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Note that when naively doing `alias = fn`, we have that `alias.__name__ == "fn"`, and `alias.__module__ == fn.__module__`. cs ||Sr2r)rSkwargsfnr)r*_fnsz_make_alias.._fnrc)rcinspect currentframef_back f_globals __module__)rlnamermr)rkr* _make_aliass rtcsdtfdd}jd}||_ttt||}ddlm}|j}||kr`|||S)z Given a function with out variant (i.e. using `out_wrapper()), it returns its in-place variant See https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-do-in-place-operations-work-in-pytorch cs|f|d|i|S)Noutr))r\rSrjrkr)r*rmsz_make_inplace.._fn_r) getmodule) rrcrUgetattratenrnrw__all__append)rlrmZ inplace_namerwZ_allr)rkr* _make_inplaces    r|cCs t|Sr2)primsr.r[r)r)r*r.scCs t|Sr2)r}r/r[r)r)r*r/scCs t|Sr2)r}r0r[r)r)r*r0scCs t|Sr2)r}r2r[r)r)r*r2scCs t|Sr2)r}r1r[r)r)r*r1#scCs t|Sr2)r}r3r[r)r)r*r3(scCs t|Sr2)r}r4r[r)r)r*r4-scCs t|Sr2)r}r5r[r)r)r*r52scCs t|Sr2)r}r6r[r)r)r*r67sinputcCs t|jSr2)rPis_complex_dtypedtyper~r)r)r*r!<scCst|js|St|Sr2)rPrrr}r7r~r)r)r*r7As cCs t|Sr2)r}r8r[r)r)r*r8IscCs t|Sr2)r}r9r[r)r)r*r9NscCs t|Sr2)r}r<r[r)r)r*r<SscCs t|Sr2)r}r=r[r)r)r*r=XscCs t|Sr2)r}Zerf_invr[r)r)r*r>]scCs t|Sr2)r}r?r[r)r)r*r?bscCs t|Sr2)r}r@r[r)r)r*r@gscCs t|Sr2)r}rAr[r)r)r*rAlscCs t|Sr2)r}rCr[r)r)r*rCqsza,r])r\valuerXcCsbt|tstt|tstt|j}tt||sVdt|d|d}t |t ||SNzvalue argument of type  cannot be safely cast to type rO) r-r$rCrrPrrrr&rGr}rD)r\r python_typemsgr)r)r*rDxs cCst||}t|||Sr2)r}rDr)r\rrr)r)r*rEs  )rrXcCs t|Sr2)torchrr~r)r)r*rscCs t|Sr2)r}rFr[r)r)r*rFs)r6rXcCs*ttt|t|}t||Sr2)rrrFr.rir)r6Ztrunc_xr)r)r*rGsr_cCs0t|tsttt|jddt |S)NcSsdS)Nz#imag only supports complex tensors.r)r)r)r)r*r9r:zimag..) r-r$rCr_checkrPrrr}rr[r)r)r*rs  )rVcCs0t|jst|jr"t|St|tjdS)Nr) rPis_float_dtyperrr}rOrrboolr[r)r)r*rOs cCsZt|jr,ttt|tt|St|jrJt |t dkStj |tj dS)Ninfr) rPrrrrrPrrrr.floatrrr[r)r)r*rPs    csFttj fddtjr6tdkStjtjdS)Ncs djS)Nz7Complex dtype is not supported for isposinf, got dtype rr)r[r)r*r9r:zisposinf..rr rrrPrrrrrrr[r)r[r*rQs    csFttj fddtjr6tdkStjtjdS)Ncs djS)Nz7Complex dtype is not supported for isneginf, got dtype rr)r[r)r*r9r:zisneginf..z-infrrr[r)r[r*rRs    cCs t||Sr2r}rr[r)r)r*rSscCs*t|jrt|dkStj|tjdS)Nrr)rPrrrrrrr[r)r)r*rTs cCs t|Sr2)r}Z bessel_i0r[r)r)r*rUscCs t|Sr2)r}rWr[r)r)r*rWscCs t|Sr2)r}rXr[r)r)r*rXscCs t|Sr2)r}rYr[r)r)r*rYscCs t|Sr2)r}rZr[r)r)r*rZscCs t|Sr2)r}r[r[r)r)r*r[s)r\dimrrXcCs6|p|j}t|}t||}t|t||dd|S)NTkeepdim)rrPget_computation_dtyper(r)r\rr result_dtypecomputation_dtypea_r)r)r*r]s   selfF)rrrrXcCst|ts|f}|dkr4tt|||Stj||dd}t|| t dkd}|rf|n t ||}tt||||}| |S)NrTrr) r-rnumelrrr@rXrrr.rrru)rrrZmaxesZmaxes_squeezedresultr)r)r*rs  )r\nanposinfneginfrXcCst|tstt|js&t|jr.|S|dkr:d}|dkrPt |jj }|dkrft |jj }t t |||}t t|||}t t|||}|S)Nr)r-r$rCrPis_boolean_dtyperis_integer_dtyperrZfinforDminrrSrRrQ)r\rrrrr)r)r*ra(sr[cCst|jtjk dddS)NcSsdS)NzNegation, the `-` operator, on a bool tensor is not supported. 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input_dtyper)r*r9r:z!view_as_complex..rcSsdS)Nz-Input tensor must have one or more dimensionsr)r)r)r)r*r9r:r?cSsdS)Nz+Tensor must have a last dimension of size 2r)r)r)r)r*r9r:r;cSsdS)Nz/Tensor must have a last dimension with stride 1r)r)r)r)r*r9r:css|]}|ddkVqdS)rrNr))r5strider)r)r*r7sz"view_as_complex..cSsdS)NzCTensor must have a stride divisible by 2 for all but last dimensionr)r)r)r)r*r9r:cSsdS)Nz0Tensor must have a storage_offset divisible by 2r)r)r)r)r*r9r:)rrrrPrsizer=rpy_allstorage_offsetr}Zview_element_typecorresponding_complex_dtyper)rsizesZ old_stridesdimsr)rr*rsD     c s$tdfdd }|S)NrYcsdkrjttddtttftttftdfdd }r^t|}|_tkrxt dk rrt ||S)Nr\br]r\rrXcstpt|t fddtp2t|t fddtp\t|toZt|t fddt||\}}||}t||g|S)Ncs dS)Nzc: Received a lhs Python scalar to an elementwise binary operation that does not accept lhs scalars!r)r)rsr)r*r9r:zQ_make_elementwise_binary_reference..inner.._ref..cs dS)Nzc: Received a rhs Python scalar to an elementwise binary operation that does not accept rhs 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(a), but it is found on rO) r-rrrrr(rHrrjrbr.r\rrr)r)r*r~us  ) rounding_mode)r\rrcCsN|dkrt||S|dkr$t||S|dkr6t||Sd|d}t|dS)z/ Reference implementation of torch.div NrtrFzLdiv expected rounding_mode to be one of None, 'trunc', or 'floor' but found .)rrrrG)r\rrrr)r)r*rs     cCs t||Sr2)r}rrr)r)r*rscCst|tst|tstt|trV|dkr2|S|dkrB||S|dkrt|SnHt|tr|dkrtt|dS|dkrt |j st |j rt |St ||S)N?g@?T)r-r%rCrrrrorDrPrrrrCr}rrr)r)r*rs$       cCsxt|trt|trtdt||}|dk s4tt|rFtj}ntj }t ||}t ||}t ||\}}t ||S)Nz=Receive two Number inputs to an elementwise binary operation!) r-rrGrPZget_higher_dtyperCrrZ complex128float64r(rTrr\rrr)r)r*rs     )r^rrcs8t|tr&t|tr&t|}t|}nt|trNt|trNt||j|jd}nt|trvt|trvt||j|jd}n`t|trt|tr|j|jkr|jtdkrd|jd|jd}t|ntj ||jd}t|trt|tst |jt r t ||St r t||Stdfdd dS) NrcpurrrOr'Fcs dS)Nz not supported for floor_divider)r)rr)r*r9r:zfloor_divide..)r-rrrrr(rrHr}Z device_putrCrPr_floor_divide_floatr_floor_divide_integerrrr)rr*rs&        cCs`t||\}}|jjs"t||St|t|kt||dk}t||t ||jSr) rTr 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  rh㈵>:0yE>)r\rrr equal_nanrXc Cstd||||dt||}|rPt|js8t|jrPt|tt|t|}|dkrd|dkrd|St|jst|jst |t }t |t }t |tt||}tt||}t|tt|t||}|S)Nz torch.iscloserr)rrrPrrrrrrSr}convert_element_typerget_default_dtyperur.rrrOr) r\rrrrcloseZ allowed_errorZ actual_errorrr)r)r*rs   cCs~|j}|tjtjfk}|r6t|tj}t|tj}t||}t|dkd|}t t |||}|sr|St||Sr) rrZint8Zint16r}rint32rrr.r)r\rrZpromote_to_intgresr)r)r*rs cCs t||Sr2)r}rrr)r)r*rsc Cst|t|k}t|||}t|||}tttt|t|t|k}t|jsvt|jrt|dk}t||t t |t |}t|||t t ||}t |} t| t tdtd|St|||t t ||SdS)Nrr)rrrrrrOrPrrrXr@rYrScomplexr) r\rrmax_min_inf_maskZ neg_min_maskZinf_valsZ non_nan_valsZnan_maskr)r)r*rs, cCstt|jpt|j dd||k}t|||}t|||}tt|||k}dt d}|t t |||}t|||S)NcSsdS)Nz)logaddexp2 doesn't support complex dtypesr)r)r)r)r*r9#r:zlogaddexp2..rr) rrrPrrrrrPrrXrYrC)r\rrrrrZ inv_log_2rr)r)r*rscCs0t|js|dk}t|js(|dk}||@SrrPrrrr)r)r*r/s   cCst|js|dkS|Srrr[r)r)r*r:s cCs2t|js|dk}t|js(|dk}t||Sr)rPrrryrr)r)r*rAs   cCs0t|js|dk}t|js(|dk}||ASrrrr)r)r*rMs   cCs t||Sr2)r}rrr)r)r*rXscCs t||Sr2)r}rrr)r)r*r`scCs t||Sr2)r}rrr)r)r*rgs)r^rcCs t||Sr2)r}rrr)r)r*rnscCs t||Sr2rrr)r)r*rvscCs t||Sr2)r}rrr)r)r*r~scCs t||Sr2)r}rrr)r)r*rsr;cCs&t|trd}t|tj|||dS)Nz?Received a Number for the first argument, but expected a Tensorr)r-rrGrr)r\rrrr)r)r*rs cCst||\}}|dkrt|tr&|jn|j}t|}tt||sddt|d|d}t|t|t j r~t ||}n||}t ||}t||g|S)z/ Reference implementation of torch.sub r;rrrO)rTr-r$rrPrrr&rGrrr}rrr/rr)r)r*rs   )r^rsrVrcCs t||Sr2)r}rrr)r)r*rsc Cstt|tpt|tddt|trHt|trHt||j|jd}n&t|trnt|trnt||j|jd}t|ts|tt|tstt t |ddt |t |}t t |td|S)NcSsdS)Nz/Expected either argument a or b to be a Tensor"r)r)r)r)r*r9r:zxlogy..rrr)rrr-r$rrrr(rCrrrrXrSr)r\rrhsr)r)r*rs$)r^rVrcCs0t|}t|r t||Stt||Sr2)rPZ get_dtyperr}rrtrr)r)r*rs   )rtensor1tensor2r)rrrrrXcsJdk r:|j}t|tttfdd|||S)z3 Reference implementation of torch.addcdiv NcsdtddSrr%r)rrr)r*r9r:zaddcdiv..rrPrrrrr&rrrrrr)rr*rs  csJdk r:|j}t|tttfdd|||S)z3 Reference implementation of torch.addcmul NcsdtddSrr%r)rr)r*r9*r:zaddcmul..rrr)rr*rs  )r\rrD)r\rrDrXcCs|dkr|dkrd}t||dk rPt|}tt|||}t|||}|dk rt|}tt|||}t|||}|S)Nz+clamp called but both min and max are none!)rGrrSryrrr)r\rrDrZa_isnan conditionr)r)r*r0s   )rrrXcCstj||dS)N)rrr)rrr)r)r*r|Ps)rrDrXcCstj||dS)N)rDr)rrDr)r)r*r}Yspredr\rcsb|dks|dkrttj||ddtjtjkfddt||\}}t ||S) NTallow_cpu_scalar_tensorscs djS)Nz#expected predicate to be bool, got rr)rr)r*r9|r:zwhere..) NotImplementedErrorrPcheck_same_devicerrrrrTr}rrr)rr*ris    memory_format)r\rrXcCstj||d}|SNr)r}r)r\rrr)r)r*rs)allow_cross_devicecCs:|s.|j|jkr.d|jd|jd}t|t||S)NzAttempting to copy from device z to device z*, but cross-device copies are not allowed!)r(rHr}r)r\rrrr)r)r*rscCs>|dkr$d|d}t|t|j}|t|S)Nr;zCan't convert a tensor with z elements to a number!)rrGrPrrr}r)r\r number_typer)r)r*rs   ) r\r(rcopylayoutr pin_memory non_blockingrXcCs\| oZ|dks|j|koZ|dks*|j|koZ|dks<|j|koZ|dkpZ|tjkpZtj||dSr)r(rrrpreserve_formatrPis_contiguous_for_memory_format)r\r(rrrrrrr)r)r*_to_will_aliass  rcOstdSr2)rrir)r)r* _to_dispatchsr)r(rrrrrXcCs|||||d}|SN)r(rrrrr)r(rrrrrjr)r)r* _to_devices r cCst|||||d}|Sr )rr(r r)r)r*_to_device_strs r )rrrrrXcCs||||d}|S)N)rrrrr))rrrrrjr)r)r* _to_dtypes r )otherrrrrXcCs(|j}|j}|j}||||||d}|S)N)r(rrrrr)r(rr)rrrrr(rrrjr)r)r* _to_othersr)r\ to_kwargscCsddddg}d|kr4t|dtr4t|d|d<|D]v}||kr8|dkrZ||tjks|dkr||j|jjkr||jr||j|jjkst||d||kr8||q8dS)Nrr(rr) r-rQrr(rr&indexrxpop)r\rZoptions_to_checkkwr)r)r*_canonicalize_to_argumentss(    rcOst|dkrt||}d|ks"tt||t|f|r<|Sd|krN|dnd}d|krd|dnd}|s|d|j|jkr|sd|krd|krd |krt ||d|jSt j |f|}t |||S) NrrrFrrrr(r) r=rrCrrrgetrr}rrr r)r\rSrjrrrr)r)r*r&s0       ) has_identityaccepts_dim_tuplerkeepdimsrru) r\rZrrrrrruoutput_dtype_kindrXcsttstjdkr*tdjd|dk rZt|ts@t|dk rZ||jkrZtd|stdkstttsttttrftj |sˆjdkpt fddD} | stdt ||\} } t | |} |r.fd d t jD} fd d t jD}t| | |} |dk r|| dk sFt|dk rd| |jkrdtd t|| j }t| |d S| j| kr| dk rt| | } | S)N@zReceived a tensor with z? dimensions, but only tensors with up to 64 dims are supported!z4dtype argument and out dtype must match in reductionrc3s|]}j|VqdSr2r>r5ir[r)r*r7nsz_reduction..zJreducing over zero-size dimension for reduction operation without identitycs"g|]}|krj|ndqSr;rrr\rr)r* ysz_reduction..csg|]}|kr|qSr)r)r)rr)r*r zsz7Expected the dtype of reduction result and out to matchZ copy_fromr)r-r$rCndimrHrrrPreduction_dimsr>rreduction_dtypesr(rFr}broadcast_in_dimr)r*r)r\rZrrrrrrurZ valid_shaperrrZ output_shapebroadcast_dimsr)rr* _reductionMsZ            r'csHj}tddfdd }|d}||_ttt||}|S)zk Given a view function (e.g. torch.diagonal) generates its copy variant (e.g. torch.diagonal_copy) Nrucs,|d|i|}|dkr(|jtjdS|S)Nrur)rrcontiguous_format)rurSrjrrkr)r*rmsz!_make_copy_from_view.._fnZ_copy)rcr-rUrxry)rlrsrmZ copy_namer)rkr*_make_copy_from_views  r*)r\rrrXcCs:ttjt|||d}|jtjkr6|jtjd}|S)Nrr)rrrruint8r)r\rrrr)r)r*rs cCsbt|tj}t|ttfr0t|dkr0|}n|j||d d}|j tj kr^t |tj S|S)Nr)rrF)r(rrr-listrAr=rrrrr+r}r)r\rrrrr)r)r*rs   rru)r\rrrrurXc Csl|dkr>|dk r|j}n&t|js0t|jr8tj}n|j}|dksN|gkrRd}t|tj||||t j dSNr)rrrrur) rrPrrrint64r'r}rr!SAMEr\rrrrur)r)r*rs" cstjddttjfddtjrRtdkrRt Sj tt t t fddt tD}tj |ddd S) NFvalidatecsddjdS)Nzsum_to_size: size "z" is not expandable to size ""rr)r\r>r)r*r9r:zsum_to_size..rc3s0|](}|dkrj|dkr|VqdSr;Nrrr\Z leading_dimsr>r)r*r7szsum_to_size..T)rrr)rPextract_shape_from_varargsrrZis_expandable_tor>Z is_same_shaper=r}view_ofr"rArFr)r\r> reduce_dimsr)r8r*rs    )r\rrrurXc Csl|dkr>|dk r|j}n&t|js0t|jr8tj}n|j}|dksN|gkrRd}t|tj||||t j dSr.) rrPrrrr0r'r}rr!r1r2r)r)r*rs" r(c Cs0|dks|gkrd}t|tj||d|dtjdSNr)Frrrrurr)r'r}rr!r1r\rrrur)r)r*r s c Cs0|dks|gkrd}t|tj||d|dtjdSr<)r'r}rr!r1r>r)r)r*r4 s cCs"|dkrt|tr|}d}||fSr2)r-r)runbiasedr)r)r*_dim_var_dispatchL sr@ correction)r\rr?rrBrXc CsVt||\}}t||}|dks*|gkr.d}t|ttj|d||dddtjd}|S)Nr)rATr=) r@rPset_correctionr'rr}rr!COMPLEX_TO_FLOAT)r\rr?rrBrr)r)r*rV s    c Csht||\}}t||}t|tj\}}t||}tj||||d}t |}|dk s^t t||SN)rBr) r@rPrCr$r!rDr(rrrorC) r\rr?rrB opmath_dtypera_vara_stdr)r)r*rs s     c s.