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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 & copy_out(const Tensor & src, Tensor & dst) {
dst.resize_(src.sizes());
dst.type().copy(src,dst);
return dst;
}
static inline Tensor & zeros_out(IntList size, Tensor & result);
static inline Tensor & ones_out(IntList size, Tensor & result);
static inline int64_t numel(const Tensor & self);
static inline Tensor & masked_select_out(const Tensor & self, const Tensor & mask, Tensor & result);
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 & squeeze_out(const Tensor & self, int64_t dim, Tensor & result);
static inline Tensor squeeze(const Tensor & self, int64_t dim);
static inline Tensor & squeeze_out(const Tensor & self, Tensor & result);
static inline Tensor squeeze(const Tensor & self);
static inline Tensor & unsqueeze_out(const Tensor & self, int64_t dim, Tensor & result);
static inline Tensor unsqueeze(const Tensor & self, int64_t dim);
static inline Tensor & nonzero_out(const Tensor & self, Tensor & result);
static inline Tensor nonzero(const Tensor & self);
static inline Tensor & index_select_out(const Tensor & self, int64_t dim, const Tensor & index, Tensor & result);
static inline Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index);
static inline Tensor & range_out(Scalar start, Scalar end, Scalar step, Tensor & result);
static inline Tensor & range_out(Scalar start, Scalar end, Tensor & result);
static inline Tensor & gather_out(const Tensor & self, int64_t dim, const Tensor & index, Tensor & result);
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(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor __and__(const Tensor & self, Scalar value);
static inline Tensor & __and___out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor __and__(const Tensor & self, const Tensor & other);
static inline Tensor & __iand__(Tensor & self, Scalar value);
static inline Tensor & __iand__(Tensor & self, const Tensor & other);
static inline Tensor & __or___out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor __or__(const Tensor & self, Scalar value);
static inline Tensor & __or___out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor __or__(const Tensor & self, const Tensor & other);
static inline Tensor & __ior__(Tensor & self, Scalar value);
static inline Tensor & __ior__(Tensor & self, const Tensor & other);
static inline Tensor & __xor___out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor __xor__(const Tensor & self, Scalar value);
static inline Tensor & __xor___out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor __xor__(const Tensor & self, const Tensor & other);
static inline Tensor & __ixor__(Tensor & self, Scalar value);
static inline Tensor & __ixor__(Tensor & self, const Tensor & other);
static inline Tensor & __lshift___out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor __lshift__(const Tensor & self, Scalar value);
static inline Tensor & __lshift___out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor __lshift__(const Tensor & self, const Tensor & other);
static inline Tensor & __ilshift__(Tensor & self, Scalar value);
static inline Tensor & __ilshift__(Tensor & self, const Tensor & other);
static inline Tensor & __rshift___out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor __rshift__(const Tensor & self, Scalar value);
static inline Tensor & __rshift___out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor __rshift__(const Tensor & self, const Tensor & other);
static inline Tensor & __irshift__(Tensor & self, Scalar value);
static inline Tensor & __irshift__(Tensor & self, const Tensor & other);
static inline Tensor & lt_out(const Tensor & tensor, Scalar value, Tensor & result);
static inline Tensor lt(const Tensor & tensor, Scalar value);
static inline Tensor & lt_out(const Tensor & tensor, const Tensor & other, Tensor & result);
static inline Tensor lt(const Tensor & tensor, const Tensor & other);
static inline Tensor & gt_out(const Tensor & tensor, Scalar value, Tensor & result);
static inline Tensor gt(const Tensor & tensor, Scalar value);
static inline Tensor & gt_out(const Tensor & tensor, const Tensor & other, Tensor & result);
static inline Tensor gt(const Tensor & tensor, const Tensor & other);
static inline Tensor & le_out(const Tensor & tensor, Scalar value, Tensor & result);
static inline Tensor le(const Tensor & tensor, Scalar value);
static inline Tensor & le_out(const Tensor & tensor, const Tensor & other, Tensor & result);
static inline Tensor le(const Tensor & tensor, const Tensor & other);
static inline Tensor & ge_out(const Tensor & tensor, Scalar value, Tensor & result);
static inline Tensor ge(const Tensor & tensor, Scalar value);
static inline Tensor & ge_out(const Tensor & tensor, const Tensor & other, Tensor & result);
static inline Tensor ge(const Tensor & tensor, const Tensor & other);
static inline Tensor & eq_out(const Tensor & tensor, Scalar value, Tensor & result);
static inline Tensor eq(const Tensor & tensor, Scalar value);
static inline Tensor & eq_out(const Tensor & tensor, const Tensor & other, Tensor & result);
static inline Tensor eq(const Tensor & tensor, const Tensor & other);
static inline Tensor & ne_out(const Tensor & tensor, Scalar value, Tensor & result);
static inline Tensor ne(const Tensor & tensor, Scalar value);
static inline Tensor & ne_out(const Tensor & tensor, const Tensor & other, Tensor & result);
static inline Tensor ne(const Tensor & tensor, const Tensor & other);
static inline std::tuple<Tensor &,Tensor &> min_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & min, Tensor & min_indices);
static inline std::tuple<Tensor,Tensor> min(const Tensor & self, int64_t dim, bool keepdim);
static inline std::tuple<Tensor &,Tensor &> min_out(const Tensor & self, int64_t dim, Tensor & min, Tensor & min_indices);
static inline std::tuple<Tensor,Tensor> min(const Tensor & self, int64_t dim);
static inline Tensor & min_out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor min(const Tensor & self, const Tensor & other);
static inline Scalar min(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> max_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & max, Tensor & max_indices);
static inline std::tuple<Tensor,Tensor> max(const Tensor & self, int64_t dim, bool keepdim);
static inline std::tuple<Tensor &,Tensor &> max_out(const Tensor & self, int64_t dim, Tensor & max, Tensor & max_indices);
static inline std::tuple<Tensor,Tensor> max(const Tensor & self, int64_t dim);
static inline Tensor & max_out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor max(const Tensor & self, const Tensor & other);
static inline Scalar max(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(const Tensor & self, int64_t k, bool keepdim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k, bool keepdim);
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(const Tensor & self, int64_t k, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k);
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(const Tensor & self, int64_t k, int64_t dim, bool keepdim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k, int64_t dim, bool keepdim);
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(const Tensor & self, int64_t k, int64_t dim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k, int64_t dim);
static inline std::tuple<Tensor &,Tensor &> mode_out(const Tensor & self, bool keepdim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self, bool keepdim);
static inline std::tuple<Tensor &,Tensor &> mode_out(const Tensor & self, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> mode_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self, int64_t dim, bool keepdim);
static inline std::tuple<Tensor &,Tensor &> mode_out(const Tensor & self, int64_t dim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self, int64_t dim);
static inline std::tuple<Tensor &,Tensor &> median_out(const Tensor & self, bool keepdim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> median(const Tensor & self, bool keepdim);
static inline std::tuple<Tensor &,Tensor &> median_out(const Tensor & self, int64_t dim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> median(const Tensor & self, int64_t dim);
static inline std::tuple<Tensor &,Tensor &> median_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> median(const Tensor & self, int64_t dim, bool keepdim);
static inline Scalar median(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> sort_out(const Tensor & self, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> sort(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> sort_out(const Tensor & self, int64_t dim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> sort(const Tensor & self, int64_t dim);
static inline std::tuple<Tensor &,Tensor &> sort_out(const Tensor & self, int64_t dim, bool descending, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> sort(const Tensor & self, int64_t dim, bool descending);
static inline std::tuple<Tensor &,Tensor &> topk_out(const Tensor & self, int64_t k, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> topk(const Tensor & self, int64_t k);
static inline std::tuple<Tensor &,Tensor &> topk_out(const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> topk(const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted);
static inline std::tuple<Tensor &,Tensor &> topk_out(const Tensor & self, int64_t k, int64_t dim, bool largest, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> topk(const Tensor & self, int64_t k, int64_t dim, bool largest);
static inline std::tuple<Tensor &,Tensor &> topk_out(const Tensor & self, int64_t k, int64_t dim, Tensor & values, Tensor & indices);
static inline std::tuple<Tensor,Tensor> topk(const Tensor & self, int64_t k, int64_t dim);
static inline Tensor & abs_out(const Tensor & self, Tensor & destination);
static inline Tensor abs(const Tensor & self);
static inline Tensor & sigmoid_out(const Tensor & self, Tensor & result);
