+ jʜaROtH0t^RIt^RIHtHt^RIHtHt^RIH t H t ^RI t .RPOt ]P!]4t] !R4t] !R4t]]]3,t]]]3,t]!RR44t]]]]],.]],3,t]!R R 44t]!4t]^k/s]^k/t]^kR R lt]] ]]3,]],3,t!/s"]^k]]]] ]]3,,3,t#/s$]^k/s%]^k/s&]^k^s']^k](!0RQm4t)]^kR Rlt*RR/RRllt+/t,]^ k]-!4t.]^ k/t/]^ kRRlt0RRlt1RRlt2RRlt3RRlt4RRlt5RR lt6R!R"lt7R#R$lt8R%RR&RR'R/R(R)llt9R*RR+RR,R/R-R.llt:R/R0lt;R1R2ltR9R/R:R;llt?Rllt@R?RR@RR>T-- -    3 3 3r!c <V^8dQhRS[S[S[,,R,RS[S[S[,,R,RS[S[S[,,R,RS[RR/#)r dsl_namesN op_symbols dispatch_keys remove_keysr1)rrr)rrs"rrr2NsnII#&-I(3-'$.IXc]*T1 I  I  Ir!c V'dV;P\\V44,unV;P\\V44,unV;P\\V44,unR#V;P\\V44,unV;P\\V44,unV;P\\V44,unR#)a Update filter sets as (current | new) or (current ~ new). Args: dsl_names: DSL names to add/remove from filter op_symbols: Operation symbols to add/remove from filter dispatch_keys: Dispatch keys to add/remove from filter remove_keys: If True, remove keys from filter; if False, add them Note: Uses set.discard as it doesn't raise an exception if the element wasn't in the set to begin with. N)r4set_resolve_iterabler5r6)r7r;r<r=r>s&&&&&rupdate_FilterState.updateNs(  OOs#4Y#?@ @O   $5j$A B B    3'8'G#H H  OOs#4Y#?@ @O   $5j$A B B    3'8'G#H H r!cF<V^8dQhRS[S[S[S[3,,/#rr1)r@tupler)rrs"rrr2ks!   s5c?';  r!c X\VPVPVP4#)z Build a set of dictionary keys based on the current filter state. Returns: set[tuple[str, str]]: Set of (op_symbol, dispatch_key) tuples )_build_key_setr4r5r6)r7s&rbuild_disable_key_set"_FilterState.build_disable_key_setks* OO        r!c <V^8dQhRS[/#rEr)rrs"rrr2xsr!c fRpVR, pVR, p\VP4Fwr#VRV RV R2, pK VR, p\VP4Fwr$VRV RV R2, pK VR, p\VP4Fwr%VRV RV R2, pK V#) z-Return string representation of filter state.zFilter State: z === DSL: ===  z:  z === OP SYMBOL: === z === DISPATCH KEYS: === ) enumerater4r5r6)r7sidslopkeys& r__str___FilterState.__str__xs   0FA 4s"SE$ $A1 %%t//0EA 4s"RD# #A1 )) 3 34FA 4s"SE$ $A5r!cn<V^8dQh/S[S[,;R&S[S[,;R&S[S[,;R&#)rr4r5r6)r@r)rrs"rrr21s8C5  S6  H9 r!r"NF)r#r$r%r&r'rr@r4r5r6r8rBrIrWr(r)r*r+s@rr-r-1sX5 5J!#6K$S9N*II:   Or!r-c$V^8dQhR\/#rEr)rs"rrrs   r!cFaRV3Rllo\;QJdV3RlV4F 'gK RM RM!V3RlV44;'gW\;QJd-V3RlVP44F 'gK R# R#!V3RlVP444#)c$V^8dQhR\/#rEr\)rs"rr%_has_cow_tensor..__annotate__sdr!c <\V\P4'd \PP V4#\V\ \ 34'd;\;QJdV3RlV4F 'gK R# R#!V3RlV44#\V\4'dW\;QJd-V3RlVP44F 'gK R# R#!V3RlVP444#R#)c34<"TF pS!V4xK R#5iNr".0item is_cow_tensors& r 9_has_cow_tensor..is_cow_tensor..s;st}T**sTFc34<"TF pS!V4xK R#5irbr"rcs& rrgrhsD|t}T**|ri) isinstancetorchTensor_C_is_cow_tensorlistrFanydictvalues)argrfs&rrf&_has_cow_tensor..is_cow_tensors c5<< ( (88**3/ / cD%= ) )3;s;33 ;3 ;3;s;; ; c4 3Dszz|D33 D3 D3Dszz|DD Dr!c34<"TF pS!V4xK R#5irbr"rdrtrfs& rrg"_has_cow_tensor..s2Tc}S!!TriTFc34<"TF pS!V4xK R#5irbr"rws& rrgrxs:&5s cori)rqrs)argskwargsrfs*,@r_has_cow_tensorr|s} 32T23332T2 2  cc:&,mmo:cccc:&,mmo:7r!c .V^8dQhR\\\,,R,R\\\,,R,R\\\,,R,R\\\\3,,/#)rr;Nr<r=r1)rrr@rF)rs"rrrsg!!Xc]"T)!hsm#d*!