�Š×Ò¢ðOx%ðP 	×Ò× Ò ¢/ðQx%ðR 	�Š×Ò¢ðSx%ðT 	×Ò× Ò ¢/ðUx%ðV 	×Ò×#Ò#¢_ðWx%ðX 	×Ò× Ò ¢/òYx%ðZ 	×Ò×$Ò$¢oð[x%ð\ 	×Ò× Ò ¢/ð]x%ð^ 	�Š×Ò¢ð_x%ð` 	�Š×Òšðax%ðb 	�Š×Òš_ðcx%ðd 	�Š×Òšðex%ðf 	�Š×Òš_ðgx%ðh 	×Ò× Ò ¢/ðix%ðj 	×Ò×$Ò$¢oðkx%ðl 	�Š×Òšoðmx%ðn 	�Š×Ò¢ðox%ðp 	�Š×Òš_ðqx%ðr 	�Š×Òš_ðsx%ðt 	×'Ò'×/Ò/²ðux%ðv 	�ŠÕNðwx%ðx 	�Š’oðyx%ðz 	�Š’oò{x%ð| 	�Ššð}x%ð~ 	�Ššðx%ð@ 	�Š’_ðAx%ðB 	×Òš_ðCx%ðD 	×ÒšOðEx%ðF 	×Òš_ðGx%ðH 	×ÒšOðIx%ðJ 	�ŠšðKx%ðL 	�‰šðMx%ðN 	�ŠÒ0ðOx%ðP 	×ÒÒ8ðQx%ðR 	×ÒÒ9ðSx%ðT 	�ŠÕkðUx%ðV 	�Š´E×4IÒ4IÔMðWx%ðX 	�Š´×0EÒ0EÔIðYx%ðZ 	�Š´×0EÒ0EÔIð[x%ð\ 	�Š´×0EÒ0EÔIò]x%ð^ 	�ŠÓMÀTÕMð_x%ð` 	�Ššðax%ðb 	×ÒÒ6ðcx%ðd 	×Ò´e×6MÒ6MÔQðex%ðf 	�ŠÕ>ðgx%ðh 	�
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¬u×/DÒ/DÔHðix%ðj 	�Š´×0EÒ0EÔIðkx%ðl 	�Š´×0EÒ0EÔIðmx%ðn 	�
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¬u×/DÒ/DÔHðox%ðp 	�
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¬u×/DÒ/DÔHðqx%ðr 	×Òšðsx%ðt 	�Ššðux%ðv 	×Òš/ðwx%ðx 	×ÒÕ!Oðyx%ðz 	�
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’Oð{x%ð| 	×ÒÕ@ð}x%ð~ 	�Š´%×2GÒ2GÔKòx%ð@ 	�Š´5×3HÒ3HÔLðAx%ðB 	×Òš_ðCx%ðD 	�ŠÒ,ðEx%ðF 	×ÒÒ0ðGx%ðH 	×ÒÓHÀÕHðIx%ðJ 	�ŠÒ,ðKx%ðL 	×ÒÒ5ðMx%ðN 	�Š´×1FÒ1FÔJðOx%ðP 	�Š´%×2GÒ2GÔKðQx%ðR 	×ÒÑ@¸Ô@ðSx%ðT 	×Òš?ðUx%ðV 	�Š´×0EÒ0EÔIðWx%ðX 	�Š´×1FÒ1FÔJðYx%ðZ 	�Ššð[x%ð\ 	�
Š
¬u×/DÒ/DÔHð]x%ð^ 	×Òš_ð_x%ð` 	×Òšoòax%ðb 	×Òš_ðcx%ðd 	×Òš/ðex%ðf 	×ÒÒ1ðgx%ðh 	×Òš/ðix%ðj 	�Š’_ðkx%ðl 	×ÒÓMÀTÕMðmx%ðn 	×ÒÒ0ðox%ðp 	�Š´×0EÒ0EÔIðqx%ðr 	�ŠÒ6ðsx%ðt 	�ŠÒ5ðux%ðv 	�	Š	Õ8ðwx%ðx 	×ÒÕ@ðyx%ðz 	×ÒÒEð{x%ð| 	×Òš?ð}x%ð~ 	�Ššðx%ð@  	×"Ò"¢OðA x%ðB  	×-Ò-ªòC x%ðD  	×%Ò%¢ðE x%ðF  	�ŠšðG x%ðH  	�Š’oðI x%ðJ  	�ŠÒ,ðK x%ðL  	×Òš?