static inline Tensor sigmoid(const Tensor & self);
static inline Tensor & log_out(const Tensor & self, Tensor & result);
static inline Tensor log(const Tensor & self);
static inline Tensor & log1p_out(const Tensor & self, Tensor & result);
static inline Tensor log1p(const Tensor & self);
static inline Tensor & lgamma_out(const Tensor & self, Tensor & result);
static inline Tensor lgamma(const Tensor & self);
static inline Tensor & exp_out(const Tensor & self, Tensor & result);
static inline Tensor exp(const Tensor & self);
static inline Tensor & cos_out(const Tensor & self, Tensor & result);
static inline Tensor cos(const Tensor & self);
static inline Tensor & acos_out(const Tensor & self, Tensor & result);
static inline Tensor acos(const Tensor & self);
static inline Tensor & cosh_out(const Tensor & self, Tensor & result);
static inline Tensor cosh(const Tensor & self);
static inline Tensor & sin_out(const Tensor & self, Tensor & result);
static inline Tensor sin(const Tensor & self);
static inline Tensor & asin_out(const Tensor & self, Tensor & result);
static inline Tensor asin(const Tensor & self);
static inline Tensor & sinh_out(const Tensor & self, Tensor & result);
static inline Tensor sinh(const Tensor & self);
static inline Tensor & tan_out(const Tensor & self, Tensor & result);
static inline Tensor tan(const Tensor & self);
static inline Tensor & atan_out(const Tensor & self, Tensor & result);
static inline Tensor atan(const Tensor & self);
static inline Tensor & tanh_out(const Tensor & self, Tensor & result);
static inline Tensor tanh(const Tensor & self);
static inline Tensor & sqrt_out(const Tensor & self, Tensor & result);
static inline Tensor sqrt(const Tensor & self);
static inline Tensor & rsqrt_out(const Tensor & self, Tensor & result);
static inline Tensor rsqrt(const Tensor & self);
static inline Tensor & ceil_out(const Tensor & self, Tensor & result);
static inline Tensor ceil(const Tensor & self);
static inline Tensor & floor_out(const Tensor & self, Tensor & result);
static inline Tensor floor(const Tensor & self);
static inline Tensor & round_out(const Tensor & self, Tensor & result);
static inline Tensor round(const Tensor & self);
static inline Tensor & trunc_out(const Tensor & self, Tensor & result);
static inline Tensor trunc(const Tensor & self);
static inline Tensor & frac_out(const Tensor & self, Tensor & result);
static inline Tensor frac(const Tensor & self);
static inline Tensor & mean_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & destination);
static inline Tensor mean(const Tensor & self, int64_t dim, bool keepdim);
static inline Tensor & mean_out(const Tensor & self, int64_t dim, Tensor & destination);
static inline Tensor mean(const Tensor & self, int64_t dim);
static inline Scalar mean(const Tensor & self);
static inline Tensor & var_out(const Tensor & self, int64_t dim, bool unbiased, bool keepdim, Tensor & destination);
static inline Tensor var(const Tensor & self, int64_t dim, bool unbiased, bool keepdim);
static inline Tensor & var_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & destination);
static inline Tensor var(const Tensor & self, int64_t dim, bool keepdim);
static inline Tensor & var_out(const Tensor & self, int64_t dim, Tensor & destination);
static inline Tensor var(const Tensor & self, int64_t dim);
static inline Scalar var(const Tensor & self, bool unbiased);
static inline Scalar var(const Tensor & self);
static inline Tensor & std_out(const Tensor & self, int64_t dim, bool unbiased, bool keepdim, Tensor & destination);
static inline Tensor std(const Tensor & self, int64_t dim, bool unbiased, bool keepdim);
static inline Tensor & std_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & destination);
static inline Tensor std(const Tensor & self, int64_t dim, bool keepdim);
static inline Tensor & std_out(const Tensor & self, int64_t dim, Tensor & destination);
static inline Tensor std(const Tensor & self, int64_t dim);
static inline Scalar std(const Tensor & self, bool unbiased);
static inline Scalar std(const Tensor & self);
static inline Tensor & norm_out(const Tensor & self, Scalar p, int64_t dim, bool keepdim, Tensor & destination);
static inline Tensor norm(const Tensor & self, Scalar p, int64_t dim, bool keepdim);
static inline Tensor & norm_out(const Tensor & self, Scalar p, int64_t dim, Tensor & destination);
static inline Tensor norm(const Tensor & self, Scalar p, int64_t dim);
static inline Scalar norm(const Tensor & self, Scalar p);
static inline Scalar norm(const Tensor & self);
static inline Tensor & renorm_out(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm, Tensor & destination);
static inline Tensor renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
static inline Scalar dist(const Tensor & self, const Tensor & other, Scalar p);
static inline Scalar dist(const Tensor & self, const Tensor & other);
static inline Tensor & reciprocal_out(const Tensor & self, Tensor & destination);
static inline Tensor reciprocal(const Tensor & self);
static inline Tensor & neg_out(const Tensor & self, Tensor & destination);
static inline Tensor neg(const Tensor & self);
static inline Tensor & atan2_out(const Tensor & self, const Tensor & other, Tensor & destination);
static inline Tensor atan2(const Tensor & self, const Tensor & other);
static inline Tensor & pow_out(const Tensor & self, Scalar exponent, Tensor & destination);
static inline Tensor pow(const Tensor & self, Scalar exponent);
static inline Tensor & pow_out(const Tensor & self, const Tensor & exponent, Tensor & destination);
static inline Tensor pow(const Tensor & self, const Tensor & exponent);
static inline Tensor & lerp_out(const Tensor & self, const Tensor & end, Scalar weight, Tensor & destination);
static inline Tensor lerp(const Tensor & self, const Tensor & end, Scalar weight);
static inline Tensor & linspace_out(Scalar start, Scalar end, int64_t steps, Tensor & result);
static inline Tensor & linspace_out(Scalar start, Scalar end, Tensor & result);
static inline Tensor & logspace_out(Scalar start, Scalar end, int64_t steps, Tensor & result);
static inline Tensor & logspace_out(Scalar start, Scalar end, Tensor & result);
static inline Tensor & histc_out(const Tensor & self, Tensor & destination);
static inline Tensor histc(const Tensor & self);
static inline Tensor & histc_out(const Tensor & self, int64_t bins, Tensor & destination);
static inline Tensor histc(const Tensor & self, int64_t bins);
static inline Tensor & histc_out(const Tensor & self, int64_t bins, Scalar min, Tensor & destination);
static inline Tensor histc(const Tensor & self, int64_t bins, Scalar min);
static inline Tensor & histc_out(const Tensor & self, int64_t bins, Scalar min, Scalar max, Tensor & destination);
static inline Tensor histc(const Tensor & self, int64_t bins, Scalar min, Scalar max);
static inline Tensor & sum_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & result);
static inline Tensor sum(const Tensor & self, int64_t dim, bool keepdim);
static inline Tensor & sum_out(const Tensor & self, int64_t dim, Tensor & result);
static inline Tensor sum(const Tensor & self, int64_t dim);
static inline Scalar sum(const Tensor & self);
static inline Tensor & prod_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & result);
static inline Tensor prod(const Tensor & self, int64_t dim, bool keepdim);
static inline Tensor & prod_out(const Tensor & self, int64_t dim, Tensor & result);
static inline Tensor prod(const Tensor & self, int64_t dim);
static inline Scalar prod(const Tensor & self);
static inline Tensor & cumsum_out(const Tensor & self, int64_t dim, Tensor & result);
static inline Tensor cumsum(const Tensor & self, int64_t dim);
static inline Tensor & cumprod_out(const Tensor & self, int64_t dim, Tensor & result);
static inline Tensor cumprod(const Tensor & self, int64_t dim);
static inline Tensor & sign_out(const Tensor & self, Tensor & result);
static inline Tensor sign(const Tensor & self);
static inline Scalar trace(const Tensor & self);
static inline Tensor & add_out(const Tensor & self, Scalar value, const Tensor & other, Tensor & result);
static inline Tensor add(const Tensor & self, Scalar value, const Tensor & other);
static inline Tensor & add_out(const Tensor & self, Scalar value, SparseTensor other, Tensor & result);
static inline Tensor add(const Tensor & self, Scalar value, SparseTensor other);
static inline Tensor & add_out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor add(const Tensor & self, Scalar value);
static inline Tensor & add_out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor add(const Tensor & self, const Tensor & other);
static inline Tensor & add_out(const Tensor & self, SparseTensor other, Tensor & result);
static inline Tensor add(const Tensor & self, SparseTensor other);
static inline Tensor & sub_out(const Tensor & self, Scalar value, const Tensor & other, Tensor & result);
static inline Tensor sub(const Tensor & self, Scalar value, const Tensor & other);
static inline Tensor & sub_out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor sub(const Tensor & self, Scalar value);
static inline Tensor & sub_out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor sub(const Tensor & self, const Tensor & other);
static inline Tensor & mul_out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor mul(const Tensor & self, Scalar value);
static inline Tensor & mul_out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor mul(const Tensor & self, const Tensor & other);
static inline Tensor & div_out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor div(const Tensor & self, Scalar value);
static inline Tensor & div_out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor div(const Tensor & self, const Tensor & other);
static inline Tensor & fmod_out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor fmod(const Tensor & self, Scalar value);