|dks|gkrd}dkr&jtdkp:jkfddttj||dtjd}tt pzt fddt |t r|f}t j|}jdkrdnttjfdd |Dd}t||}dkrjn}t||}dk r*t tstt|jt|d S|S) Nr)csddjdS)Nz"Expected out tensor to have dtype z , but got  insteadrr)r-r)r*r9 r:zmean..r/csddkrdnddS)Nz&mean(): could not infer output dtype. ZInputr z> dtype must be either a floating point or complex dtype. Got: r)r))r orig_dtyper)r*r9 srr;c3s|]}j|VqdSr2rrr[r)r*r7 szmean..r!)rrrr'r}rr!ZKEEP_PROMOTED_TYPErPrrr-rr#r>r"roperatorrrr(r$rCr)r*) r\rrrrurrZnelemrr))r\rrJrur*r sD     *     Zout0Zout1)r?rrB)r\rr?rrBc Cs|t||\}}t||}t|tj\}}|j}t||}tj ||||d\}} t |} |dk sht t| |t| |fSrE) r@rPrCr$r!rDrr(rrrorC) r\rr?rrBrFrZoriginal_dtyperGZa_meanrHr)r)r*r s     cCs4t||\}}t|||||d}t|||}||fS)NrA)r@rr)r\rr?rrBvmr)r)r*r s  )rvec1vec2)betar)rrNrOrPrrXcs\tjdkfddtjdkfddjdjdtjrttt t fddttt t fddsrt St dSt rt n t dSn~ttt tjfd dttt tjfd ddkr@t St SdS) Nr;csdjdS)Nz*addr: Expected 1-D argument vec1, but got -Dr"r))rNr)r*r9 r:zaddr..csdjdS)Nz*addr: Expected 1-D argument vec2, but got rQrRr))rOr)r*r9 r:rcsdtS)Nzexpected bool/int beta but got r%r)rPr)r*r9 r:csdtS)Nz expected bool/int alpha but got r%r)rSr)r*r9 r:FcsdtdjSNzcannot safely convert z to r&rr))rPrr)r*r9 r:csdtdjSrTrUr))rrr)r*r9 r:)rrr"rr>rPrrrr&intouterrrr)rrNrOrPrr))rrPrrNrOr*r sF          .)argrSrXcGs^|st|tjjr|}nt|tjjr*t|f|}tdd|D}t|dkrV|S|dS)z5Reference implementation of :func:`torch.atleast_1d`.css&|]}|jdkr|nt|dVqdS)r;rN)r"rr5r\r)r)r*r7) szatleast_1d..r;r)r- collectionsabcrrCrAr=rXrSZargs_rr)r)r*r s  ) at_least_fnrrXrXcCs ||}t|tstt||Sr2)r-r$rCr)r]rrXZarg_r)r)r*_unsqueeze_atleast/ sr^csn|st|tjjr|}nt|tjjr*t|f|}tttdtfdd|D}t |dkrf|S|dS)z5Reference implementation of :func:`torch.atleast_2d`.rc3s$|]}|jdkr|n|VqdS)rNrRrYZunsqueeze_atleast_1dr)r*r7B szatleast_2d..r;) r-rZr[rrCrr^rrAr=r\r)r_r*r8 s  csn|st|tjjr|}nt|tjjr*t|f|}tttdtfdd|D}t |dkrf|S|dS)z5Reference implementation of :func:`torch.atleast_3d`.r?c3s$|]}|jdkr|n|VqdS)NrRrYZunsqueeze_atleast_2dr)r*r7Q szatleast_3d..r;r) r-rZr[rrCrr^rrAr=r\r)rar*rG s  )r\rrrrXcCs$|dk r |n|}t||||Sr2)rr}r)r\rrrstorage_offset_intr)r)r*rU s)rsrcrrrrXcCs"|dkr dn|}t|||||Sr)r}r)rrcrrrrbr)r)r*ra s cGstt|Sr2)rSizerL)rIr)r)r*rn scGs4t|dkr"t|dts"|d}tt|ddiS)Nr;rrMF)r=r-rr,rTtensorsr)r)r*rr s)r\rrXcCs8t|t|j}tt|t|j|}t|||Sr2)r=r>rArFr}r%)r\rstartrr)r)r*r{ sre)rfrrXc sdd}t|dkr d}t||D]}t|ts$tq$tj|ddiddlm}dt |D]J\dkrj dkrq`j dkr`t j j kfd d q`dkr|dj }g}t |D]x\}t|t|j kr|j dkstt |j ddkfd d q|j dkr<||j ddkr}t|}|tjkr(|S|dk rB||krBtjS|}q|dk sTt|Sr2)rPsuggest_memory_formatrr)rC)inputsformatrfr)r)r* cat_compute_output_memory_format s    z-cat..cat_compute_output_memory_formatrz3cat expects at least one tensor, but received zero!rFr0r;csdjdjddS)N6Number of dimensions of tensors must match. Expected z-D tensors, but got z-D for tensor number in the listrRr))examplerrr)r*r9 r:zcat..csdjddS)Nrmz*-D tensors, but got 1-D for tensor number rnrRr))ro tensor_idxr)r*r9 r:css|] }|jVqdSr2) requires_gradr4r)r)r*r7 szcat..rrr(rqrr)r=rGr-r$rCrPrr@r1rEr"rrr>r{r Exceptionr rr(canonicalize_dimZ validate_idxr}rr) rfrrlrtensorr1r>filteredrrqr))rorrrpr*r sb          )rfrXcCstdd|D}t|dS)Ncss.|]&}|jdkr|n||dfVqdSr7)r"rrr4r)r)r*r7 szcolumn_stack..r;)rArrfZaligned_tensorsr)r)r*r scCs*t|js|S|jr t|St|Sr2)rPrrZ is_sparserr7r}rr~r)r)r*r s   )rpadrrXc sZttddkfdd|jttd}|t|kfdd|}tD]rdddkr| |j}ddkrj|d|jd}qjtddDr|St d}t|D]dtddd}t|dkfd d| |qt |}tj ||j|j|j|d }|dkr|jtjkrd }t||}|} tD]vdddkr| | j} ddkr| d| jd} qt| ||S) NrrcsdtS)Nz1Length of pad must be even but instead it equals r<r))ryr)r*r9 r:z!constant_pad_nd..csdtddS)Nz`Length of pad should be no more than twice the number of dimensions of the input. Pad length is z while the input has dimensions.r<r))l_inpryr)r*r9 r:r;css|]}|dkVqdS)rNr))r5pr)r)r*r7! sz"constant_pad_nd..c s6dddddd S)NzThe input size z, plus negative padding rr;zG resulted in a negative output size, which is invalid. Check dimension z of your input.r)r))r input_sizesl_diffrypad_idxr)r*r9+ r:rsF)rrr=r>rFrbuiltinsrrr,r{rPrhr rr(rqrrDr}r) rryrZl_padZc_input new_shapeZnew_dimrr.Zc_outputr))rr}r~r{ryrr*r sl    ""    $ cCs6t|tjkddtj||dr(|Stj||dS)NcSsdS)Nz@preserve memory format is unsupported by the contiguous operatorr)r)r)r)r*r9R r:zcontiguous..r)rrrrPrr)r\rr)r)r*rM scCs*tt|dkddt|}t|dS)NrcSsdS)Nz%dstack expects a non-empty TensorListr)r)r)r)r*r9] r:zdstack..r)rrr=rrrxr)r)r*r[ sc sddlm}t|dkr2t|dtr2t|d}tt|t|jkddt|t|j}t |}t |jD]\\}||}||}t||kp|dkp|dkfdd|dkr|n||<qtt |t ||tt|t|j|S)Nrr0r;cSsdS)Nz3expand: the requested shape has too few dimensions!r)r)r)r)r*r9n r:zexpand..r?cs ddS)Nz3expand: attempting to expand a dimension of length rOr)r)r8r)r*r9z r:)r@r1r=r-rrArrr>r,rErPvalidate_shaper}r%rF)r\r>r1rshape_rKZ offset_idxZrequested_lengthr)r8r*rb s4      cCs ||jSr2)rr>rr)r)r*r s)r\chunksrrXc Cs|dkrd|d}t|t|j|}|j|}t||}t||}||}g}t|D]} | t ||| ||qd|dkr| t |||||t |S)Nrz%Expected at least one chunk, but got rO) rGrPrur"r>rr6rFrFr{rrA) r\rrrlength chunk_sizeZ full_chunksZtail_chunk_sizerrr)r)r*r s   r?)r\ start_dimend_dimrXcCsht|j|}t|j|}||kr2|jdkr2|St|||\}}|dk rZt|||St|||Sr)rPrur"r}_collapse_view_helperZ collapse_viewZcollapse)r\rrr new_stridesr)r)r*r s)r\rrXcCs@t|tst|tstdt|j|}t|t ||S)Nz!dims has to be a sequence of ints) r-rAr,rGrPcanonicalize_dimsr"Zvalidate_no_repeating_dimsr}revrr)r)r*r s  cCs|jdkrtdt|dS)NrzInput must be >= 2-d.rr"rHrr[r)r)r*r s cCs|jdkrtdt|dS)Nr;zInput must be >= 1-d.rrrr[r)r)r*r s )r\rrgrrXcsttr6tdko$tjddt|dkddtdkddt |j |}| |t t kokfdddkrtkfddtj||dS) NrcSsdS)Nz'start must be an 0-dim integral Tensor.r)r)r)r)r*r9 r:znarrow..cSsdS)Nz-narrow() cannot be applied to a 0-dim tensor.r)r)r)r)r*r9 r:cSsdS)Nz&narrow(): length must be non-negative.r)r)r)r)r*r9 r:csd dddS)Nz0start out of range (expected to be in range of [, z ], but got )r)r)) dim_lengthrgr)r*r9 r:csddddS)Nzstart (z ) + length (z) exceeds dimension size (z).r)r)rrrgr)r*r9 r:axis)r-r$rrrrPrrrrur"rZ _check_with IndexErrorr} slice_in_dim)r\rrgrr)rr*r s,    )r\ norm_dimsepsrXc Cslt|j|}t|j}t||}t|ts2tt j ||ddd\}}t ||}|||}|||fS)aComputes mean and 1/std of a tensor along norm_dims. Used as a helper function for normalization layers. Args: a (Tensor): input tensor norm_dims (DimsType): dimensions to normalize over eps (float): epsilon for numerical stability Returns: out (Tensor): normalized tensor. mean (Tensor): mean of the tensor along norm_dims. rstd (Tensor): 1/std of the tensor along norm_dims. FT)rr?r) rPrr"rrr(r-r$rCrrr) r\rrrZa_accZ biased_varrrstdrur)r)r* _normalize s    r)r6 dimensionsrXcCst|D]}t||}q|Sr2)sortedrr)r6rrr)r)r*_unsqueeze_multiple s r) rweightbias batch_size num_channelsflattened_inner_size