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Functions.h
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Functions.h
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#pragma once
#include "ATen/Scalar.h"
#include "ATen/Type.h"
#include "ATen/Tensor.h"
#include "ATen/Storage.h"
#include "ATen/Generator.h"
namespace at {
static inline Tensor & zeros_out(Tensor & result, IntList size);
static inline Tensor & zeros_like_out(Tensor & result, const Tensor & input);
static inline Tensor zeros_like(const Tensor & input);
static inline Tensor & ones_out(Tensor & result, IntList size);
static inline Tensor & ones_like_out(Tensor & result, const Tensor & input);
static inline Tensor ones_like(const Tensor & input);
static inline int64_t numel(const Tensor & self);
static inline Tensor & masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask);
static inline Tensor masked_select(const Tensor & self, const Tensor & mask);
static inline Tensor transpose(const Tensor & self, int64_t dim0, int64_t dim1);
static inline Tensor t(const Tensor & self);
static inline Tensor & nonzero_out(Tensor & result, const Tensor & self);
static inline Tensor nonzero(const Tensor & self);
static inline Tensor & index_select_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index);
static inline Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index);
static inline Tensor & take_out(Tensor & result, const Tensor & self, const Tensor & index);
static inline Tensor take(const Tensor & self, const Tensor & index);
static inline Tensor & range_out(Tensor & result, Scalar start, Scalar end, Scalar step=1);
static inline Tensor & arange_out(Tensor & result, Scalar start, Scalar end, Scalar step=1);
static inline Tensor & arange_out(Tensor & result, Scalar end);
static inline Tensor & gather_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index);
static inline Tensor gather(const Tensor & self, int64_t dim, const Tensor & index);
static inline bool equal(const Tensor & self, const Tensor & other);
static inline Tensor & __and___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __and__(const Tensor & self, Scalar other);
static inline Tensor & __and___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __and__(const Tensor & self, const Tensor & other);
static inline Tensor & __iand__(Tensor & self, Scalar other);
static inline Tensor & __iand__(Tensor & self, const Tensor & other);
static inline Tensor & __or___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __or__(const Tensor & self, Scalar other);
static inline Tensor & __or___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __or__(const Tensor & self, const Tensor & other);
static inline Tensor & __ior__(Tensor & self, Scalar other);
static inline Tensor & __ior__(Tensor & self, const Tensor & other);
static inline Tensor & __xor___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __xor__(const Tensor & self, Scalar other);
static inline Tensor & __xor___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __xor__(const Tensor & self, const Tensor & other);
static inline Tensor & __ixor__(Tensor & self, Scalar other);
static inline Tensor & __ixor__(Tensor & self, const Tensor & other);
static inline Tensor & __lshift___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __lshift__(const Tensor & self, Scalar other);
static inline Tensor & __lshift___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __lshift__(const Tensor & self, const Tensor & other);
static inline Tensor & __ilshift__(Tensor & self, Scalar other);
static inline Tensor & __ilshift__(Tensor & self, const Tensor & other);
static inline Tensor & __rshift___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __rshift__(const Tensor & self, Scalar other);
static inline Tensor & __rshift___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __rshift__(const Tensor & self, const Tensor & other);
static inline Tensor & __irshift__(Tensor & self, Scalar other);
static inline Tensor & __irshift__(Tensor & self, const Tensor & other);
static inline Tensor & lt_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor lt(const Tensor & self, Scalar other);
static inline Tensor & lt_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor lt(const Tensor & self, const Tensor & other);
static inline Tensor & gt_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor gt(const Tensor & self, Scalar other);
static inline Tensor & gt_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor gt(const Tensor & self, const Tensor & other);
static inline Tensor & le_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor le(const Tensor & self, Scalar other);
static inline Tensor & le_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor le(const Tensor & self, const Tensor & other);
static inline Tensor & ge_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor ge(const Tensor & self, Scalar other);
static inline Tensor & ge_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor ge(const Tensor & self, const Tensor & other);
static inline Tensor & eq_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor eq(const Tensor & self, Scalar other);
static inline Tensor & eq_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor eq(const Tensor & self, const Tensor & other);
static inline Tensor & ne_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor ne(const Tensor & self, Scalar other);
static inline Tensor & ne_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor ne(const Tensor & self, const Tensor & other);
static inline std::tuple<Tensor &,Tensor &> min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> min(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor & min_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor min(const Tensor & self, const Tensor & other);
static inline Tensor min(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> max_out(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> max(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor & max_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor max(const Tensor & self, const Tensor & other);
static inline Tensor max(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false);
static inline std::tuple<Tensor &,Tensor &> mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self, int64_t dim=-1, bool keepdim=false);
static inline std::tuple<Tensor &,Tensor &> median_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> median(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor median(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool descending=false);
static inline std::tuple<Tensor,Tensor> sort(const Tensor & self, int64_t dim=-1, bool descending=false);
static inline std::tuple<Tensor &,Tensor &> topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
static inline std::tuple<Tensor,Tensor> topk(const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
static inline Tensor & abs_out(Tensor & result, const Tensor & self);
static inline Tensor abs(const Tensor & self);
static inline Tensor & sigmoid_out(Tensor & result, const Tensor & self);
static inline Tensor sigmoid(const Tensor & self);
static inline Tensor & log_out(Tensor & result, const Tensor & self);
static inline Tensor log(const Tensor & self);
static inline Tensor & log1p_out(Tensor & result, const Tensor & self);
static inline Tensor log1p(const Tensor & self);
static inline Tensor & lgamma_out(Tensor & result, const Tensor & self);
static inline Tensor lgamma(const Tensor & self);
static inline Tensor & digamma_out(Tensor & result, const Tensor & self);
static inline Tensor digamma(const Tensor & self);
static inline Tensor & polygamma_out(Tensor & result, int64_t n, const Tensor & self);
static inline Tensor polygamma(int64_t n, const Tensor & self);
static inline Tensor & exp_out(Tensor & result, const Tensor & self);
static inline Tensor exp(const Tensor & self);
static inline Tensor & expm1_out(Tensor & result, const Tensor & self);
static inline Tensor expm1(const Tensor & self);
static inline Tensor & cos_out(Tensor & result, const Tensor & self);
static inline Tensor cos(const Tensor & self);
static inline Tensor & acos_out(Tensor & result, const Tensor & self);
static inline Tensor acos(const Tensor & self);
static inline Tensor & cosh_out(Tensor & result, const Tensor & self);
static inline Tensor cosh(const Tensor & self);
static inline Tensor & sin_out(Tensor & result, const Tensor & self);
static inline Tensor sin(const Tensor & self);
static inline Tensor & asin_out(Tensor & result, const Tensor & self);
static inline Tensor asin(const Tensor & self);
static inline Tensor & sinh_out(Tensor & result, const Tensor & self);
static inline Tensor sinh(const Tensor & self);
static inline Tensor & tan_out(Tensor & result, const Tensor & self);
static inline Tensor tan(const Tensor & self);
static inline Tensor & atan_out(Tensor & result, const Tensor & self);
static inline Tensor atan(const Tensor & self);
static inline Tensor & tanh_out(Tensor & result, const Tensor & self);
static inline Tensor tanh(const Tensor & self);
static inline Tensor & erf_out(Tensor & result, const Tensor & self);
static inline Tensor erf(const Tensor & self);
static inline Tensor & erfinv_out(Tensor & result, const Tensor & self);
static inline Tensor erfinv(const Tensor & self);
static inline Tensor & sqrt_out(Tensor & result, const Tensor & self);
static inline Tensor sqrt(const Tensor & self);
static inline Tensor & rsqrt_out(Tensor & result, const Tensor & self);
static inline Tensor rsqrt(const Tensor & self);
static inline Tensor & ceil_out(Tensor & result, const Tensor & self);
static inline Tensor ceil(const Tensor & self);
static inline Tensor & floor_out(Tensor & result, const Tensor & self);
static inline Tensor floor(const Tensor & self);