static inline Tensor & fmod_out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor fmod(const Tensor & self, const Tensor & other);
static inline Tensor & remainder_out(const Tensor & self, Scalar value, Tensor & result);
static inline Tensor remainder(const Tensor & self, Scalar value);
static inline Tensor & remainder_out(const Tensor & self, const Tensor & other, Tensor & result);
static inline Tensor remainder(const Tensor & self, const Tensor & other);
static inline Tensor & clamp_out(const Tensor & self, Scalar min, Scalar max, Tensor & destination);
static inline Tensor clamp(const Tensor & self, Scalar min, Scalar max);
static inline Tensor & clamp_out(const Tensor & self, Scalar min, Tensor & result);
static inline Tensor clamp(const Tensor & self, Scalar min);
static inline Scalar dot(const Tensor & self, const Tensor & tensor);
static inline Tensor & tril_out(const Tensor & self, int64_t diagonal, Tensor & destination);
static inline Tensor tril(const Tensor & self, int64_t diagonal);
static inline Tensor & tril_out(const Tensor & self, Tensor & destination);
static inline Tensor tril(const Tensor & self);
static inline Tensor & triu_out(const Tensor & self, int64_t diagonal, Tensor & destination);
static inline Tensor triu(const Tensor & self, int64_t diagonal);
static inline Tensor & triu_out(const Tensor & self, Tensor & destination);
static inline Tensor triu(const Tensor & self);
static inline Tensor & cross_out(const Tensor & self, const Tensor & other, int64_t dim, Tensor & destination);
static inline Tensor cross(const Tensor & self, const Tensor & other, int64_t dim);
static inline Tensor & cross_out(const Tensor & self, const Tensor & other, Tensor & destination);
static inline Tensor cross(const Tensor & self, const Tensor & other);
static inline Tensor & eye_out(int64_t n, Tensor & result);
static inline Tensor & eye_out(int64_t n, int64_t m, Tensor & result);
static inline Tensor & diag_out(const Tensor & self, int64_t diagonal, Tensor & result);
static inline Tensor diag(const Tensor & self, int64_t diagonal);
static inline Tensor & diag_out(const Tensor & self, Tensor & result);
static inline Tensor diag(const Tensor & self);
static inline Tensor & addmm_out(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & mat1, const Tensor & mat2, Tensor & result);
static inline Tensor addmm(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & mat1, const Tensor & mat2);
static inline Tensor & addmm_out(Scalar beta, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Tensor & result);
static inline Tensor addmm(Scalar beta, const Tensor & self, const Tensor & mat1, const Tensor & mat2);
static inline Tensor & addmm_out(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Tensor & result);
static inline Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2);
static inline Tensor & addmv_out(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & mat, const Tensor & vec, Tensor & result);
static inline Tensor addmv(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & mat, const Tensor & vec);
static inline Tensor & addmv_out(Scalar beta, const Tensor & self, const Tensor & mat, const Tensor & vec, Tensor & result);
static inline Tensor addmv(Scalar beta, const Tensor & self, const Tensor & mat, const Tensor & vec);
static inline Tensor & addmv_out(const Tensor & self, const Tensor & mat, const Tensor & vec, Tensor & result);
static inline Tensor addmv(const Tensor & self, const Tensor & mat, const Tensor & vec);
static inline Tensor & addr_out(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & vec1, const Tensor & vec2, Tensor & result);
static inline Tensor addr(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & vec1, const Tensor & vec2);
static inline Tensor & addr_out(Scalar beta, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Tensor & result);
static inline Tensor addr(Scalar beta, const Tensor & self, const Tensor & vec1, const Tensor & vec2);
static inline Tensor & addr_out(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Tensor & result);
static inline Tensor addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2);
static inline Tensor & ger_out(const Tensor & self, const Tensor & vec2, Tensor & result);
static inline Tensor ger(const Tensor & self, const Tensor & vec2);
static inline Tensor & mv_out(const Tensor & self, const Tensor & vec, Tensor & result);
static inline Tensor mv(const Tensor & self, const Tensor & vec);
static inline Tensor & mm_out(const Tensor & self, const Tensor & mat2, Tensor & result);
static inline Tensor mm(const Tensor & self, const Tensor & mat2);
static inline Tensor & bmm_out(const Tensor & self, const Tensor & mat2, Tensor & result);
static inline Tensor bmm(const Tensor & self, const Tensor & mat2);
static inline Tensor & addbmm_out(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & batch1, const Tensor & batch2, Tensor & result);
static inline Tensor addbmm(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & batch1, const Tensor & batch2);
static inline Tensor & addbmm_out(Scalar beta, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Tensor & result);
static inline Tensor addbmm(Scalar beta, const Tensor & self, const Tensor & batch1, const Tensor & batch2);
static inline Tensor & addbmm_out(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Tensor & result);
static inline Tensor addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2);
static inline Tensor & baddbmm_out(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & batch1, const Tensor & batch2, Tensor & result);
static inline Tensor baddbmm(Scalar beta, const Tensor & self, Scalar alpha, const Tensor & batch1, const Tensor & batch2);
static inline Tensor & baddbmm_out(Scalar beta, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Tensor & result);
static inline Tensor baddbmm(Scalar beta, const Tensor & self, const Tensor & batch1, const Tensor & batch2);
static inline Tensor & baddbmm_out(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Tensor & result);
static inline Tensor baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2);
static inline Tensor & addcmul_out(const Tensor & self, Scalar value, const Tensor & tensor1, const Tensor & tensor2, Tensor & result);
static inline Tensor addcmul(const Tensor & self, Scalar value, const Tensor & tensor1, const Tensor & tensor2);
static inline Tensor & addcmul_out(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Tensor & result);
static inline Tensor addcmul(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2);
static inline Tensor & addcdiv_out(const Tensor & self, Scalar value, const Tensor & tensor1, const Tensor & tensor2, Tensor & result);
static inline Tensor addcdiv(const Tensor & self, Scalar value, const Tensor & tensor1, const Tensor & tensor2);
static inline Tensor & addcdiv_out(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Tensor & result);
static inline Tensor addcdiv(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2);
static inline std::tuple<Tensor &,Tensor &> gesv_out(const Tensor & self, const Tensor & A, Tensor & solution, Tensor & lu);
static inline std::tuple<Tensor,Tensor> gesv(const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor &,Tensor &> gels_out(const Tensor & self, const Tensor & A, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> gels(const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor &,Tensor &> trtrs_out(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> trtrs(const Tensor & self, const Tensor & A, bool upper, bool transpose, bool unitriangular);
static inline std::tuple<Tensor &,Tensor &> trtrs_out(const Tensor & self, const Tensor & A, bool upper, bool transpose, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> trtrs(const Tensor & self, const Tensor & A, bool upper, bool transpose);
static inline std::tuple<Tensor &,Tensor &> trtrs_out(const Tensor & self, const Tensor & A, bool upper, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> trtrs(const Tensor & self, const Tensor & A, bool upper);
static inline std::tuple<Tensor &,Tensor &> trtrs_out(const Tensor & self, const Tensor & A, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> trtrs(const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor &,Tensor &> symeig_out(const Tensor & self, bool eigenvectors, bool upper, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> symeig(const Tensor & self, bool eigenvectors, bool upper);
static inline std::tuple<Tensor &,Tensor &> symeig_out(const Tensor & self, bool eigenvectors, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> symeig(const Tensor & self, bool eigenvectors);
static inline std::tuple<Tensor &,Tensor &> symeig_out(const Tensor & self, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> symeig(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> eig_out(const Tensor & self, bool eigenvectors, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> eig(const Tensor & self, bool eigenvectors);
static inline std::tuple<Tensor &,Tensor &> eig_out(const Tensor & self, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> eig(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &,Tensor &> svd_out(const Tensor & self, bool some, Tensor & res1, Tensor & res2, Tensor & res3);
static inline std::tuple<Tensor,Tensor,Tensor> svd(const Tensor & self, bool some);
static inline std::tuple<Tensor &,Tensor &,Tensor &> svd_out(const Tensor & self, Tensor & res1, Tensor & res2, Tensor & res3);
static inline std::tuple<Tensor,Tensor,Tensor> svd(const Tensor & self);
static inline Tensor & inverse_out(const Tensor & self, Tensor & output);
static inline Tensor inverse(const Tensor & self);
static inline Tensor & potrf_out(const Tensor & self, bool upper, Tensor & output);
static inline Tensor potrf(const Tensor & self, bool upper);
static inline Tensor & potrf_out(const Tensor & self, Tensor & output);
static inline Tensor potrf(const Tensor & self);
static inline Tensor & potrs_out(const Tensor & self, const Tensor & input2, bool upper, Tensor & result);
