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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 slice_in_dim 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 tensorlikerrrrct|trZ|s|t|tsJ|s|t|tsJd}d}tt |}t j d}nu|ct|t jsJt|j}t|j}|j}|j }n|J|J|J|J|n t|}|n t|}|n|}|n|}t|trt j |}t j||||S)Ncpurr) isinstancerr r typetorchrrtuplerstriderstrr) rrrrrinferred_shapeinferred_stridesinferred_dtypeinferred_devices H/var/www/html/venv/lib/python3.12/site-packages/torch/_prims/__init__.py TensorMetarsN*f%emz%/JKK:gx3PQQ*,,.&tJ'78,,u-  *ell333z//0 !2!2!45#))$++   """   !!!#mNuE")/uW~G#mNE &_FF&#f%   ugU6 JJF)tagsuse_old_custom_ops_apiregister_conj_neg_fallthroughschema return_type.meta impl_atendocrrrcJfdfd}fd} |jdd} |t| d}tjj | |z} |s t | svt j| |ztjjtj| tj| |tj| ng} | jD]A} | j| jj s'| j#| j$Ctj&j)d| zt+| | }|j-|t.j0k(s|rj|j2j| tj&j4d |j2j| tj&j4d t7tj8j:j<| }|j>|r|_ nt7tj:jB| dx}r|jEDcgc]}t7||jF}}tI|djJ|d djMtjjNjMtjjPt+fd |dD_ ddl)m*tWfdjXjDr t[dvrt\j| | 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overloadsrsetintersection_updatediscardcoredata_dependent_outputtorch._subclasses.fake_tensorrr_schemarprim_backend_select_impl__doc__rrr)rrrrrrrrrrr cpp_schemarargprim_def _prim_packet aten_packetoverload overload_tagsprrrrs `` @@@@r _make_primr s *?/ << Q D CIK F&&tf}5J%9*%E D6M (C(C DtZ(n5D$' '' .C~~)cnn.E.E##CHH- .==**   |, +  t$ +** *.K MM  tU]]%E%E{ S MM  tU]]%E%Ez R5::>>//6L  E   d; ; ;@K@U@U@W 4t@t&j:t&j<fsJd| xs t| t@t&j<f} c| r tC| } tE| S)z Meta function for elementwise operations that produce outputs in the same dtype as their inputs. 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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)startendctd|j}tj|tj|t j k\fdy)NrcdddS)Nz Attempting to collapse but end, z, is less than start, !r)rrsrrGz)_validate_collapse_args..Ls23%7MeWTUVr)maxdimr- validate_idxr _check_value)rrrrs `` r_validate_collapse_argsrBsN q!%%'?D tU# tS!  u Vrct|dk(rdn t|}d}|||dzD]}||z} |d||fz||dzdzS)zZ Returns the shape of a with dims in [start, end) merged into a single dimension. rrrN)rr)rrr dim_lengthrs r_collapsed_shaperPsf J!ODuEJ 537 #$!^ $ 5>ZM )E#'),< < AA14fu  )E#'),< Tensor(a)c|jjs td|j|j|j |j }tjj||j |S)Nz$Expected complex dtype in prims.conj) rr\rbrrrr~rr _set_conjis_conj)routs r _conj_metarsa 77  ABB ,,qww A,<,<,> ?C HHs O, Jrz2 Returns a conjugated view of the original tensor zconj(Tensor(a) a) -> Tensor(a) dimensionsc| ttj||}n)ttj|j|}t t |t |k7rdt |}t|t|j}|D]}|j|dtt |Dcgc] }||vs| }}t|||Scc}w)z Creates a view of a with a.ndim + len(dimensions) dimensions, with new dimensions of length one at the dimensions specified by dimensions. z+Received duplicate dimensions to expand in r) sortedr-canonicalize_dimsrrr rrr/rinsertrr)rrrdimsrrrrs rrrs e--dJ?