static inline Tensor & round_out(Tensor & result, const Tensor & self);
static inline Tensor round(const Tensor & self);
static inline Tensor & trunc_out(Tensor & result, const Tensor & self);
static inline Tensor trunc(const Tensor & self);
static inline Tensor & frac_out(Tensor & result, const Tensor & self);
static inline Tensor frac(const Tensor & self);
static inline Tensor & mean_out(Tensor & result, const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor mean(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor mean(const Tensor & self);
static inline Tensor & var_out(Tensor & result, const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false);
static inline Tensor var(const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false);
static inline Tensor var(const Tensor & self, bool unbiased=true);
static inline Tensor & std_out(Tensor & result, const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false);
static inline Tensor std(const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false);
static inline Tensor std(const Tensor & self, bool unbiased=true);
static inline Tensor & norm_out(Tensor & result, const Tensor & self, Scalar p, int64_t dim, bool keepdim=false);
static inline Tensor norm(const Tensor & self, Scalar p, int64_t dim, bool keepdim=false);
static inline Tensor norm(const Tensor & self, Scalar p=2);
static inline Tensor & renorm_out(Tensor & result, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
static inline Tensor renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
static inline Tensor dist(const Tensor & self, const Tensor & other, Scalar p=2);
static inline Tensor & reciprocal_out(Tensor & result, const Tensor & self);
static inline Tensor reciprocal(const Tensor & self);
static inline Tensor & neg_out(Tensor & result, const Tensor & self);
static inline Tensor neg(const Tensor & self);
static inline Tensor & atan2_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor atan2(const Tensor & self, const Tensor & other);
static inline Tensor & pow_out(Tensor & result, const Tensor & self, Scalar exponent);
static inline Tensor pow(const Tensor & self, Scalar exponent);
static inline Tensor & pow_out(Tensor & result, const Tensor & self, const Tensor & exponent);
static inline Tensor pow(const Tensor & self, const Tensor & exponent);
static inline Tensor & pow_out(Tensor & result, Scalar base, const Tensor & self);
static inline Tensor pow(Scalar base, const Tensor & self);
static inline Tensor & lerp_out(Tensor & result, const Tensor & self, const Tensor & end, Scalar weight);
static inline Tensor lerp(const Tensor & self, const Tensor & end, Scalar weight);
static inline Tensor & linspace_out(Tensor & result, Scalar start, Scalar end, int64_t steps=100);
static inline Tensor & logspace_out(Tensor & result, Scalar start, Scalar end, int64_t steps=100);
static inline Tensor & histc_out(Tensor & result, const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0);
static inline Tensor histc(const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0);
static inline Tensor & sum_out(Tensor & result, const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor sum(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor sum(const Tensor & self);
static inline Tensor & prod_out(Tensor & result, const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor prod(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor prod(const Tensor & self);
static inline Tensor & cumsum_out(Tensor & result, const Tensor & self, int64_t dim);
static inline Tensor cumsum(const Tensor & self, int64_t dim);
static inline Tensor & cumprod_out(Tensor & result, const Tensor & self, int64_t dim);
static inline Tensor cumprod(const Tensor & self, int64_t dim);
static inline Tensor & sign_out(Tensor & result, const Tensor & self);
static inline Tensor sign(const Tensor & self);
static inline Tensor trace(const Tensor & self);
static inline Tensor & add_out(Tensor & result, const Tensor & self, Scalar other, Scalar alpha=1);
static inline Tensor add(const Tensor & self, Scalar other, Scalar alpha=1);
static inline Tensor & add_out(Tensor & result, const Tensor & self, const Tensor & other, Scalar alpha=1);
static inline Tensor add(const Tensor & self, const Tensor & other, Scalar alpha=1);
static inline Tensor & add_out(Tensor & result, const Tensor & self, SparseTensor other, Scalar alpha=1);
static inline Tensor add(const Tensor & self, SparseTensor other, Scalar alpha=1);
static inline Tensor & sub_out(Tensor & result, const Tensor & self, Scalar other, Scalar alpha=1);
static inline Tensor sub(const Tensor & self, Scalar other, Scalar alpha=1);
static inline Tensor & sub_out(Tensor & result, const Tensor & self, const Tensor & other, Scalar alpha=1);
static inline Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha=1);
static inline Tensor & mul_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor mul(const Tensor & self, Scalar other);
static inline Tensor & mul_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor mul(const Tensor & self, const Tensor & other);
static inline Tensor & div_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor div(const Tensor & self, Scalar other);
static inline Tensor & div_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor div(const Tensor & self, const Tensor & other);
static inline Tensor & fmod_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor fmod(const Tensor & self, Scalar other);
static inline Tensor & fmod_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor fmod(const Tensor & self, const Tensor & other);
static inline Tensor & remainder_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor remainder(const Tensor & self, Scalar other);
static inline Tensor & remainder_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor remainder(const Tensor & self, const Tensor & other);
static inline Tensor & clamp_out(Tensor & result, const Tensor & self, Scalar min, Scalar max);
static inline Tensor clamp(const Tensor & self, Scalar min, Scalar max);
static inline Tensor & clamp_(Tensor & self, Scalar min, Scalar max);
static inline Tensor & clamp_min_out(Tensor & result, const Tensor & self, Scalar min);
static inline Tensor clamp_min(const Tensor & self, Scalar min);
static inline Tensor & clamp_min_(Tensor & self, Scalar min);
static inline Tensor & clamp_max_out(Tensor & result, const Tensor & self, Scalar max);
static inline Tensor clamp_max(const Tensor & self, Scalar max);
static inline Tensor & clamp_max_(Tensor & self, Scalar max);
static inline Tensor _dot(const Tensor & self, const Tensor & tensor);
static inline Tensor & tril_out(Tensor & result, const Tensor & self, int64_t diagonal=0);
static inline Tensor tril(const Tensor & self, int64_t diagonal=0);
static inline Tensor & triu_out(Tensor & result, const Tensor & self, int64_t diagonal=0);
static inline Tensor triu(const Tensor & self, int64_t diagonal=0);
static inline Tensor & cross_out(Tensor & result, const Tensor & self, const Tensor & other, int64_t dim=-1);
static inline Tensor cross(const Tensor & self, const Tensor & other, int64_t dim=-1);
static inline Tensor & eye_out(Tensor & result, int64_t n, int64_t m=-1);
static inline Tensor & diag_out(Tensor & result, const Tensor & self, int64_t diagonal=0);
static inline Tensor diag(const Tensor & self, int64_t diagonal=0);
static inline Tensor & addmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1);
static inline Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & addmm_out(Tensor & result, const Tensor & self, SparseTensor mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1);
static inline Tensor addmm(const Tensor & self, SparseTensor mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & _addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1);
static inline Tensor _addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1);
static inline Tensor & _addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1);
static inline Tensor _addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & _ger_out(Tensor & result, const Tensor & self, const Tensor & vec2);
static inline Tensor _ger(const Tensor & self, const Tensor & vec2);
static inline Tensor & _mv_out(Tensor & result, const Tensor & self, const Tensor & vec);
static inline Tensor _mv(const Tensor & self, const Tensor & vec);
static inline Tensor & _mm_out(Tensor & result, const Tensor & self, const Tensor & mat2);
static inline Tensor _mm(const Tensor & self, const Tensor & mat2);
static inline Tensor & bmm_out(Tensor & result, const Tensor & self, const Tensor & mat2);
static inline Tensor bmm(const Tensor & self, const Tensor & mat2);
static inline Tensor & addbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1);
static inline Tensor addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & baddbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1);
static inline Tensor baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & addcmul_out(Tensor & result, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1);
static inline Tensor addcmul(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1);
static inline Tensor & addcdiv_out(Tensor & result, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1);
static inline Tensor addcdiv(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1);
static inline std::tuple<Tensor &,Tensor &> gesv_out(Tensor & solution, Tensor & lu, const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor,Tensor> gesv(const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor &,Tensor &> gels_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor,Tensor> gels(const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor &,Tensor &> trtrs_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
static inline std::tuple<Tensor,Tensor> trtrs(const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
