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NumberType RETURN_TYPE ShapeType StrideType TensorLikeTensorLikeType type_to_dtypebackwards_not_supported) FakeTensorFakeTensorMode)handle_torch_functionhas_torch_function) tree_flattentree_maptree_unflattenprimsDEFIMPLCompositeExplicitAutograd BackendSelectAutogradMeta)rabsacosacoshasinasinhatanatanhcoscosh bessel_i0 bessel_i0e bessel_i1 bessel_i1e bessel_j0 bessel_j1 bitwise_notcbrtceil conj_physicaldigammaerferf_inverfcerfcxexpexpm1exp2fillfloorimagisfinitelgammaloglog1plog2log10ndtrinegreal reciprocalroundsignsignbitsinsinhspherical_bessel_j0sqrttantanhtruncaddatan2 bitwise_and bitwise_or bitwise_xordiveqfmaxfminfmodfrexpgcdgegthypotigammaigammacleltmaximumminimummulne nextafterpow remainderrsqrt shift_leftshift_right_arithmeticshift_right_logicalsubzeta as_stridedbroadcast_in_dim collapse_viewconj expand_dimsslice split_dimsqueeze transposeview_ofview_element_typeas_strided_scattercollapsecatreshaperevwherecloneconvert_element_type device_putitem maximum_value minimum_value copy_stridedcopy_toresizeamaxaminprodsumxor_sumvar empty_stridedempty_permuted scalar_tensoriotasvdnormal_uniform_helperfft_r2cfft_c2cfft_c2r _make_token _sink_tokensshapestridesdtypedevice tensorlikerrrrc[U[5(aU(a,[U[5(d[S[ U535eU(a,[U[5(d[S[ U535eSnSn[ [ U55n[ R"S5nOUb}[U[ R5(d[S[ U535e[UR5n[UR55nURnURnO8Uc [S5eUc [S5eUc [S5eUc [S 5eUcWO [U5nUcWO [U5nUcWOUnUcWOUn[U[5(a[ R"U5n[ R"XX4S 9$) Nz7shape must be None or a Sequence for Number input, got z9strides must be None or a Sequence for Number input, got cpuz%tensorlike must be torch.Tensor, got z.shape must be provided when tensorlike is Nonez0strides must be provided when tensorlike is Nonez.dtype must be provided when tensorlike is Nonez/device must be provided when tensorlike is Nonerr) isinstancerrAssertionErrortypertorchrr tuplerstriderstrr) rrrrrinferred_shapeinferred_stridesinferred_dtypeinferred_devices R/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torch/_prims/__init__.py TensorMetars*f%% E844 I$u+W  :gx88 KDQXM?[ +-,.&tJ'78,,u-  *ell33 7Z8H7IJ z//0 !2!2!45#))$++ = !QR R ? !ST T = !QR R > !RS S#mNuE")/uW~G#mNE &_FF&#f%   uU JJF)tagsuse_old_custom_ops_apiregister_conj_neg_fallthroughschema return_type.meta impl_atendocrrrc^^^^^^UU4SjmU4SjnUU4Sjn URSSS9Sn U[U 5Sn[RR X-5n U(d[ U 5(dq[ RX-[RRS 9 [RU T5 [RX5 [RU T5 GOU RV s/sH;n U RcMU RR (dM/U R"PM= n n [R$R'S U -T[)U 5US 9nUR+T5 U[,R.:XdU(ajUR0RU [R$R2S 5 UR0RU [R$R2S 5 [5[R6R8R:U 5nUR<mU(aUTlO[5[R8R@U S5=n(aURC5Vs/sHn[5UU5RDPM nn[GUS5mTRH"USS6 TRK[RRL5 TRK[RRN5 [)U4SjUS55TlSSK(J)m [UU4SjTRVR55(a[YT5S:Xa[ZRX5 UT4H-nUUl.UUl/UUl0TUl TUl TUl1M/ T$s sn fs snf)z! Creates a primitive operation. c$>T"U0UD6 T"U0UD6$Nr)argskwargsrrs r _prim_impl_make_prim.._prim_impl(s" df$)&))rc&>[T5"U0UD6$rr)rr_prims r_autograd_impl"_make_prim.._autograd_impl3s&u-t>v>>rc>URS5(aUSRS:XaT"U0UD6$[SU55(aT"U0UD6$T"U0UD6$)Nrrc3# UH5n[U[R5=(a URS:Hv M7 g7f)rN)rrrr).0xs r ;_make_prim.._backend_select_impl..9s,NAz!U\\*?qvv/??s=?)getrany)rrrrs r_backend_select_impl(_make_prim.._backend_select_impl6se ::h  F8$4$9$9V$C(( ( NN N N(( (t.v. .r()maxsplitrN)rzprims::) mutates_argsr ConjugateNegativec36># UHoT;dM Uv M g7frr)rttags_intersections rr_make_prim..msR'7!@Q;QAA'7s  )contains_tensor_typesc3H># UHnT"UR5v M g7fr)r)rars rrrrs O7N!%aff--7Ns"zprims.device_put.default)2splitlenr_C parse_schemarprimdefineTagpt2_compliant_tag prim_implimplprim_autograd_implprim_meta_impl arguments alias_infois_writenamelibrary custom_opr register_fakerVIEW_libfallthrough_kernelgetattr_opsopsr'default_tagsaten overloadsrsetintersection_updatediscardcoredata_dependent_outputtorch._subclasses.fake_tensorrr_schemarprim_backend_select_impl__doc__rrr)rrrrrrrrrrr cpp_schemaargrprim_def _prim_packet aten_packetoverload overload_tagsprrrrs `` @@@@r _make_primrs *?