static inline Tensor potrs(const Tensor & self, const Tensor & input2, bool upper);
static inline Tensor & potrs_out(const Tensor & self, const Tensor & input2, Tensor & result);
static inline Tensor potrs(const Tensor & self, const Tensor & input2);
static inline Tensor & potri_out(const Tensor & self, bool upper, Tensor & output);
static inline Tensor potri(const Tensor & self, bool upper);
static inline Tensor & potri_out(const Tensor & self, Tensor & output);
static inline Tensor potri(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> pstrf_out(const Tensor & self, bool upper, Scalar tol, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> pstrf(const Tensor & self, bool upper, Scalar tol);
static inline std::tuple<Tensor &,Tensor &> pstrf_out(const Tensor & self, bool upper, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> pstrf(const Tensor & self, bool upper);
static inline std::tuple<Tensor &,Tensor &> pstrf_out(const Tensor & self, Scalar tol, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> pstrf(const Tensor & self, Scalar tol);
static inline std::tuple<Tensor &,Tensor &> pstrf_out(const Tensor & self, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> pstrf(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> qr_out(const Tensor & self, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> qr(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> geqrf_out(const Tensor & self, Tensor & res1, Tensor & res2);
static inline std::tuple<Tensor,Tensor> geqrf(const Tensor & self);
static inline std::tuple<Tensor &,const Tensor &> orgqr_out(const Tensor & self, const Tensor & input2, Tensor & result);
static inline std::tuple<Tensor,const Tensor &> orgqr(const Tensor & self, const Tensor & input2);
static inline std::tuple<Tensor &,const Tensor &> ormqr_out(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left, bool transpose, Tensor & result);
static inline std::tuple<Tensor,const Tensor &> ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left, bool transpose);
static inline std::tuple<Tensor &,const Tensor &> ormqr_out(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left, Tensor & result);
static inline std::tuple<Tensor,const Tensor &> ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left);
static inline std::tuple<Tensor &,const Tensor &> ormqr_out(const Tensor & self, const Tensor & input2, const Tensor & input3, Tensor & result);
static inline std::tuple<Tensor,const Tensor &> ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3);
static inline std::tuple<Tensor &,Tensor &> btrifact_out(const Tensor & info, bool pivot, const Tensor & self, Tensor & result, Tensor & pivots);
static inline std::tuple<Tensor,Tensor> btrifact(const Tensor & info, bool pivot, const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> btrifact_out(const Tensor & info, const Tensor & self, Tensor & result, Tensor & pivots);
static inline std::tuple<Tensor,Tensor> btrifact(const Tensor & info, const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> btrifact_out(bool pivot, const Tensor & self, Tensor & result, Tensor & pivots);
static inline std::tuple<Tensor,Tensor> btrifact(bool pivot, const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> btrifact_out(const Tensor & self, Tensor & result, Tensor & pivots);
static inline std::tuple<Tensor,Tensor> btrifact(const Tensor & self);
static inline Tensor & btrisolve_out(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots, Tensor & result);
static inline Tensor btrisolve(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots);
static inline Tensor & randperm_out(Generator & generator, int64_t n, Tensor & result);
static inline Tensor & randperm_out(int64_t n, Tensor & result);
static inline Tensor & multinomial_out(Generator & generator, const Tensor & self, int64_t num_samples, bool replacement, Tensor & result);
static inline Tensor multinomial(Generator & generator, const Tensor & self, int64_t num_samples, bool replacement);
static inline Tensor & multinomial_out(Generator & generator, const Tensor & self, int64_t num_samples, Tensor & result);
static inline Tensor multinomial(Generator & generator, const Tensor & self, int64_t num_samples);
static inline Tensor & multinomial_out(const Tensor & self, int64_t num_samples, bool replacement, Tensor & result);
static inline Tensor multinomial(const Tensor & self, int64_t num_samples, bool replacement);
static inline Tensor & multinomial_out(const Tensor & self, int64_t num_samples, Tensor & result);
static inline Tensor multinomial(const Tensor & self, int64_t num_samples);
static inline Tensor & rand_out(Generator & generator, IntList size, Tensor & result);
static inline Tensor & rand_out(IntList size, Tensor & result);
static inline Tensor & randn_out(Generator & generator, IntList size, Tensor & result);
static inline Tensor & randn_out(IntList size, Tensor & result);
static inline Tensor & select_out(const Tensor & self, int dim, int64_t sliceIndex, Tensor & result);
static inline Tensor select(const Tensor & self, int dim, int64_t sliceIndex);
static inline Tensor & cat_out(TensorList tensors, int dim, Tensor & self);
static inline Tensor cat(TensorList tensors, int dim);
static inline void Abs_updateOutput(const Tensor & input, const Tensor & output);
static inline void Abs_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput);
static inline void AbsCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage);
static inline void AbsCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage);
static inline void BCECriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage, const Tensor & weights);
static inline void BCECriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage);
static inline void BCECriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage, const Tensor & weights);
static inline void BCECriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage);
static inline void ClassNLLCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage, const Tensor & weights, const Tensor & total_weight, int64_t ignore_index);
static inline void ClassNLLCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage, const Tensor & total_weight, int64_t ignore_index);
static inline void ClassNLLCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage, const Tensor & weights, const Tensor & total_weight, int64_t ignore_index);
static inline void ClassNLLCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage, const Tensor & total_weight, int64_t ignore_index);
static inline void SpatialClassNLLCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage, const Tensor & weights, const Tensor & total_weight, int64_t ignore_index);
static inline void SpatialClassNLLCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage, const Tensor & total_weight, int64_t ignore_index);
static inline void SpatialClassNLLCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage, const Tensor & weights, const Tensor & total_weight, int64_t ignore_index);
static inline void SpatialClassNLLCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage, const Tensor & total_weight, int64_t ignore_index);
static inline void ELU_updateOutput(const Tensor & input, const Tensor & output, Scalar alpha, bool inplace);
static inline void ELU_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & output, Scalar alpha, bool inplace);
static inline void DistKLDivCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage);
static inline void DistKLDivCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage);
static inline void GatedLinear_updateOutput(const Tensor & input, const Tensor & output, int dim);
static inline void GatedLinear_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int dim);
static inline void HardShrink_updateOutput(const Tensor & input, const Tensor & output, Scalar lambda);
static inline void HardShrink_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, Scalar lambda);
static inline void HardTanh_updateOutput(const Tensor & input, const Tensor & output, Scalar min_val, Scalar max_val, bool inplace);
static inline void HardTanh_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, Scalar min_val, Scalar max_val, bool inplace);
static inline void L1Cost_updateOutput(const Tensor & input, const Tensor & output);
static inline void L1Cost_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput);
static inline void L1Cost_updateGradInput(const Tensor & input, const Tensor & gradInput);
static inline void LeakyReLU_updateOutput(const Tensor & input, const Tensor & output, Scalar negval, bool inplace);
static inline void LeakyReLU_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, Scalar negval, bool inplace);
static inline void GRUFused_updateOutput(const Tensor & input, const Tensor & hidden, const Tensor & bias1, const Tensor & bias2, const Tensor & hx, const Tensor & output, const Tensor & storage);
static inline void GRUFused_updateOutput(const Tensor & input, const Tensor & hidden, const Tensor & bias1, const Tensor & hx, const Tensor & output, const Tensor & storage);
static inline void GRUFused_updateOutput(const Tensor & input, const Tensor & hidden, const Tensor & hx, const Tensor & output, const Tensor & storage);
static inline void GRUFused_updateGradInput(const Tensor & gradInInput, const Tensor & gradInHidden, const Tensor & gradOutput, const Tensor & gradInputHx, const Tensor & storage);
static inline void LSTMFused_updateOutput(const Tensor & input, const Tensor & hidden, const Tensor & bias1, const Tensor & bias2, const Tensor & cell, const Tensor & output, const Tensor & outputCell);
static inline void LSTMFused_updateOutput(const Tensor & input, const Tensor & hidden, const Tensor & bias1, const Tensor & cell, const Tensor & output, const Tensor & outputCell);
static inline void LSTMFused_updateOutput(const Tensor & input, const Tensor & hidden, const Tensor & cell, const Tensor & output, const Tensor & outputCell);