static inline std::tuple<Tensor &,Tensor &> symeig_out(Tensor & res1, Tensor & res2, const Tensor & self, bool eigenvectors=false, bool upper=true);
static inline std::tuple<Tensor,Tensor> symeig(const Tensor & self, bool eigenvectors=false, bool upper=true);
static inline std::tuple<Tensor &,Tensor &> eig_out(Tensor & res1, Tensor & res2, const Tensor & self, bool eigenvectors=false);
static inline std::tuple<Tensor,Tensor> eig(const Tensor & self, bool eigenvectors=false);
static inline std::tuple<Tensor &,Tensor &,Tensor &> svd_out(Tensor & res1, Tensor & res2, Tensor & res3, const Tensor & self, bool some=true);
static inline std::tuple<Tensor,Tensor,Tensor> svd(const Tensor & self, bool some=true);
static inline Tensor & inverse_out(Tensor & output, const Tensor & self);
static inline Tensor inverse(const Tensor & self);
static inline Tensor & potrf_out(Tensor & output, const Tensor & self, bool upper=true);
static inline Tensor potrf(const Tensor & self, bool upper=true);
static inline Tensor & potrs_out(Tensor & result, const Tensor & self, const Tensor & input2, bool upper=true);
static inline Tensor potrs(const Tensor & self, const Tensor & input2, bool upper=true);
static inline Tensor & potri_out(Tensor & output, const Tensor & self, bool upper=true);
static inline Tensor potri(const Tensor & self, bool upper=true);
static inline std::tuple<Tensor &,Tensor &> pstrf_out(Tensor & res1, Tensor & res2, const Tensor & self, bool upper=true, Scalar tol=-1);
static inline std::tuple<Tensor,Tensor> pstrf(const Tensor & self, bool upper=true, Scalar tol=-1);
static inline std::tuple<Tensor &,Tensor &> qr_out(Tensor & res1, Tensor & res2, const Tensor & self);
static inline std::tuple<Tensor,Tensor> qr(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> geqrf_out(Tensor & res1, Tensor & res2, const Tensor & self);
static inline std::tuple<Tensor,Tensor> geqrf(const Tensor & self);
static inline Tensor & orgqr_out(Tensor & result, const Tensor & self, const Tensor & input2);
static inline Tensor orgqr(const Tensor & self, const Tensor & input2);
static inline Tensor & ormqr_out(Tensor & result, const Tensor & self, const Tensor & input2, const Tensor & input3, bool left=true, bool transpose=false);
static inline Tensor ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left=true, bool transpose=false);
static inline std::tuple<Tensor &,Tensor &> btrifact_out(Tensor & result, Tensor & pivots, const Tensor & self, bool pivot=true);
static inline std::tuple<Tensor,Tensor> btrifact(const Tensor & self, bool pivot=true);
static inline std::tuple<Tensor &,Tensor &,Tensor &> btrifact_with_info_out(Tensor & result, Tensor & pivots, Tensor & info, const Tensor & self, bool pivot=true);
static inline std::tuple<Tensor,Tensor,Tensor> btrifact_with_info(const Tensor & self, bool pivot=true);
static inline Tensor & btrisolve_out(Tensor & result, const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots);
static inline Tensor btrisolve(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots);
static inline Tensor & randperm_out(Tensor & result, int64_t n, Generator * generator=nullptr);
static inline Tensor & multinomial_out(Tensor & result, const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr);
static inline Tensor multinomial(const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr);
static inline Tensor & normal_out(Tensor & output, const Tensor & mean, double std=1, Generator * generator=nullptr);
static inline Tensor normal(const Tensor & mean, double std=1, Generator * generator=nullptr);
static inline Tensor & normal_out(Tensor & output, double mean, const Tensor & std, Generator * generator=nullptr);
static inline Tensor normal(double mean, const Tensor & std, Generator * generator=nullptr);
static inline Tensor & normal_out(Tensor & output, const Tensor & mean, const Tensor & std, Generator * generator=nullptr);
static inline Tensor normal(const Tensor & mean, const Tensor & std, Generator * generator=nullptr);
static inline Tensor & rand_out(Tensor & result, IntList size, Generator * generator=nullptr);
static inline Tensor & randn_out(Tensor & result, IntList size, Generator * generator=nullptr);
static inline Tensor & bernoulli_out(Tensor & output, const Tensor & self, Generator * generator=nullptr);
static inline Tensor bernoulli(const Tensor & self, Generator * generator=nullptr);
static inline Tensor & _standard_gamma_out(Tensor & output, const Tensor & self, Generator * generator=nullptr);
static inline Tensor _standard_gamma(const Tensor & self, Generator * generator=nullptr);
static inline Tensor & _dirichlet_grad_out(Tensor & output, const Tensor & x, const Tensor & alpha, const Tensor & total);
static inline Tensor _dirichlet_grad(const Tensor & x, const Tensor & alpha, const Tensor & total);
static inline Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values, IntList size);
static inline Tensor sparse_coo_tensor(const Tensor & indices, const Tensor & values);
static inline Tensor alias(const Tensor & self);
static inline Tensor & as_strided_out(Tensor & result, const Tensor & self, IntList size, IntList stride, int64_t storage_offset=-1);
static inline Tensor as_strided(const Tensor & self, IntList size, IntList stride, int64_t storage_offset=-1);
static inline Tensor & as_strided_(Tensor & self, IntList size, IntList stride, int64_t storage_offset=-1);
static inline Tensor & _cat_out(Tensor & self, TensorList tensors, int64_t dim=0);
static inline Tensor _cat(TensorList tensors, int64_t dim=0);
static inline Tensor & binary_cross_entropy_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, bool reduce=true);
static inline Tensor binary_cross_entropy(const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, bool reduce=true);
static inline Tensor & binary_cross_entropy_forward_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, bool reduce);
static inline Tensor binary_cross_entropy_forward(const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, bool reduce);
static inline Tensor & binary_cross_entropy_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, bool reduce);
static inline Tensor binary_cross_entropy_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, bool reduce);
static inline Tensor & kl_div_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor kl_div(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor & kl_div_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor kl_div_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & kl_div_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor kl_div_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & l1_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor l1_loss(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor & l1_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor l1_loss_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & mse_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor mse_loss(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor & mse_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor mse_loss_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & mse_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor mse_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & multi_margin_loss_out(Tensor & output, const Tensor & self, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, bool size_average=true);
static inline Tensor multi_margin_loss(const Tensor & self, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, bool size_average=true);
static inline Tensor & multi_margin_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average);
static inline Tensor multi_margin_loss_forward(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average);
static inline Tensor & multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average);
static inline Tensor multi_margin_loss_backward(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average);
static inline Tensor & multilabel_margin_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor multilabel_margin_loss(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline std::tuple<Tensor &,Tensor &> multilabel_margin_loss_forward_out(Tensor & output, Tensor & is_target, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline std::tuple<Tensor,Tensor> multilabel_margin_loss_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & multilabel_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce, const Tensor & is_target);
static inline Tensor multilabel_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce, const Tensor & is_target);
static inline Tensor & nll_loss_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100, bool reduce=true);
static inline Tensor nll_loss(const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100, bool reduce=true);
static inline std::tuple<Tensor &,Tensor &> nll_loss_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce);
static inline std::tuple<Tensor,Tensor> nll_loss_forward(const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce);
static inline Tensor & nll_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce, const Tensor & total_weight);
static inline Tensor nll_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce, const Tensor & total_weight);
static inline Tensor & nll_loss2d_out(Tensor & output, const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100, bool reduce=true);
static inline Tensor nll_loss2d(const Tensor & self, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100, bool reduce=true);
static inline std::tuple<Tensor &,Tensor &> nll_loss2d_forward_out(Tensor & output, Tensor & total_weight, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce);
static inline std::tuple<Tensor,Tensor> nll_loss2d_forward(const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce);