@e--affjAB 3t9~T";C O;LMoQWW I!a !S^,:0E Ay*> ??s C&C&pyslice start_indices limit_indicescp||ndgt|z}|jt|k7r'd|jdt|d}t||jt|k7r'd|jdt|d}t||jt|k7r'd|jdt|d}t|t||jD]=\}}|dkrd|d}t|||kDs"d |d |j}t|t||j|D]L\}}}|dkrd |d}t|||kDrd |d |j}t|||ksEd |d|}N|D]}|dks d|d}t|g} t|||D]#\}}}| j d||z dz |zz%g} t|j |D]\}}| j ||z|j| | |jS)Nrz#Attempting to slice tensor of rank z with start_indices of length rz with limit_indices of length z with strides of length rz/1aa!eaiA--./KAHHJ)"11q5!" << ;0@0@0B CCrc||ndgt|z}g}t|||D]"\}}}|jt|||$t j ||SNr)rrrrrgetitem) rrrrrslicesrstopsteps r _slice_atenr4sj "-wA3]9K3KH F  xH2tT geT4012   Av &&ra Creates a view of a "bounding box" within the tensor. The bounding box is specified independently in each of the tensor's dimensions. start_indices and limit_indices describe the box's boundaries for their corresponding dimensions. If strides is specified then they specify the step size between elements in their corresponding dimension. This operation is analogous to slicing in NumPy, but does not permit slices where the stop indices are less than the start indices. zgslice(Tensor(a) a, SymInt[] start_indices, SymInt[] limit_indices, SymInt[]? strides=None) -> Tensor(a) start_index limit_indexaxiscD|dkrd|}t|||jk\rd|d|jd}t||dkrd|}t|||j|kDrd|d|}t|||j|kDrd |d|}t|||krd |d |}t||dkrd |d }t|dg|jz}t|j}dg|jz}|||<|||<|||<t ||||S)Nrz'slice_in_dim: received a negative axis zslice_in_dim: axis z! is greater or equal to the rank z of the tensorz.slice_in_dim: received a negative start_index z5slice_in_dim: start_index is greater than the length z of dimension z5slice_in_dim: limit_index is greater than the length z%slice_in_dim: received a limit_index z less than the start_index z0slice_in_dim: received a non-positive stride of rr)rrrr/r) rrrrrrrrrs r_slice_in_dim_metarXsj ax7v>o qvv~#D6)J166(R`aoQ>{mLoQWWT]"Ek]R`ae`fgoQWWT]"Ek]R`ae`fgo[ 5k]B]^i]jko z@JoC!&&LMMMcAFFlG%M$%M$GDM q- @@rcdg|jz}t|j}dg|jz}|||<|||<|||<t||||SNrr)rr/rr)rrrrrrrrs r_slice_in_dim_atenrs]C!&&LMMMcAFFlG%M$%M$GDM M=' ::rzI Convenience wrapper for slicing just one dimension using slice. zhslice_in_dim(Tensor(a) a, SymInt start_index, SymInt limit_index, int stride=1, int axis=0) -> Tensor(a)r outer_lengthct|tsJtj|j|tj ||j ||z}|j ||zdk7r!d|j |d|d}t|g}g}t|jD]}||k(rL|j||f|j|j||z|j|fT|j|j ||j|j||j|||jS)Nrz(Attempting to split dimension of length z, but outer length of z divides it with a remainder!)rrr-rrvalidate_dim_lengthrrrextendrrrr~)rrr inner_lengthrrrrs r_split_dim_metarsD a $$ $ qvvs# l+773<</L  |#)6qwws|nE##/.0M O oIKQVV}0 #:   lL9 :    3, > 3P Q   QWWS\ *   qxxz# / 0 << ;0@0@0B CCrc|j||z}|jd|||fz|j|dzdz}|j|Sr)rr)rrrrrs r_split_dim_atenrsP773<</L#, !==a @RRI 66) rz Creates a view of a with the given dimension (of length l) split into two dimensions, with the outer of the two having length outer_length and the inner of the two having computed length inner_length such outer_length * inner_length = l. zAsplit_dim(Tensor(a) a, int dim, SymInt outer_length) -> Tensor(a)ct|tsJ|D]6}tj|j||j |dk(r6Jg}g}t t|j D]G}||vr|j|j ||j|j|I|j|||jSr) rrr-rrrrrrrrr~)rrrrrs r _squeeze_metars a $$ $! 