#&-! sCx !r!c|a\4oRV3RllpV!V\4V!V\4V!V\4S#)a+ Build a set of dictionary keys based on filter criteria. Args: dsl_names: DSL names to include in key set op_symbols: Operation symbols to include in key set dispatch_keys: Dispatch keys to include in key set Returns: set[tuple[str, str]]: Set of (op_symbol, dispatch_key) tuples cnV^8dQhR\\\,,R,R\RR/#)rentriesNgraph_lib_dictr1)rr _MappingType)rs"rr$_build_key_set..__annotate__s2 % %x}$t+ %=I %  %r!c<\V4pVF+pW19gK W,FpSPV4K K- R#)z;Helper to add matching keys from graph_lib_dict to key_set.N)rAadd)rrresolved_entriesentryrVkey_sets&& r_append_to_set&_build_key_set.._append_to_sets<-W5%E&)00CKK$1&r!)r@_dsl_name_to_lib_graph_op_symbol_to_lib_graph_dispatch_key_to_lib_graph)r;r<r=rrs&&& @rrHrHs> %(EG % %945:67="<= Nr!print_inactiveFc(V^8dQhR\RR/#)rrr1Nr\)rs"rrrsdtr!cb\P4Fwwrp\RV: RV: 24\V4FlwrEVP'g V'gK!RV RVP : RVP : 2pV'dVRVP: 2, p\V4Kn K R#)zn Print all override graphs for debugging purposes. Args: print_inactive: Whether to print inactive nodes zop=z, key=rOz: node.dsl_name=z, node.unconditional_override=z node.active=N)_graphsitemsprintrQrrr)rrUrV node_listrSr0rRs$ r_print_override_graphsrs!(  9 Wv +GA{{{nns"3T]]$44St7R7R6TU!>T[[N++Aa ,!0r!cXV^8dQhR\R\PP/#)r namespacer1rrllibraryLibrary)rs"rrrs$    (=(= r!cV\9d(\PPVR4\V&\V,#)FRAGMENT) _def_libsrlrr)rs&r_get_def_libraryrs0 !$}}44Y K ) Y r!cXV^8dQhR\R\PP/#)rrr1r)rs"rrrs$   1F1F r!cRV3pV\9d)\PPRRV4\V&\V,#)z Get or create the _native IMPL library for a given dispatch key. One library per dispatch key is shared across all overridden ops. _nativeIMPL)_libsrlrr)rrVs& r_get_or_create_libraryrs< l #C %]]**9flKc :r!c@V^8dQhR\R\R\RR/#)rrrkernelr1N)rr)rs"rrr)s)##c##h#SW#r!cW3p\PVR4pVeVP4\PP RRV4pVP WVRR7V\V&R#)z Install (or replace) an aten kernel at (op_symbol, dispatch_key). Creates a fresh Library per (op, key) so we can tear down just this one override via `_destroy_aten_override` without affecting any other override at the same dispatch key. NatenrT) with_keyset)_aten_override_libspop_destroyrlrrimpl)rrrrVexistinglibs&&& r_install_aten_overrider)sg  #C#&&sD1H --   =CHHY $H?"r!c4V^8dQhR\R\RR/#)rrrr1NrL)rs"rrr>s!cr!c`\PW3R4pVeVP4R#R#)zATear down the aten override at (op_symbol, dispatch_key), if any.N)rrr)rrrs&& r_destroy_aten_overrider>s*  ! !9";T BC  r!c(V^8dQhR\RR/#)rrr1ztorch._ops.OpOverload | NonerL)rs"rrrEsc.Lr!cVPR4wrpT;'gRp\\PPV4p\WC4# \ dR#i;i)u  Resolve `op_symbol` to a concrete OpOverload on `torch.ops.aten`. Accepts bare names ("bmm" → aten.bmm.default) and overload-qualified names ("add_.Tensor" → aten.add_.Tensor). Returns None if the op is not registered (e.g. a test-only op_symbol that never hit the C++ dispatcher). .defaultN) partitiongetattrrlopsrAttributeError)rname_ overload_namepackets& r_resolve_aten_overloadrEs\'005D]!..YM.v-- s.A A A c0V^8dQhR\R\/#)rrr1rL)rs"rrrVs    r!c\V4pVf\RV: 24h\VP4pVP R^4wr4VP R^4wr5RV 2#)uReturn the schema of at:: with the `aten::` prefix stripped. Accepts bare names ("bmm" → aten.bmm.default) and overload-qualified names ("add_.Tensor" → aten.add_.Tensor). z aten op not found for op_symbol=z::()rrr_schemasplit)roverloadrRrrestrzs& r_aten_schema_tailrVsg &i0H? }MNN H  AggdAGAjja GA tf:r!c4V^8dQhR\R\RR/#)rrrr1NrL)rs"rrrfs!"""""r!cV\9dR#\R4PV \V4 24\ V4p\ P PRV 24!RV/Rl4\PV4R#)Nrz _native::_aten_overloadcV!V/VB#rbr")rrzr{s$*,r(_define_native_op_once..tsn ? ? r!) _defined_native_opsrdefinerrrlr register_faker)rr aten_overloads&& r_define_native_op_oncerfsy  ""Y&&$0A)0L/M'NO+95M MM)D6 23 ] D!r!chV^8dQhR\PPR\R\RR/#)rrr0rr1N)rlrrr r)rs"rrrs6   &3CF r!cVP'g)\RVP: RVP: R24h\ VPVP4VP VPVP VRRR7R#)z Register a single node implementation with the library. Args: lib: The torch.library.Library instance node: The override node to register dispatch_key: The dispatch key for registration zJ_OverrideNode must have a non-empty node_id before registration (dsl_name=z , op_symbol=)FT)rallow_overrideN)r ValueErrorrrrrr)rr0rs&&&r_register_node_implrsx <<<)dnn5Gq J  4<<8HH    r!cV^8dQhR\\\,,R,R\\,/#)riterableNr1)rr)rs"rrrs,hsm 3d :x}r!cpVf.#\V\4'd\V\4'dV3#V#)z Resolve various input types to a consistent iterable of strings. Args: iterable: String, iterable of strings, or None Returns: Iterable[str]: Consistent iterable output )rkrr)rs&rrArAs5 h ) )Z#-F-F{ Or!enable_dsl_namesenable_op_symbolsenable_dispatch_keyscV^8dQhR\\\,,R,R\\\,,R,R\\\,,R,RR/#)rrNrrr1rrp)rs"rrrsY22DIo,2T#Y-2S /D0 2  2r!c\PRVVV4\PVVVRR7\ VVV4pVF1pVwrVV\ 9gK\ WV\ V,\R7K3 R#)aO Re-enable overrides by removing them from filter state and reregistering. Args: enable_dsl_names: DSL names to re-enable enable_op_symbols: Operation symbols to re-enable enable_dispatch_keys: Dispatch keys to re-enable Note: This function uses reverse filter state management (removing from filters to enable). z>Re-registering ops by dsl: %s, op_symbol: %s, dispatch_key: %sT)r> filter_stateN)loginfo _filter_staterBrHr_register_overrides_from_graph)rrrrrVrrs$$$ rreenable_op_overridesrs$HHH  %3%G"% '> +M  r!disable_dsl_namesdisable_op_symbolsdisable_dispatch_keyscV^8dQhR\\\,,R,R\\\,,R,R\\\,,R,RR/#)rrNrrr1r)rs"rrrsY))T#Y-)d3i$.)c?T1 )  )r!c\PRVVV4\PWV4\P 4pVF2pVwrVV\ 9gK\ VV\ V,\R7K4 R#)aC De-register overrides by updating filter state and reregistering graphs. Args: disable_dsl_names: DSL names to disable disable_op_symbols: Operation symbols to disable disable_dispatch_keys: Dispatch keys to disable Note: This function uses filter state management to selectively disable operations. z>De-registering ops by dsl: %s, op_symbol: %s, dispatch_key: %srN)rrrrBrIr_cleanup_and_reregister_graph)rrrrrVrrs$$$ rderegister_op_overridesrss$HHH *@UV%2$G$G$IG"% '> ) *   r!cFV^8dQhR\R\\,/#)rrr1r)rs"rrrscr!c\4p\P4F6wwr#pVF(pVPV8XgKVP V4K4 K8 \ V4#)zGet list of operations registered by a specific DSL. Args: dsl_name: Name of the DSL to query. Returns: Sorted list of operation names registered by the DSL. )r@rrrrsorted)r operationsrrnodesr0s& rget_dsl_operationsrsTJ!