ðM x%ðN  	�ŠÕGðO x%ðP  	�ŠšðQ x%ðR  	�ŠÓLÀDÕLðS x%ðT  	×ÒÒ5ðU x%ðV  	×ÒÒ3ðW x%ðX  	×1Ò1Ò3HðY x%ðZ  	�ŠÒ-ð[ x%ð\  	×ÒÕBð] x%ð^  	×ÒÒ1ð_ x%ð`  	�ŠÒ-ða x%ðb  	�ŠÒ-ðc x%ðd  	×ÒÒ0òe x%ðf  	× Ò Ò"8ðg x%ðh  	×ÒšOði x%ðj  	�ŠÕ[ðk x%ðl  	×ÒÒ?ðm x%ðn  	×Òšoðo x%ðp  	�Š´×1FÒ1FÔJðq x%ðr  	�Š’_ðs x%ðt  	×ÒÕWðu x%ðv  	×Òš?ðw x%ðx  	×ÒÒ1ðy x%ðz  	×&Ò&Õ(Wð{ x%ð|  	×ÒÒGð} x%ð~  	×'Ò'Ò)Qð x%ð@! 	�ŠÕOðA!x%ðB! 	�ŠšðC!x%ðD! 	×Ò¢ðE!x%ðF! 	×ÒšòG!x%ðH! 	×Òš_ðI!x%ðJ! 	×ÒÒ1ðK!x%ðL! 	�ŠÒ+ðM!x%ðN! 	�	Š	°EÀÔUZ×UjÒUjÔnðO!x%ðP! 	�ŠÓIÀÕIðQ!x%ðR! 	×ÒÕGðS!x%ðT! 	×Òš/ðU!x%ðV! 	�Š’ðW!x%ðX! 	×Òš/ðY!x%ðZ! 	�ŠÒ.ð[!x%ð\! 	�ŠÒ=ð]!x%ð^! 	�ŠÕ7ð_!x%ð`! 	�Š’ða!x%ðb! 	�ŠÒ+ðc!x%ðd! 	�ŠÒ.ðe!x%ðf! 	�Š’oðg!x%ðh! 	×ÒÕuñi!x%ðj! 	× Ò ¢/Ø�ŠÒ+Ü�‰×ÒÕFño!x%€Côv! 	�Š×(Ò(×BÒBð õ ˆv×0Ò0åFð 	�G�FÓ5Ñ6ò JYˆ�G�F™cÐ":Ð!;Ð<Ó=×EÒEÑFà€DÝ#Ó%€Gà—	’	—‰ˆð �JŠJØ�JŠJ™ÕÙ�1—:’:Õ¡Õ$Ù�A—J’JÕ¡Õ%Ù�A—J’JÕ¡Õ%ð
ˆð �:Š:× Ò ¡×,Ò,ð —j’j¥¡Z£Ð!2Ð3ˆGØ�LŠLÙ˜•¡$Õ&©°­¹$Õ(>ÁÈÅÑRVÕ@VÐWôó ˆDÝ˜6¨Ó.ˆDÝ˜�~Œ~ $¦/°dÖ6IØ�T“
ô ñ% ð. ‡J‚JˆtÔô
 ×Ò×%Ò%×'Ó'ß(à�
Š
ðØ—’Õ gðà—’Õ!Wðð —’Õmðð ×)Ò)Õ+bð	ð
 —’Õ![ðð ×&Ò&Õ(jðð ×)Ò)Õ+xðð —’Õvðð —’Õxðð ×#Ò#Õ%gðð ×*Ò*Õ,iðð ×&Ò&õ  )Nðð —’Õ!mðð —
’
ÕZðð —
’
ÕZðð  —	’	ÕYð!ð" —	’	ÕYð#ô	
ð, €Jr   c                óÐ   € V ^8„  d   QhR\         \        \        \        ,          3,          R\         \         \        \        3,          .\         \        \        3,          3,          /# )r   Ú
dispatcherr   )r   r   r   r   r   )r   s   "r   r   r   A  sI   € ÷ 'ñ 'ÜœœX¤c�]Ð*Õ+ð'äŒxœœB˜ÕÐ ¤(¬2¬r¨6Õ"2Ð2Õ3ñ'r   c                ó   a € R V 3R llpV# )a=  Wraps a given function with ``__torch_function__`` -related functionality.