static inline Tensor & nll_loss2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce, const Tensor & total_weight);
static inline Tensor nll_loss2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, bool reduce, const Tensor & total_weight);
static inline Tensor & smooth_l1_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor smooth_l1_loss(const Tensor & self, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor & smooth_l1_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor smooth_l1_loss_forward(const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & smooth_l1_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor smooth_l1_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & soft_margin_loss_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average=true);
static inline Tensor soft_margin_loss(const Tensor & self, const Tensor & target, bool size_average=true);
static inline Tensor & soft_margin_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, bool size_average);
static inline Tensor soft_margin_loss_forward(const Tensor & self, const Tensor & target, bool size_average);
static inline Tensor & soft_margin_loss_backward_out(Tensor & grad_input, const Tensor & self, const Tensor & target, bool size_average);
static inline Tensor soft_margin_loss_backward(const Tensor & self, const Tensor & target, bool size_average);
static inline Tensor & elu_out(Tensor & output, const Tensor & self, Scalar alpha=1, Scalar scale=1);
static inline Tensor elu(const Tensor & self, Scalar alpha=1, Scalar scale=1);
static inline Tensor & elu_forward_out(Tensor & output, const Tensor & self, Scalar alpha, Scalar scale);
static inline Tensor elu_forward(const Tensor & self, Scalar alpha, Scalar scale);
static inline Tensor & elu_backward_out(Tensor & grad_input, const Tensor & grad_output, Scalar alpha, Scalar scale, const Tensor & output);
static inline Tensor elu_backward(const Tensor & grad_output, Scalar alpha, Scalar scale, const Tensor & output);
static inline Tensor & elu_(Tensor & self, Scalar alpha=1, Scalar scale=1);
static inline Tensor & elu_forward_(Tensor & self, Scalar alpha, Scalar scale);
static inline Tensor & glu_out(Tensor & output, const Tensor & self, int64_t dim=-1);
static inline Tensor glu(const Tensor & self, int64_t dim=-1);
static inline Tensor & glu_forward_out(Tensor & output, const Tensor & self, int64_t dim);
static inline Tensor glu_forward(const Tensor & self, int64_t dim);
static inline Tensor & glu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim);
static inline Tensor glu_backward(const Tensor & grad_output, const Tensor & self, int64_t dim);
static inline Tensor & hardshrink_out(Tensor & output, const Tensor & self, Scalar lambd=0.5);
static inline Tensor hardshrink(const Tensor & self, Scalar lambd=0.5);
static inline Tensor & hardshrink_forward_out(Tensor & output, const Tensor & self, Scalar lambd);
static inline Tensor hardshrink_forward(const Tensor & self, Scalar lambd);
static inline Tensor & hardshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar lambd);
static inline Tensor hardshrink_backward(const Tensor & grad_output, const Tensor & self, Scalar lambd);
static inline Tensor & hardtanh_out(Tensor & output, const Tensor & self, Scalar min_val=-1, Scalar max_val=1);
static inline Tensor hardtanh(const Tensor & self, Scalar min_val=-1, Scalar max_val=1);
static inline Tensor & hardtanh_forward_out(Tensor & output, const Tensor & self, Scalar min_val, Scalar max_val);
static inline Tensor hardtanh_forward(const Tensor & self, Scalar min_val, Scalar max_val);
static inline Tensor & hardtanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val);
static inline Tensor hardtanh_backward(const Tensor & grad_output, const Tensor & self, Scalar min_val, Scalar max_val);
static inline Tensor & hardtanh_(Tensor & self, Scalar min_val=-1, Scalar max_val=1);
static inline Tensor & hardtanh_forward_(Tensor & self, Scalar min_val, Scalar max_val);
static inline Tensor & leaky_relu_out(Tensor & output, const Tensor & self, Scalar negative_slope=0.01);
static inline Tensor leaky_relu(const Tensor & self, Scalar negative_slope=0.01);
static inline Tensor & leaky_relu_forward_out(Tensor & output, const Tensor & self, Scalar negative_slope);
static inline Tensor leaky_relu_forward(const Tensor & self, Scalar negative_slope);
static inline Tensor & leaky_relu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar negative_slope);
static inline Tensor leaky_relu_backward(const Tensor & grad_output, const Tensor & self, Scalar negative_slope);
static inline Tensor & leaky_relu_(Tensor & self, Scalar negative_slope=0.01);
static inline Tensor & leaky_relu_forward_(Tensor & self, Scalar negative_slope);
static inline Tensor & log_sigmoid_out(Tensor & output, const Tensor & self);
static inline Tensor log_sigmoid(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> log_sigmoid_forward_out(Tensor & output, Tensor & buffer, const Tensor & self);
static inline std::tuple<Tensor,Tensor> log_sigmoid_forward(const Tensor & self);
static inline Tensor & log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & buffer);
static inline Tensor log_sigmoid_backward(const Tensor & grad_output, const Tensor & self, const Tensor & buffer);
static inline Tensor & log_softmax_out(Tensor & output, const Tensor & self, int64_t dim);
static inline Tensor log_softmax(const Tensor & self, int64_t dim);
static inline Tensor & log_softmax_forward_out(Tensor & output, const Tensor & self, int64_t dim);
static inline Tensor log_softmax_forward(const Tensor & self, int64_t dim);
static inline Tensor & log_softmax_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim, const Tensor & output);
static inline Tensor log_softmax_backward(const Tensor & grad_output, const Tensor & self, int64_t dim, const Tensor & output);
static inline Tensor & prelu_out(Tensor & output, const Tensor & self, const Tensor & weight);
static inline Tensor prelu(const Tensor & self, const Tensor & weight);
static inline Tensor & prelu_forward_out(Tensor & output, const Tensor & self, const Tensor & weight);
static inline Tensor prelu_forward(const Tensor & self, const Tensor & weight);
static inline std::tuple<Tensor &,Tensor &> prelu_backward_out(Tensor & grad_input, Tensor & grad_weight, const Tensor & grad_output, const Tensor & self, const Tensor & weight);
static inline std::tuple<Tensor,Tensor> prelu_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, std::array<bool,2> output_mask={{true, true}});
static inline Tensor & rrelu_with_noise_out(Tensor & output, const Tensor & self, const Tensor & noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr);
static inline Tensor rrelu_with_noise(const Tensor & self, const Tensor & noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr);
static inline Tensor & rrelu_with_noise_forward_out(Tensor & output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator);
static inline Tensor rrelu_with_noise_forward(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator);
static inline Tensor & rrelu_with_noise_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training);
static inline Tensor rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training);
static inline Tensor & rrelu_with_noise_(Tensor & self, const Tensor & noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr);
static inline Tensor & rrelu_with_noise_forward_(Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, Generator * generator);
static inline Tensor & softmax_out(Tensor & output, const Tensor & self, int64_t dim);
static inline Tensor softmax(const Tensor & self, int64_t dim);
static inline Tensor & softmax_forward_out(Tensor & output, const Tensor & self, int64_t dim);
static inline Tensor softmax_forward(const Tensor & self, int64_t dim);
static inline Tensor & softmax_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t dim, const Tensor & output);
static inline Tensor softmax_backward(const Tensor & grad_output, const Tensor & self, int64_t dim, const Tensor & output);
static inline Tensor & softplus_out(Tensor & output, const Tensor & self, Scalar beta=1, Scalar threshold=20);
static inline Tensor softplus(const Tensor & self, Scalar beta=1, Scalar threshold=20);
static inline Tensor & softplus_forward_out(Tensor & output, const Tensor & self, Scalar beta, Scalar threshold);
static inline Tensor softplus_forward(const Tensor & self, Scalar beta, Scalar threshold);
static inline Tensor & softplus_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output);
static inline Tensor softplus_backward(const Tensor & grad_output, const Tensor & self, Scalar beta, Scalar threshold, const Tensor & output);
static inline Tensor & softshrink_out(Tensor & output, const Tensor & self, Scalar lambd=0.5);
static inline Tensor softshrink(const Tensor & self, Scalar lambd=0.5);
static inline Tensor & softshrink_forward_out(Tensor & output, const Tensor & self, Scalar lambd);
static inline Tensor softshrink_forward(const Tensor & self, Scalar lambd);
static inline Tensor & softshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar lambd);
static inline Tensor softshrink_backward(const Tensor & grad_output, const Tensor & self, Scalar lambd);
static inline Tensor & threshold_out(Tensor & output, const Tensor & self, Scalar threshold, Scalar value);
static inline Tensor threshold(const Tensor & self, Scalar threshold, Scalar value);
static inline Tensor & threshold_forward_out(Tensor & output, const Tensor & self, Scalar threshold, Scalar value);
static inline Tensor threshold_forward(const Tensor & self, Scalar threshold, Scalar value);
static inline Tensor & threshold_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, Scalar threshold, Scalar value);
static inline Tensor threshold_backward(const Tensor & grad_output, const Tensor & self, Scalar threshold, Scalar value);