num_groupsrrXcstjdkfddt|dkfddddg}t|||g} t| ||\} } } | j} dgttdj} d}|dk rt || }d}|dk rt || }|dk r| |} |dk r| |} t | j } t | j } t | j } t | |} t | |} | | | fS)Nrcs djS)Nz=Expected at least 2 dimensions for input tensor but received rRr)r~r)r*r9$ r:z#native_group_norm..rcsddjdS)NzDExpected number of channels in input to be divisible by num_groups, zbut got input of shape z and num_groups = rr)rrr)r*r9( sr`) rrr"rrrr>r,rFrr(rr)rrrrrrrrr#Zinput_reshapedrurrr&Zunsqueeze_biasZunsqueeze_weightr)rr*r s@            Zout2)rnormalized_shaperrrrXc st}t|dkfddtdkp8jtkfddtdkp`jtkfddtj|kojj|dtkfdddk rdk rЈj|}tt|j}t ||\}} } dkrdk r|}n>dk r8dkr8|}n dk rXdk rX|}t |j }j j dkrt | j } t | j } || | fS)Nr;cs dtS)NzzExpected normalized_shape to be at least 1-dimensional, i.e., containing at least one element, but got normalized_shape = )rQr))rr)r*r9X sz#native_layer_norm..csdtjdtS)NzQExpected weight to be of same shape as normalized_shape, but got weight of shape  and normalized_shape = rQr>r))rrr)r*r9a s csdtjdtS)NzMExpected bias to be of same shape as normalized_shape, but got bias of shape rrr))rrr)r*r9i s cs&dtdtdtjS)NzGiven normalized_shape=z, expected input with shape z, but got input of size rr))rrr)r*r9r sr)r=rrr>rAr"rr,rFrr(rr(r&) rrrrrZnormalized_ndimrr#rurrr))rrrrr*rL sJ               cGs t|jt|}t||Sr2)rPrr"Zextract_dims_from_varargsr}r)r\r _permutationr)r)r*r s )rr|rmaxnormrXc stt|t ddt|dkddttt ddtdkfdd|jtdkfddt|}tt}||=t |j }||j krtj j |||d |d }ntj j |||d d }d }t |k||d }||j krt||j }||S)NcSsdS)Nzrenorm: p must be real-valuedr)r)r)r)r*r9 r:zrenorm..rcSsdS)Nz'renorm: non-positive norm not supportedr)r)r)r)r*r9 r:cSsdS)Nz#renorm: maxnorm must be real-valuedr)r)r)r)r*r9 r:cs dS)Nz,renorm: expected maxnorm to be >= 0 but got r)r))rr)r*r9 r:r;cs ddS)Nz/renorm: input needs at least 2 dimensions, got dimensionsr)r)rRr)r*r9 r:T)rrrgHz>r)rrr-rr"rPrur,rFrrlinalg vector_normrr}rr) rr|rrr;Zacc_typer_rZ norm_factorr))rr"r*r" sB        reflect) rn_fft hop_length win_lengthwindowcenterpad_mode normalizedonesidedreturn_complexrXc stdkpjjkfdd|dk r2|nd|dk rF|n| dkr|pldk oltj} t| dn| } ttjptjddtdjkodknddj} | dkr d |r:d j} d} t d| j }t || | g|| dd }dtd kofknfd dtd kfd dtd koknfd dtdkpԈj fkfddkr0dkr tjjjdd}t ||gjdddk rRtj}|dk rl|n| }|r|dnd}|rt| ddtjjd|d}ntjjd|d}|dd| dkr|d }| r|St|S)NcsdjdjS)NzAstft input and window must be on the same device but got self on  and window on r'r)rrr)r*r9 s  zstft..zstft requires the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release.cSsdS)Nz:stft expected a tensor of floating point or complex valuesr)r)r)r)r*r9 r:r;rcSsdS)Nzstft expected a 1D or 2D tensorr)r)r)r)r*r9 r:rr`csddS)Nzstft expected 0 < n_fft <= z, but got n_fft=r)r))rrr)r*r9 r:cs dS)Nz0stft expected hop_length > 0 but got hop_length=r)r)) hop_length_r)r*r9 r:cs dS)Nz9stft expected 0 < win_length <= n_fft but got win_length=r)r)) win_length_r)r*r9 r:csdddjS)Nz8expected a 1D window tensor of size equal to win_length=rzbut got window with size rr))rrr)r*r9 s  rr?) dimensionrsteporthocSsdS)Nz9Cannot have onesided output if window or input is complexr)r)r)r)r*r9" r:rr_)rrr(r!rPrrrr"r itertoolsrr>ryryrrrrrfftZrfftZ transpose_Zsqueeze_Z view_as_real)rrrrrrrrrrZreturn_complex_ original_ndimZ extra_dimsZ pad_amountZextended_shapebatchleftZ complex_fftr_rur))rrrrrrr*r# s  &                ) rrrrrrrrrrXc stdkpjjkfdd|dk r2|n|d} |dk rF|n|} ttjfddd} d} || | d}tdkd dtd jkod knfd d|dk r|n| |k}|rt|d d| kd dnt|| kddtd| ko2| knddtd| koX|knddtdkp~j | fkdddkrt j}tj | |jd}n}| |kr|| d }t |||| |fd}j}jd krddd |rdnd}| rHt| ddtjjd|dnTtdkpbtj dd|sjdd|d ddtjjd|dd |kst|dd|g}t j||d|fd|| d}t j|d d| |f|d|fd|| d}||dks&t||dks:t|rH|d nd}|dk r`||}n|rt||d }n|}td||}|jd||d}|jd||d}|d}t| dd||}|d kr|d}||krt dt |d||fd}|S)NcsdjdjSNzBistft input and window must be on the same device but got self on rr'r)rr)r*r9@ s  zistft..rcsdjdjSrr'r)rr)r*r9K sr?r;rcSsdS)Nz"istft input tensor cannot be emptyr)r)r)r)r*r9T r:rr`cs djS)Nz8istft expected a tensor with 2 or 3 dimensions, but got rRr)r~r)r*r9W r:cSsdS)Nzistft expected the frequency dimension (3rd to the last) of the input tensor to match n_fft / 2 + 1 when onesided=True, but got {fft_size}r)r)r)r)r*r9^ scSsdS)N)zistft expected the frequency dimension (3rd to the last) of the input tensor to match n_fft when onesided=False, but got {fft_size}r)r)r)r)r*r9f r:cSsdS)Nz+istft expected 0 < hop_length <= win_lengthr)r)r)r)r*r9n r:cSsdS)Nz&istft expected 0 < win_length <= n_fftr)r)r)r)r*r9q r:cSsdS)NzDInvalid window shape. window has to be 1D and length of `win_length`r)r)r)r)r*r9u r:rrcSsdS)Nz9cannot have onesided output if window or input is complexr)r)r)r)r*r9 r:rcSsdS)Nz:Complex windows are incompatible with return_complex=Falser)r)r)r)r*r9 r:)rrgr)r}rrrgdy=cSsdS)Nz&window overlap add min less than 1e-11r)r)r)r)r*r9 r:zThe length of signal is shorter than the length parameter. Result is being padded with zeros in the tail. Please check your center and hop_length settings)!rrr(rPrrrrr"r>Zcorresponding_real_dtyperryrrrrZifftrZirfftrCrZunfold_backwardrrrDr.rrrrwarningswarn)rrrrrrrrrrrrZn_framesZfft_sizeZexpected_output_signal_lenZ onesided_Z real_dtypeZwindow_rrr_Zy_tmpyZwindow_enveloprgendZwindow_envelop_lowestr)rr*r$1 s                     )a_shapea_striderrrc st|}tj||dd|dkr$dn||dkr8dn|}tkfddtdkfddt|}t|}||||kr|d|<|9<||fS)NT)Z wrap_scalarrr;csdddS)Nz%Maximum size for tensor at dimension z is z but size is r)r))rmax_sizerr)r*r9 r:z*_get_unfold_shape_stride..cs ddS)NzStep is z but must be > 0r)r)rr)r*r9 r:)r=rPrurrr,r{) rrrrrZa_ndimZ last_strider>stridesr))rrrrr*_get_unfold_shape_stride s(   rc Gs>tj|dd}tt|t|jkddt|dkrBt|St||j}dg|}|jD]}||q`t ddt ||D}d|krtj ||j |j |jt|d S|}t|}t|D]"\}}t||||t|d\}}qtt|} | jtdd d t | \} } ||} t| } | | }||S) NFr3cSsdS)Nzbrepeat: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensorr)r)r)r)r*r9 