static inline void LSTMFused_updateGradInput(const Tensor & storage, const Tensor & gradInGates, const Tensor & cx, const Tensor & cy, const Tensor & gradOutput, const Tensor & gradOutputCell, const Tensor & gradInputCx);
static inline void LogSigmoid_updateOutput(const Tensor & input, const Tensor & output, const Tensor & buffer);
static inline void LogSigmoid_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & buffer);
static inline void LogSoftMax_updateOutput(const Tensor & input, const Tensor & output);
static inline void LogSoftMax_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & output);
static inline void MarginCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage, Scalar margin);
static inline void MarginCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage, Scalar margin);
static inline void SoftMarginCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage);
static inline void SoftMarginCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage);
static inline void MSECriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage);
static inline void MSECriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage);
static inline void MultiLabelMarginCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, const Tensor & isTarget, bool sizeAverage);
static inline void MultiLabelMarginCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, const Tensor & isTarget, bool sizeAverage);
static inline void MultiMarginCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage, int p, const Tensor & weights, Scalar margin);
static inline void MultiMarginCriterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage, int p, Scalar margin);
static inline void MultiMarginCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage, int p, const Tensor & weights, Scalar margin);
static inline void MultiMarginCriterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage, int p, Scalar margin);
static inline void PReLU_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, int64_t nOutputPlane);
static inline void PReLU_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, int64_t nOutputPlane);
static inline void PReLU_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & gradWeight, const Tensor & gradWeightBuf, const Tensor & gradWeightBuf2, int64_t nOutputPlane, Scalar scale);
static inline void Linear_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & addBuffer);
static inline void Linear_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight);
static inline void Linear_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & bias, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & addBuffer, Scalar scale);
static inline void RReLU_updateOutput(const Tensor & input, const Tensor & output, const Tensor & noise, Scalar lower, Scalar upper, bool train, bool inplace, Generator & generator);
static inline void RReLU_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & noise, Scalar lower, Scalar upper, bool train, bool inplace);
static inline void Sigmoid_updateOutput(const Tensor & input, const Tensor & output);
static inline void Sigmoid_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & output);
static inline void Sigmoid_updateGradInput(const Tensor & gradOutput, const Tensor & gradInput, const Tensor & output);
static inline void SmoothL1Criterion_updateOutput(const Tensor & input, const Tensor & target, const Tensor & output, bool sizeAverage);
static inline void SmoothL1Criterion_updateGradInput(const Tensor & input, const Tensor & target, const Tensor & gradInput, bool sizeAverage);
static inline void SoftMax_updateOutput(const Tensor & input, const Tensor & output);
static inline void SoftMax_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & output);
static inline void SoftPlus_updateOutput(const Tensor & input, const Tensor & output, Scalar beta, Scalar threshold);
static inline void SoftPlus_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & output, Scalar beta, Scalar threshold);
static inline void SoftShrink_updateOutput(const Tensor & input, const Tensor & output, Scalar lambda);
static inline void SoftShrink_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, Scalar lambda);
static inline void IndexLinear_updateOutput(const Tensor & keys, int64_t keysOffset, const Tensor & values, const Tensor & sizes, const Tensor & cumSumSizes, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & normalizedValues, int train);
static inline void IndexLinear_accGradParameters(const Tensor & keys, int64_t keysOffset, const Tensor & values, const Tensor & sizes, const Tensor & cumSumSizes, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & weight, const Tensor & bias, const Tensor & valuesBuffer, Scalar weightDecay, Scalar scale);
static inline void SparseLinear_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias);
static inline void SparseLinear_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & weight, const Tensor & bias, Scalar weightDecay, Scalar scale);
static inline void Sqrt_updateOutput(const Tensor & input, const Tensor & output, Scalar eps);
static inline void Sqrt_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & output);
static inline void Square_updateOutput(const Tensor & input, const Tensor & output);
static inline void Square_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput);
static inline void Tanh_updateOutput(const Tensor & input, const Tensor & output);
static inline void Tanh_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & output);
static inline void Tanh_updateGradInput(const Tensor & gradOutput, const Tensor & gradInput, const Tensor & output);
static inline void Threshold_updateOutput(const Tensor & input, const Tensor & output, Scalar threshold, Scalar val, bool inplace);
static inline void Threshold_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, Scalar threshold, Scalar val, bool inplace);
static inline void TemporalConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, int kW, int dW, int inputFrameSize, int outputFrameSize);
static inline void TemporalConvolution_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, int kW, int dW);
static inline void TemporalConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, int kW, int dW, Scalar scale);
static inline void TemporalMaxPooling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & indices, int kW, int dW);
static inline void TemporalMaxPooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & indices, int kW, int dW);
static inline void TemporalSubSampling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, int kW, int dW, int inputFrameSize);
static inline void TemporalSubSampling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, int kW, int dW);
static inline void TemporalSubSampling_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, int kW, int dW, Scalar scale);
static inline void TemporalRowConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & finput, const Tensor & fgradInput, int kW, int dW, int padW, bool featFirst);
static inline void TemporalRowConvolution_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int kW, int dW, int padW, bool featFirst);
static inline void TemporalRowConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & finput, const Tensor & fgradInput, int kW, int dW, int padW, bool featFirst, Scalar scale);
static inline void BatchNormalization_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_std, bool train, double momentum, double eps);
static inline void BatchNormalization_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_std, bool train, double momentum, double eps);
static inline void BatchNormalization_updateOutput(const Tensor & input, const Tensor & output, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_std, bool train, double momentum, double eps);
static inline void BatchNormalization_backward(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_std, bool train, double scale, double eps);
static inline void BatchNormalization_backward(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_std, bool train, double scale, double eps);
static inline void BatchNormalization_backward(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & gradWeight, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_std, bool train, double scale, double eps);
static inline void BatchNormalization_backward(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_std, bool train, double scale, double eps);
static inline void BatchNormalization_backward(const Tensor & input, const Tensor & gradOutput, const Tensor & running_mean, const Tensor & running_var, const Tensor & save_mean, const Tensor & save_std, bool train, double scale, double eps);
static inline void SpatialConvolutionMap_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & connTable, int nInputPlane, int nOutputPlane, int dW, int dH);
static inline void SpatialConvolutionMap_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & bias, const Tensor & connTable, int nInputPlane, int nOutputPlane, int dW, int dH);
static inline void SpatialConvolutionMap_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & connTable, int nInputPlane, int nOutputPlane, int dW, int dH, Scalar scale);
static inline void SpatialConvolutionMM_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH);
static inline void SpatialConvolutionMM_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH);
static inline void SpatialConvolutionMM_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH);
static inline void SpatialConvolutionMM_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH, Scalar scale);
static inline void SpatialConvolutionMM_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH, Scalar scale);
static inline void SpatialDepthWiseConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH);
static inline void SpatialDepthWiseConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH);
static inline void SpatialDepthWiseConvolution_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH);