static inline Tensor & threshold_(Tensor & self, Scalar threshold, Scalar value);
static inline Tensor & threshold_forward_(Tensor & self, Scalar threshold, Scalar value);
static inline Tensor & adaptive_avg_pool2d_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor adaptive_avg_pool2d(const Tensor & self, IntList output_size);
static inline Tensor & adaptive_avg_pool2d_forward_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor adaptive_avg_pool2d_forward(const Tensor & self, IntList output_size);
static inline Tensor & adaptive_avg_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self);
static inline Tensor adaptive_avg_pool2d_backward(const Tensor & grad_output, const Tensor & self);
static inline Tensor & adaptive_avg_pool3d_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor adaptive_avg_pool3d(const Tensor & self, IntList output_size);
static inline Tensor & adaptive_avg_pool3d_forward_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor adaptive_avg_pool3d_forward(const Tensor & self, IntList output_size);
static inline Tensor & adaptive_avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self);
static inline Tensor adaptive_avg_pool3d_backward(const Tensor & grad_output, const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> adaptive_max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList output_size);
static inline std::tuple<Tensor,Tensor> adaptive_max_pool2d(const Tensor & self, IntList output_size);
static inline std::tuple<Tensor &,Tensor &> adaptive_max_pool2d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList output_size);
static inline std::tuple<Tensor,Tensor> adaptive_max_pool2d_forward(const Tensor & self, IntList output_size);
static inline Tensor & adaptive_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices);
static inline Tensor adaptive_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices);
static inline std::tuple<Tensor &,Tensor &> adaptive_max_pool3d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList output_size);
static inline std::tuple<Tensor,Tensor> adaptive_max_pool3d(const Tensor & self, IntList output_size);
static inline std::tuple<Tensor &,Tensor &> adaptive_max_pool3d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList output_size);
static inline std::tuple<Tensor,Tensor> adaptive_max_pool3d_forward(const Tensor & self, IntList output_size);
static inline Tensor & adaptive_max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices);
static inline Tensor adaptive_max_pool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices);
static inline Tensor & avg_pool2d_out(Tensor & output, const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false);
static inline Tensor avg_pool2d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false);
static inline Tensor & avg_pool2d_forward_out(Tensor & output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor avg_pool2d_forward(const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor & avg_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor avg_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor & avg_pool3d_out(Tensor & output, const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false);
static inline Tensor avg_pool3d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false);
static inline Tensor & avg_pool3d_forward_out(Tensor & output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor avg_pool3d_forward(const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor & avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor avg_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline std::tuple<Tensor &,Tensor &> fractional_max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & random_samples);
static inline std::tuple<Tensor,Tensor> fractional_max_pool2d(const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & random_samples);
static inline std::tuple<Tensor &,Tensor &> fractional_max_pool2d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & random_samples);
static inline std::tuple<Tensor,Tensor> fractional_max_pool2d_forward(const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & random_samples);
static inline Tensor & fractional_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & indices);
static inline Tensor fractional_max_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList output_size, const Tensor & indices);
static inline std::tuple<Tensor &,Tensor &> max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false);
static inline std::tuple<Tensor,Tensor> max_pool2d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false);
static inline std::tuple<Tensor &,Tensor &> max_pool2d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode);
static inline std::tuple<Tensor,Tensor> max_pool2d_forward(const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode);
static inline Tensor & max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices);
static inline Tensor max_pool2d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices);
static inline std::tuple<Tensor &,Tensor &> max_pool3d_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false);
static inline std::tuple<Tensor,Tensor> max_pool3d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false);
static inline std::tuple<Tensor &,Tensor &> max_pool3d_forward_out(Tensor & output, Tensor & indices, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode);
static inline std::tuple<Tensor,Tensor> max_pool3d_forward(const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode);
static inline Tensor & max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices);
static inline Tensor max_pool3d_backward(const Tensor & grad_output, const Tensor & self, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices);
static inline Tensor & max_unpool2d_out(Tensor & output, const Tensor & self, const Tensor & indices, IntList output_size);
static inline Tensor max_unpool2d(const Tensor & self, const Tensor & indices, IntList output_size);
static inline Tensor & max_unpool2d_forward_out(Tensor & output, const Tensor & self, const Tensor & indices, IntList output_size);
static inline Tensor max_unpool2d_forward(const Tensor & self, const Tensor & indices, IntList output_size);
static inline Tensor & max_unpool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntList output_size);
static inline Tensor max_unpool2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntList output_size);
static inline Tensor & max_unpool3d_out(Tensor & output, const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor max_unpool3d(const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor & max_unpool3d_forward_out(Tensor & output, const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor max_unpool3d_forward(const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor & max_unpool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor max_unpool3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor & reflection_pad1d_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor reflection_pad1d(const Tensor & self, IntList padding);
static inline Tensor & reflection_pad1d_forward_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor reflection_pad1d_forward(const Tensor & self, IntList padding);
static inline Tensor & reflection_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor reflection_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor & reflection_pad2d_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor reflection_pad2d(const Tensor & self, IntList padding);
static inline Tensor & reflection_pad2d_forward_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor reflection_pad2d_forward(const Tensor & self, IntList padding);
static inline Tensor & reflection_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor reflection_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor & replication_pad1d_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor replication_pad1d(const Tensor & self, IntList padding);
static inline Tensor & replication_pad1d_forward_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor replication_pad1d_forward(const Tensor & self, IntList padding);
static inline Tensor & replication_pad1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor replication_pad1d_backward(const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor & replication_pad2d_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor replication_pad2d(const Tensor & self, IntList padding);
static inline Tensor & replication_pad2d_forward_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor replication_pad2d_forward(const Tensor & self, IntList padding);
static inline Tensor & replication_pad2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor replication_pad2d_backward(const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor & replication_pad3d_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor replication_pad3d(const Tensor & self, IntList padding);
static inline Tensor & replication_pad3d_forward_out(Tensor & output, const Tensor & self, IntList padding);
static inline Tensor replication_pad3d_forward(const Tensor & self, IntList padding);
static inline Tensor & replication_pad3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor replication_pad3d_backward(const Tensor & grad_output, const Tensor & self, IntList padding);
static inline Tensor & upsample_linear1d_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor upsample_linear1d(const Tensor & self, IntList output_size);
static inline Tensor & upsample_linear1d_forward_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor upsample_linear1d_forward(const Tensor & self, IntList output_size);
static inline Tensor & upsample_linear1d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntList output_size, IntList input_size);