r:zrepeat..rr;css|]\}}||VqdSr2r))r5Z padded_sizeZ repeat_sizer)r)r*r7 szrepeat..rsT)keyreverse)rPr9rrr=r>rr"r{rAzipr rr(rqrhmake_contiguous_strides_forrErrDr,sortrK itemgetterrrr)r\Z repeat_shapeZnum_new_dimensionsZ padded_shapedim_sizeZ target_shapeZurtensor_shapeZurtensor_striderZenumerated_strideZ permute_orderZ sorted_strideZrepeat_xtensorZ cloned_resultZpermuted_resultr)r)r*r sN            )r\ allow_copyrXcsddlm}m}tj|dd}t||}||dkrPt||t|S|j dkr|}|D]}|dksrt t |d}qb||krt |S|St|dkr|}|jD]}|dkst t|d}q||krt |S|S|rVt|dkr|j dkrt||gdgSt|dkrV|j dkrV|d}|d}t|||g|dgSd||D]6}j kr|dkst j d} t | j| dqb||jkr̈dqbj} } || |dkr| d} | j| } q| krtt | \} } | dkrh|rBt ||Sd|jd |d |d }t|t| || |krt |dqbj krtjdkfd d tq|krt |SSdS)Nr)r1sym_eqFr3r;r?rz Cannot view a tensor with shape z and strides z as a tensor with shape rOcsddjS)Nza.size(z) expected to be 1 but got rr)rrKr)r*r9r:z&_reshape_view_helper..)r@r1rrPr9Z infer_sizerrrr"rCrr}r:r=r>rZ is_contiguousrZ split_dimrrrrGrr)r\rr>r1r_ardim0dim1last_dimaccumrrrrr)rr*_reshape_view_helper"s                       r)r\r>rXcGst|f|ddiS)NrTrr6r)r)r*rs)rrrXcCs||Sr2)rrrrr)r)r*rs)r\shiftsrrXc Cs|t|j|}t|ts|f}t|ts.|f}|dkrB|S|dkrnt|dkrnt d|ddt|}t|}|dks|dkr2|dkrt d|dkr|dkrt t ||d|jS||krt d|d||dkst|dd}|dd}t ||df|d}t |||S|d}|j|} | |d| } t j| |jd } ||t | | | S) z/Reference implementation of :func:`torch.roll`.rzDimension specified as z but tensor has no dimensionsr;z`shifts` requiredz*shifts and dimensions must align. shifts: z, dims: Nr')rPrr"r-rrrrr=rrHrrrrr>rCrr(rLr) r\rrZ len_shiftsZlen_dimsZ tail_shiftsZ tail_dimsZfirst_dim_rolledrrrgrKr)r)r*rs@       rr;)r\krrXcCst|dkrtdt||jdkr8td|jt|j|}|d|dkrrtd|dd|d|d}|dkrtt||df|d|dS|dkrt||S|d krtt||df|d|dSt|tj d Sd S) z0Reference implementation of :func:`torch.rot90`.rz2expected total rotation dims == 2, but got dims = z/expected total dims >= 2, but got total dims = rr;z7expected rotation dims to be different, but got dim0 = z and dim1 = rr`rN) r=rHr"rPrrrrrr))r\rrr)r)r*rs&   $ $cCsP|dj}tdt|D]2}||j|kstd|d||jd|qdS)Nrr;z4stack expects each tensor to be equal size, but got z at entry 0 and z at entry )r>rFr=rC)rfZ entry_shaperr)r)r*_check_stack_inputss  rcst|dkstdt|djd||djkrtt|t|dj}|t|t |}| |St fdd|D|S)Nrz$stack expects a non-empty TensorListr;csg|]}|qSr))rrNZ wrapped_dimr)r*r szstack..) r=rCrPrur"rr,r>insertrrr)rfrZ result_sizesrur)rr*r s  cCsh|p|j}t|}t||}|dkr4t|}nt||dd}t||}tt|t||dd|S)NrTr) rrPrr(rr@rrr)r\rrrrrZa_expZa_maxr)r)r*rns      cCsBtt|dkddt|}|djdkr8t|dSt|dS)NrcSsdS)Nz%hstack expects a non-empty TensorListr)r)r)r)r*r91r:zhstack..r;)rrr=rr"rrxr)r)r*r/s  cCs*tt|dkddt|}t|dS)NrcSsdS)Nz%vstack expects a non-empty TensorListr)r)r)r)r*r9;r:zvstack..)rrr=rrrxr)r)r*r9s)r\rrrXcCs\t|j|}tt|dkdd|t|jd|t|t|j|ddS)NrcSsdS)Nz"unflatten: sizes must be non-emptyr)r)r)r)r*r9Cr:zunflatten..r;) rPrur"rrr=rrAr>)r\rrr)r)r*rAs)rrrXcsvddlm}t|jtt|jdkdd||jdkrLt St fddt ||jDSdS)Nrr0cSsdS)Nz5Dimension specified as 0 but tensor has no dimensionsr)r)r)r)r*r9Nr:zunbind..c3s|]}t|VqdSr2)rr)r5srr)r*r7Sszunbind..) r@r1rPrur"rZ _check_indexr=r>rAr)rrr1r)rr*rGs   r6rrrvcCs|jtjd|||Sr)rrr)rKrr)r)r*rJXs cs`t|j|}tjdkfdd|jdkr<|dn|}tdf|f}|||<|S)Nr;csdjdSNz(Index should have dimension 1 or 0 (got rrRr)rr)r*r9cr:zindex_copy_..r)rPrr"rrrslice)r6rrrvrrKr)rr*rK_s r6rrrcCst||||ddS)NFinplace _index_fillrr)r)r*rMlscCst||||ddS)NTrrrr)r)r*rNts)r6rrrrc stjdkfddttr@tjdkfddntj|j|j|jd|jdk}|rp| dn|}t |j } ||< ||rtjntj}|||} |r|S|r| d} | |krt|} | | | } | SdS)Nr;csdjdSrrRr)rr)r*r9r:z_index_fill..rcsdjdS)NzrrrrKrJrrrr copy_) r6rrrrZzero_dimrr>rJruZnew_outr))rrr*r{s>         rr6rrrvrcCs|jtjdj||||dS)Nrr)rrr)Z index_add_rr)r)r*rIs )r6rrcs|t|j|}tjdkfddjdkr<d|jdkr`t|d|St df|f}||S)Nr;csdjdSrrRr)rr)r*r9r:zindex_select..r) rPrr"rrrr rJrr)r6rrrKr)rr*rLs    )r\rrXcsddlm|dkrFtddtjD}|r.rrc3s$|]}j|dkr|VqdSr7r)r5dr\r1r)r*r7sr;T)r)r@r1rArEr>r}rr:r"rPrr-rr=rCr,r)r\rrr"Z dims_listrr)rr*rs(         )r\indices_or_sectionsrrXc Cst|j|}|jdkr$d}t|t|trr|jjdksNd|j}t||jt j krrd}d|jt|t|t st|tr@|jdkr@t|t r|n| }|dkrd|}t|g}|j|}t||}||} d} t|D]B} | | kr |dn|} tj|| | | |d } || | | } qt|S|}t|tr~|jdkrvd }d |jd t||}g}d} |D]"}|tj|| ||d |} q|tj|| |j||d t|SdS) NrzXtensor_split: received a rank zero tensor, but expected a tensor of rank one or greater!rz\tensor_split: if indices_or_sections is a tensor it must be on the CPU, but received one on zJtensor_split: if indices_or_sections is a tensor it must have long dtype, z but received one with dtype zAtensor_split: number of sections must be greater than 0, but was r;rzStensor_split: non-scalar indices_or_sections tensors must have only one dimension, zbut received a tensor with r)rPrur"rGr-r$r(r&rrlongrrrr>rrFrFr}rr{rAtolist)r\rrZ_dimrsectionsZsplitsrZmin_split_sizeZnum_splits_one_extraZ start_idxZ split_idx split_sizerindicesr6r)r)r*rsj               )r\rrXcstjdkfddjdkr(dndttrttdkoVjdkfddtStttt ffdd}t|S)Nr;csdtjdS)NzPtorch.hsplit requires a tensor with at least 1 dimension, but got a tensor with dimensions!rQr"r)r[r)r*r99s zhsplit..rcs.dtdtjdtdS)Nz0torch.hsplit attempted to split along dimension z , but the size of the dimension $ is not divisible by the split_size rOrr))r\rrr)r*r9Ds csdtS)Nzhsplit(): received an invalid combination of arguments. Expected indices_or_sections to be of type int, list of ints or tuple of ints but got type r%r)rr)r*r9Rs rrr"r-rr>r _check_typer,rAr\rZ split_sizesr))r\rrrr*r4s$      cstjdkfddttr`tdkoDjddkfddtdStttt ffdd}t|dS)NrcsdtjdS)NzPtorch.vsplit requires a tensor with at least 2 dimension, but got a tensor with rrr)r[r)r*r9cs zvsplit..rcsdjdddS)NzQtorch.vsplit attempted to split along dimension 0, but the size of the dimension rrrOrr))r\rr)r*r9mscsdtS)Nzvsplit(): received an invalid combination of arguments. Expected indices_or_sections to be of type int, list of ints or tuple of ints but