Parameters
----------
dispatcher: Callable
    A callable that returns an iterable of Tensor-likes passed into the function.

Note
----
This decorator may reduce the performance of your code. Generally, it's enough to express
your code as a series of functions that, themselves, support __torch_function__. If you
find yourself in the rare situation where this is not the case, e.g. if you're wrapping a
low-level library and you also need it to work for Tensor-likes, then this function is available.

Examples
--------
>>> def dispatcher(a):  # Must have the same signature as func
...     return (a,)
>>> @torch.overrides.wrap_torch_function(dispatcher)
>>> def func(a):  # This will make func dispatchable by __torch_function__
...     return a + 0
c                ót   € V ^8„  d   QhR\         \        \        3,          R\         \        \        3,          /# ©r   r   r   )r   r   r   )r   s   "r   r   Ú)wrap_torch_function.<locals>.__annotate__[  s,   € ÷ /ñ /”HœR¤˜VÕ$ð /¬´"´b°&Õ)9ñ /r   c                 ó’   <a a€ \         P                  ! S 4      R  VV V3R ll4       o\        \        \        \
        3,          S4      # )c                ód   € V ^8„  d   QhR\         P                  R\         P                  R\        /# r"   r%   )r   s   "r   r   Ú8wrap_torch_function.<locals>.inner.<locals>.__annotate__]  s)   € ÷ 	)ñ 	)œ2Ÿ7™7ð 	)¬b¯i©ið 	)¼Bñ 	)r   c                  ó¤   <€ S! V / VB p\        V4      '       d0   \        \        \        \        \
        3,          S4      V.V O5/ VB # S! V / VB # r›  )r¾   rÁ   r   r   r   r   )r#   r$   Úrelevant_argsr  r   Úwrappeds   *, €€€r   r   Ú3wrap_torch_function.<locals>.inner.<locals>.wrapped\  sa   ø€ á&¨Ð7°Ñ7ˆMÜ! -×0Ò0Ü,Üœ¤"¤b &Õ)¨7Ó3°]ðØEIòØMSñð ñ ˜Ð( Ñ(Ð(r   )Ú	functoolsr   r   r   r   r   )r   r   r  s   f@€r   r‡  Ú"wrap_torch_function.<locals>.inner[  s;   ú€ Ü	�Š˜Ó	÷	)ñ 	)ó 
ð	)ô ”HœR¤˜VÕ$ gÓ.Ð.r   rS  )r  r‡  s   f r   Úwrap_torch_functionr$  A  s   ø€ ÷4/ð /ð €Lr   c                óš   € V ^8„  d   QhR\         \        ,          R\        \        .\        3,          R,          R\        \        ,          /# )r   r  Úget_type_fnNr   )r   r   r   rŽ
  Úlist)r   s   "r   r   r   k  sC   € ÷ Lñ LÜœC•=ðLäœ3˜%¤˜+Õ&¨Õ-ðLô 
Œ#…YñLr   c                ó  € Vf   \         p\        P                  P                  4       '       g   . # \	        4       p. pV  F½  pV! V4      pWR9  g   K  \        VR4      '       g   K'  VP                  \        P                  P                  Jg   KQ  V'       d_   VP                  V4       \        V4      p\        V4       F   w  rx\        WQ! V4      4      '       g   K  Tp M	  VP                  Wd4       K·  V0pV.pK¿  	  V# )a¦  Returns a list of arguments on which to call __torch_function__.

Checks arguments in relevant_args for __torch_function__ implementations,
storing references to the arguments and their types in overloaded_args and
overloaded_types in order of calling precedence. Only distinct types are
considered. If a type is a subclass of another type it will have higher
precedence, otherwise the precedence order is the same as the order of
arguments in relevant_args, that is, from left-to-right in the argument list.

The precedence-determining algorithm implemented in this function is
described in `NEP-0018`_.

See torch::append_overloaded_arg for the equivalent function in the C++
implementation.

Parameters
----------
relevant_args : iterable of array-like
    Iterable of array-like arguments to check for __torch_function__
    methods.

get_type_fn : callable, optional
    Function to call on each argument in relevant_args to get its type.

Returns
-------
overloaded_args : list
    Arguments from relevant_args on which to call __torch_function__
    methods, in the order in which they should be called.

.. _NEP-0018:
   https://numpy.org/neps/nep-0018-array-function-protocol.html
r	  )rŽ
  r9   Ú_CÚ_is_torch_function_enabledr5   rö
  r	  Ú_disabled_torch_function_implrC  rû
  Ú	enumerateÚ
issubclassÚinsert)	r  r&  Úoverloaded_typesÚoverloaded_argsÚargÚarg_typerA  ÚiÚold_args	   &&       r   Ú_get_overloaded_argsr5  k  sí   € ðJ ÒÜˆô �8‰8×.Ñ.×0Ò0Øˆ	ä"%£%ÐØ!#€OÛˆÙ˜sÓ#ˆð Ö,Ü˜Ð"6×7Ô7Ø×+Ñ+Ü—8‘8×9Ñ9õ:÷
  Ø ×$Ñ$ XÔ.ô ˜OÓ,�Ü"+¨OÖ"<‘J�AÜ! (¨K¸Ó,@×AÔAØ !˜Ùñ #=ð  ×&Ñ& uÖ2à$, :Ð Ø#& %’ñ; ð< Ðr   c          
      ó´   € V ^8„  d   QhR\         \        \        3,          R\        \        ,          R\        P
                  R\        P                  R\        /# )r   Ú
public_apir  r#   r$   r   )r   r   r   r   r   r#   r$   )r   s   "r   r   r   º  sV   € ÷ Vñ VÜœœR˜Õ ðVäœC•=ðVô �7‰7ðVô �i‰ið	Vô
 ñVr   c           	     óö  € \        V4      p\        \        \        V4      4      p\	        4       '       d:   \        4       ;_uu_ 4       pVP                  WW#4      pRRR4       X\        Jd   V# V Fƒ  pVP                  p	\        V	R4      '       dL   V	P                  VJ d<   V	\        P                  P                  Jd   \        P                  ! R\        ^R7       V	! WW#4      pV\        Jg   K�  Vu # 	  V P                    RV P"                   2p
RT
 RV Uu. uF  p\        V4      NK  	  up 2p\	        4       '       d   VR\%        4        2,          p\'        V4      h  + '       g   i     EL; iu upi )	aÉ  Implement a function with checks for ``__torch_function__`` overrides.