static inline void SpatialDepthWiseConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH, Scalar scale);
static inline void SpatialDepthWiseConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH, Scalar scale);
static inline void SpatialConvolutionLocal_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH, int64_t inputWidth, int64_t inputHeight, int64_t outputWidth, int64_t outputHeight);
static inline void SpatialConvolutionLocal_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH, int64_t inputWidth, int64_t inputHeight, int64_t outputWidth, int64_t outputHeight);
static inline void SpatialConvolutionLocal_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & finput, const Tensor & fgradInput, int kW, int kH, int dW, int dH, int padW, int padH, int64_t inputWidth, int64_t inputHeight, int64_t outputWidth, int64_t outputHeight, Scalar scale);
static inline void SpatialAdaptiveMaxPooling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & indices, int owidth, int oheight);
static inline void SpatialAdaptiveMaxPooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & indices);
static inline void SpatialAdaptiveAveragePooling_updateOutput(const Tensor & input, const Tensor & output, int owidth, int oheight);
static inline void SpatialAdaptiveAveragePooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput);
static inline void SpatialAveragePooling_updateOutput(const Tensor & input, const Tensor & output, int kW, int kH, int dW, int dH, int padW, int padH, bool ceil_mode, bool count_include_pad);
static inline void SpatialAveragePooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int kW, int kH, int dW, int dH, int padW, int padH, bool ceil_mode, bool count_include_pad);
static inline void SpatialFractionalMaxPooling_updateOutput(const Tensor & input, const Tensor & output, int outputW, int outputH, int poolSizeW, int poolSizeH, const Tensor & indices, const Tensor & randomSamples);
static inline void SpatialFractionalMaxPooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int outputW, int outputH, int poolSizeW, int poolSizeH, const Tensor & indices);
static inline void SpatialFullConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & columns, const Tensor & ones, int kW, int kH, int dW, int dH, int padW, int padH, int adjW, int adjH);
static inline void SpatialFullConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & columns, const Tensor & ones, int kW, int kH, int dW, int dH, int padW, int padH, int adjW, int adjH);
static inline void SpatialFullConvolution_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & gradColumns, int kW, int kH, int dW, int dH, int padW, int padH, int adjW, int adjH);
static inline void SpatialFullConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & columns, const Tensor & ones, int kW, int kH, int dW, int dH, int padW, int padH, int adjW, int adjH, Scalar scale);
static inline void SpatialFullConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & columns, const Tensor & ones, int kW, int kH, int dW, int dH, int padW, int padH, int adjW, int adjH, Scalar scale);
static inline void SpatialFullConvolutionMap_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & connTable, int nInputPlane, int nOutputPlane, int dW, int dH);
static inline void SpatialFullConvolutionMap_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & bias, const Tensor & connTable, int nInputPlane, int nOutputPlane, int dW, int dH);
static inline void SpatialFullConvolutionMap_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & connTable, int nInputPlane, int nOutputPlane, int dW, int dH, Scalar scale);
static inline void SpatialDilatedConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & columns, const Tensor & ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH);
static inline void SpatialDilatedConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & columns, const Tensor & ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH);
static inline void SpatialDilatedConvolution_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & gradColumns, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH);
static inline void SpatialDilatedConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & columns, const Tensor & ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, Scalar scale);
static inline void SpatialDilatedConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & columns, const Tensor & ones, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, Scalar scale);
static inline void SpatialMaxPooling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & indices, int kW, int kH, int dW, int dH, int padW, int padH, bool ceil_mode);
static inline void SpatialMaxPooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & indices, int kW, int kH, int dW, int dH, int padW, int padH, bool ceil_mode);
static inline void SpatialDilatedMaxPooling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & indices, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, bool ceil_mode);
static inline void SpatialDilatedMaxPooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & indices, int kW, int kH, int dW, int dH, int padW, int padH, int dilationW, int dilationH, bool ceil_mode);
static inline void SpatialMaxUnpooling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & indices, int owidth, int oheight);
static inline void SpatialMaxUnpooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & indices, int owidth, int oheight);
static inline void SpatialSubSampling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, int kW, int kH, int dW, int dH);
static inline void SpatialSubSampling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, int kW, int kH, int dW, int dH);
static inline void SpatialSubSampling_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, int kW, int kH, int dW, int dH, Scalar scale);
static inline void SpatialUpSamplingNearest_updateOutput(const Tensor & input, const Tensor & output, int scale_factor);
static inline void SpatialUpSamplingNearest_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int scale_factor);
static inline void SpatialUpSamplingBilinear_updateOutput(const Tensor & input, const Tensor & output, int outputHeight, int outputWidth);
static inline void SpatialUpSamplingBilinear_updateGradInput(const Tensor & gradOutput, const Tensor & gradInput, int nbatch, int nchannels, int inputHeight, int inputWidth, int outputHeight, int outputWidth);
static inline void SpatialGridSamplerBilinear_updateOutput(const Tensor & input, const Tensor & grid, const Tensor & output);
static inline void SpatialGridSamplerBilinear_updateGradInput(const Tensor & input, const Tensor & gradInput, const Tensor & grid, const Tensor & gradGrid, const Tensor & gradOutput);
static inline void VolumetricAveragePooling_updateOutput(const Tensor & input, const Tensor & output, int kT, int kW, int kH, int dT, int dW, int dH);
static inline void VolumetricAveragePooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int kT, int kW, int kH, int dT, int dW, int dH);
static inline void VolumetricConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & finput, const Tensor & fgradInput, int dT, int dW, int dH, int pT, int pW, int pH);
static inline void VolumetricConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int dT, int dW, int dH, int pT, int pW, int pH);
static inline void VolumetricConvolution_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & finput, int dT, int dW, int dH, int pT, int pW, int pH);
static inline void VolumetricConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & finput, const Tensor & fgradInput, int dT, int dW, int dH, int pT, int pW, int pH, Scalar scale);
static inline void VolumetricConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & finput, const Tensor & fgradInput, int dT, int dW, int dH, int pT, int pW, int pH, Scalar scale);
static inline void VolumetricConvolutionMM_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & finput, int kT, int kW, int kH, int dT, int dW, int dH, int pT, int pW, int pH);
static inline void VolumetricConvolutionMM_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & finput, int kT, int kW, int kH, int dT, int dW, int dH, int pT, int pW, int pH);
static inline void VolumetricConvolutionMM_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int kT, int kW, int kH, int dT, int dW, int dH, int pT, int pW, int pH);
static inline void VolumetricConvolutionMM_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & finput, int kT, int kW, int kH, int dT, int dW, int dH, int pT, int pW, int pH, Scalar scale);
static inline void VolumetricConvolutionMM_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & finput, int kT, int kW, int kH, int dT, int dW, int dH, int pT, int pW, int pH, Scalar scale);
static inline void VolumetricFractionalMaxPooling_updateOutput(const Tensor & input, const Tensor & output, int outputT, int outputW, int outputH, int poolSizeT, int poolSizeW, int poolSizeH, const Tensor & indices, const Tensor & randomSamples);
static inline void VolumetricFractionalMaxPooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int outputT, int outputW, int outputH, int poolSizeT, int poolSizeW, int poolSizeH, const Tensor & indices);
static inline void VolumetricFullConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & finput, const Tensor & fgradInput, int dT, int dW, int dH, int pT, int pW, int pH, int aT, int aW, int aH);
static inline void VolumetricFullConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int dT, int dW, int dH, int pT, int pW, int pH, int aT, int aW, int aH);
static inline void VolumetricFullConvolution_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & finput, const Tensor & fgradInput, int dT, int dW, int dH, int pT, int pW, int pH, int aT, int aW, int aH);
static inline void VolumetricFullConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & finput, const Tensor & fgradInput, int dT, int dW, int dH, int pT, int pW, int pH, int aT, int aW, int aH, Scalar scale);