got type r%r)rr)r*r9zsrrr))r\rrr*r^s"       )rrrXcsD|tdkfdddkr4t||St||SdS)N)r;rcs ddS)Nz'diag(): Supports 1D or 2D tensors. Got Dr)r)rRr)r*r9r:zdiag..r;)rrrrr)rrr)rRr*rs  )rrcrrdim2rXcsDt|}||||tjjkfddt|S)NcsdjdjS)NzCexpected src to have a size equal to the diagonal of the input.Got z for a diagonal of shape rr)rrcr)r*r9r:z"diagonal_scatter..)rPZclone_preserve_stridesrrrr>r)rrcrrrrur)rr*rs    )rrrrrXc sF|}tj|dtj|dtkfdd|}|dkrttt|||d}n$tt|||d}|dkr|dkr||| 7}n||| 8}fddt |D}| |fddt | D}| | | |j |||d} | S) z4 Reference implementation of torch.diagonal )rKrankcsddSNz(diagonal dimensions cannot be identical rr)r)rrr)r*r9r:zdiagonal..rcs g|]\}}|fkr|qSr)r)r5rrr r)r*r s zdiagonal..cs g|]\}}|fkr|qSr)r)r r r)r*r s )rrr) rrPrurrrrDrrrrEr{r) rrrrZnum_dimsrZ diag_sizerrrr)r r*rs*  &$ r)rrrrrXc sT|jd}tj|dtj|dkr>| }tkfdd|d|dkrt|j}t ||d<tj ||j |j dd}|dkr||fn||f}tj |dd }t |7|d}tj|j tjd }tj|||j tjd } || dk} fd d tt|jD} | | } t| |S) z6 Reference implementation of torch.diag_embed r;)rrKcsddSrr)r)r r)r*r9r:zdiag_embed..r?rFrr(rqrr(rcs g|]}|fkrndqSrr)rrrrr)r*r szdiag_embed..)r"rPrurrrr,r>rr.rrr(rrrrr0rFr=r mask_tensorr) rrrrrZt_shapezpairZa_rangeZb_rangecondZ cond_shaper)r r*rs<       c sdd|D}tdd|D}|djg}d}t|D]\tdkfddtjkfd dj\}}tj||fj d }tj||||fj d }|tj ||fd d g7}||7}q.css|]}|jdVqdSr7rrr)r)r*r7sz'_block_diag_iterable..rrcsdddS)Nz5Input tensors must have 2 or fewer dimensions. Input z has rrr))rrvr)r*r9#r:z&_block_diag_iterable..csdddjdS)NzCInput tensors must all be on the same device. Input 0 is on device z and input z is on device rr'r)r(rrvr)r*r9(r:r r;r) rrr(rErrrr>rrr) rfZ tensors_2dZncolsrZ col_startrowcolrrightr)rr*_block_diag_iterables4     rcGst|S)z This is used as an input to PythonRefInfo. `torch.block_diag` expects arguments splatted, but `aten.block_diag` expects only one argument that is a list of Tensors. )rrer)r)r*r6s)r\rrXcCsl|jdkrtd|jdt|tr`|dks@|jd|dkr`tdd|jdd|d t||dS) Nr`zPtorch.dsplit requires a tensor with at least 3 dimension, but got a tensor with rrrz3torch.dsplit attempted to split along dimension 2, zbut the size of the dimension rrO)r"rHr-rr>r)r\rr)r)r*r@s  $cCs8|jdkrtd|jdt|d|jdkr2dndS)Nrz7t() expects a tensor with <= 2 dimensions, but self is rrr;)r"rHrrr[r)r)r*rMs  cCst|jdkdd|S)N)rrcSsdS)NzcThe use of `x.T` on tensors of dimension other than 0 or 2 to reverse their shape is not supported.r)r)r)r)r*r9dszT..)rrr"rr[r)r)r*r`s cCs t|Sr2)r}r:r[r)r)r*rls)r\rrrXcCs`t|j||f\}}|jdks(||kr4tj|Sttd|j}|||<|||<t ||S)Nr;r) rPrr"ryrdefaultr,rFrr)r\rrZ_dim0Z_dim1rr)r)r*rqs )rrrrrXcCs&t|j||||\}}|||Sr2)rr>rr)rrrrr>rr)r)r*rsrrrrcCs||||jtjdSr)rrrr)rr)r)r*rs)r\rrrurXc Cs|j}t||}|dkr0||dd||dS||d}tj|j||jd}|d|k}t||dD]} |d}qrt |||} || |||dS)Nr)rrrur;r'r?) r"rPrurrrr>r(rFr) funcinitr\rrrur"rgrrvZmasked_ar)r)r*_cumsumprod_commons   rcCsttd||||dS)Nrrrr\rrru)rrr\rrrur)r)r*rscCsttd||||dS)Nr;r)rrrr)r)r*rscCs(|jd}t||}tj||f|dS)Nr;rR)r"rPrur}Z expand_dims)r\rr"r)r)r*rs  cGst|f|ddiS)NrFrr6r)r)r*rscCs||Sr2)rrrr)r)r*rscCs t|dS)N)r?)rr[r)r)r*rs)r\rrrXcstjjkfddttjfdd|dkr\tdddStj } |||<t | }t |}tj } |||<t | }t |}t|||SdS)NcsdjdjdS)Nz]torch.take_along_dim(): input and indices should have the same number of dimensions, but got z dimensions for input, and z dimensions for indicesrRr)r\rr)r*r9sz take_along_dim..csdjdS)Nz?torch.take_along_dim(): dtype of indices should be int but got rIrr))rr)r*r9sr?r) rrr"rPrrZgatherrr,r>rZinfer_size_shapesr)r\rrZ self_sizesZbroadcast_shapeZindices_broadcastZ indices_sizesZself_broadcastr)r r*rs&         rrr(rqrr)rrr(rqrrrXc sttjkddt|}tjkr6t|}n:tjkrLt|}n$ttj kfddt |}tj |||||||dS)NcSsdS)Nz8torch.empty: the Preserve memory format is not supportedr)r)r)r)r*r9r:zempty..cs ddS)Nz/torch.empty: received an unknown memory format rOr)r)rr)r*r9r:rrr(rrq) rrrrPr9r)rZchannels_last_3dZ!make_channels_last_3d_strides_forZ channels_lastZ!make_channels_last_2d_strides_forr )rrr(rqrrr>rr)rr*r s.        )rrr(rqrrXcCstj|||||dS)Nr )r}r )r>Zphysical_layoutrrr(rqrr)r)r*r -s rrr(r)r\rrrr(rrXcCsJ|dkr|jn|}|dkr |jn|}|dkr2|jn|}tj|||||dS)Nrr(rr)rrr(rr )r\rrrr(rr)r)r*r@s )r\rrrrr(rrXcCsL|dkr|jn|}|dkr |jn|}|dkr2|jn|}tj||||||dS)zD Reference implementation of torch.Tensor.new_empty_strided Nr$)rrr(rr )r\rrrrr(rr)r)r*rXsr")rrr(rrqrXc Gs@t|}|dkrt}tj||tjkr.dnd|||||dSNFrr"rPr9rrrrrrr(rrqrr)r)r*rvs )r\rrrr(rrqrXc Csb|dkr|jn|}|dkr |jn|}|dkr2|jn|}tj||pD|jtjkrPdnd|||||dSr%rrr(rrrr\rrrr(rrqr)r)r*rs c Gs@t|}|dkrt}tj||tjkr.dnd|||||dSNTr;r"r&r'r)r)r*rs c Csb|dkr|jn|}|dkr |jn|}|dkr2|jn|}tj||pD|jtjkrPdnd|||||dSr*r(r)r)r)r*rs )r\r fill_valuerrr(rrXcCsL|dkr|jn|}|dkr |jn|}|dkr2|jn|}tj||||||dS)Nr#)rrr(rr)r\rr+rrr(rr)r)r*rs )rr(rrrqr)r\rr(rrrqrrXc Cs~|dkr|jn|}|dkr |jn|}|dkr2|jn|}|tjkrZtj|j||||||dSt|}tj |j||||||dS)Nr!r") rrr(rrr r>rPZ3compute_elementwise_output_logical_to_physical_permr )r\rr(rrrqrZlogical_to_physical_permr)r)r*r s0   ) rgrrrrr(rrqrXcst|t|tt|}ttr2tttr@tttrNtdkr^dt dkdddkrt kddndkrt kdddd}t |o|fddt |fd df} t d d | D} |dkr*| r"tj nt }t|} | rRt} t} t}|tj krt|dkt|dk}| | |||}nt}| rtj|| ||||d Stj|dd tj |dd }| rtjn t||}t||}|}t||}|r|d|S)NrcSsdS)Nzstep must be nonzeror)r)r)r)r*r9>r:zarange..cSsdSNz7upper bound and lower bound inconsistent with step signr)r)r)r)r*r9Br:cSsdSr,r)r)r)r)r*r9Gr:cSst|t pt|Sr2)r-rrrOr8r)r)r* is_finiteJszarange..is_finitecsddS)Nzunsupported range: z -> r)r))rrgr)r*r9Or:cs dS)Nzstep must be finite but got r)r)rr)r*r9Sr:css|]}t|tVqdSr2r3r5rXr)r)r*r7Wszarange..)rgrrr(rqr;FT)rP check_layoutcheck_pin_memoryrr(device_or_defaultr-rrCrrrr0rrrrrr6r}iotarZ get_acc_typer(requires_grad_)rgrrrrr(rrqr-rSZ integer_args is_integerZxstartZxendZxsteprhrrrrr))rrgrr*r%s              )rgrrc Cs||g}t|tr |d|}n ||t|ts8t|dk}t||d|}t|||}||||}t j t |}| |krt ||}t||S)Nr)rr;)r-rrr{rrCr.rrrPZ"compute_elementwise_output_stridesrTrr}Z copy_stridedr/) rgrrrirZcoeffbaser.rr)r)r*rVs     )rr(rrrq) rgrstepsrr(rrrqrXc