See torch::autograd::handle_torch_function for the equivalent of this
function in the C++ implementation.

Arguments
---------
public_api : function
    Function exposed by the public torch API originally called like
    ``public_api(*args, **kwargs)`` on which arguments are now being
    checked.
relevant_args : iterable
    Iterable of arguments to check for __torch_function__ methods.
args : tuple
    Arbitrary positional arguments originally passed into ``public_api``.
kwargs : tuple
    Arbitrary keyword arguments originally passed into ``public_api``.

Returns
-------
object
    Result from calling ``implementation`` or an ``__torch_function__``
    method, as appropriate.

Raises
------
TypeError : if no implementation is found.

Example
-------
>>> def func(a):
...     if has_torch_function_unary(a):
...         return handle_torch_function(func, (a,), a)
...     return a + 0
NÚ__self__z„Defining your `__torch_function__ as a plain method is deprecated and will be an error in future, please define it as a classmethod.©Ú
stacklevelÚ.zno implementation found for 'z.' on types that implement __torch_function__: z nor in mode )r5  ÚtupleÚmaprŽ
  r   Ú_pop_mode_temporarilyr	  ÚNotImplementedrö
  r9  r9   r)  r+  r+   ÚwarnÚDeprecationWarningÚ
__module__rù
  Ú_get_current_function_modeÚ	TypeError)r7  r  r#   r$   r0  ÚtypesrÔ  ÚresultÚoverloaded_argÚtorch_func_methodÚ	func_namer1  r°  s   &&*,         r   rÁ   rÁ   º  se  € ôT +¨=Ó9€Oä”#”d˜OÓ,Ó-€Eô '×(Ò(ô #×$Ô$¨Ø×,Ñ,¨ZÀÓMˆF÷ %àœÓ'ØˆMó *ˆð +×=Ñ=ÐäÐ% z×2Ò2Ø!×*Ñ*¨nÓ<Ø!¬¯©×)OÑ)OÓOä�MŠMðQä"Øõ	ñ # :°dÓCˆàœÕ'ØŠMñ+ *ð. ×(Ñ(Ð)¨¨:×+>Ñ+>Ð*?Ð@€Ià
'¨	 {ð 3Ù5DÓE±_¨c¤ S¦	±_ÑEÐFð	Hð ô '×(Ò(Ø�Ô9Ó;Ð<Ð=Õ=ˆÜ
�C‹.Ð÷I %×$Ð$üò@  Fs   ÁE"ÄE6
Å"E3	aú  Check for __torch_function__ implementations in the elements of an iterable
    or if a __torch_function__ mode is enabled.  Considers exact ``Tensor`` s
    and ``Parameter`` s non-dispatchable.  Use this to guard a call to
    :func:`handle_torch_function`; don't use it to test if something
    is Tensor-like, use :func:`is_tensor_like` instead.
    Arguments
    ---------
    relevant_args : iterable
        Iterable of arguments to check for __torch_function__ methods.
    Returns
    -------
    bool
        True if any of the elements of relevant_args have __torch_function__
        implementations, False otherwise.
    See Also
    ________
    torch.is_tensor_like
        Checks if something is a Tensor-like, including an exact ``Tensor``.
    zÓSpecial case of `has_torch_function` for single inputs.
    Instead of:
      `has_torch_function((t,))`
    call:
      `has_torch_function_unary(t)`
    which skips unnecessary packing and unpacking work.
    a'  Special case of `has_torch_function` that skips tuple creation.