/ <<a< ( +D CIK F&&t}5J%9*%E%E DM (C(C DtZ(5D$'"++ +~~ .1nn.E.E CHH+  ==**   |, +  t$ +** *.K MM  tU]]%E%E{ S MM  tU]]%E%Ez R5::>>//6L  E   d; ; ;@K@U@U@W @WHGK * / /@W   a 01--}QR/@A !!%))..1 !!%))"A"ABR}Q'7RR C Ou}}7N7NO O O    &  & !%%dAE " #    # Lw . s3OO%O Oc$\rSrSrSrSrSrSrSrg)$ELEMENTWISE_PRIM_TYPE_PROMOTION_KINDi)r)))rN) __name__ __module__ __qualname____firstlineno__DEFAULT INT_TO_FLOAT ALWAYS_BOOLCOMPLEX_TO_FLOAT__static_attributes__rrrrrsGLKrr)args_with_fixed_dtypestype_promotionr+returnc[U5S:Xa [S5e[R"U6 [ U5nUb[ U5U-n[R "USS06 [R "USS06 [R"U6upE[R"USS06nSnSnUHqn [U [5(a7[R"U 5(dU Rn O4U RnMO[U [5(dMf[U 5nMs UcUb[R"U5nSn Sn UHrn [U [5(a<[R"U 5(aU cU R n MDMFU R n O"[U [5(dMkU bMpU n Mt U bUc [S5eU["R$:Xa[&R(nOU["R*:XaL[R,"U5(d[R."U5(a[&R0"5nOEU["R2:Xa1[R4"U5(a[R6"U5nUc [S5e[&R8"XdXS9$S n [U [&R:[&R<45(aUH|n [U [>[@[&R:[&R<45(d[S [U 535eU =(d [U [@[&R<45n M~ U (a [CU 5n [EU 5$) z Meta function for elementwise operations that produce outputs in the same dtype as their inputs. Stride logic is currently incorrect. rz4elementwise operation requires at least one argumentNallow_cpu_scalar_tensorsTz.dtype must not be None when device is not Nonez.shape must not be None when device is not None)rrFz.Expected int, float, SymInt, or SymFloat, got )#rrutilscheck_same_dtypelistcheck_same_devicecheck_same_shape3compute_elementwise_output_logical_to_physical_perm extract_shaperris_cpu_scalar_tensorrrrrrrr(rboolr'is_integer_dtypeis_boolean_dtypeget_default_dtyper)is_complex_dtypecorresponding_real_dtyperSymIntSymFloatintfloatr r)r,r+rargs_l2p_perm_rr scalar_typerrnumber seen_floatrs r_prim_elementwise_metarHs 4yA~STT D! JE)+,u4 UBTB EADAKKUSKH    F FE EK c: & &--c22   V $ $s)K }0##K0F F c: & &))#..> ZZF" V $ $~" = !QR R AMM MJJE CPP P%%e,,0F0Fu0M0M//1 CTT T%%e,,66u= = !QR R##EFPP J&5<<899Aa#uellENN!KLL$DT!WIN$Mz!eU^^5L'MJ  v&F f rc[R"[R"USR5S5 [ U0UD6$)Nrcg)NzOnly complex dtype is supportedrrrr0_complex_only_elementwise_meta..s7Xr)r_checkr0r<rrHrrs r_complex_only_elementwise_metarOs9 LL tAw}}-/X "4 26 22rrc Z[SUS3[[US9[RS.UD6$)z$ Creates an elementwise unary prim. z(Tensor self) -> Tensorr,rrrrrrrHrNEWrr,rs r_make_elementwise_unary_primrVs=  ./ +N KOO    rc Z[SUS3[[US9[RS.UD6$)z% Creates an elementwise binary prim. z%(Tensor self, Tensor other) -> TensorrQrRrrSrUs r_make_elementwise_binary_primrXs=  <= +N KOO    rc[erNotImplementedErrorrNs r _not_implr\s rr.)rrr,r/r0r1r2r3r4r5r6r;r<r7r8r9r:r=rc[R"UR5(+S5 [R"[R"UR 5S5U5$)Ncg)NzJcbrt: Complex inputs not supported. Consider calling torch.pow(a, 1.0/3.0)rrrrrK_cbrt_aten..s\rgUUUUUU?)rrM is_complexcopysignrxr.rs r _cbrt_atenrdsA LL LLN\ >>%))AEEGU3Q 77rr>r?inputcURR(d [S5e[R"U5n[ XS9$)Nz6prims.conj_physical is only defined for complex dtypes)r)rra RuntimeErrorr0"compute_elementwise_output_stridesr)rers r_conj_physical_metaris6 ;; ! !STT66u=G e --rz$conj_physical(Tensor self) -> Tensorz4Returns the physical conjugation of a complex tensor)rrrrr memory_formatrkcVU[R:wa@[R"URURUR UR US9$[R"U5n[R"URUURUR UR S9$)N)rlayoutrrk)rrmr) rpreserve_formatemptyrrrmrr0rhr)rerkcomputed_strides r _clone_metarqs---{{ KK++<<<<'    BB5I"" KK ++<<<<   rzAclone(Tensor self, *, MemoryFormat? memory_format=None) -> TensorzReturns the copy of a tensorT)rrrrrrrArBrCrDrErFrGrHvaluec2[U[RS9$)NrQrHrr&)rrrs r _fill_metarus ! >FF rz)fill(Tensor self, Scalar value) -> Tensor)rrrrrrJz!imag(Tensor(a) self) -> Tensor(a)rQ)rrrrrrLrMrNrOrPrQz!real(Tensor(a) self) -> Tensor(a)rUrRrSrVrzrWrXrYrZr[r\r]r^r_r`)rrrr,rarbrcrdc@[U[[[R45=(dA [U[R 5=(a [ R"UR5nU(a[R"XSS9$[R"X5$)Nr_) rounding_mode) rr8r@rr>r r0r9rre true_divide)rb is_integrals r _div_atenr{sjQsELL 9:1ell#G(>(>qww(GyyW55  &&rrerfrgrhrirkrlrmrnrorprqrrrycP[U[5(a4[U[5(a[XRUR S9nOH[U[5(a3[U[5(a[XRUR S9n[ R"X5$Nr)rrrrrrrrsrrys r _maximum_atenrjm!Z  Z6%:%: !77188 < Az " "z!V'<'< !77188 < == rrscP[U[5(a4[U[5(a[XRUR S9nOH[U[5(a3[U[5(a[XRUR S9n[ R"X5$r})rrrrrrrrtr~s r _minimum_atenr}rrrtrurvrwrxryr{r|r~rsizerstorage_offsetc[U5[U5:wa#[S[U5S[U535eUS:a[SU35e[R"U5 [R"U5 [ [ RU5S:XaOE[U[R5(a&[R"UR5XU5 [R"XX#5$)Nz len(size)=z != len(stride)=rz!storage_offset must be >= 0, got )rrr0validate_stridesvalidate_shaperoperatorrurrr check_in_bounds_for_storage_typed_storagerrrrrs r_as_strided_metars 4yCKz#d)4DS[MRSS@@PQRR 6"  hllD!Q& Au|| $ $ ))   n    AV < Tensor(a!)rrrrrbroadcast_dimensionsc &^^^^ ^ SSKJnJnJn [ T[ 5(d[ S[T535e[ T[5(d[ S[T535e[ U[5(d[ S[U535eTR[U5:wa%[ STRS[U5S35e[T5TR:a%[ S [T5S TRS35eU4S jn[XbS 5 [U5HNumm [R"U"TRTS :HTT TRT:H5UUU U4Sj5 MP /nSm [![T55GHVmTU;aU"TRT S :H5(aUU"TRT TT:H5(a#UR#TR%5T 5 OeUR#S5 OS[R"TRT TT:HUUU U4Sj5 UR#TR%5T 5 T S -m MU"TTS :g5(aUR#S5 MT TR:XaUR#S 5 GM UR#TR%5T TR'5T -5 GMY TR)TUTR+55$)Nr)guard_or_false guard_or_truesym_ora must be TensorLike, got zshape must be a Sequence, got z-broadcast_dimensions must be a Sequence, got za.ndim (z ) != len(broadcast_dimensions) ()z len(shape) (z) must be >= a.ndim (c>[U[5(d[S[U535eX::a[SUSU35eU[ T5:a[SUS[ T535eU$)Nz.broadcast_dimensions element must be Dim, got z1broadcast_dimensions must be strictly ascending: z <= zbroadcast_dimension z# out of bounds for shape of length )rrrrr)accrrs r_greater_than_reduce4_broadcast_in_dim_meta.._greater_than_reduces!S!! @a J  8 CA3d3%P  E ? &qc)LSQVZLY rrc2>TRTSTT3$)Nz must be broadcastable to r)ridxnew_idxrsrrK(_broadcast_in_dim_meta..4sqwws|n$>uW~>NOrc4>STRTSTT3$)Nz#non-broadcasting semantics require z == r)rr original_idxrsrrKrEs&A!'',BWAXX\]bcf]g\hir)%torch.fx.experimental.symbolic_shapesrrrrrrrrndimrr enumeraterrMrrangeappendrrrr) rrrrrrr new_stridesrrrs `` @@@r_broadcast_in_dim_metars a $ $9$q'CDD eX & &=d5k]KLL *H 5 5;DAUsCW?X>YYZ [   5zAFF|CJ<7LQVVHTUVWW   r:""67 W 1773<1$eGn &D E O 8 KLSZ  & &aggl3q899!!'',"75:"EFF&&qxxz,'?@&&q) GGL)U3Z7i""188:l#;<'!+LU3Z1_--""1%'""1%""188:l#;affh|>T#TU/!2 <<{A,<,<,> ??rc[U5nUHnSX4'M Un[U5HupgUS:wdM URU5nM URU5$Nr)r2r unsqueezeexpand)rrrsbroadcast_dimensionvrrs r_broadcast_in_dim_atenrUsZ U A3!# 4 AA, 7 C A 88E?raD Creates a view of a with the specified shape. Allows adding dimensions of any length and broadcasting dimensions of length one in a to any length. The location of the broadcast dimensions must be specified using the broadcast_dimensions argument. Changing the relative order of dimensions is not supported. zVbroadcast_in_dim(Tensor(a) a, SymInt[] shape, int[] broadcast_dimensions) -> Tensor(a)startendc^^[SUR55n[R"UT5 [R"UT5 [R "TT:UU4Sj5 g)Nrc>STSTS3$)Nz Attempting to collapse but end, z, is less than start, !r)rrsrrK)_validate_collapse_args..s23%7MeWTUVr)maxdimr0 validate_idxr _check_value)rrrrs `` r_validate_collapse_argsrvsN q!%%'?D tU# tS!  u Vrc[U5S:XaSO [U5nSnXUS-HnX4-nM USUU4-XS-S-$)zR Returns the shape of a with dims in [start, end) merged into a single dimension. rrrN)rr)rrr dim_lengthrs r_collapsed_shapers[ J!ODuEJ 37 #^ $ 5>ZM )E'),< <T$rr)rsrrK'_collapse_view_helper..s}r)NN)rrrrrrrrrrrrrnumelrrrMminr0make_contiguous_strides_for)rrrrrrrrrrvalid_oprrlength new_shapers ` r_collapse_view_helperrsZ a $ $9$q'CDDAc* vv{((*vv{s|~Haggi1n%%q%!)R0CJ!O'Na'LG!G$4u1W~$EEHh%/001h Q/H X45h''\FS1Weai, :?.F -ZFVq[!!q%!)R0CejAo..Cj(F 1fu  )E'),< Tensor(a)c(URR(d [S5eURURUR 5UR 55n[RRXR5(+5 U$)Nz$Expected complex dtype in prims.conj) rrargrrrrrr _set_conjis_conj)routs r _conj_metars_ 77  ABB ,,qww A,<,<,> ?C HHs O, Jrz2 Returns a conjugated view of the original tensor zconj(Tensor(a) a) -> Tensor(a) dimensionscUb [[R"X!55nO*[[R"URU55n[ [ U55[ U5:waS[ U53n[U5e[UR5nUHnURUS5 M [[ U55Vs/sH ofU;dM UPM nn[XU5$s snf)z Creates a view of a with a.ndim + len(dimensions) dimensions, with new dimensions of length one at the dimensions specified by dimensions. z+Received duplicate dimensions to expand in r) sortedr0canonicalize_dimsrrr r ValueErrorr2rinsertrr)rrrdimsmsgrrrs rrr+s e--d?