1663'wws|q   !IKS\", *  &188:c?+ , << ;0@0@0B CCrz~ Creates a view of the tensor with the specified dimensions removed. The removed dimensions must each have length one. z3squeeze(Tensor(a) a, int[] dimensions) -> Tensor(a) permutationc|jt|k7r'd|jdt|d}t|tj|j|sd|d}t|dg|jz}dg|jz}t |D]-\}}|j |||<|j|||</|jt|t||jS)Nz'Attempting to permute a tensor of rank z', but received a permutation of length rz!Received an invalid permutation, r) rrrr-is_valid_permutationrrrrrr~)rrrrrrrs r_transpose_metarsvv[!!7x?fgjkvgwfxxyzo  % %affk :1+a@oaff I#,Kk*+S #88:c? C+ <<i(% *N>N>P QQrc.tj||Sr)rpermute)rrs r_transpose_atenrs ==K ((rz Creates a view of the tensor with its dimensions permuted. The length of the permutation must be the rank of the tensor, and each element of the permutation specifies the new order for the corresponding dimension. z6transpose(Tensor(a) a, int[] permutation) -> Tensor(a)ct|j|j|j|jSr)rrrr~r^s r _view_of_metar#s( <<Q-=-=-? @@rc8|j|jSr)rrr^s r _view_of_atenr's 66!''?rz' Creates a view of the tensor. z!view_of(Tensor(a) a) -> Tensor(a)c$|j|Srrrrs r_view_element_type_metar8 66%=rc$|j|Srrrs r_view_element_type_atenr<rrz> Creates a view of the tensor with a different dtype. z9view_of_dtype(Tensor(a) a, ScalarType dtype) -> Tensor(a)srcctjtjtjt j j k\fdt j tjjfdtjS)Ncddddjdjzdjjz S)Nzas_strided_scatter: sizes z , strides z, storage offset z and itemsize z requiring a storage size of z' are out of bounds for storage of size ) element_sizer)r` required_sizer}r~rsrrGz*_as_strided_scatter_meta..^so(j@QR`Qab"//122Ou113345##(;;=53E3E3G#G"H Jrc(djdS)NzCexpected src to have a size equal to the slice of self. src size = z, slice size = r)r}rsrrGz*_as_strided_scatter_meta..gs UVYV_V_U``optouvr) r-rrcompute_required_storage_lengthrrIr is_same_shaperclone_preserve_strides)r`rr}rr~rs`````@r_as_strided_scatter_metar Qs  6"99$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) -> Tensorclt|||t|j||}|j|Sr)rrr new_emptyrs r_collapse_metar s/Auc* %5I ;;y !!rct|j||}|j|}tj5|j |j |ddd|S#1swY|SxYwr)rrr rno_gradview_ascopy_)rrrrrs r_collapse_atenrs[ %5I ++i C   AQ J Js !A((A2zn Collapse a span of neighboring dimensions into one. See collapse_view for the corresponding view operation. z0collapse(Tensor a, int start, int end) -> Tensortensorsc  dk\sJ|dj}d}t|D]|\ }t|t|jk(sJtt||jD]2\}\|k(r|z}t j k( fd4~t |djj}||<t|d|tj|S)Nrc "ddddd S)Nz0Sizes of tensors must match except in dimension z . Expected z but got z for tensor number z in the listr) common_lengthrr tensor_idxsrrGz_cat_meta..s,NseT -ix?R!l,0rrr) rrrrrrIr/copyrr-r) rrr concat_lengthtensorrrrrrs ` @@@r _cat_metars !8O8 AJ  