forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Functions.h
2208 lines (2200 loc) · 163 KB
/
Functions.h
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#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(Tensor & result, IntList size);
static inline Tensor & zeros_like_out(Tensor & result, const Tensor & input);
static inline Tensor zeros_like(const Tensor & input);
static inline Tensor & ones_out(Tensor & result, IntList size);
static inline Tensor & ones_like_out(Tensor & result, const Tensor & input);
static inline Tensor ones_like(const Tensor & input);
static inline int64_t numel(const Tensor & self);
static inline Tensor & masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask);
static inline Tensor masked_select(const Tensor & self, const Tensor & mask);
static inline Tensor transpose(const Tensor & self, int64_t dim0, int64_t dim1);
static inline Tensor t(const Tensor & self);
static inline Tensor & squeeze_out(Tensor & result, const Tensor & self, int64_t dim);
static inline Tensor squeeze(const Tensor & self, int64_t dim);
static inline Tensor & squeeze_out(Tensor & result, const Tensor & self);
static inline Tensor squeeze(const Tensor & self);
static inline Tensor & unsqueeze_out(Tensor & result, const Tensor & self, int64_t dim);
static inline Tensor unsqueeze(const Tensor & self, int64_t dim);
static inline Tensor & nonzero_out(Tensor & result, const Tensor & self);
static inline Tensor nonzero(const Tensor & self);
static inline Tensor & index_select_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index);
static inline Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index);
static inline Tensor & range_out(Tensor & result, Scalar start, Scalar end, Scalar step=1);
static inline Tensor & arange_out(Tensor & result, Scalar start, Scalar end, Scalar step=1);
static inline Tensor & gather_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index);
static inline Tensor gather(const Tensor & self, int64_t dim, const Tensor & index);
static inline bool equal(const Tensor & self, const Tensor & other);
static inline Tensor & __and___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __and__(const Tensor & self, Scalar other);
static inline Tensor & __and___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __and__(const Tensor & self, const Tensor & other);
static inline Tensor & __iand__(Tensor & self, Scalar other);
static inline Tensor & __iand__(Tensor & self, const Tensor & other);
static inline Tensor & __or___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __or__(const Tensor & self, Scalar other);
static inline Tensor & __or___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __or__(const Tensor & self, const Tensor & other);
static inline Tensor & __ior__(Tensor & self, Scalar other);
static inline Tensor & __ior__(Tensor & self, const Tensor & other);
static inline Tensor & __xor___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __xor__(const Tensor & self, Scalar other);
static inline Tensor & __xor___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __xor__(const Tensor & self, const Tensor & other);
static inline Tensor & __ixor__(Tensor & self, Scalar other);
static inline Tensor & __ixor__(Tensor & self, const Tensor & other);
static inline Tensor & __lshift___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __lshift__(const Tensor & self, Scalar other);
static inline Tensor & __lshift___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __lshift__(const Tensor & self, const Tensor & other);
static inline Tensor & __ilshift__(Tensor & self, Scalar other);
static inline Tensor & __ilshift__(Tensor & self, const Tensor & other);
static inline Tensor & __rshift___out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor __rshift__(const Tensor & self, Scalar other);
static inline Tensor & __rshift___out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor __rshift__(const Tensor & self, const Tensor & other);
static inline Tensor & __irshift__(Tensor & self, Scalar other);
static inline Tensor & __irshift__(Tensor & self, const Tensor & other);
static inline Tensor & lt_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor lt(const Tensor & self, Scalar other);
static inline Tensor & lt_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor lt(const Tensor & self, const Tensor & other);
static inline Tensor & gt_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor gt(const Tensor & self, Scalar other);
static inline Tensor & gt_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor gt(const Tensor & self, const Tensor & other);
static inline Tensor & le_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor le(const Tensor & self, Scalar other);
static inline Tensor & le_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor le(const Tensor & self, const Tensor & other);
static inline Tensor & ge_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor ge(const Tensor & self, Scalar other);
static inline Tensor & ge_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor ge(const Tensor & self, const Tensor & other);