(D}}(y)"1 * r!c nV^8dQhR\R\R\R\\\3,RR/#)rrrrrVr1N)rrF)rs"rrr"sI#L#L#L#L#L sCx #L  #Lr!c`RRlpV!\W4V!\W4V!\W#4R#)z Update the registration mapping dictionaries. Args: dsl_name: The DSL name op_symbol: The operation symbol dispatch_key: The dispatch key key: The dictionary key tuple cV^8dQhR\\\\\\3,,3,R\R\\\3,RR/#)r registrationsymbolrVr1N)rrrrprF)rs"rr/_update_registration_maps..__annotate__5sO # #3U38_ 556 # #38_ #  #r!cdVPV4pVf V.pW0V&R#VPV4R#)z;Helper to add key to registration list or create new entry.N)getappend)rrrV entry_lists&&& r_get_new_entry_or_append;_update_registration_maps.._get_new_entry_or_append5s7 "%%f-  J#-   c "r!N)rrr)rrrrVrs&&&& r_update_registration_mapsr"s*& #3XC4iE7Kr!c<V^8dQhR\R\R\/#)rrzr{r1)objectr)rs"rrrHs!&Tr!cR#)Tr")rzr{s*,r _always_truerHs r!allow_multiple_overridercV^8dQhR\R\R\R\R\R,R\R\R \R R/ #) rbackend lib_symbolrrcondNrrrr1r)rs"rrrLs}MIMI MIMIMI MI d  MI  MI"MI!MI MIr!c VR8wd\RV R24hV\9d\RV: R24hVfV'g \R4h\pW#3p\P V.4p VP RR 4p V R V R \ 2p \ ^, sV P\VVVVVVV R 74V \V&\WW8R 7R#) a- Register a passed override function to the dispatcher. Actually a graph-building operation; real registration happens later. Args: backend: The backend name (DSL name) lib_symbol: Library you're overriding symbols in (must be "aten") op_symbol: Name of the operation you're overriding dispatch_key: Dispatch key to override cond: Predicate choosing whether `impl` applies to a given call. May be None if `unconditional_override=True`. impl: Implementation function for the override allow_multiple_override: Allow overriding an existing override unconditional_override: Implementation doesn't have a fallback and doesn't require torch.DispatchKeySet as the first argument. When True, a trivially-True predicate is supplied for the router if `cond` is None. Raises: ValueError: If lib_symbol is not "aten", if dispatch_key is in _DISALLOWED_DISPATCH_KEYS (Meta / CompositeImplicitAutograd / CompositeExplicitAutograd), or if cond is None without unconditional_override=True. rz.Unsupported lib_symbol (must be "aten", got: ""z dispatch_key=z is not supported. Overrides must be installed at a backend key (e.g. CPU, CUDA, XPU); the router's fake kernel redispatches to aten and would recurse otherwise.Nz8cond must be provided unless unconditional_override=Truerr)rrrrrrr)rV) r_DISALLOWED_DISPATCH_KEYSrrrreplace_node_id_counterrr r) r r rrr rrrrVop_graph _sanitizedrs &&&&&&$$ rregister_op_overriderLsHVI*UVWXX00L+,G