static inline void VolumetricFullConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & finput, const Tensor & fgradInput, int dT, int dW, int dH, int pT, int pW, int pH, int aT, int aW, int aH, Scalar scale);
static inline void VolumetricDilatedConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & bias, const Tensor & columns, const Tensor & ones, int kT, int kW, int kH, int dT, int dW, int dH, int padT, int padW, int padH, int dilationT, int dilationW, int dilationH);
static inline void VolumetricDilatedConvolution_updateOutput(const Tensor & input, const Tensor & output, const Tensor & weight, const Tensor & columns, const Tensor & ones, int kT, int kW, int kH, int dT, int dW, int dH, int padT, int padW, int padH, int dilationT, int dilationW, int dilationH);
static inline void VolumetricDilatedConvolution_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & weight, const Tensor & gradColumns, int kT, int kW, int kH, int dT, int dW, int dH, int padT, int padW, int padH, int dilationT, int dilationW, int dilationH);
static inline void VolumetricDilatedConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & gradBias, const Tensor & columns, const Tensor & ones, int kT, int kW, int kH, int dT, int dW, int dH, int padT, int padW, int padH, int dilationT, int dilationW, int dilationH, Scalar scale);
static inline void VolumetricDilatedConvolution_accGradParameters(const Tensor & input, const Tensor & gradOutput, const Tensor & gradWeight, const Tensor & columns, const Tensor & ones, int kT, int kW, int kH, int dT, int dW, int dH, int padT, int padW, int padH, int dilationT, int dilationW, int dilationH, Scalar scale);
static inline void VolumetricMaxPooling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & indices, int kT, int kW, int kH, int dT, int dW, int dH, int pT, int pW, int pH, bool ceilMode);
static inline void VolumetricMaxPooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & indices, int kT, int kW, int kH, int dT, int dW, int dH, int pT, int pW, int pH, bool ceilMode);
static inline void VolumetricDilatedMaxPooling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & indices, int kT, int kW, int kH, int dT, int dW, int dH, int pT, int pW, int pH, int dilationT, int dilationW, int dilationH, bool ceilMode);
static inline void VolumetricDilatedMaxPooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & indices, int kT, int kW, int kH, int dT, int dW, int dH, int pT, int pW, int pH, int dilationT, int dilationW, int dilationH, bool ceilMode);
static inline void VolumetricMaxUnpooling_updateOutput(const Tensor & input, const Tensor & output, const Tensor & indices, int oT, int oW, int oH, int dT, int dW, int dH, int pT, int pW, int pH);
static inline void VolumetricMaxUnpooling_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & indices, int oT, int oW, int oH, int dT, int dW, int dH, int pT, int pW, int pH);
static inline void SpatialReflectionPadding_updateOutput(const Tensor & input, const Tensor & output, int pad_l, int pad_r, int pad_t, int pad_b);
static inline void SpatialReflectionPadding_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int pad_l, int pad_r, int pad_t, int pad_b);
static inline void SpatialReplicationPadding_updateOutput(const Tensor & input, const Tensor & output, int pad_l, int pad_r, int pad_t, int pad_b);
static inline void SpatialReplicationPadding_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int pad_l, int pad_r, int pad_t, int pad_b);
static inline void VolumetricReplicationPadding_updateOutput(const Tensor & input, const Tensor & output, int pleft, int pright, int ptop, int pbottom, int pfront, int pback);
static inline void VolumetricReplicationPadding_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int pleft, int pright, int ptop, int pbottom, int pfront, int pback);
static inline void VolumetricUpSamplingNearest_updateOutput(const Tensor & input, const Tensor & output, int scale_factor);
static inline void VolumetricUpSamplingNearest_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, int scale_factor);
static inline void VolumetricUpSamplingTrilinear_updateOutput(const Tensor & input, const Tensor & output, int outputDepth, int outputHeight, int outputWidth);
static inline void VolumetricUpSamplingTrilinear_updateGradInput(const Tensor & gradOutput, const Tensor & gradInput, int nbatch, int nchannels, int inputDepth, int inputHeight, int inputWidth, int outputDepth, int outputHeight, int outputWidth);
static inline void SpatialCrossMapLRN_updateOutput(const Tensor & input, const Tensor & output, const Tensor & scale, int size, Scalar alpha, Scalar beta, Scalar k);
static inline void SpatialCrossMapLRN_updateGradInput(const Tensor & input, const Tensor & gradOutput, const Tensor & gradInput, const Tensor & scale, const Tensor & output, int size, Scalar alpha, Scalar beta, Scalar k);
static inline Type & infer_type(const Tensor & t) {
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(IntList size, Tensor & result) {
return infer_type(result).zeros_out(size, result);
}
static inline Tensor & ones_out(IntList size, Tensor & result) {
return infer_type(result).ones_out(size, result);
}
static inline int64_t numel(const Tensor & self) {
return infer_type(self).numel(self);
}
static inline Tensor & masked_select_out(const Tensor & self, const Tensor & mask, Tensor & result) {
return infer_type(self).masked_select_out(self, mask, result);
}
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 & squeeze_out(const Tensor & self, int64_t dim, Tensor & result) {
return infer_type(self).squeeze_out(self, dim, result);
}
static inline Tensor squeeze(const Tensor & self, int64_t dim) {
return infer_type(self).squeeze(self, dim);
}
static inline Tensor & squeeze_out(const Tensor & self, Tensor & result) {
return infer_type(self).squeeze_out(self, result);
}
static inline Tensor squeeze(const Tensor & self) {
return infer_type(self).squeeze(self);
}
static inline Tensor & unsqueeze_out(const Tensor & self, int64_t dim, Tensor & result) {
return infer_type(self).unsqueeze_out(self, dim, result);
}
static inline Tensor unsqueeze(const Tensor & self, int64_t dim) {
return infer_type(self).unsqueeze(self, dim);
}
static inline Tensor & nonzero_out(const Tensor & self, Tensor & result) {
return infer_type(self).nonzero_out(self, result);
}
static inline Tensor nonzero(const Tensor & self) {
return infer_type(self).nonzero(self);
}
static inline Tensor & index_select_out(const Tensor & self, int64_t dim, const Tensor & index, Tensor & result) {
return infer_type(self).index_select_out(self, dim, index, result);
}
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 & range_out(Scalar start, Scalar end, Scalar step, Tensor & result) {
return infer_type(result).range_out(start, end, step, result);
}
static inline Tensor & range_out(Scalar start, Scalar end, Tensor & result) {
return infer_type(result).range_out(start, end, result);
}
static inline Tensor & gather_out(const Tensor & self, int64_t dim, const Tensor & index, Tensor & result) {
return infer_type(self).gather_out(self, dim, index, result);
}
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(const Tensor & self, Scalar value, Tensor & result) {
return infer_type(self).__and___out(self, value, result);
}
static inline Tensor __and__(const Tensor & self, Scalar value) {
return infer_type(self).__and__(self, value);
}
static inline Tensor & __and___out(const Tensor & self, const Tensor & other, Tensor & result) {
return infer_type(self).__and___out(self, other, result);
}
static inline Tensor __and__(const Tensor & self, const Tensor & other) {
return infer_type(self).__and__(self, other);
}
static inline Tensor & __iand__(Tensor & self, Scalar value) {
return infer_type(self).__iand__(self, value);
}
static inline Tensor & __iand__(Tensor & self, const Tensor & other) {
return infer_type(self).__iand__(self, other);
}
static inline Tensor & __or___out(const Tensor & self, Scalar value, Tensor & result) {
return infer_type(self).__or___out(self, value, result);
}
static inline Tensor __or__(const Tensor & self, Scalar value) {
return infer_type(self).__or__(self, value);
}
static inline Tensor & __or___out(const Tensor & self, const Tensor & other, Tensor & result) {
return infer_type(self).__or___out(self, other, result);
}
static inline Tensor __or__(const Tensor & self, const Tensor & other) {
return infer_type(self).__or__(self, other);
}
static inline Tensor & __ior__(Tensor & self, Scalar value) {
return infer_type(self).__ior__(self, value);
}
static inline Tensor & __ior__(Tensor & self, const Tensor & other) {
return infer_type(self).__ior__(self, other);
}
static inline Tensor & __xor___out(const Tensor & self, Scalar value, Tensor & result) {
return infer_type(self).__xor___out(self, value, result);
}
static inline Tensor __xor__(const Tensor & self, Scalar value) {
return infer_type(self).__xor__(self, value);
}
static inline Tensor & __xor___out(const Tensor & self, const Tensor & other, Tensor & result) {
return infer_type(self).__xor___out(self, other, result);
}
static inline Tensor __xor__(const Tensor & self, const Tensor & other) {
return infer_type(self).__xor__(self, other);
}
static inline Tensor & __ixor__(Tensor & self, Scalar value) {
return infer_type(self).__ixor__(self, value);
}
static inline Tensor & __ixor__(Tensor & self, const Tensor & other) {
return infer_type(self).__ixor__(self, other);
}
static inline Tensor & __lshift___out(const Tensor & self, Scalar value, Tensor & result) {
return infer_type(self).__lshift___out(self, value, result);
}
static inline Tensor __lshift__(const Tensor & self, Scalar value) {
return infer_type(self).__lshift__(self, value);
}
static inline Tensor & __lshift___out(const Tensor & self, const Tensor & other, Tensor & result) {
return infer_type(self).__lshift___out(self, other, result);
}
static inline Tensor __lshift__(const Tensor & self, const Tensor & other) {