    This uses the METH_FASTCALL protocol introduced in Python 3.7

    Instead of:
      `has_torch_function((a, b))`
    call:
      `has_torch_function_variadic(a, b)`
    which skips unnecessary packing and unpacking work.
    c                ó    € V ^8„  d   QhR\         \        \        \        \        ,          3,          \        \        \
        3,          3,          /# r3   )r=  rM  r   r'  r   r   )r   s   "r   r   r   E  s:   € ÷ Q$ñ Q$¤EÜŒŒd”8�nÐ	Õœt¤H¬c MÕ2Ð2õ%ñ Q$r   c                  óT  € \         P                  ! \        4      p / pR \        \        P                  3R\        P
                  \        P
                  P                  3R\        P                  P
                  \        \        P                  P
                  4      3R\        P                  P                  \        \        P                  P                  4      3R\        P                  \        \        P                  4      3R\        P                  \        \        P                  4      3R\        P                  \        \        P                  4      3R\        P                  \        \        P                  4      3.pV EF£  w  r4pV EF•  pRpV\        P                  Jdx   VP                  R	4      '       d   K3  VP                  R
4      '       d   RpMpVP                  R
4      '       d   RpMVV^ ,          P                  4       '       g   RpM6VR8X  d   K�  M,\!        WF4      p\!        \"        VR4      V8X  d   K´  VR8X  d   K½  \!        WF4      pV\        P                  J d   \!        \"        VR4      V8X  d   Kô  \%        V\&        P(                  4      '       d   EK  \%        V\*        P,                  4      '       d   EK:  \/        V4      '       gÅ   \1        VR4      '       d³   V RV R2WP2                  &   V RV R2WP4                  &   V'       d   EK�  VP2                  \7        4       9   dC   Rp	VP2                  \9        4       9   d%   \;        V	P=                  WHP>                  4      4      hEKë  W,          PA                  VP2                  4       EK  \/        V4      '       g   EK#  V RV 2W&   V'       d   EK7  V\7        4       9   d9   Rp	V\9        4       9   d%   \;        V	P=                  WHP>                  4      4      hEK~  W,          PA                  V4       EK˜  	  EK¦  	  W3# )r9   ztorch.functionalztorch.nn.functionalztorch.nn.initztorch.Tensorztorch.linalgz	torch.fftztorch.specialFrs  rr  TÚ
unique_dimNÚ__weakref__r4  r<  z.__get__z.__set__zk{}.{} is in the tuple returned by torch._overrides.get_ignored_functions but still has an explicit override)!ÚcollectionsÚdefaultdictr'  r9   Ú__all__r¹   r¸   ÚdirrÊ   r:   r  r‹   rÕ	  rú
  ÚendswithÚislowerr÷
  ÚobjectÚ
isinstancerF  Ú
ModuleTypeÚ
__future__Ú_Featurerâ  rö
  r4  Ú__set__rE  r  ÚAssertionErrorr   rù
  r¦  )
Úoverridable_funcsrA  Útested_namespacesÚnamespace_strÚ	namespaceÚns_funcsrJ  r(   r   r°  s
             r   Ú_get_overridable_functionsra  D  s/  € ô $×/Ò/´Ó5ÐØ€Eà	”%œŸ™Ð'Ø	œU×-Ñ-¬u×/?Ñ/?×/GÑ/GÐHØ	¤§¡× 3Ñ 3´S¼¿¹×9LÑ9LÓ5MÐNØ	œ%Ÿ(™(Ÿ-™-¬¬U¯X©X¯]©]Ó);Ð<Ø	œŸ™¤s¬5¯<©<Ó'8Ð9Ø	œŸ™¤s¬5¯<©<Ó'8Ð9Ø	”e—i‘i¤¤U§Y¡Y£Ð0Ø	œ%Ÿ-™-¬¬U¯]©]Ó);Ð<ð	Ðô /@Ñ*ˆ (Ü!ˆIØˆFà¤§¡Ó,Ø×'Ñ'¨×-Ò-ÙØ×)Ñ)¨#×.Ò.Ø!‘FØ×'Ñ'¨×,Ò,Ø!‘FØ" 1�×-Ñ-×/Ò/Ø!‘FØ ,Ô.Ùð /ô ˜yÓ4�Üœ6 9¨dÓ3°tÔ;ÙØ Ô-ÙÜ˜9Ó0ˆDØœEŸL™LÓ(¬W´V¸YÈÓ-MÐQUÔ-UÙä˜$¤× 0Ñ 0×1Ò1Úä˜$¤
× 3Ñ 3×4Ò4Úä˜D—>’>¤g¨d°I×&>Ò&>Ø)6¨°q¸¸À8Ð&L�—l‘lÑ#Ø)6¨°q¸¸À8Ð&L�—l‘lÑ#ßÚØ—<‘<Ô#8Ó#:Ô:ð=ð ð —|‘|Ô'<Ó'>Ô>Ü,¨S¯Z©Z¸	Ç=Á=Ó-QÓRÐRÚà%Õ+×2Ñ2°4·<±<Ô@Úä˜D—>’>Úà*˜O¨1¨Y¨KÐ8ˆE‰KçÚð Ô,Ó.Ô.ð9ð ð Ô0Ó2Ô2Ü(¨¯©°I¿}¹}Ó)MÓNÐNÚØÕ(×/Ñ/°×5ôA "ñ /@ðD Ð#Ð#r   c                ó\   € V ^8„  d   QhR\         \        \        \        ,          3,          /# r3   )rM  r   r'  r   )r   s   "r   r   r   š  s!   € ÷ 	+ñ 	+¤4¬¬T´(­^Ð(;Õ#<ñ 	+r   c                 ó$   € \        4       ^ ,          # )zêList functions that are overridable via __torch_function__

Returns
-------
Dict[Any, List[Callable]]
    A dictionary that maps namespaces that contain overridable functions
    to functions in that namespace that can be overridden.
)ra  rS  r   r   Úget_overridable_functionsrd  ™  s   € ô &Ó'¨Õ*Ð*r   c                óà   € \        V \        P                  P                  \        P                  P                  34      '       d   \        V 4      # \        4       ^,          P                  V 4      # )zúGet a human readable string name for a function passed to
__torch_function__

Arguments
---------
f : Callable
    Function to resolve the name of.