static inline Tensor upsample_linear1d_backward(const Tensor & grad_output, IntList output_size, IntList input_size);
static inline Tensor & upsample_bilinear2d_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor upsample_bilinear2d(const Tensor & self, IntList output_size);
static inline Tensor & upsample_bilinear2d_forward_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor upsample_bilinear2d_forward(const Tensor & self, IntList output_size);
static inline Tensor & upsample_bilinear2d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntList output_size, IntList input_size);
static inline Tensor upsample_bilinear2d_backward(const Tensor & grad_output, IntList output_size, IntList input_size);
static inline Tensor & upsample_trilinear3d_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor upsample_trilinear3d(const Tensor & self, IntList output_size);
static inline Tensor & upsample_trilinear3d_forward_out(Tensor & output, const Tensor & self, IntList output_size);
static inline Tensor upsample_trilinear3d_forward(const Tensor & self, IntList output_size);
static inline Tensor & upsample_trilinear3d_backward_out(Tensor & grad_input, const Tensor & grad_output, IntList output_size, IntList input_size);
static inline Tensor upsample_trilinear3d_backward(const Tensor & grad_output, IntList output_size, IntList input_size);
static inline Tensor & upsample_nearest1d_out(Tensor & output, const Tensor & self, int64_t scale_factor);
static inline Tensor upsample_nearest1d(const Tensor & self, int64_t scale_factor);
static inline Tensor & upsample_nearest1d_forward_out(Tensor & output, const Tensor & self, int64_t scale_factor);
static inline Tensor upsample_nearest1d_forward(const Tensor & self, int64_t scale_factor);
static inline Tensor & upsample_nearest1d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t scale_factor);
static inline Tensor upsample_nearest1d_backward(const Tensor & grad_output, const Tensor & self, int64_t scale_factor);
static inline Tensor & upsample_nearest2d_out(Tensor & output, const Tensor & self, int64_t scale_factor);
static inline Tensor upsample_nearest2d(const Tensor & self, int64_t scale_factor);
static inline Tensor & upsample_nearest2d_forward_out(Tensor & output, const Tensor & self, int64_t scale_factor);
static inline Tensor upsample_nearest2d_forward(const Tensor & self, int64_t scale_factor);
static inline Tensor & upsample_nearest2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t scale_factor);
static inline Tensor upsample_nearest2d_backward(const Tensor & grad_output, const Tensor & self, int64_t scale_factor);
static inline Tensor & upsample_nearest3d_out(Tensor & output, const Tensor & self, int64_t scale_factor);
static inline Tensor upsample_nearest3d(const Tensor & self, int64_t scale_factor);
static inline Tensor & upsample_nearest3d_forward_out(Tensor & output, const Tensor & self, int64_t scale_factor);
static inline Tensor upsample_nearest3d_forward(const Tensor & self, int64_t scale_factor);
static inline Tensor & upsample_nearest3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, int64_t scale_factor);
static inline Tensor upsample_nearest3d_backward(const Tensor & grad_output, const Tensor & self, int64_t scale_factor);
static inline Tensor & _sigmoid_out(Tensor & output, const Tensor & self);
static inline Tensor _sigmoid(const Tensor & self);
static inline Tensor & _sigmoid_forward_out(Tensor & output, const Tensor & self);
static inline Tensor _sigmoid_forward(const Tensor & self);
static inline Tensor & _sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output);
static inline Tensor _sigmoid_backward(const Tensor & grad_output, const Tensor & output);
static inline Tensor & _tanh_out(Tensor & output, const Tensor & self);
static inline Tensor _tanh(const Tensor & self);
static inline Tensor & _tanh_forward_out(Tensor & output, const Tensor & self);
static inline Tensor _tanh_forward(const Tensor & self);
static inline Tensor & _tanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output);
static inline Tensor _tanh_backward(const Tensor & grad_output, const Tensor & output);
static inline Tensor & thnn_batch_norm_out(Tensor & output, const Tensor & self, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps);
static inline Tensor thnn_batch_norm(const Tensor & self, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_batch_norm_forward_out(Tensor & output, Tensor & save_mean, Tensor & save_std, const Tensor & self, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_batch_norm_forward(const Tensor & self, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_batch_norm_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, bool training, double eps, const Tensor & save_mean, const Tensor & save_std);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_batch_norm_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, bool training, double eps, const Tensor & save_mean, const Tensor & save_std, std::array<bool,3> output_mask={{true, true, true}});
static inline Tensor & thnn_conv_transpose2d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1);
static inline Tensor thnn_conv_transpose2d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_transpose2d_forward_out(Tensor & output, Tensor & columns, Tensor & ones, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv_transpose2d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_transpose2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv_transpose2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & columns, const Tensor & ones, std::array<bool,3> output_mask={{true, true, true}});
static inline Tensor & thnn_conv_transpose3d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1);
static inline Tensor thnn_conv_transpose3d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_transpose3d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv_transpose3d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_transpose3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & finput, const Tensor & fgrad_input);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv_transpose3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & finput, const Tensor & fgrad_input, std::array<bool,3> output_mask={{true, true, true}});
static inline Tensor & thnn_conv2d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0);
static inline Tensor thnn_conv2d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv2d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv2d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input, std::array<bool,3> output_mask={{true, true, true}});
static inline Tensor & thnn_conv_depthwise2d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline Tensor thnn_conv_depthwise2d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline Tensor & thnn_conv_depthwise2d_forward_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation);
static inline Tensor thnn_conv_depthwise2d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation);
static inline std::tuple<Tensor &,Tensor &> thnn_conv_depthwise2d_backward_out(Tensor & grad_input, Tensor & grad_weight, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation);
static inline std::tuple<Tensor,Tensor> thnn_conv_depthwise2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, std::array<bool,2> output_mask={{true, true}});
static inline Tensor & thnn_conv3d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0);
static inline Tensor thnn_conv3d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv3d_forward_out(Tensor & output, Tensor & finput, Tensor & fgrad_input, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv3d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input, std::array<bool,3> output_mask={{true, true, true}});
static inline Tensor & thnn_conv_dilated2d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline Tensor thnn_conv_dilated2d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_dilated2d_forward_out(Tensor & output, Tensor & columns, Tensor & ones, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv_dilated2d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_dilated2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv_dilated2d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones, std::array<bool,3> output_mask={{true, true, true}});
static inline Tensor & thnn_conv_dilated3d_out(Tensor & output, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline Tensor thnn_conv_dilated3d(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_dilated3d_forward_out(Tensor & output, Tensor & columns, Tensor & ones, const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv_dilated3d_forward(const Tensor & self, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation);
static inline std::tuple<Tensor &,Tensor &,Tensor &> thnn_conv_dilated3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline std::tuple<Tensor,Tensor,Tensor> thnn_conv_dilated3d_backward(const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones, std::array<bool,3> output_mask={{true, true, true}});
static inline Tensor adaptive_avg_pool1d(const Tensor & self, IntList output_size);
static inline std::tuple<Tensor,Tensor> adaptive_max_pool1d(const Tensor & self, IntList output_size);
static inline bool allclose(const Tensor & self, const Tensor & other, double rtol=1e-05, double atol=1e-08);
static inline Tensor addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1);
static inline Tensor & addmv_(Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1);
static inline Tensor & addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1);
static inline Tensor addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & addr_(Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1);