static inline Tensor & eq_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor eq(const Tensor & self, Scalar other);
static inline Tensor & eq_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor eq(const Tensor & self, const Tensor & other);
static inline Tensor & ne_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor ne(const Tensor & self, Scalar other);
static inline Tensor & ne_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor ne(const Tensor & self, const Tensor & other);
static inline std::tuple<Tensor &,Tensor &> min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> min(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor & min_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor min(const Tensor & self, const Tensor & other);
static inline Scalar min(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> max_out(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> max(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor & max_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor max(const Tensor & self, const Tensor & other);
static inline Scalar max(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> kthvalue_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> kthvalue(const Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false);
static inline std::tuple<Tensor &,Tensor &> mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> mode(const Tensor & self, int64_t dim=-1, bool keepdim=false);
static inline std::tuple<Tensor &,Tensor &> median_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim=false);
static inline std::tuple<Tensor,Tensor> median(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Scalar median(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim=-1, bool descending=false);
static inline std::tuple<Tensor,Tensor> sort(const Tensor & self, int64_t dim=-1, bool descending=false);
static inline std::tuple<Tensor &,Tensor &> topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
static inline std::tuple<Tensor,Tensor> topk(const Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
static inline Tensor & abs_out(Tensor & destination, const Tensor & self);
static inline Tensor abs(const Tensor & self);
static inline Tensor & sigmoid_out(Tensor & result, const Tensor & self);
static inline Tensor sigmoid(const Tensor & self);
static inline Tensor & log_out(Tensor & result, const Tensor & self);
static inline Tensor log(const Tensor & self);
static inline Tensor & log1p_out(Tensor & result, const Tensor & self);
static inline Tensor log1p(const Tensor & self);
static inline Tensor & lgamma_out(Tensor & result, const Tensor & self);
static inline Tensor lgamma(const Tensor & self);
static inline Tensor & exp_out(Tensor & result, const Tensor & self);
static inline Tensor exp(const Tensor & self);
static inline Tensor & expm1_out(Tensor & result, const Tensor & self);
static inline Tensor expm1(const Tensor & self);
static inline Tensor & cos_out(Tensor & result, const Tensor & self);
static inline Tensor cos(const Tensor & self);
static inline Tensor & acos_out(Tensor & result, const Tensor & self);
static inline Tensor acos(const Tensor & self);
static inline Tensor & cosh_out(Tensor & result, const Tensor & self);
static inline Tensor cosh(const Tensor & self);
static inline Tensor & sin_out(Tensor & result, const Tensor & self);
static inline Tensor sin(const Tensor & self);
static inline Tensor & asin_out(Tensor & result, const Tensor & self);
static inline Tensor asin(const Tensor & self);
static inline Tensor & sinh_out(Tensor & result, const Tensor & self);
static inline Tensor sinh(const Tensor & self);
static inline Tensor & tan_out(Tensor & result, const Tensor & self);
static inline Tensor tan(const Tensor & self);
static inline Tensor & atan_out(Tensor & result, const Tensor & self);
static inline Tensor atan(const Tensor & self);
static inline Tensor & tanh_out(Tensor & result, const Tensor & self);
static inline Tensor tanh(const Tensor & self);
static inline Tensor & erf_out(Tensor & result, const Tensor & self);
static inline Tensor erf(const Tensor & self);
static inline Tensor & erfinv_out(Tensor & result, const Tensor & self);
static inline Tensor erfinv(const Tensor & self);
static inline Tensor & sqrt_out(Tensor & result, const Tensor & self);
static inline Tensor sqrt(const Tensor & self);
static inline Tensor & rsqrt_out(Tensor & result, const Tensor & self);
static inline Tensor rsqrt(const Tensor & self);
static inline Tensor & ceil_out(Tensor & result, const Tensor & self);
static inline Tensor ceil(const Tensor & self);
static inline Tensor & floor_out(Tensor & result, const Tensor & self);
static inline Tensor floor(const Tensor & self);
static inline Tensor & round_out(Tensor & result, const Tensor & self);
static inline Tensor round(const Tensor & self);
static inline Tensor & trunc_out(Tensor & result, const Tensor & self);
static inline Tensor trunc(const Tensor & self);