H   |%WX X  #C{{3#H ""3,J AgYa(8'9:G OO%#9  GCLg,Hr!force_reregisterctV^8dQhR\\,R\\,R\R\/#)roriginal_graph new_graphrr1)rpr r)rs"rrrs:''''M"' '  'r!cV'dR#W8g#)a' Determine if a graph needs reregistration based on changes. Args: original_graph: The original graph before modification new_graph: The graph after modification force_reregister: If True, always reregister regardless of changes Returns: bool: True if reregistration is needed Tr")rrrs&&$r_should_reregister_graphrs"  &&r!rc pV^8dQhR\R\R\\,R\R,RR/#rrrgraphrNr1rrpr r-)rs"rrrsD   %   r!c$\VVVVR7R#)ag Reregister a graph's routes from scratch. Used by reorder / deregister / reenable. Libraries are intentionally long-lived singletons; we rebuild the per-op router closure here. Args: op_symbol: The operation symbol dispatch_key: The dispatch key graph: The graph to register filter_state: Optional filter state for conditional registration rN)r)rrrrs&&&$rrrs&# ! r!keys_to_processreregister_overridesc V^8dQhR\R\\\\3,,R,R\R\ R,RR/#)rtransformation_fnr Nr!rr1)UserOrderingFnr@rFrrr-)rs"rrrsZNN%NsCx)D0N N % N  Nr!c VeTM\\P44p\V4FwrVWV3\9dK\\WV3,4pV!WVV4p\T\4'g.\PR\T4PTT4KwT\YV3&T'gK\YxRR7'gK\!TTTTR 7K R# \\ \ \3d\PRTTRR7K\d\PRTT4EKi;i) ad Apply a transformation function to graphs and optionally reregister. This is the core pattern used by reorder_graphs_from_user_function and can be reused for other graph transformation operations. Args: transformation_fn: Function to transform each graph keys_to_process: Keys to process, or None for all graphs reregister_overrides: Whether to reregister changed graphs filter_state: Optional filter state for conditional registration Note: If transformation_fn raises an exception for a specific graph, that graph will be skipped and processing will continue with remaining graphs. NAGraph transformation failed for %s/%s. Preserving original graph.Texc_infoNUnexpected error in graph transformation for %s/%s. Preserving original graph.zbGraph transformation returned invalid type %s for %s/%s. Expected list. Preserving original graph.F)rr)r@rkeysrp TypeErrorrr RuntimeErrorrwarning Exception exceptionrktyper#rr) r#r r!r target_keysrrrrs &$$$ r_apply_graph_transformationr2s76+6C RI$)T** KKtY((   .7)* $< % %  *)  U$5:~|D  KKS     MM`    s C$$4EE$EEcRV^8dQhR\R\\\3,/#)roverrides_onlyr1)rrrrr)rs"rrr s&333 &( 3r!crV'd/pM^RIHp\V!44pVP\4V#)aT Return a decomposition table suitable for passing to ``ExportedProgram.run_decompositions`` or to Inductor / Dynamo as a decomposition set for ``torch.compile``. This is the canonical way to apply native overrides outside of eager. Callers opt in explicitly -- the registry does **not** install into any global compile/export decomp table on its own. This preserves three properties: * ``import torch._native`` doesn't transitively import