Returns
-------
str
    Name of the function; if eval'ed it should give back the input
    function.
)rV  r9   Ú_opsÚ
OpOverloadÚOpOverloadPacketr   ra  Úget)Úfs   &r   Úresolve_namerk  ¦  sL   € ô  �!”e—j‘j×+Ñ+¬U¯Z©Z×-HÑ-HÐI×JÒJÜ�1‹vˆÜ%Ó'¨Õ*×.Ñ.¨qÓ1Ð1r   c                ó:   € V ^8„  d   QhR\         \        ,          /# r3   r4   )r   s   "r   r   r   ¼  s   € ÷ ñ œS¤�]ñ r   c                 óZ   € \        4       p \        V \        P                  ,          4      pV# )z<Returns a set of the overridable methods on ``torch.Tensor``)rd  r5   r9   r:   )r\  Úmethodss     r   Ú_get_tensor_methodsro  »  s&   € ô 2Ó3ÐÜÐ#¤E§L¡LÕ1Ó2€GØ€Nr   c                ó0   € V ^8„  d   QhR\         R\        /# r  )r   rž
  )r   s   "r   r   r   Ä  s   € ÷ Gñ G¤xð G´Dñ Gr   c                óJ   € V \        4       9   ;'       g    V P                  R8H  # )a7  
Returns True if the function passed in is a handler for a
method or property belonging to ``torch.Tensor``, as passed
into ``__torch_function__``.

.. note::
   For properties, their ``__get__`` method must be passed in.

This may be needed, in particular, for the following reasons:

1. Methods/properties sometimes don't contain a `__module__` slot.
2. They require that the first passed-in argument is an instance
   of ``torch.Tensor``.

Examples
--------
>>> is_tensor_method_or_property(torch.Tensor.add)
True
>>> is_tensor_method_or_property(torch.add)
False
r4  )ro  rù
  )r   s   &r   Úis_tensor_method_or_propertyrr  Ã  s$   € ð. Ô&Ó(Ñ(×FÐF¨D¯M©M¸YÑ,FÐFr   c                ó`   € \        V 4      \        P                  J ;'       g    \        V R4      # )aÕ  
Returns ``True`` if the passed-in input is a Tensor-like.

Currently, this occurs whenever there's a ``__torch_function__``
attribute on the type of the input.

Examples
--------
A subclass of tensor is generally a Tensor-like.

>>> class SubTensor(torch.Tensor): ...
>>> is_tensor_like(SubTensor([0]))
True

Built-in or user types aren't usually Tensor-like.

>>> is_tensor_like(6)
False
>>> is_tensor_like(None)
False
>>> class NotATensor: ...
>>> is_tensor_like(NotATensor())
False

But, they can be made Tensor-like by implementing __torch_function__.

>>> class TensorLike:
...     @classmethod
...     def __torch_function__(cls, func, types, args, kwargs):
...         return -1
>>> is_tensor_like(TensorLike())
True
r	  )rŽ
  r9   r:   rö
  )Úinps   &r   Úis_tensor_likeru  Ý  s(   € ôD �‹9œŸ™Ð$×JÐJ¬°Ð5IÓ(JÐJr   c                   óh   a € ] tR tRt o RtV 3R lR ltRR ltR tR t]	R	 4       t
V 3R
 ltRtV tR# )ÚTorchFunctionModei  a¹  
A ``TorchFunctionMode`` allows you to override the meaning of all
``__torch_function__`` overridable functions within a dynamic scope,
without having to actually create a tensor subclass or manually
monkey-patch functions in the PyTorch API.  Some common situations
where you should use a mode:

    * You want to override the meaning of factory functions, or other
      functions that do not otherwise take a tensor as an argument
      (these cannot be overridden with tensor subclasses).

    * You want to override the behavior of all functions without needing
      to wrap your inputs in tensor subclasses; e.g., if you are just
      interested in logging intermediate computations.

    * You want to control the order of execution of various tensor
      subclasses explicitly, rather than implicitly via the return of
      ``NotImplemented``.