static inline Tensor batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, bool cudnn_enabled);
static inline Tensor & bernoulli_(Tensor & self, const Tensor & p, Generator * generator=nullptr);
static inline Tensor & bernoulli_(Tensor & self, double p=0.5, Generator * generator=nullptr);
static inline Tensor cat(TensorList tensors, int64_t dim=0);
static inline Tensor & cat_out(Tensor & result, TensorList tensors, int64_t dim=0);
static inline Tensor sspaddmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & sspaddmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1);
static inline std::vector<Tensor> chunk(const Tensor & self, int64_t chunks, int64_t dim=0);
static inline bool cudnn_is_acceptable(const Tensor & self);
static inline Tensor convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding, int64_t groups);
static inline Tensor _convolution(const Tensor & input, const Tensor & weight, const Tensor & bias, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled);
static inline Tensor _convolution_nogroup(const Tensor & input, const Tensor & weight, const Tensor & bias, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding);
static inline std::tuple<Tensor,Tensor,Tensor> _convolution_double_backward(const Tensor & ggI, const Tensor & ggW, const Tensor & ggb, const Tensor & gO, const Tensor & weight, const Tensor & self, IntList stride, IntList padding, IntList dilation, bool transposed, IntList output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, std::array<bool,3> output_mask);
static inline Tensor conv1d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1, int64_t groups=1);
static inline Tensor conv2d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1, int64_t groups=1);
static inline Tensor conv3d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1, int64_t groups=1);
static inline Tensor conv_tbc(const Tensor & self, const Tensor & weight, const Tensor & bias, int64_t pad);
static inline std::tuple<Tensor,Tensor,Tensor> conv_tbc_backward(const Tensor & self, const Tensor & input, const Tensor & weight, const Tensor & bias, int64_t pad);
static inline Tensor conv_transpose1d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, int64_t groups=1, IntList dilation=1);
static inline Tensor conv_transpose2d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, int64_t groups=1, IntList dilation=1);
static inline Tensor conv_transpose3d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, int64_t groups=1, IntList dilation=1);
static inline Tensor cudnn_affine_grid_generator(const Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W);
static inline Tensor cudnn_affine_grid_generator_backward(const Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W);
static inline std::tuple<Tensor,Tensor,Tensor> cudnn_batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double exponential_average_factor, double epsilon);
static inline std::tuple<Tensor,Tensor,Tensor> cudnn_batch_norm_backward(const Tensor & input, const Tensor & grad_output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_var, double epsilon);
static inline Tensor cudnn_convolution(const Tensor & self, const Tensor & weight, const Tensor & bias, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic);
static inline Tensor cudnn_convolution_backward_input(IntList self_size, const Tensor & grad_output, const Tensor & weight, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic);
static inline std::tuple<Tensor,Tensor,Tensor> cudnn_convolution_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask);
static inline Tensor cudnn_convolution_backward_bias(const Tensor & grad_output);
static inline Tensor cudnn_convolution_backward_weight(IntList weight_size, const Tensor & grad_output, const Tensor & self, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic);
static inline Tensor cudnn_convolution_transpose(const Tensor & self, const Tensor & weight, const Tensor & bias, IntList padding, IntList output_padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic);
static inline std::tuple<Tensor,Tensor,Tensor> cudnn_convolution_transpose_backward(const Tensor & self, const Tensor & grad_output, const Tensor & weight, IntList padding, IntList output_padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic, std::array<bool,3> output_mask);
static inline Tensor cudnn_convolution_transpose_backward_bias(const Tensor & grad_output);
static inline Tensor cudnn_convolution_transpose_backward_input(const Tensor & grad_output, const Tensor & weight, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic);
static inline Tensor cudnn_convolution_transpose_backward_weight(IntList weight_size, const Tensor & grad_output, const Tensor & self, IntList padding, IntList stride, IntList dilation, int64_t groups, bool benchmark, bool deterministic);
static inline Tensor cudnn_grid_sampler(const Tensor & self, const Tensor & grid);
static inline std::tuple<Tensor,Tensor> cudnn_grid_sampler_backward(const Tensor & self, const Tensor & grid, const Tensor & grad_output);
static inline Tensor det(const Tensor & self);
static inline std::tuple<Tensor,Tensor,Tensor,Tensor> _det_with_svd(const Tensor & self);
static inline Tensor dot(const Tensor & self, const Tensor & tensor);
static inline Tensor embedding(const Tensor & weight, const Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false);
static inline Tensor embedding_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse);
static inline Tensor embedding_dense_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq);
static inline Tensor & embedding_renorm_(Tensor & self, const Tensor & indices, double max_norm, double norm_type);
static inline Tensor embedding_sparse_backward(const Tensor & grad, const Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq);
static inline Tensor empty_like(const Tensor & self);
static inline std::tuple<Tensor,Tensor,Tensor> embedding_bag(const Tensor & weight, const Tensor & indices, const Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false);
static inline Tensor embedding_bag_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse);
static inline Tensor embedding_bag_sparse_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode);
static inline Tensor embedding_bag_dense_backward(const Tensor & grad, const Tensor & indices, const Tensor & offsets, const Tensor & offset2bag, const Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode);
static inline Tensor hinge_embedding_loss(const Tensor & self, const Tensor & target, double margin, bool size_average, bool reduce);
static inline Tensor ger(const Tensor & self, const Tensor & vec2);
static inline Tensor & ger_out(Tensor & result, const Tensor & self, const Tensor & vec2);
static inline Tensor index(const Tensor & self, TensorList indices);
static inline Tensor & index_put_(Tensor & self, TensorList indices, const Tensor & values);
static inline bool is_cuda(const Tensor & self);
static inline bool is_distributed(const Tensor & self);
static inline bool is_floating_point(const Tensor & self);
static inline bool is_nonzero(const Tensor & self);
static inline bool is_same_size(const Tensor & self, const Tensor & other);
static inline bool is_signed(const Tensor & self);
static inline bool is_sparse(const Tensor & self);
static inline Tensor matmul(const Tensor & self, const Tensor & other);
static inline std::tuple<Tensor,Tensor> max_pool1d(const Tensor & self, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false);
static inline Tensor mm(const Tensor & self, const Tensor & mat2);
static inline Tensor & mm_out(Tensor & result, const Tensor & self, const Tensor & mat2);
static inline Tensor mv(const Tensor & self, const Tensor & vec);
static inline Tensor & mv_out(Tensor & result, const Tensor & self, const Tensor & vec);
static inline Tensor narrow(const Tensor & self, int64_t dim, int64_t start, int64_t length);
static inline Tensor pin_memory(const Tensor & self);
static inline Tensor rand_like(const Tensor & self);
static inline Tensor randn_like(const Tensor & self);
static inline Tensor repeat(const Tensor & self, IntList repeats);
static inline std::tuple<Tensor,Tensor> RoiPooling2d_forward(const Tensor & input, const Tensor & rois, int64_t pooledHeight, int64_t pooledWidth, double spatialScale);
static inline Tensor RoiPooling2d_backward(const Tensor & input, const Tensor & rois, int64_t pooledHeight, int64_t pooledWidth, double spatialScale, const Tensor & gradOutput, const Tensor & argmaxes);
static inline Tensor rrelu(const Tensor & self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr);
static inline Tensor & rrelu_(Tensor & self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=false, Generator * generator=nullptr);
static inline Tensor select(const Tensor & self, int64_t dim, int64_t index);
static inline Tensor selu(const Tensor & self);
static inline Tensor & selu_(Tensor & self);
static inline int64_t size(const Tensor & self, int64_t dim);
static inline Tensor slice(const Tensor & self, int64_t dim=0, int64_t start=0, int64_t end=9223372036854775807, int64_t step=1);
static inline std::vector<Tensor> split(const Tensor & self, int64_t split_size, int64_t dim=0);
static inline Tensor squeeze(const Tensor & self);
static inline Tensor squeeze(const Tensor & self, int64_t dim);
static inline Tensor & squeeze_(Tensor & self);
static inline Tensor & squeeze_(Tensor & self, int64_t dim);
static inline Tensor stack(TensorList tensors, int64_t dim=0);
static inline Tensor & stack_out(Tensor & result, TensorList tensors, int64_t dim=0);
static inline Tensor stft(const Tensor & self, int64_t frame_length, int64_t hop, int64_t fft_size, bool return_onesided=true, const Tensor & window={}, int64_t pad_end=0);
static inline int64_t stride(const Tensor & self, int64_t dim);
static inline Tensor & transpose_(Tensor & self, int64_t dim0, int64_t dim1);