EM'0  F5zS....,5c%6N,O  (C(-cz - 6  m+0  WQZ%%&++-I"IcN  11)< rc.tj||Sr)rr)rrs r _cat_atenrs 99Wc ""rz Concatenates tensors along the specified dimension. The tensors' shapes must have the same rank and same length for other dimensions. z(cat(Tensor[] tensors, int dim) -> Tensorc0t|tsJtj|t t j |}||jk7r"d|jd|d}t|t||tj|S)Nz$Attempting to reshape a tensor with z elements to a shape with elements!r) rrr-rrrrxrrrr)rrrrs r _reshape_metar!s a $$ $  8<< 'E  4QWWYK?YZ_Y``jko aue.O.OPU.V WWrc\|j|jjSr)r contiguousrrrs r _reshape_atenr%s# 99U  & & ( . . 00rz` Creates a contiguous tensor with the specified shape containing a copy of the data in a. z+reshape(Tensor a, SymInt[] shape) -> Tensorrctj|j|tj|tj S)Nre)r-validate_dimension_indicesrr empty_likeri)rrs r _rev_metar)s/ $$QVVT2   AU-B-B CCrzD Reverses the order of elements along the given dimensions. z#rev(Tensor a, int[] dims) -> Tensorpredc>t||tj|fS)N)r(r'rn)r*rrss 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) -> Tensorct|tsJt|tjsJtjj |r|j }ntj|}t|||S)N)rr) rrrr _prims_commonis_non_overlapping_and_denserr-rcr)rrrs r_convert_element_type_metar0!sf a $$ $ eU[[ )) ) 77:((*::1= a 66rc.tj|sd}n |j}t j ||j ||}t j5t||cdddS#t$r }d}Yd}~]d}~wwxYw#1swYyxYw)NF)rr requires_grad) r- is_grad_dtyper2 Exceptionrr(rrr)rrr2eresults r_convert_element_type_atenr7/s   u %  "OOM   !((%}F "vq!""  "!M " ""s# A3 B 3 B<BB Bz6 Creates a copy of a tensor with the given dtype. z:convert_element_type(Tensor a, ScalarType dtype) -> Tensor)rrrrrrct|tsJt|ttjfsJt |t j|S)N)r)rrrrrrr-canonicalize_devicerrs r_device_put_metar;OsE a $$ $ fsELL1 22 2 a 9 9& A BBrc$|j|Sr)tor:s r_device_put_atenr>Xs 44<rz5 Creates a copy of a tensor on the given device. z-device_put(Tensor a, Device device) -> Tensorcbtj|j}t|dSr)r- dtype_to_typerr)r number_types r _item_metarBks%%%agg.K k"o &&rz< Converts a tensor with one element to a Python number. zitem(Tensor a) -> ScalarcNtj|}t|dSrr-r@rrrAs r_maximum_value_metarF!%%e,K k"o &&rc|tjk(ry|js |jrtj|j Stj |j S)NT)rr5r\is_floating_pointfinforiinfors r_maximum_value_atenrMsL     U44{{5!%%%{{5!%%%rz2 Return the maximum finite value for a dtype. z)maximum_value(ScalarType dtype) -> ScalarcNtj|}t|dSrrDrEs r_minimum_value_metarOrGrc|tjk(ry|js |jrtj|j Stj |j S)NF)rr5r\rIrJminrKrLs r_minimum_value_atenrRsL     U44{{5!%%%{{5!%%%rz2 Return the minimum finite value for a dtype. z)minimum_value(ScalarType dtype) -> Scalarct|tsJt|tsJ|j|jk7r0d|jd|jd}t||S)NzAttempting to copy z elements to a tensor with r )rrrrb)rrsrs r _copy_to_metarTsj a $$ $ a $$ $ wwyAGGI#AGGI;.I!'')T^_3 Hrc$|j|Sr)rrxs r _copy_to_atenrVs 771:rz; Copies the data in b to a and returns the modified a. z-copy_to(Tensor(a!) a, Tensor b) -> Tensor(a!))rrrrrrct|tsJtj|j||j |j |j|jS)N)rrhrr2) rrrrrrrhrr2)rrs r_copy_strided_metarXsK a $$ $    ggxxxxoo  rctj|j||j|j|j |j }|j||S)N)rrrhrr2)rrr}rrhrr2r)rrrs r_copy_strided_atenrZsL    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$|j|Srresize_r$s r _resize_metar^  99U rc$|j|Srr\r$s r _resize_atenra r_rz Gives a tensor with no elements a new shape, returning the modified tensor. The tensor's strides are contiguous and its values are unitialized. z2resize(Tensor(a!) a, SymInt[] shape) -> Tensor(a!) output_dtypect|tsJ| |j}tj|j |}t |tj|||jS)z\ Meta function for single output reduction operations Stride logic is incorrect r) rrrr-compute_reduction_output_shaperrrr)inprrc output_shapes r_reduction_metarh) sa c: && &yy 77 4HL 11,?zz  rctj|jr tj|j}n |j}t |||S)Nrb)r-r9rr:rh)rfr correctionrcs r_var_reduction_metark: s@ 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 cLt|dt|tj|S)Creates a reduction prim.zE(Tensor inp, int[]? dims, *, ScalarType? output_dtype=None) -> Tensorr)rrhrrPrrrs r_make_reduction_primro[ s* \] OO   rcLt|dt|tj|S)rmzZ(Tensor inp, int[]? dims, float? correction=1, *, ScalarType? output_dtype=None) -> Tensorr)rrkrrPrns r_make_var_reduction_primrqf s* qr OO   rrrnrLrfctd)Nz&xor_sum only implemented with inductorrV)rfrrs r _xor_sum_atenrsx s F GGrrc|Qt|dk(r|jSt|dD]!}|dk\sJtj|||}#|Stj|||S)NrT)reverserL)rrrrr)rfrrds r _prod_atenrw sn   t9>99; d+ 2A6M6**S!51C 2 zz#t511rrc 4tj|f||d|S)N)rrj)rr)r`rrjrs r torch_varry s 99U E Ef EErrrrzC Constructs a 1-D tensor t where ``t[i] == start + i * step``. rrr2ctjtj|dtj|dk7dtj||||S)Ncy)Nz'prims.iota only supports integer dtypesrrrrrGz_iota_meta.. rHrrcy)Nzstep must be nonzerorrrrrGz_iota_meta.. rHrrrr2)rrIr-r6rj)rrrrrr2s r _iota_metar~ sS LL u%9 LL:; ;;#  rcH|||zz}tj||||||SNr})rarange)rrrrrr2rs r _iota_atenr s0 &4- C << sDfM rzpiota(SymInt length, *, SymInt start, SymInt step, ScalarType dtype, Device device, bool requires_grad) -> TensorcJtj|}t||||SNrr-rr)rrrr2rs r _empty_metar s%//6G E7% OOrc4tj||||Sr)rrj)rrrr2s r _empty_atenr s ;;uE& 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 t||||Sr)r)rrrrr2s r_empty_strided_metar s E7% OOrz1 Creates a tensor with uninitialized values. zqempty_strided(SymInt[] shape, SymInt[] strides, *, ScalarType dtype, Device device, bool requires_grad) -> Tensorphysical_layoutc tjDcgc]}|| c}}t| tjt k( fddgt|z}t }t D]`\ tjdcxkxr knc  fdtj|vd| |<|jbt||||Scc}w)Nc&ddtS)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)rrsrrGz&_empty_permuted_meta... s) ??BeD669/6J5K Mrrc"ddz dddS)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)rlrsrrGz&_empty_permuted_meta..9 s-GayPZ#Zs#IIrcy)NzDuplicate dim not allowedrrrrrGz&_empty_permuted_meta..? rHrr) r-rrrrIr rrcr) rrrrr2r p_stridesr seen_dimsrrs ` ` @@r_empty_permuted_metar" s11_2U582UVI e*C LL O# cCJGI/* 1 LSL    Qi')LMq\  a    13Vs C2z 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_valuecJtj|}t||||Srr)rrrrr2rs r _full_metarY s%//6G