@e--affjAB 3t9~T";C O;LMoQWW Ia S^,,:0E, A*> ??s  C/C/r outer_lengthc[U[5(d[S[U535e[R "UR U5 [R"U5 URUU-nURUU-S:wa!SURUSUS3n[U5e/n/n[UR 5HnXq:XaLURX#45 URUR5UU-UR5U45 MTURURU5 URUR5U5 M URXVUR55$)Nrrz(Attempting to split dimension of length z, but outer length of z divides it with a remainder!)rrrrr0rrvalidate_dim_lengthrrrextendrrrr)rrr inner_lengthrrrrs r_split_dim_metarEsM a $ $9$q'CDD qvvs# l+773<</L  |#)6qwws|nE##/.0M O oIKQVV} :   l9 :    3, > 3P Q   QWWS\ *   qxxz# /  << 0@0@0B CCrcURUU-nURSUX#4-URUS-S-nURU5$)Nrr)rr)rrrrrs r_split_dim_atenrbsN773<</L#,!==a @RRI 66) rz Creates a view of a with the given dimension (of length l) split into two dimensions, with the outer of the two having length outer_length and the inner of the two having computed length inner_length such outer_length * inner_length = l. zAsplit_dim(Tensor(a) a, int dim, SymInt outer_length) -> Tensor(a)c2[U[5(d[S[U535eUHVn[R "UR U5 URUS:wdM9[SUSURUS35e /n/n[[UR55HJnX!;aM URURU5 URUR5U5 ML URX4UR55$)NrrzCannot squeeze dimension z with size z (must be 1))rrrrr0rrrrrrrrr)rrrrrs r _squeeze_metar{s a $ $9$q'CDD 1663' 773<1  +C5 AGGCL>V IKS\"   &188:c?+ # << 0@0@0B CCrz~ Creates a view of the tensor with the specified dimensions removed. The removed dimensions must each have length one. z3squeeze(Tensor(a) a, int[] dimensions) -> Tensor(a) permutationc UR[U5:wa'SURS[U5S3n[U5e[R"URU5(dSUS3n[U5eS/UR-nS/UR-n[ U5H+upVUR UX5'UR5UXE'M- UR[U5[U5UR55$)Nz'Attempting to permute a tensor of rank z', but received a permutation of length rz!Received an invalid permutation, r) rrrr0is_valid_permutationrrrrrr)rrrrrrrs r_transpose_metarsvv[!!7x?fgjkvgwfxxyzo  % %affk : :1+a@oaff I#,Kk* 88:c? + <<i(% *N>N>P QQrc.[R"X5$r)rpermute)rrs r_transpose_atenrs == ((rz Creates a view of the tensor with its dimensions permuted. The length of the permutation must be the rank of the tensor, and each element of the permutation specifies the new order for the corresponding dimension. z6transpose(Tensor(a) a, int[] permutation) -> Tensor(a)ctURURUR5UR55$r)rrrrrcs r _view_of_metars( <<Q-=-=-? @@rc8URUR5$r)rrrcs r _view_of_atenrs 66!''?rz' Creates a view of the tensor. z!view_of(Tensor(a) a) -> Tensor(a)c$URU5$rrrrs r_view_element_type_metar 66%=rc$URU5$rrrs r_view_element_type_atenrrrz> Creates a view of the tensor with a different dtype. z9view_of_dtype(Tensor(a) a, ScalarType dtype) -> Tensor(a)srcc^^^^^^[R"T5 [R"T5 [R"TTT5m[R "TR 5T:UUUUU4Sj5 [R "[R"TRT5UU4Sj5 [R"T5$)Nc>STSTSTSTR5STTR5-STR5TR5-3 $)Nzas_strided_scatter: sizes z , strides z, storage offset z and itemsize z requiring a storage size of z' are out of bounds for storage of size ) element_sizer)re required_sizerrrsrrK*_as_strided_scatter_meta..sj(j@QR`Qab"//122Ou113345##(;;=53E3E3G#G"H Jrc(>STRST3$)NzCexpected src to have a size equal to the slice of self. src size = z, slice size = r)rrsrrKr sUVYV_V_U``optouvr) r0rrcompute_required_storage_lengthrrMr is_same_shaperclone_preserve_strides)rerrrrrs`````@r_as_strided_scatter_metars  6"99$WM LL &   LL CIIt,v  ' ' ..rz Creates a new tensor equivalent to ``out = input.clone()`` after mutation by ``out.as_strided(size, stride, storage_offset).copy_(src)``. zlas_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt storage_offset) -> Tensorch[XU5 [URX5nURU5$r)rrr new_emptyrs r_collapse_metar %s+Ac* %5I ;;y !!rc[URX5nURU5n[R"5 UR U5R U5 SSS5 U$!,(df  U$=fr)rrrrno_gradview_ascopy_)rrrrrs r_collapse_atenr,sV %5I ++i C  AQ  J  Js !A(( A7zn Collapse a span of neighboring dimensions into one. See collapse_view for the corresponding view operation. z0collapse(Tensor a, int start, int end) -> Tensortensorsc ^^^^^ TS:a[ST35eUSRn/n[U5Hum n[U5[UR5:wa0[S[U5ST S[UR535e[[ X$R55HDumummTT:XaUR T5 M"[ R"TT:HUUUUU 4Sj5 MF M [USR5R5n[ R"U5UT'[USU[R"U5S9$)Nrzdim must be non-negative, got z>All tensors must have the same number of dimensions. Expected z but tensor z has c (>STSTSTSTSTS3 $)Nz0Sizes of tensors must match except in dimension z . Expected z in dimension z but got z for tensor number z in the listr) common_lengthrrr