s&ttr.tdkddttjttr\tdkddttjtddfDrt t dkrqtt fddn pt ttj st tttfddttst tdkd d||||d }dkr6tjdd i|Sd krttrjtjffd i|Stjffd i|Stjdf|} tstrtjn} t| tj| \} } tt| d} d }t| dk|| | || d | }t|S)NrcSsdSNz:linspace only supports 0-dimensional start and end tensorsr)r)r)r)r*r9r:zlinspace..cSsdSr7r)r)r)r)r*r9r:css|]}t|tVqdSr2r-rr.r)r)r*r7szlinspace..csddS)Nzlinspace(): inferred dtype z& can't be safely cast to passed dtype r)r))default_complex_dtyperr)r*r9r:cs*dtjdtjdtjdS)Nz4received an invalid combination of arguments - got (rr)r&rcr))rrgr6r)r*r9s cSsdS)Nz$number of steps must be non-negativer)r)r)r)r*r9r:)rr(rrqrrrr;rr)rrr)r-r%rrrr(rpy_anyrPrrrrrCrrrr rrrrr0r$r!r1rr)rgrr6rr(rrrqZfactory_kwargsrZ dtype_redrrvZcast_rgrrur))r9rrrgr6r*rsz              r) rgrr6r5rr(rrrqrXc Cs|dkrt}tj|rt|tr0t|}n,t|tr\t | dkddt ||}t|trpt|}n,t|trt | dkddt ||}t dd|||fDrt t} | }d} ntj} t|trt|dkrttj|||| ||||d} t t|| |S)NrcSsdSNz:logspace only supports 0-dimensional start and end tensorsr)r)r)r)r*r9r:zlogspace..cSsdSr;r)r)r)r)r*r9"r:css|]}t|tVqdSr2r8r.r)r)r*r7&szlogspace..r")rrr}rPrr-rrr%rrr(r:rrrrCrrr) rgrr6r5rr(rrrqr9Z_dtyperetr)r)r*rsP            rfindexingcCsdSr2r)r=r)r)r*r?scGsdSr2r))r>rfr)r)r*rDs)rfr>rXcst|dttfr.t|dks"tt|d}ttdd|Dddtt|dkddtt|dD]L}t||j ||dj kddt||j ||dj kd dqtd }d krt|d k}|r|d|df|d d}ntd kfddg}|D]Jtt s0ttj dkpHj dkfdd| qg}t|D]F\}tt stj dkrd| t||fqt|r|d|d|d<|d<|S)Nrr;css|]}t|tVqdSr2r-r$rYr)r)r*r7Vszmeshgrid..cSsdS)Nz)meshgrid expects its inputs to be tensorsr)r)r)r)r*r9Wr:zmeshgrid..cSsdS)Nz'meshgrid expects a non-empty TensorListr)r)r)r)r*r9Zr:cSsdS)Nz3meshgrid expects all tensors to have the same dtyper)r)r)r)r*r9_r:cSsdS)Nz4meshgrid expects all tensors to have the same devicer)r)r)r)r*r9cr:FZxyrZijcs dS)NzDtorch.meshgrid: indexing must be one of "xy" or "ij", but received: r)r))r>r)r*r9nscs dS)NzEtorch.meshgrid: Expected 0D or 1D tensor in the tensor list but got: r)r))rr)r*r9yr:r)r-r,rAr=rCrrrrFrr(r$r"r{rrErr}r%)r>rfrZswap_first_and_second_tensorsZ result_shapeZgridsr))r>rr*rIsV      )rsource destinationrXcs@ttkrfttkr$ftttkfdd|j}ttj|d}ttj|d}t |}t |}tt|t|kfddtt|t|kfddt t ||}g} d} t |D]L} | | } | dk r| | q| |kr| d7} q| | | d7} qt|t| } | S) z3 Reference implementation of torch.movedim csdtdtdS)Nz5movedim: Invalid source or destination dims: source (z> dims) should contain the same number of dims as destination (z dims)r,r)rAr@r)r*r9szmovedim..)rrcsdtdS)Nz#movedim: repeated dim in `source` (rrBr))r@r)r*r9r:csdtdS)Nz(movedim: repeated dim in `destination` (rrBr))rAr)r*r9r:rNr;)r&rVrrr=r"rArPrsetdictrrFrr{r)rr@rArssZdsZsssZdssrMrsiZdirrr)rCr*rsD              )rr(rrqr)r>rrr(rrqrrXcCs\t|t|t|}|dkr.tn|}|dkrDtdn|}tj|||||dS)Nrr ) rPr/r0r9rrr(r}r )r>rrr(rrqrr)r)r*r s   )nrMrrr(rrqrXc sdkr tdkfddtdkfddtjtj|dd}tjtj|dd}|d|k} |tjkr| Stjd ||||dd } t| | dSdS) z/ Reference implementation of torch.eye Nrcs dS)Nz%n must be greater or equal to 0, got r)r))rHr)r*r9r:zeye..cs dS)Nz%m must be greater or equal to 0, got r)r))rMr)r*r9r:Fr r?rr")rrrr0rrrr) rHrMrrr(rrqZrange_nZrange_mroner))rMrHr*r s$ )r>r+rrr(rrqrXcCsdt|t||dk r |n tt|}|dk r:|ntd}t||||||d}t||S)Nrr") rPr/r0 type_to_dtyper&rr(r rD)r>r+rrr(rrqer)r)r*rs  rrr(rrqr) r\r+rrr(rrqrrXc Cs"tj|||||||d}t||S)NrL)rr rD) r\r+rrr(rrqrrKr)r)r*r/s  )r\rrr(rrqrrXc Cs.tj||p|jtjkrdnd||||||dS)NFrrLrrrrr\rrr(rrqrr)r)r*rFs c Cs.tj||p|jtjkrdnd||||||dS)NTr;rLrMrNr)r)r*r^s )rr(rrqrrXcGs>t|t|}t|}t|}tj|dd|||dS)Nrr)rrrr(rq)rPr0r9Zdtype_or_defaultr1r}r`)rr(rrqrr>rr)r)r*rvs    )r\rrr(rrXcCsTt|t||dk r |n tt|}|dk r:|ntd}tj|||dS)Nrr) rPr/r0rJr&rr(r}r)r\rrr(rr)r)r*rs   r)r>lowhighrr(rXcCsdt|t|tstt|ts&tt|}t|}t|tjsFtt|}t j |||||dS)N)rOrPrr() rPrr-rrCrrrZcanonicalize_devicer}_uniform_helper)r>rOrPrr(r)r)r*rQs  rQ)r\rrcst|jt|tr t|}nj|jtdkfdd|j jddtj fko`|j jdk}t|pt|j |j kddt|j}|t krtt |fddt||j}t|||}|S) Nrcs ddS)Nz@only supports a 0-dimensional value tensor, but got tensor with z dimensionr)r)) value_ndimr)r*r9r:zmasked_fill..cudaZxpurcSsdS)Nz,Expected `value` to be on same device as `a`r)r)r)r)r*r9r:cs ddS)Nzcould not convert to type z without overflowr)r))rr)r*r9r:)rPrrr-rr&r"rrr(_CZ_get_privateuse1_backend_namerrr(rr)r\rrZ value_typeZ is_cpu_scalarrr))rrRr*rs0         )r\rrrXcCst|||}|||Sr2)rrr)r\rrrr)r)r*rs c Cs4td||||dtttj|||||dS)z4 Reference implementation of torch.allclose ztorch.allcloser)rrr)rrrrrr)r\rrrrr)r)r*rs cCsttj||ddt|||j|jkr,dSt|j|jD]\}}||kr:dSq:|dkrbdSttt ||S)NFrrT) rPrZcheck_same_dtyper"rr>rrrr)r\rr6rr)r)r*rs   )Z exact_dtypefror)rr|rrrrXcCs|dkr&|dks.t|ts.t|dks.|dkr2d}t|trB|g}t|trx|dkrbtt|j}tjj |||||dStjj |||||dSdS)NrUrr) r-rr=rQrArFr"rrZ matrix_normr)rr|rrrr)r)r*r_s$    cCs(t|jdkddtt|dS)NrcSsdS)Nz6expected a matrix, but got tensor with dim {self.ndim}r)r)r)r)r*r94r:ztrace..r)rrr"rrrr)r)r*rs0s cs*tttftttftdfdd }|S)Nrcs ||Sr2r)rbase_opr)r*rop:sz_make_r_binary_op..rop)rr%r)rWrXr)rVr*_make_r_binary_op9s   rY)r\rrXcCsht|jdkdd|jdd\}}tj||jddtj||jdd|k}t|| S)NrcSsdS)Nz2triu: input tensor must have at least 2 dimensionsr)r)r)r)r*r9Lr:ztriu..rr'r? rrr"r>rr(rrPrrr\rhwrr)r)r*rHscCsht|jdkdd|jdd\}}tj||jddtj||jdd|k}t|| S)NrcSsdS)Nz2tril: input tensor must have at least 2 dimensionsr)r)r)r)r*r9]r:ztril..rr'r?rZr[r)r)r*rYs)rrrrXc Cs|dks|dkrdS|dkr*t|d|nt||dk}tdt|||}tdt|||}||d}|||d}||}td||} || |fS)Nrrrrr;r)rrVrD) rrr m_first_rowZ m_last_rowZ n_row_allZn_row_trapezoidtrapezoid_sizeZdiff_rowrectangle_sizer)r)r*_get_tril_sizesos& rbrsrrrrrcsVtdkfddtdkfddttjtjfkfdddS)Nrcs dS)Nzrow must be non-negative, got r)r))rr)r*r9r:z_trilu_checks..cs dS)Nzcol must be non-negative, got r)r))rr)r*r9r:csdddS)Nr5z" not implemented for ''r)r))rrsr)r*r9r:)rrrr0rcr))rrrsrr* _trilu_checkss  rer)rrrrrr(rrXcCstd|||||t|||\}}} td| } ttj|||d} | d|tjd} | d} t| t| | d| }t| d| d||d}t || |}t ||}| d||d}|||| d| }||}t t ||ft ||ffS)Nrrrr(rrrrr;) rerbrDrrrrrFror(rr)rrrrrr(rr`rar_ row_offset arange_kwxs1r row_inds1 col_inds1xs2 row_inds2 