Independent subclasses of :class:`TorchFunctionMode` are compositional:
modes can be pushed onto a stack using ``with MyMode():``.
When you call functions in the PyTorch API inside your
``__torch_function__`` implementation, by default, they will forward on to
the next mode on the mode stack.  If you want recursively call back into
your current ``__torch_function__`` implementation, either explicitly
invoke ``self.__torch_function__(...)``, or use the context manager
``enable_torch_function_mode(self, replace=self.inner)`` to make PyTorch
API self-referential (beware of infinite loops, in this case!)
c                ó   <€ V ^8„  d   QhRR/# )r   r   NrS  )r   Ú__classdict__s   "€r   r   ÚTorchFunctionMode.__annotate__$  s   ø€ ÷ ñ ˜$ñ r   c                ó   € R # r›  rS  rI  s   &r   r  ÚTorchFunctionMode.__init__$  s   € Ùr   Nc                ó   € \         hr›  )ÚNotImplementedError©r  r   rF  r#   r$   s   &&&&&r   r	  Ú$TorchFunctionMode.__torch_function__'  s   € Ü!Ð!r   c                ó   € \        V 4       V # r›  )Ú
_push_moderI  s   &r   Ú	__enter__ÚTorchFunctionMode.__enter__*  s   € Ü�4ÔØˆr   c                ó   € \        4        R # r›  )Ú	_pop_mode)r  Úexc_typeÚexc_valÚexc_tbs   &&&&r   Ú__exit__ÚTorchFunctionMode.__exit__.  s   € ÜŽr   c                óF   € \         P                  ! R ^R7       V ! V/ VB pV# )zP`Mode.push()` is no longer necessary and can be replaced with just `with Mode()`r:  )r+   rA  )Úclsr#   r$   Úinstances   &*, r   ÚpushÚTorchFunctionMode.push1  s*   € ä�ŠØ^Øõ	
ñ ˜Ð' Ñ'ˆØˆr   c                ó$   <€ V ^8„  d   Qh/ R;R&   # )r   rw  r‡  rS  )r   ry  s   "€r   r   rz    s   ø‡ ‚ ð> Ñò? r   rS  ©rS  N)rù
  rC  Ú__qualname__Ú__firstlineno__Ú__doc__r  r	  rƒ  rŠ  Úclassmethodr�  Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__©ry  s   @r   rw  rw    s@   ø‡ € ñ÷Bð ô"òòð ñó ð÷a ƒ r   rw  c                  óL   € \        4       p V ^ 8”  d   \        V ^,
          4      # R# rŸ  )r   r   )Ú	stack_lens    r   rD  rD  ;  s%   € Ü)Ó+€IØ4=À´MÔ! )¨a¥-Ó0ÐKÀtÐKr   c                  óh   € \        4       p \        V 4       Uu. uF  p\        V4      NK  	  up# u upi r›  )r   r¦   r   )rœ  r3  s     r   Ú _get_current_function_mode_stackrž  @  s/   € Ü)Ó+€IÜ/4°YÔ/?Ó@Ñ/?¨!Ô" 1Ö%Ñ/?Ñ@Ð@ùÒ@s   ˜/c                 ó   € \        V 4       R # r›  )r   )rÔ  s   &r   r‚  r‚  E  s
   € Ü! $Ö'r   c                  ó   € \        4       p V # r›  )r   ©Úolds    r   r†  r†  I  s   € Ü
#Ó
%€CØ€Jr   c               #   ób   "  € \        4       p  V x € \        V 4       R #   \        T 4       i ; i5ir›  )r†  r‚  r¡  s    r   r?  r?  N  s$   é € ä
‹+€CðØŠ	ä�3ŽøŒ
�3�üs   ‚/Ž ’/Ÿ,¬/c                   ó*   a € ] tR tRt o RR ltRtV tR# )ÚBaseTorchFunctionModeiW  Nc                ó   € Vf   / pV! V/ VB # r›  rS  r  s   &&&&&r   r	  Ú(BaseTorchFunctionMode.__torch_function__X  s   € ØŠ>ØˆFÙ�TÐ$˜VÑ$Ð$r   rS  r’  )rù
  rC  r“  r”  r	  r˜  r™  rš  s   @r   r¥  r¥  W  s   ø‡ € ÷%ò %r   r¥  c               #   ó\  "  € \         P                  P                  4       p  \         P                  P                  \         P                  P                  P
                  4       R x € \         P                  P                  V 4       R #   \         P                  P                  T 4       i ; i5ir›  )r9   r)  Ú_get_torch_function_stateÚ_set_torch_function_stateÚ_TorchFunctionStateÚENABLED)Ú	old_states    r   Ú_enable_torch_functionr®  ^  se   é € ä—‘×2Ñ2Ó4€Ið6Ü�‰×*Ñ*¬5¯8©8×+GÑ+G×+OÑ+OÔPÛä�‰×*Ñ*¨9Ö5øŒ�‰×*Ñ*¨9Õ5üs   ‚B,¢AB Á'!B,Â!B)Â)B,c               #   óª   "  € \         P                  P                  4       ;_uu_ 4         R x €  R R R 4       R #   i ; i  + '       g   i     R # ; i5ir›  )r9   r)  Ú_RestorePythonTLSSnapshotrS  r   r   Úenable_reentrant_dispatchr±  h  s?   é € ô 
�‰×	+Ñ	+×	-Õ	-ð	Ûà÷	 
.Ñ	-øð ú÷	 
.×	-Ð	-üs%   ‚'A©?«:°
Aº<¼?¿A	Á
	Ac                óB   € \         P                  P                  WW#4      # )aá	  Skip one level of ``__torch_function__`` dispatch and call the function.