tensor_idxsrrK_cat_meta..Ws0NseT -nSE6(Re!l,0rrr)rrrrziprrrMr2copysym_sumrr0r) rrr sym_sum_argstensorrrrrrs ` @@@@r _cat_metarEs? Qw=cUCDD AJ  EL'0 F u:V\\* * J<|J Tensorc`[U[5(d[S[U535e[R "U5 [ [RU5nX R5:wa"SUR5SUS3n[U5e[X[R"U5S9$)Nrz$Attempting to reshape a tensor with z elements to a shape with elements!r) rrrrr0rrrrurrrr)rrrrs r _reshape_metar xs a $ $9$q'CDD  8<< 'E  4QWWYK?YZ_Y``jko ae.O.OPU.V WWrcZURU5R[RS9$Nrj)rrrcontiguous_formatrrs r _reshape_atenr%s% 99U  ! !0G0G ! HHrz` Creates a contiguous tensor with the specified shape containing a copy of the data in a. z+reshape(Tensor a, SymInt[] shape) -> Tensorrc[R"URU5 [R"U[R S9$r")r0validate_dimension_indicesrr empty_likern)rrs r _rev_metar)s/ $$QVVT2   AU-B-B CCrzD Reverses the order of elements along the given dimensions. z#rev(Tensor a, int[] dims) -> Tensorpredc8[UU[RU4S9$)N)r,r+rt)r*rrys r _where_metar,s% "  ;CC $w  rz Selects elements from a and b according to pred. Where pred is true the result contains the element from a, and where pred is false the result contains the element from b. z0where(Tensor pred, Tensor a, Tensor b) -> Tensorcp[U[5(d[S[U535e[U[R 5(d[S[U535e[R RU5(aUR5nO[R"U5n[XUS9$)Nrzdtype must be torch.dtype, got )rr) rrrrrr _prims_common%is_non_overlapping_and_dense_or_falserr0rhr)rrrs r_convert_element_type_metar0s a $ $9$q'CDD eU[[ ) )>tE{mLMM @@CC((*::1= a 66rc0[R"U5(dSnO URn[R "XR XS9n[R"5 [X05sSSS5 $![a SnNWf=f!,(df  g=f)NF)rr requires_grad) r0 is_grad_dtyper2 Exceptionrr(rr r)rrr2results r_convert_element_type_atenr6sy   u % %  "OOM   ((%F v!   "!M " s A5 B5 BB Bz6 Creates a copy of a tensor with the given dtype. z:convert_element_type(Tensor a, ScalarType dtype) -> Tensor)rrrrrrcf[U[5(d[S[U535e[U[[ R 45(d[S[U535e[U[5(d[S[U535e[U[R"U5S9$)Nrz(device must be str or torch.device, got znon_blocking must be bool, got )r) rrrrrrrr8rr0canonicalize_devicerr non_blockings r_device_put_metar;s a $ $9$q'CDD fsELL1 2 2GV ~VWW lD ) )>tL?Q>RSTT a 9 9& A BBrc URXS9$)N)r:)tor9s r_device_put_atenr> s 444 22rz5 Creates a copy of a tensor on the given device. zFdevice_put(Tensor a, Device device, bool non_blocking=False) -> Tensorcd[R"UR5n[U"S55$r)r0 dtype_to_typerr)r number_types r _item_metarBs%%%agg.K k"o &&rz< Converts a tensor with one element to a Python number. c[RRR5 [RR "U0UD6sSSS5 $!,(df  g=fr)r _dispatchpythonno_python_dispatcherr rrNs r_item_aten_no_python_dispatcherrG+s;    4 4 6||  $1&1 7 6 6s A A!zitem(Tensor a) -> ScalarcP[R"U5n[U"S55$rr0r@rrrAs r_maximum_value_metarK>!%%e,K k"o &&rcU[R:XagUR(dUR(a [R"U5R $[R "U5R $)NT)rr8rais_floating_pointfinforiinfors r_maximum_value_atenrRCsL     U44{{5!%%%{{5!%%%rz2 Return the maximum finite value for a dtype. z)maximum_value(ScalarType dtype) -> ScalarcP[R"U5n[U"S55$rrIrJs r_minimum_value_metarT^rLrcU[R:XagUR(dUR(a [R"U5R $[R "U5R $)NF)rr8rarNrOrrPrQs r_minimum_value_atenrVcsL     U44{{5!%%%{{5!%%%rz2 Return the minimum finite value for a dtype. z)minimum_value(ScalarType dtype) -> ScalarcZ[U[5(d[S[U535e[U[5(d[S[U535eUR 5UR 5:wa0SUR 5SUR 5S3n[ U5eU$)Nrzb must be TensorLike, got zAttempting to copy z elements to a tensor with r)rrrrrrg)rryrs r _copy_to_metarXs a $ $9$q'CDD a $ $9$q'CDD wwyAGGI#AGGI;.I!'')T^_3 Hrc$URU5$r)r r~s r _copy_to_atenrZs 771:rz; Copies the data in b to a and returns the modified a. z-copy_to(Tensor(a!) a, Tensor b) -> Tensor(a!))rrrrrrc [U[5(d[S[U535e[R "UR UURURURURS9$)Nr)rrmrr2) rrrrrrrrrmrr2)rrs r_copy_strided_metar\s_ a $ $9$q'CDD    ggxxxxoo  rc [R"UR5UURURUR UR S9nURU5 U$)N)rrrmrr2)rrrrrmrr2r )rrrs r_copy_strided_atenr^sL    ggxxxxoo  CIIaL Jrzp Copies the data in a to a new tensor, the new tensor has same shape with a size, but has different stride. z1copy_strided(Tensor a, SymInt[] stride) -> Tensorc$URU5$rresize_r$s r _resize_metarb 99U rc$URU5$rr`r$s r _resize_atenrercrz Gives a tensor with no elements a new shape, returning the modified tensor. The tensor's strides