inductor / dynamo / triton. * No other consumer (ONNX, tests, third-party backends) accidentally picks up our overrides. * Scoping is up to the caller: pass the table only where routing is desired. By default, the returned table contains ``torch.export.default_decompositions()`` with native-registered overrides layered on top -- matching typical "run the usual decompositions, plus my overrides" intent. The override entries win over any same-op default, because they're merged last. Example: ep = torch.export.export(model, args) ep = ep.run_decompositions( torch._native.registry.native_decomp_table() ) Args: overrides_only: If True, return only the native-registered overrides and no default aten decompositions. Useful for inspection / debugging, or when composing tables manually. Returns: A dict mapping ``OpOverload`` to decomposition callable. )default_decompositions) torch.exportr6rrrB_native_decomp_overrides)r4tabler6s& rnative_decomp_tabler: s2R(* 8+-. LL)* Lr!c pV^8dQhR\R\R\\,R\R,RR/#rr)rs"rrrVsKy<y<y<y<  y< % y<  y.__annotate__s   r!c<SFBwr4V!V/VBpT'gK\\PPT4!T/TBu# S# \dT'ghK_i;irb)r.rrlrr)rzr{r>r  impl_namematched _NO_MATCH cond_impls&&& r _dispatch1_register_overrides_from_graph.._dispatchsm(OD // wuyy00))rlcompileris_dynamo_compilingr| call_boxed)keysetrGrrzr{resultrCrEs&$$*, r eager_router4_register_overrides_from_graph..eager_routersr NN . . 0 0*#T4V44!4262 24G Y ''@@@ @ r!c4<S!WRR7pVSJd\#V#)TrI)NotImplemented)rzr{rNrCrEs*, rcompile_router6_register_overrides_from_graph..compile_routers"4F Y ! ! r!)rr8rrrrrrrr8rrlr get_kernelrr)rrrrrr0enablerfallback_kernelrOrSrCrErDs&&&$ @@@rrrVs ! .C-/I  !//5F  < 8   dllDLL9 :DKDK&i0H 93    $ ( (4 8mm.. {/C\ROI  !0AI.9LA -; *r!cV^8dQhRR/#)rr1Nr")rs"rrrs     r!cb\P4FwrVwr#\VVV4K R#)zG Perform all registration calls from previously-built override graphs. N)rrr)rVrrrs r_register_all_overridesrZs/mmo "% &    &r!c4V^8dQhR\R\RR/#)rfnr!r1N)r$r)rs"rrrs( r!c \VVR7R#)a Reorder override graphs using a user-provided ordering function. Args: fn: User-provided function that takes (op_symbol, dispatch_key, graph) and returns a reordered graph reregister_overrides: Whether to reregister graphs that have changed Note: This function uses the common graph transformation pattern and can serve as an example for other graph manipulation operations. r#r!Nr2)r\r!s&$r!reorder_graphs_from_user_functionr`s" 1r!clV^8dQhR\\\\.\3,R\RR/#)r filter_fnr!r1N)rrr r)rs"rrrs955c=147855 5r!c4aRV3Rllp\VVR7R#)a Apply a filter function to remove nodes from graphs. This is a convenience function that uses the graph transformation pattern to filter out unwanted nodes. Args: filter_fn: Function that takes (op_symbol, dispatch_key, node) and returns True to keep the node, False to remove it reregister_overrides: Whether to reregister modified graphs Example: # Remove all nodes with "deprecated" in the DSL name _apply_graph_filter( lambda op, dk, node: "deprecated" not in node.dsl_name, reregister_overrides=True ) Note: If filter_fn raises an exception for a specific graph, the original graph will be preserved and processing will continue. c tV^8dQhR\R\R\\,R\\,/#rrrrr1rrpr )rs"rr)_apply_graph_filter..