static inline Tensor & t_(Tensor & self);
static inline Tensor type_as(const Tensor & self, const Tensor & other);
static inline Tensor unsqueeze(const Tensor & self, int64_t dim);
static inline Tensor & unsqueeze_(Tensor & self, int64_t dim);
static inline Tensor view_as(const Tensor & self, const Tensor & other);
static inline Tensor where(const Tensor & condition, const Tensor & self, const Tensor & other);
static inline Tensor _s_where(const Tensor & condition, const Tensor & self, const Tensor & other);
static inline Tensor _standard_gamma_grad(const Tensor & self, const Tensor & output);
static inline Tensor poisson(const Tensor & self, Generator * generator=nullptr);
static inline Tensor _cudnn_rnn_flatten_weight(TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, bool bidirectional);
static inline std::tuple<Tensor,Tensor,Tensor,Tensor,Tensor> _cudnn_rnn(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntList batch_sizes, const Tensor & dropout_state);
static inline std::tuple<Tensor,Tensor,Tensor,std::vector<Tensor>> _cudnn_rnn_backward(const Tensor & input, TensorList weight, int64_t weight_stride0, const Tensor & weight_buf, const Tensor & hx, const Tensor & cx, const Tensor & output, const Tensor & grad_output, const Tensor & grad_hy, const Tensor & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, IntList batch_sizes, const Tensor & dropout_state, const Tensor & reserve, std::array<bool,4> output_mask);
static inline Type & infer_type(const Tensor & t) {
AT_ASSERT(t.defined(), "undefined Tensor");
return t.type();
}
static inline Type & infer_type(const TensorList & tl) {
AT_ASSERT(tl.size() > 0, "expected a non-empty list of Tensors");
return tl[0].type();
}
// function definitions are all static inline because
// they are one-line statically dispatched functions that
// invoke the actual dynamic dispatch on the correct argument
static inline Tensor & zeros_out(Tensor & result, IntList size) {
return infer_type(result).zeros_out(result, size);
}
static inline Tensor & zeros_like_out(Tensor & result, const Tensor & input) {
return infer_type(result).zeros_like_out(result, input);
}
static inline Tensor zeros_like(const Tensor & input) {
return infer_type(input).zeros_like(input);
}
static inline Tensor & ones_out(Tensor & result, IntList size) {
return infer_type(result).ones_out(result, size);
}
static inline Tensor & ones_like_out(Tensor & result, const Tensor & input) {
return infer_type(result).ones_like_out(result, input);
}
static inline Tensor ones_like(const Tensor & input) {
return infer_type(input).ones_like(input);
}
static inline int64_t numel(const Tensor & self) {
return infer_type(self).numel(self);
}
static inline Tensor & masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask) {
return infer_type(self).masked_select_out(result, self, mask);
}
static inline Tensor masked_select(const Tensor & self, const Tensor & mask) {
return infer_type(self).masked_select(self, mask);
}
static inline Tensor transpose(const Tensor & self, int64_t dim0, int64_t dim1) {
return infer_type(self).transpose(self, dim0, dim1);
}
static inline Tensor t(const Tensor & self) {
return infer_type(self).t(self);
}
static inline Tensor & nonzero_out(Tensor & result, const Tensor & self) {
return infer_type(self).nonzero_out(result, self);
}
static inline Tensor nonzero(const Tensor & self) {
return infer_type(self).nonzero(self);
}
static inline Tensor & index_select_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index) {
return infer_type(self).index_select_out(result, self, dim, index);
}
static inline Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index) {
return infer_type(self).index_select(self, dim, index);
}
static inline Tensor & take_out(Tensor & result, const Tensor & self, const Tensor & index) {
return infer_type(self).take_out(result, self, index);
}
static inline Tensor take(const Tensor & self, const Tensor & index) {
return infer_type(self).take(self, index);
}
static inline Tensor & range_out(Tensor & result, Scalar start, Scalar end, Scalar step) {
return infer_type(result).range_out(result, start, end, step);
}
static inline Tensor & arange_out(Tensor & result, Scalar start, Scalar end, Scalar step) {
return infer_type(result).arange_out(result, start, end, step);
}
static inline Tensor & arange_out(Tensor & result, Scalar end) {
return infer_type(result).arange_out(result, end);
}
static inline Tensor & gather_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index) {
return infer_type(self).gather_out(result, self, dim, index);
}
static inline Tensor gather(const Tensor & self, int64_t dim, const Tensor & index) {
return infer_type(self).gather(self, dim, index);
}
static inline bool equal(const Tensor & self, const Tensor & other) {
return infer_type(self).equal(self, other);
}
static inline Tensor & __and___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__and___out(result, self, other);
}
static inline Tensor __and__(const Tensor & self, Scalar other) {
return infer_type(self).__and__(self, other);
}
static inline Tensor & __and___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__and___out(result, self, other);
}
static inline Tensor __and__(const Tensor & self, const Tensor & other) {
return infer_type(self).__and__(self, other);
}
static inline Tensor & __iand__(Tensor & self, Scalar other) {
return infer_type(self).__iand__(self, other);
}
static inline Tensor & __iand__(Tensor & self, const Tensor & other) {
return infer_type(self).__iand__(self, other);
}
static inline Tensor & __or___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__or___out(result, self, other);
}
static inline Tensor __or__(const Tensor & self, Scalar other) {
return infer_type(self).__or__(self, other);
}
static inline Tensor & __or___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__or___out(result, self, other);
}
static inline Tensor __or__(const Tensor & self, const Tensor & other) {
return infer_type(self).__or__(self, other);
}
static inline Tensor & __ior__(Tensor & self, Scalar other) {
return infer_type(self).__ior__(self, other);
}
static inline Tensor & __ior__(Tensor & self, const Tensor & other) {
return infer_type(self).__ior__(self, other);
}
static inline Tensor & __xor___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__xor___out(result, self, other);
}
static inline Tensor __xor__(const Tensor & self, Scalar other) {
return infer_type(self).__xor__(self, other);
}
static inline Tensor & __xor___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__xor___out(result, self, other);
}
static inline Tensor __xor__(const Tensor & self, const Tensor & other) {
return infer_type(self).__xor__(self, other);
}
static inline Tensor & __ixor__(Tensor & self, Scalar other) {
return infer_type(self).__ixor__(self, other);
}
static inline Tensor & __ixor__(Tensor & self, const Tensor & other) {
return infer_type(self).__ixor__(self, other);
}
static inline Tensor & __lshift___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__lshift___out(result, self, other);
}
static inline Tensor __lshift__(const Tensor & self, Scalar other) {
return infer_type(self).__lshift__(self, other);
}
static inline Tensor & __lshift___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__lshift___out(result, self, other);
}
static inline Tensor __lshift__(const Tensor & self, const Tensor & other) {
return infer_type(self).__lshift__(self, other);
}
static inline Tensor & __ilshift__(Tensor & self, Scalar other) {
return infer_type(self).__ilshift__(self, other);
}
static inline Tensor & __ilshift__(Tensor & self, const Tensor & other) {
return infer_type(self).__ilshift__(self, other);
}
static inline Tensor & __rshift___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__rshift___out(result, self, other);
}
static inline Tensor __rshift__(const Tensor & self, Scalar other) {
return infer_type(self).__rshift__(self, other);
}
static inline Tensor & __rshift___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__rshift___out(result, self, other);
}
static inline Tensor __rshift__(const Tensor & self, const Tensor & other) {
return infer_type(self).__rshift__(self, other);
}
static inline Tensor & __irshift__(Tensor & self, Scalar other) {
return infer_type(self).__irshift__(self, other);
}
static inline Tensor & __irshift__(Tensor & self, const Tensor & other) {
return infer_type(self).__irshift__(self, other);
}
static inline Tensor & lt_out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).lt_out(result, self, other);
}
static inline Tensor lt(const Tensor & self, Scalar other) {
return infer_type(self).lt(self, other);
}
static inline Tensor & lt_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).lt_out(result, self, other);
}
static inline Tensor lt(const Tensor & self, const Tensor & other) {
return infer_type(self).lt(self, other);
}
static inline Tensor & gt_out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).gt_out(result, self, other);
}
static inline Tensor gt(const Tensor & self, Scalar other) {
return infer_type(self).gt(self, other);
}
static inline Tensor & gt_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).gt_out(result, self, other);
}
static inline Tensor gt(const Tensor & self, const Tensor & other) {
return infer_type(self).gt(self, other);
}
static inline Tensor & le_out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).le_out(result, self, other);
}
static inline Tensor le(const Tensor & self, Scalar other) {
return infer_type(self).le(self, other);
}
static inline Tensor & le_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).le_out(result, self, other);
}
static inline Tensor le(const Tensor & self, const Tensor & other) {
return infer_type(self).le(self, other);
}
static inline Tensor & ge_out(Tensor & result, const Tensor & self, Scalar other) {