are contiguous and its values are uninitialized. z2resize(Tensor(a!) a, SymInt[] shape) -> Tensor(a!) output_dtypec[U[5(d[S[U535eUc URn[ R "URU5n[U[ R"U5UURS9$)zP Meta function for single output reduction operations Stride logic is incorrect zinp must be TensorLike, got r) rrrrrr0compute_reduction_output_shaperrrr)inprrg output_shapes r_reduction_metarlsu c: & &;DI;GHHyy 77 4HL 11,?zz  rc[R"UR5(a![R"UR5nO URn[ XUS9$)Nrf)r0r<rr=rl)rjr correctionrgs r_var_reduction_metarosA cii((55cii@ yy 3< @@rzx Computes the sum of elements in the input tensor over the list of dimensions specified in the dim argument z| Computes the xor sum of elements in the input tensor over the list of dimensions specified in the dim argument z| Computes the product of elements in the input tensor over the list of dimensions specified in the dim argument z Computes the maximum value of elements in the input tensor over the list of dimensions specified in the dim argument z Computes the minimum value of elements in the input tensor over the list of dimensions specified in the dim argument ze Computes the biased variance of x over the list of dimensions specified in the dim argument cF[US3[U[RUS9$)Creates a reduction prim.zE(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensorr)rrlrrTrrrs r_make_reduction_primrs s* \] OO   rcF[US3[U[RUS9$)rqzZ(Tensor inp, int[]? dims, float? correction=1, *, ScalarType? output_dtype=None) -> Tensorr)rrorrTrrs r_make_var_reduction_primru& s* qr OO   rrrrrQrjc[S5e)Nz&xor_sum only implemented with inductorrZ)rjrrs r _xor_sum_atenrw8 s F GGrrcUb[[U5S:XaUR5$[USS9H,nUS:a[SU35e[R "XUS9nM. U$[R "XUS9$)NrT)reversez$dimension must be non-negative, got rQ)rrrrrr)rjrrds r _prod_atenr{H st   t9>99; d+A1u$'KA3%OPP**S51C, zz#511rrc 4[R"U4XS.UD6$)N)rrn)rr)rerrnrs r torch_varr}b s 99U E Ef EErrrrzC Constructs a 1-D tensor t where ``t[i] == start + i * step``. rstepr2c[R"[R"U5S5 [R"US:gS5 [R"UUUUS9$)Ncg)Nz'prims.iota only supports integer dtypesrrrrrK_iota_meta.. s9rrcg)Nzstep must be nonzerorrrrrKr s$:rrrr2)rrMr0r9ro)rrr~rrr2s r _iota_metar sS LL u%9 LL:; ;;#  rc <XU--n[R"XX#XES9$Nr)rarange)rrr~rrr2rs r _iota_atenr s( 4- C << Df rzpiota(SymInt length, *, SymInt start, SymInt step, ScalarType dtype, Device device, bool requires_grad) -> TensorcB[R"U5n[XXS9$Nrr0rr)rrrr2rs r _empty_metar s!//6G E% OOrc,[R"XX#S9$r)rro)rrrr2s r _empty_atenr s ;;u& VVrz\ Creates a tensor with uninitialized values and the specified shape, dtype, and device. zWempty(SymInt[] shape, *, ScalarType dtype, Device device, bool requires_grad) -> Tensorc[XX#S9$rr)rrrrr2s r_empty_strided_metar s E% OOrz1 Creates a tensor with uninitialized values. zqempty_strided(SymInt[] shape, SymInt[] strides, *, ScalarType dtype, Device device, bool requires_grad) -> Tensorphysical_layoutc^^^ ^ [R"TVs/sHoPUPM sn5n[U5m [R"[T5T :HU U4Sj5 S/[U5-n[ 5n[ T5Hjum m[R"STs=:*=(a T :Os U UU 4Sj5 [R"TU;S5 UT UT'URT5 Ml [UUUUS9$s snf)Nc&>STS[T53$)NzlNumber of dimensions in the tensor input does not match the length of the physical layout; i.e. len(size) = z( is not equal to len(physical_layout) = )r)rrsrrK&_empty_permuted_meta.. s$ ??BeD669/6J5K Mrrc">STS- STSTS3$)Nz5Dimension out of range (expected to be between 0 and rz , but got z at index zL). NB: negative dims not currently supported; file an issue if you want it.r)rlrsrrKr s(GayPZ#Zs#IIrcg)NzDuplicate dim not allowedrrrrrKr s1Lrr) r0rrrrMr rr`r) rrrrr2r p_stridesr seen_dimsrrs ` ` @@r_empty_permuted_metar s11_2U_8_2UVI e*C LL O# cCJGI/*1 LLSL    Qi')LMq\  a+   13VsC?z Creates a tensor with uninitialized values according to some physical layout, that is guaranteed to be non-overlapping and dense. zwempty_permuted(SymInt[] shape, int[] physical_layout, *, ScalarType dtype, Device device, bool requires_grad) -> Tensor fill_valuecB[R"U5n[XX#S9$rr)rrrrr2rs r _full_metar s!