__annotate__s6&)26}2E m r!c<VUu.uFpS!WV4'gKVNK up#uupi \\\\3d\P RTTRR7Tu#\ d\PRTT4Tu#i;i)z-Apply filter_fn to graph with error handling.r&Tr'r)r+rrr,rr-r.r/)rrrr0rbs&&& rfiltering_transformation5_apply_graph_filter..filtering_transformations %*WUTi QU.VDDUW WW:~|D  KKS   L  MM`  L  s++&&++6B #B ,B B r^Nr_)rbr!rjsf$ r_apply_graph_filterrls8, 21r!cnV^8dQhR\\\.\3,R\R\RR/#)r condition_fn ordering_fnr!r1N)rrrr$)rs"rrr/sDLLC:t+,LL L  Lr!c8aaRVV3Rllp\VVR7R#)a Apply reordering only to graphs that match a condition. This allows for more targeted reordering operations. Args: condition_fn: Function that takes (op_symbol, dispatch_key) and returns True if the graph should be reordered ordering_fn: Ordering function to apply to matching graphs reregister_overrides: Whether to reregister modified graphs Example: # Only reorder CUDA operations _apply_selective_reordering( condition_fn=lambda op, dk: dk == "CUDA", ordering_fn=lambda op, dk, g: sorted(g, key=lambda n: n.dsl_name), reregister_overrides=True ) Note: If condition_fn or ordering_fn raises an exception for a specific graph, the original graph will be preserved and processing will continue. c tV^8dQhR\R\R\\,R\\,/#rerf)rs"rr1_apply_selective_reordering..__annotate__Ms6)))&))26}2E) m )r!c<S!W4pT'd S!YT4#T# \\\\3d\P RTTRR7Tu#\ d\PRTT4Tu#i;i \\\\3d\P RTTRR7Tu#\ d\PRTT4Tu#i;i)z=Apply ordering_fn conditionally based on condition_fn result.r&Tr'r)ri)rrrshould_reorderrnros&&& rconditional_transformation?_apply_selective_reordering..conditional_transformationMs ))BN"  "9EBB" G:~|D  KKS   L  MM`  L  z><H  W !     d    s8B6A=A= A=<A=6C8CCCr^Nr_)rnror!rusff$ r_apply_selective_reorderingrw/s<))V 41r!chV^8dQh/^\9d \;R&^\9d@\\\\3,\ P P3,;R&^\9d\\\3,;R&^\9d \;R&^\9d \;R&^\9d \;R&^\9d \;R&^\9d \;R&^\9d\\,;R &^ \9d/\\\ P P3,;R &^ \9d\\,;R &^ \9d@\\\\3,\ P P3,;R &#) rrrr8rrrrrrrrr)__conditional_annotations__r-rrrFrrlrrrr _GraphsTyperint frozensetr@)rs"rrrsYR-,|,SX98tE#s(OU]]22 238YF65$vx/05Gnox*) )yz.-L-{|+**}F#GV9S>WP104U]]*** +0QT&%SX%U`GFT%S/5==+@+@@AFar!)r$rr`rrrr:>MetaCompositeExplicitAutogradCompositeImplicitAutogradrZ)Iryloggingcollections.abcrr dataclassesrrtypingrr torch.libraryrl__all__ getLoggerr#rr r rrrr rrpr$r-rrr8r|rrrFrzrrrrrrr|rrHrrr@rrrrrrrrrrrArrrrrrrrr2r:rrZr`rlrwr)rys@rrs.(% ! cN CL QW  QTN   "3T-%894 ;NNO TT Tp+n ,798.465  5c?D$778 CeCHo../ (*)+--(** -6F- !HU(/1 0 #% EGF #*"  "84&2/3215248 2j)04)26)59 )X$#LLMI%*MI$)MI`'# '0 )- 6N48N"' N )- Nb3ly< )- y