return infer_type(self).__lshift__(self, other);
}
static inline Tensor & __ilshift__(Tensor & self, Scalar value) {
return infer_type(self).__ilshift__(self, value);
}
static inline Tensor & __ilshift__(Tensor & self, const Tensor & other) {
return infer_type(self).__ilshift__(self, other);
}
static inline Tensor & __rshift___out(const Tensor & self, Scalar value, Tensor & result) {
return infer_type(self).__rshift___out(self, value, result);
}
static inline Tensor __rshift__(const Tensor & self, Scalar value) {
return infer_type(self).__rshift__(self, value);
}
static inline Tensor & __rshift___out(const Tensor & self, const Tensor & other, Tensor & result) {
return infer_type(self).__rshift___out(self, other, result);
}
static inline Tensor __rshift__(const Tensor & self, const Tensor & other) {
return infer_type(self).__rshift__(self, other);
}
static inline Tensor & __irshift__(Tensor & self, Scalar value) {
return infer_type(self).__irshift__(self, value);
}
static inline Tensor & __irshift__(Tensor & self, const Tensor & other) {
return infer_type(self).__irshift__(self, other);
}
static inline Tensor & lt_out(const Tensor & tensor, Scalar value, Tensor & result) {
return infer_type(tensor).lt_out(tensor, value, result);
}
static inline Tensor lt(const Tensor & tensor, Scalar value) {
return infer_type(tensor).lt(tensor, value);
}
static inline Tensor & lt_out(const Tensor & tensor, const Tensor & other, Tensor & result) {
return infer_type(tensor).lt_out(tensor, other, result);
}
static inline Tensor lt(const Tensor & tensor, const Tensor & other) {
return infer_type(tensor).lt(tensor, other);
}
static inline Tensor & gt_out(const Tensor & tensor, Scalar value, Tensor & result) {
return infer_type(tensor).gt_out(tensor, value, result);
}
static inline Tensor gt(const Tensor & tensor, Scalar value) {
return infer_type(tensor).gt(tensor, value);
}
static inline Tensor & gt_out(const Tensor & tensor, const Tensor & other, Tensor & result) {
return infer_type(tensor).gt_out(tensor, other, result);
}
static inline Tensor gt(const Tensor & tensor, const Tensor & other) {
return infer_type(tensor).gt(tensor, other);
}
static inline Tensor & le_out(const Tensor & tensor, Scalar value, Tensor & result) {
return infer_type(tensor).le_out(tensor, value, result);
}
static inline Tensor le(const Tensor & tensor, Scalar value) {
return infer_type(tensor).le(tensor, value);
}
static inline Tensor & le_out(const Tensor & tensor, const Tensor & other, Tensor & result) {
return infer_type(tensor).le_out(tensor, other, result);
}
static inline Tensor le(const Tensor & tensor, const Tensor & other) {
return infer_type(tensor).le(tensor, other);
}
static inline Tensor & ge_out(const Tensor & tensor, Scalar value, Tensor & result) {
return infer_type(tensor).ge_out(tensor, value, result);
}
static inline Tensor ge(const Tensor & tensor, Scalar value) {
return infer_type(tensor).ge(tensor, value);
}
static inline Tensor & ge_out(const Tensor & tensor, const Tensor & other, Tensor & result) {
return infer_type(tensor).ge_out(tensor, other, result);
}
static inline Tensor ge(const Tensor & tensor, const Tensor & other) {
return infer_type(tensor).ge(tensor, other);
}
static inline Tensor & eq_out(const Tensor & tensor, Scalar value, Tensor & result) {
return infer_type(tensor).eq_out(tensor, value, result);
}
static inline Tensor eq(const Tensor & tensor, Scalar value) {
return infer_type(tensor).eq(tensor, value);
}
static inline Tensor & eq_out(const Tensor & tensor, const Tensor & other, Tensor & result) {
return infer_type(tensor).eq_out(tensor, other, result);
}
static inline Tensor eq(const Tensor & tensor, const Tensor & other) {
return infer_type(tensor).eq(tensor, other);
}
static inline Tensor & ne_out(const Tensor & tensor, Scalar value, Tensor & result) {
return infer_type(tensor).ne_out(tensor, value, result);
}
static inline Tensor ne(const Tensor & tensor, Scalar value) {
return infer_type(tensor).ne(tensor, value);
}
static inline Tensor & ne_out(const Tensor & tensor, const Tensor & other, Tensor & result) {
return infer_type(tensor).ne_out(tensor, other, result);
}
static inline Tensor ne(const Tensor & tensor, const Tensor & other) {
return infer_type(tensor).ne(tensor, other);
}
static inline std::tuple<Tensor &,Tensor &> min_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & min, Tensor & min_indices) {
return infer_type(self).min_out(self, dim, keepdim, min, min_indices);
}
static inline std::tuple<Tensor,Tensor> min(const Tensor & self, int64_t dim, bool keepdim) {
return infer_type(self).min(self, dim, keepdim);
}
static inline std::tuple<Tensor &,Tensor &> min_out(const Tensor & self, int64_t dim, Tensor & min, Tensor & min_indices) {
return infer_type(self).min_out(self, dim, min, min_indices);
}
static inline std::tuple<Tensor,Tensor> min(const Tensor & self, int64_t dim) {
return infer_type(self).min(self, dim);
}
static inline Tensor & min_out(const Tensor & self, const Tensor & other, Tensor & result) {
return infer_type(self).min_out(self, other, result);
}
static inline Tensor min(const Tensor & self, const Tensor & other) {
return infer_type(self).min(self, other);
}
static inline Scalar min(const Tensor & self) {
return infer_type(self).min(self);
}
static inline std::tuple<Tensor &,Tensor &> max_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & max, Tensor & max_indices) {
return infer_type(self).max_out(self, dim, keepdim, max, max_indices);
}
static inline std::tuple<Tensor,Tensor> max(const Tensor & self, int64_t dim, bool keepdim) {
return infer_type(self).max(self, dim, keepdim);
}
static inline std::tuple<Tensor &,Tensor &> max_out(const Tensor & self, int64_t dim, Tensor & max, Tensor & max_indices) {
return infer_type(self).max_out(self, dim, max, max_indices);
}
static inline std::tuple<Tensor,Tensor> max(const Tensor & self, int64_t dim) {
return infer_type(self).max(self, dim);
}
static inline Tensor & max_out(const Tensor & self, const Tensor & other, Tensor & result) {
return infer_type(self).max_out(self, other, result);
}
static inline Tensor max(const Tensor & self, const Tensor & other) {
return infer_type(self).max(self, other);
}
static inline Scalar max(const Tensor & self) {
return infer_type(self).max(self);
}
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(const Tensor & self, int64_t k, bool keepdim, Tensor & values, Tensor & indices) {
return infer_type(self).kthvalue_out(self, k, keepdim, values, indices);
}
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k, bool keepdim) {
return infer_type(self).kthvalue(self, k, keepdim);
}
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(const Tensor & self, int64_t k, Tensor & values, Tensor & indices) {
return infer_type(self).kthvalue_out(self, k, values, indices);
}
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k) {
return infer_type(self).kthvalue(self, k);
}
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(const Tensor & self, int64_t k, int64_t dim, bool keepdim, Tensor & values, Tensor & indices) {
return infer_type(self).kthvalue_out(self, k, dim, keepdim, values, indices);
}
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k, int64_t dim, bool keepdim) {
return infer_type(self).kthvalue(self, k, dim, keepdim);
}
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(const Tensor & self, int64_t k, int64_t dim, Tensor & values, Tensor & indices) {
return infer_type(self).kthvalue_out(self, k, dim, values, indices);
}
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k, int64_t dim) {
return infer_type(self).kthvalue(self, k, dim);
}
static inline std::tuple<Tensor &,Tensor &> mode_out(const Tensor & self, bool keepdim, Tensor & values, Tensor & indices) {
return infer_type(self).mode_out(self, keepdim, values, indices);
}
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self, bool keepdim) {
return infer_type(self).mode(self, keepdim);
}
static inline std::tuple<Tensor &,Tensor &> mode_out(const Tensor & self, Tensor & values, Tensor & indices) {
return infer_type(self).mode_out(self, values, indices);
}
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self) {
return infer_type(self).mode(self);
}
static inline std::tuple<Tensor &,Tensor &> mode_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & values, Tensor & indices) {
return infer_type(self).mode_out(self, dim, keepdim, values, indices);
}
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self, int64_t dim, bool keepdim) {
return infer_type(self).mode(self, dim, keepdim);
}
static inline std::tuple<Tensor &,Tensor &> mode_out(const Tensor & self, int64_t dim, Tensor & values, Tensor & indices) {
return infer_type(self).mode_out(self, dim, values, indices);
}
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self, int64_t dim) {
return infer_type(self).mode(self, dim);
}
static inline std::tuple<Tensor &,Tensor &> median_out(const Tensor & self, bool keepdim, Tensor & values, Tensor & indices) {
return infer_type(self).median_out(self, keepdim, values, indices);
}
static inline std::tuple<Tensor,Tensor> median(const Tensor & self, bool keepdim) {
return infer_type(self).median(self, keepdim);
}
static inline std::tuple<Tensor &,Tensor &> median_out(const Tensor & self, int64_t dim, Tensor & values, Tensor & indices) {
return infer_type(self).median_out(self, dim, values, indices);
}
static inline std::tuple<Tensor,Tensor> median(const Tensor & self, int64_t dim) {
return infer_type(self).median(self, dim);
}
static inline std::tuple<Tensor &,Tensor &> median_out(const Tensor & self, int64_t dim, bool keepdim, Tensor & values, Tensor & indices) {
return infer_type(self).median_out(self, dim, keepdim, values, indices);
}
static inline std::tuple<Tensor,Tensor> median(const Tensor & self, int64_t dim, bool keepdim) {
return infer_type(self).median(self, dim, keepdim);
}
static inline Scalar median(const Tensor & self) {
return infer_type(self).median(self);
}