//6G E% OOrc2[R"UUUUUS9$r)rfull)rrrrr2s r _full_atenr& s$ :: #  rzi Creates a tensor filled with the given fill value, and with the specified shape, dtype, and device. zifull(SymInt[] shape, Scalar fill_value, *, ScalarType dtype, Device device, bool requires_grad) -> Tensorc[R"U5nUR5S:XaUR5n[ XX#S9$)Nr)rrr)r0rhrrr)rrrrr2rs r_full_like_metarF s966q9GwwyA~((* a EErc2[R"UUUUUS9$r)r full_like)rrrrr2s r_full_like_atenrU s$ ?? #  rz Creates a tensor filled with the given fill value, and the same shape, dtype, and device as the given tensor by default. The dtype and device settings can be overridden by specifying them explicitly. zhfull_like(Tensor a, Scalar fill_value, *, ScalarType dtype, Device device, bool requires_grad) -> TensorscalarcH/n[R"U5n[XXAUS9$rr)rrrrrs r_scalar_tensor_metarv s) E//6G f7PV WWrc[U[5(a)Ub[R"U5(d [ S5e[ R "XUS9$)Nz-Complex scalar requires complex tensor dtype.r)rcomplexr0r< TypeErrorrr)rrrs r_scalar_tensor_atenr sE &'"" U33E::GHH   v6 BBrzG Wraps a Number into a Tensor with the specified dtype and device. zQscalar_tensor(Scalar s, *, ScalarType? dtype=None, Device? device=None) -> TensorA full_matricesc[R"US5 [R"URSSS9 URnUSSnUSSupE[ XE5nX4U(aUOU4-n[R "USS9n[XxURURS9n X64-n [R "U 5n [U U UR5(a [R"UR5O URURS9n X1(aUOUU4-n URRS:Hn[R "XS9n[XURURS9nUR5S:waHUR5(a3[RR5(aUR!5nXU4$) Nz linalg.svdF)allow_low_precision_dtypes) row_majorrcudar)r0check_is_matrixcheck_fp_or_complexrrrrrrrar=rrrr is_availabler)rrA_shapebatchmnkshape_U strides_UUshape_S strides_SSshape_Vhis_cuda strides_VhVhs r _svd_metar sn !\* agg|PUVggG CRLE 23 (Tensor U, Tensor S, Tensor Vh) generatormeanstdrc^^[R"TS:U4Sj5 [R"[R"T5=(d [R"T5U4Sj5 [R "U5n[ XTUS9$)Ngc>ST3$)Nz6expected non-negative standard deviation, but got std=r)rsrrK_normal_meta.. s HNrc>ST3$)Nz:expected a floating-point or complex dtype, but got dtype=rrQsrrKr s LUGTrr)rrMr0is_float_dtyper<rr)rrrrrr2rrs `` r _normal_metar sk LL s N  LL U#Du'='=e'DT //6G E% OOrc[R"XXES9n[R"5 URXUS9 SSS5 U$!,(df  U$=f)Nrr)rror normal_)rrrrrr2rrs r _normal_atenr sH  EvSA  $y 1  H  Hs A Az Constructs a tensor filled with values drawn from a normal distribution with the specified mean and standard deviation. Only supports floating-point types. znormal(SymInt[] shape, *, Scalar mean, Scalar std, ScalarType dtype, Device device, bool requires_grad, Generator? generator=None) -> Tensorlowhighc[XX4S9$rr)rrrrrrrs r _uniform_metar s E NNrcP[R"XX4S9nURXUS9 U$)N)rrrr)rruniform_)rrrrrrrrs r _uniform_atenr) s+ EMAJJsIJ. HrzN Constructs a tensor filled with values drawn uniformly from low to high. zuniform(SymInt[] shape, *, Scalar low, Scalar high, ScalarType dtype, Device device, SymInt[] stride, Generator? generator=None) -> Tensoronesidedc^[R"URU5n[R"U5 [ UR 5nU(aUSnX4S-S-X4'[R "UR5n[R"U5n[X6XPRS9$)Nrrrr) r0rrvalidate_no_repeating_dimsr2rcorresponding_complex_dtyperrrr)rerrrlast_dimrrs r _fft_r2c_metarM s  ! !%**c 2C $$S)  Er7/Q.2  - -ekk :E//6G E% UUrc4Sn[R"XX25$Nr)r_fft_r2c)rerr normalizations r _fft_r2c_atenr` s M >>%m >>rz7 Performs a real to complex Fast Fourier Transform z;fft_r2c(Tensor self, *, int[] dim, bool onesided) -> Tensorforwardc[R"URU5n[R"U5 URn[R "U5n[ X4URURS9$r) r0rrrrrrrr)rerrrrs r _fft_c2c_metarx s]  ! !%**c 2C $$S) KKE//6G EKK  rc4Sn[R"XX25$r)r_fft_c2c)rerrrs r _fft_c2c_atenr s M >>%m ==rz> Performs either a Fast Fourier Transform, or its inverse z:fft_c2c(Tensor self, *, int[] dim, bool forward) -> Tensor last_dim_sizec<[R"URU5n[R"U5 [ UR 5nX#US'[R "UR5n[R"U5n[X5X@RS9$)Nrr) r0rrrr2rr=rrrr)rerrrrrs r _fft_c2r_metar sv  ! !