E7% OOrc6tj|||||Sr)rfull)rrrrr2s r _full_atenre s  :: zv] 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) -> Tensorctj|}|jdk(r|j}t ||||S)Nr)rrr)r-rcrrr)rrrrr2rs r_full_like_metar s=66q9GwwyA~((* af EErc6tj|||||Sr)r full_like)rrrrr2s r_full_like_atenr s  ?? :U6 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) -> TensorscalarcPg}tj|}t|||||Srr)rrrrrs r_scalar_tensor_metar s- E//6G fE7%PV WWrct|tr"|tj|s t dt j |||S)Nz-Complex scalar requires complex tensor dtype.r)rcomplexr-r9 TypeErrorrr)rrrs r_scalar_tensor_atenr sA &'" U33E:GHH   vU6 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_matricesctj|dtj|jdd|j}|dd}|dd\}}t ||}|||r|n|fz}tj |d}t|||j|j} ||fz} tj | } t| | |jrtj|jn |j|j} ||r|n||fz} |jjdk(}tj | |}t| ||j|j}|jdk7r>|jr.tjjr|j!}| | |fS) Nz linalg.svdF)allow_low_precision_dtypes) row_majorrcudar)r-check_is_matrixcheck_fp_or_complexrrrQrrrr\r:rrrr is_availabler)rrA_shapebatchmnkshape_U strides_UUshape_S strides_SSshape_Vhis_cuda strides_VhVhs r _svd_metar sw !\* agg|PUVggG CRLE 23 (Tensor U, Tensor S, Tensor Vh) generatormeanstdrc tjdk\fdtjtjxstjfdtj |}t |||S)NgcdS)Nz6expected non-negative standard deviation, but got std=r)rsrrGz_normal_meta..$ sHNrcdS)Nz:expected a floating-point or complex dtype, but got dtype=rrLsrrGz_normal_meta..) sLUGTrr)rrIr-is_float_dtyper9rr)rrrrrr2rrs `` r _normal_metar sm LL s N  LL U#Du'='=e'DT //6G E7% OOrctj||||}tj5|j|||ddd|S#1swY|SxYw)Nr}r)rrjrnormal_)rrrrrr2rrs r _normal_atenr0 sQ  Ev]SA 2 $y 12 H2 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) -> TensorlowhighcJtj|}t||||Srr)rrrrrrrs r _uniform_metarS s%//6G E7% OOrc^tj|||}|j||||S)Nrr)rrjuniform_)rrrrrrrs r _uniform_atenr` s-  Ev6AJJsDIJ. HrzN Constructs a tensor filled with values drawn uniformly from low to high. zyuniform(SymInt[] shape, *, Scalar low, Scalar high, ScalarType dtype, Device device, Generator? generator=None) -> TensoronesidedcZtj|j|}tj|t |j }|r|d}||dzdz||<tj |j}tj|}t||||jS)Nrrrr) r-rrvalidate_no_repeating_dimsr/rcorresponding_complex_dtyperrrr)r`rrrlast_dimrrs r _fft_r2c_metar s  ! !%**c 2C $$S)  Er7/Q.2h  - -ekk :E//6G E7% UUrc6d}tj||||Sr)r_fft_r2c)r`rr normalizations r _fft_r2c_atenr s M >>%mX >>rz7 Performs a real to complex Fast Fourier Transform z;fft_r2c(Tensor self, *, int[] dim, bool onesided) -> Tensorforwardctj|j|}tj||j}tj |}t |||j|jSr) r-rrrrrrrr)r`rrrrs r _fft_c2c_metar s_  ! !%**c 2C $$S) KKE//6G WEKK  rc6d}tj||||Sr)r_fft_c2c)r`rrrs r _fft_c2c_atenr s M >>%mW ==rz> Performs either a Fast Fourier Transform, or its inverse z:fft_c2c(Tensor self, *, int[] dim, bool forward) -> Tensor last_dim_sizec@tj|j|}tj|t |j }|||d<tj |j}tj|}t||||jS)Nrr) r-rrrr/rr:rrrr)r`rrrrrs r _fft_c2r_metar s|  ! !%**c 2C $$S)  E"E#b'N  * *5;; 7E//6G E7% UUrc6d}tj||||Sr)r_fft_c2r)r`rrrs r _fft_c2r_atenr s M >>%m] CCrz? 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