This is primarily useful for **Tensor subclasses** that want to call into
a function's implementation while still intercepting PyTorch operations
inside that function.

Example with Tensor subclass. Only ops whose inputs include a
``LoggingTensor`` are intercepted; once ``redispatch_function`` returns
a plain ``torch.Tensor``, subsequent ops (here ``+ 1``) are not logged.

    >>> from torch.overrides import has_torch_function, handle_torch_function
    >>> class LoggingTensor(torch.Tensor):
    ...     depth = 0
    ...
    ...     @classmethod
    ...     def __torch_function__(cls, func, types, args, kwargs=None):
    ...         print(f"{'  ' * cls.depth}Calling {func.__name__}")
    ...         cls.depth += 1
    ...         r = torch.overrides.redispatch_function(func, types, args, kwargs)
    ...         cls.depth -= 1
    ...         return r
    >>> def scaled_mul(a, b):
    ...     if has_torch_function((a, b)):
    ...         return handle_torch_function(scaled_mul, (a, b), a, b)
    ...     return a * b + 1
    >>> x = LoggingTensor(torch.tensor([3.0]))
    >>> y = LoggingTensor(torch.tensor([4.0]))
    >>> result = scaled_mul(x, y)
    Calling scaled_mul
      Calling mul
    >>> result
    tensor([13.])

Note that only ``mul`` is logged, not ``add``: ``redispatch_function``
returns a plain ``torch.Tensor``, so the ``+ 1`` inside ``scaled_mul``
no longer sees a ``LoggingTensor`` input and ``__torch_function__`` is
not triggered.

With ``TorchFunctionMode`` the mode stays active across all inner ops,
so the ``+ 1`` is now visible too.  Use ``with self:`` after
``redispatch_function`` to re-enable the mode for those inner calls.

    >>> from torch.overrides import TorchFunctionMode
    >>> class LoggingMode(TorchFunctionMode):
    ...     def __init__(self):
    ...         self.depth = 0
    ...
    ...     def __torch_function__(self, func, types, args, kwargs=None):
    ...         print(f"{'  ' * self.depth}Calling {func.__name__}")
    ...         self.depth += 1
    ...         with self:
    ...             r = torch.overrides.redispatch_function(
    ...                 func, types, args, kwargs
    ...             )
    ...         self.depth -= 1
    ...         return r
    >>> a = torch.tensor([3.0])
    >>> b = torch.tensor([4.0])
    >>> with LoggingMode():
    ...     result = scaled_mul(a, b)
    Calling scaled_mul
      Calling mul
      Calling add
    >>> result
    tensor([13.])
)r9   r)  Ú_skip_one_hop_torch_function)r   rF  r#   r$   s   &&&&r   Úredispatch_functionr´  x  s   € ôF �8‰8×0Ñ0°¸dÓKÐKr   )rE  rd  r  rÁ   r¾   rk  ru  rr  r$  r±  r´  )z.*is deprecated, please use.*r9   r›  )<r•  rX  rO  Ú
contextlibr"  rA  rF  r+   Úcollections.abcr   r   r   Útypingr   r   r   Útyping_extensionsr	   r9   Útorch._Cr
   r   r   r   r   r   r   r   r   rQ  r   r   r1   ÚcacherE  rK  r  r$  r5  rÁ   r¾   r¿   rÀ   ra  rd  rk  ro  rr  ru  rw  rD  rž  r‚  r†  Úcontextmanagerr?  r¥  r®  r±  r´  rS  r   r   Ú<module>r¼     s  ðñó, ã Û Û Û 
Û Û ß .Ý ß %Ñ %Ý 'ã ÷
÷ 
õ 
ò€ñ ˆtƒ_€ÙˆTƒ]€÷ ðF ‡�Øôaó ó ðaðH	 ‡�ôó ðð2 ‡�ØôUó ó ðUõp$'÷TLõ^Vñr !ØðóÐ ñ. 'Øðó	Ð ñ *Ø ð	óÐ ð ‡�ôQ$ó ðQ$ðh ô	+ó ð	+ð ñ2ó ð2ð( ‡�ôó ðð ôGó ðGò2"K÷J6ñ 6òrLò
Aò
(òð
 ×Ññó ðô%Ð-ô %ð ×Ññ6ó ð6ð ×Ññó ðôCLr   