%**c 2C $$S)  E"#b'N  * *5;; 7E//6G E% UUrc4Sn[R"XX25$r)r_fft_c2r)rerrrs r _fft_c2r_atenr s M >>%m CCrz? Performs a complex to real Inverse Fast Fourier Transform zBfft_c2r(Tensor self, *, int[] dim, SymInt last_dim_size) -> Tensorselfc[R"URRS5 [R"U5[R"U[R S94$)Ncg)Nz1torch.frexp() only supports floating-point dtypesrrrrrK_frexp_meta.. sCrrQ)rrMrrNr(int32)rs r _frexp_metar sG LL $$C   D !5#3#3D #L LLrz8frexp(Tensor self) -> (Tensor mantissa, Tensor exponent)c[5$rr rrr_make_token_atenr s  rz_make_token() -> Tensorz7Creates a token used for keeping track of side effects.cgrr)tokenss r_sink_tokens_atenr srz#_sink_tokens(Tensor[] tokens) -> ()zTSink all of the tokens which were previously used for keeping track of side effects.r)F)Nr)r-N(hrcollections.abcrrenumr functoolsrrtypingrr rtorch._prims_commonr.r0 torch.libraryr r torch._Cr torch._higher_order_ops.effectsrtorch._library.utilsrtorch._prims.debug_primsrtorch._prims.rng_primsrrrrrrrrrrrrrtorch._prims_common.wrappersrrr r!torch.overridesr"r#torch.utils._pytreer$r%r&rLibraryrrrrr__all__rrrrrrr8rrrHrOrVrXr\r.r)r/r&r0r1r2r3r4r5r6specialr;r<i0r7i0er8i1r9i1er:r=rdr>r?ri_conj_physicalrTr@rnrkrqrrArBerfinvrCrDrErFrGrHrurIrJrrKrLr(rMrNrOrPrQrTrUrRrSrVrzrWrXrYrZr[r\r]r^r_r`rarbrcrdr{rerfrgrhrirkrlrmrngammaincro gammainccrprqrrrrsrrtrurvrwrxrybitwise_left_shiftr{bitwise_right_shiftr|r}r~rr@rr_as_strided_docrrr_broadcast_in_dim_docrrrrrr_collapse_view_docrr _conj_docrrrr_split_dim_docrr _squeeze_docrrr_transpose_docrrr _view_of_docrrr_view_element_type_docrr_as_strided_scatter_docrr r _collapse_docrrr2r_cat_docrr r% _reshape_docrr)_rev_docfliprr, _where_docrr0r6_convert_element_type_doc pointwiserr;r>_device_put_docrrB _item_docrGrrKrR_maximum_value_docrrTrV_minimum_value_docrrXrZ _copy_to_docINPLACErr\r^_copy_strided_docrrbre _resize_docrrlro_sum_doc _xor_sum_doc _prod_doc _amax_doc _amin_doc_var_docrsrurrwrr{rr}rrr _iota_docrrrrr _empty_docror_empty_strided_docrr_empty_permuted_docrrr _full_docrrr_full_like_docrrr_scalar_tensor_docrrr_svd_docrrAr Generatorrr _normal_docrrr _uniform_docrrr _fft_r2c_docrrr _fft_c2c_docrrr _fft_c2r_docrrrjrrrNONErfxnodehas_side_effectrrrrNs8".%" ##(<595    ADEFF }}We, MM ! !'63N O  ==00&/R]]**7FJG&&w?p h485K#!% $(, 5KU\\)D05K t 5K$  5K ;;  5K LL3  % 5K~(,#(*/l lu[#%566l  l  l l 599  $l!l$(l^4AE_8_".#"56=_ _D3   "F    "F # ii 7HH $ jj 7??  % kk 7??  $ jj 7??  % kk 7??  $ jj 7??  % kk 7??  # ii 7?? $ jj 7??  )mm%% 7??    )mm%% 7??    )hh 7??   *mm 7??   )mm 7??   *mm 7??  + 7??   8%,, 86 8$  7?? $ jj 7?? .~... 1 "">  DICXCX  -2-@-@  0  N kk&"&   ' mm 7??  # ii 7??  ' mm"" 7??  $ mm   7??  % mm!! 7??  # ii 7??  % mm!! 7??  $ mm   7?? . 6 jj  % kk 7??   . &;LL   jj   (nn 7CC   & ll 7??  # ii 7??  % kk 7??  $ jj 7??  % kk 7??   . &;LL   jj  * 7??   % mm!! 7??  # ii 7??  % kk 7??   % kk 7??  $ jj 7??  ' mm 7??  # ii 7?? $ jj 7?? 3mm// 7?? $ jj 7?? # ii 7?? $ jj 7??  % kk 7??  $ ii 7??  & kk 7??  , 7??  + 7??  , 7??  '$  7?? #hh 7CC % jj 7?? % jj 7?? % jj 7?? $ ii 7?? #hh 7CC #hh 7CC  & kk 7??   ' mm$$ 7??   ( mm%% 7??  #hh 7CC #hh 7CC  "'5 'B (  7??   "'5 'B (  7??  $ ii 7?? #hh 7CC  *oo 7??   $ ii 7??  *oo 7??   +&& 7??  7'' 7??  # ii 7?? % mm   7?? ==&=0:=LO==,= ==(2=DG= =   j     T@T@'T@?G}T@n   c $    v c    =I =c = =c3h =&P"P"!P"(+P" D# DC DN D6#CF " H !    .^   + jj   ;?@@#3@@4D~DCDsD~D:vCsv  N      D^DDnD.  @ mm    R~R4DRR$)v),<))  C      A^AAV   .     ~ekknvekkf F %   / / / /  /  /  /8  y !&& "f"S"s"v"fSsv  =   x/cn@#uVS[)DL8#s#v#   5   X^ XI XIVIII&I   8   DD'7DND   0 jj  +0>   = kk   7. 7 7 7 "&"""$" G #(! ))    AF C C"U\\1 C C9>3 3U\\)3 3  S   '.'Z'   2 % - 'u{{'z' &u{{& 6 !  'u{{'z' &u{{& 6 !   ^  *V6  : ##"&   . )  & )    >    N9F9   ? ##  04$A          s3 ii!% H H T !H ;;  H  H   !% 2 2 T !2 ;;  2  2$ F   jj  jj       ;;  LL ,        ;;  LL    }  P P % P5:\\PRVPPW W % W5:\\WRVW W    d   P P P ;; P LL P  PP  ~ !!  % %%% ;; % LL %  %%P  E ""  P  P P ;; P LL P  P P  ;;  LL    $   v   F F F ;; F LL F  F F  ;;  LL    $  u    X X ;;X LL X  X C  C ;; C LL C  C  ^ !  "")-" >>> 9:"J<<)-< 666 !"<    T +//;??C ,)-P P '/P  P ;; P LL PP%PP@)-   '/    ;;  LL   %   "   W  &)- O  O  O  O ;; O LL O  O% O O*)-          ;;  LL     %       O   V V V V  V&? ? ? ?  ?  H            > > > >  >  G    V  V  V V  V D D D D  D  O   MnM~~/M)NM  E +//2kk  . $ A    0   ^   l+r