col_inds2r)r)r*rs*  "" c Cs||dks|dkrdS|dkr*td||n|}tdt|| |}t|||d\}}}||||}||} | ||fS)Nrr^r;)rDrrb) rrrr_raZtrapezoid_size_trilZrectangle_size_trilrvZ triu_sizer`r)r)r*_get_triu_sizessrocCstd|||||t|||\}}} td|} ttj|||d} | d||d} | |} | |}| d|tjd}d| }t| t||d|}t|d| d||d}t ||}t ||}|r|||}|| }t t | |ft ||ffS) Nrrrfrgrr;r) rerorDrrrrrFror(rr)rrrrrr(rr`rar_ col_offsetrhrlrmrnrirrjrkr)r)r*rs0  ""   ) out_int32r)r\ boundariesrqrcsTtdkfdd|r&tjntj}jd}|dkrHt|Stj|j|jtjd}||}|||d}|} |r| |k} n| |k} t | ||d}|dkrHtj |tj d} t t |} t| D]p} t | | @||}||k} t | |||dd}|} |r&| |k} n| |k} t | | @|d|}q|j|dS) Nr;csddS)Nz2boundaries tensor must be 1 dimension but got dim(rrr)rrr)r*r9r:zbucketize..r?rr rr)rrrrr0r>rrr(rrrrVrrZrFr)r\rrrqrZ out_dtypeZ n_boundariesrgrmidZmid_valZcond_midZ cond_updateZnitersrvr)rsr*r s:         cs|dks tttj o8tj o8tj fddtdkfdd|tt j t dS)Ncs djS)NzwCauchy distribution is a continuous probability distribution. dtype must be a floating point but you specified rr)rr)r*r9@szcauchy..rcs dS)Nz-cauchy_ expects sigma > 0.0, but found sigma=r)r))sigmar)r*r9Er:r) rCrrrPrrrrrqrr rand_like)rZmedianru generatorr))rrur*r4s     csx|dks tttj o8tj o8tj fddtdkfdddtt  S)Ncs djS)Nz|Exponential distribution is a continuous probability distribution. dtype must be a floating point but you specified rr)rr)r*r9Vszexponential..rcs dS)Nz4exponential_ expects lambda > 0.0, but found lambda=r)r))rater)r*r9[r:r?) rCrrrPrrrrrYrv)rrxrwr))rxrr*rBJs     cs|dks tttj o*tj fddtdkoJdkfddttt  t  dS)Ncs djS)Nzgeometric not implemented for rr)rr)r*r9lr:zgeometric..rr;cs dS)Nz0geometric_ expects p to be in (0, 1), but got p=r)r))r|r)r*r9pr:) rCrrrPrrrrFrYrvr)rr|rwr))r|rr*rH`s    rcsv|dks tttj o8tj o8tj fddtdkfddtt |S)Ncs djS)Nzlog_normal not implemented for rr)rr)r*r9r:zlog_normal..rcs dS)Nz-log_normal_ expects std > 0.0, but found std=r)r)rr)r*r9r:) rCrrrPrrrrr@Z randn_likerrrrwr))rrr*r\us     )rr)rwrrr(rc s$|dks|tjksttts8tdkfdd|dkrtdd|fD}tt|dkddt|dko|dkddtdd|D}|dj }|dj }nNtt|t ott d d|dkrt n|}|dkrt d n|}t j |d d ||d |d} | |S)Nrcs dS)Nz)normal expects std >= 0.0, but found std r)r)ryr)r*r9r:znormal..css|]}t|tr|VqdSr2r?rNr)r)r*r7s znormal..cSsdS)NzFnormal expects that either mean or std is a tensor, or size is definedr)r)r)r)r*r9r:cSsdS)Nz.Cannot pass layout, or pin_memory without sizer)r)r)r)r*r9r:css|] }|jVqdSr2rrNr)r)r*r7scSsdS)Nz>normal expects mean and std to be scalars when size is definedr)r)r)r)r*r9r:rrrF)rrrr(rqrw)rstridedrCr-r$rrAr=rLrr(rr}r`) rrrrwrrr(rrfZnormal_samplesr)ryr*r`sF      )rwcCst|||j||dS)N)rurw)r`r>rzr)r)r*normal_sr|cCs&tt|j ddd}||S)NcSsdS)Nz-rad2deg is not supported for complex tensors.r)r)r)r)r*r9r:zrad2deg..gcܥL@rrrPrr)rZM_180_PIr)r)r*rds  cCs&tt|j ddd}||S)NcSsdS)Nz-deg2rad is not supported for complex tensors.r)r)r)r)r*r9r:zdeg2rad..g9RFߑ?r})rZM_PI_180r)r)r*r;s  rcCs|dk|Sr)r)rrr)r)r*r:scsTtdkodkfddfdd}tk|dS)Nr;csdddS)Nz1D tensors expected, but got zD and z D tensorsrr)rrr)r*r9r:z_dot_check..c s.ddddd S)Nz+inconsistent tensor size, expected tensor [z ] and src [z.] to have thesame number of elements, but got rz elements respectively)rr)r~r)r* numel_errors,z_dot_check..numel_error)rrrr)rrrr)r~r* _dot_checks  rrcCsp|rZ|rB|r0t||St||Sn|rZt||St||||Sr2)r!is_conjrrrrrrrr)r)r*rs cCs|st||S|rJ|r8t||St||Sn|rft||St||||Sr2) r!rrrrrrr7rrr)r)r*r s  )r6rcrrcCszt|j|}dg|j}d||<|dkr8||j|}tj|j||jd||k}t|| |j}t |||S)Nr;r?rr') rPrur"r>rrr(rrrr)r6rcrrZ mask_shaperr)r)r*select_scatter"s  rcCst|tjtjfSr2)r-rZ TypedStorageZUntypedStorage)objr)r)r* _isStoragesrcCsd}t|}g}t|ttfrt|}|r4||j}||t||kr`tdt|j d|dkrjqz |d}Wn*t k rtdt|j dYnX|}q|S)Nztoo many dimensions 'rdrz.could not determine the shape of object type ') rr-r,rAr=itemsizer{rGr&rcrt)seq scalar_typeZMAX_DIMSZ is_storagerrhandler)r)r*_compute_sizess(     rcCsLt|trtSt|tr,t|ts,tjSt|tr.) r-rrdetachrrrrrr)rrrr)rr*rs  rc Cst|tjr`t| dd|}|r.|}|r8|jn|} |dk rH|n|j} |j| | d|dSt|drnt S|dk rz|n|d} |rt |n|} t |rt St| j dkrt St | |} | j| | ddd} | S) NcSsdS)Nz,Can't pin tensor constructed from a variabler)r)r)r)r*r9r:z)_internal_new_from_data..F)r(rrrZ__cuda_array_interface__r(meta)rr)r-rrrrrr(rhasattrNotImplementedrrr&r) optionsrZ device_optdatacopy_variables copy_numpytype_inferencerrZinferred_scalar_typer(rvr)r)r*_internal_new_from_datas6   r)rr(rrqc Csdt|tjrtd|dk}tddi|dk r2|nt||dd||d}||r`|||S)NzTo copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor)r(rT)rrrr) r-rrrrrrZdetach_r3)rrr(rrqrZ new_tensorr)r)r*rv4s&   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start_outrVrrrrr r rrrrrrrQrrrrr_rsrYrrrrrrbrerrrorr rrBrHr\r`r|rdr;r:rrrrZabs_Zacos_Zacosh_Zadd_Zaddcmul_Zaddcdiv_Zasin_Zasinh_Zatan_Zatanh_Zatan2_Z bitwise_and_Zbitwise_left_shift_Z bitwise_not_Z bitwise_or_Zbitwise_right_shift_Z bitwise_xor_Zceil_Zclamp_Z clamp_min_Z clamp_max_Zconj_physical_Z copysign_Zcos_Zcosh_Zcumsum_Zcumprod_Zdeg2rad_Zdigamma_Zdiv_eq_Zerf_Zerfc_Zerfinv_Zexp_Zexp2_Zexpm1_Z float_power_Zfloor_Z floor_divide_Zfmod_Zfrac_Zgcd_Zge_Zgt_Z heaviside_Zhypot_Zigamma_Zigammac_Zi0_Zlcm_Zle_Zlerp_Zlgamma_Zlog10_Zlog1p_Zlog2_Zlog_Z logical_and_Z logical_not_Z logical_or_Z logical_xor_Zlt_Zmul_Z mvlgamma_Z nan_to_num_Zne_Zneg_Z nextafter_Zpow_Zrad2deg_Z reciprocal_Z remainder_Zrsqrt_Zsgn_Zsigmoid_Zsign_Zsin_Zsinc_Zsinh_Zsqrt_Zsquare_Zsub_Ztan_Ztanh_Ztril_Ztriu_Z true_divide_Ztrunc_Zxlogy_Zcauchy_Z exponential_Z geometric_Z log_normal_Zzero_rrrrrrvZtorch._refs._conversionsZtorch._refs.fftZtorch._refs.linalgZtorch._refs.nn.functionalZtorch._refs.specialr)r)r)r*s  ,  \ %    (!                                   $            & :          6   "+     *       +?  2   4    "h L %(       4  F  'k 8 1          . "!  K + '  )  8  $            $&      )c &   W   ; C  ?  (     +            & )& )  9     4                                                                                                      4 9 $