static inline Tensor & frac_out(Tensor & result, const Tensor & self);
static inline Tensor frac(const Tensor & self);
static inline Tensor & mean_out(Tensor & destination, const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor mean(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Scalar mean(const Tensor & self);
static inline Tensor & var_out(Tensor & destination, const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false);
static inline Tensor var(const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false);
static inline Scalar var(const Tensor & self, bool unbiased=true);
static inline Tensor & std_out(Tensor & destination, const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false);
static inline Tensor std(const Tensor & self, int64_t dim, bool unbiased=true, bool keepdim=false);
static inline Scalar std(const Tensor & self, bool unbiased=true);
static inline Tensor & norm_out(Tensor & destination, const Tensor & self, Scalar p, int64_t dim, bool keepdim=false);
static inline Tensor norm(const Tensor & self, Scalar p, int64_t dim, bool keepdim=false);
static inline Scalar norm(const Tensor & self, Scalar p=2);
static inline Tensor & renorm_out(Tensor & destination, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
static inline Tensor renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
static inline Scalar dist(const Tensor & self, const Tensor & other, Scalar p=2);
static inline Tensor & reciprocal_out(Tensor & destination, const Tensor & self);
static inline Tensor reciprocal(const Tensor & self);
static inline Tensor & neg_out(Tensor & destination, const Tensor & self);
static inline Tensor neg(const Tensor & self);
static inline Tensor & atan2_out(Tensor & destination, const Tensor & self, const Tensor & other);
static inline Tensor atan2(const Tensor & self, const Tensor & other);
static inline Tensor & pow_out(Tensor & destination, const Tensor & self, Scalar exponent);
static inline Tensor pow(const Tensor & self, Scalar exponent);
static inline Tensor & pow_out(Tensor & destination, const Tensor & self, const Tensor & exponent);
static inline Tensor pow(const Tensor & self, const Tensor & exponent);
static inline Tensor & lerp_out(Tensor & destination, const Tensor & self, const Tensor & end, Scalar weight);
static inline Tensor lerp(const Tensor & self, const Tensor & end, Scalar weight);
static inline Tensor & linspace_out(Tensor & result, Scalar start, Scalar end, int64_t steps=100);
static inline Tensor & logspace_out(Tensor & result, Scalar start, Scalar end, int64_t steps=100);
static inline Tensor & histc_out(Tensor & destination, const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0);
static inline Tensor histc(const Tensor & self, int64_t bins=100, Scalar min=0, Scalar max=0);
static inline Tensor & sum_out(Tensor & result, const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor sum(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Scalar sum(const Tensor & self);
static inline Tensor & prod_out(Tensor & result, const Tensor & self, int64_t dim, bool keepdim=false);
static inline Tensor prod(const Tensor & self, int64_t dim, bool keepdim=false);
static inline Scalar prod(const Tensor & self);
static inline Tensor & cumsum_out(Tensor & result, const Tensor & self, int64_t dim);
static inline Tensor cumsum(const Tensor & self, int64_t dim);
static inline Tensor & cumprod_out(Tensor & result, const Tensor & self, int64_t dim);
static inline Tensor cumprod(const Tensor & self, int64_t dim);
static inline Tensor & sign_out(Tensor & result, const Tensor & self);
static inline Tensor sign(const Tensor & self);
static inline Scalar trace(const Tensor & self);
static inline Tensor & add_out(Tensor & result, const Tensor & self, Scalar other, Scalar alpha=1);
static inline Tensor add(const Tensor & self, Scalar other, Scalar alpha=1);
static inline Tensor & add_out(Tensor & result, const Tensor & self, const Tensor & other, Scalar alpha=1);
static inline Tensor add(const Tensor & self, const Tensor & other, Scalar alpha=1);
static inline Tensor & add_out(Tensor & result, const Tensor & self, SparseTensor other, Scalar alpha=1);
static inline Tensor add(const Tensor & self, SparseTensor other, Scalar alpha=1);
static inline Tensor & sub_out(Tensor & result, const Tensor & self, Scalar other, Scalar alpha=1);
static inline Tensor sub(const Tensor & self, Scalar other, Scalar alpha=1);
static inline Tensor & sub_out(Tensor & result, const Tensor & self, const Tensor & other, Scalar alpha=1);
static inline Tensor sub(const Tensor & self, const Tensor & other, Scalar alpha=1);
static inline Tensor & mul_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor mul(const Tensor & self, Scalar other);
static inline Tensor & mul_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor mul(const Tensor & self, const Tensor & other);
static inline Tensor & div_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor div(const Tensor & self, Scalar other);
static inline Tensor & div_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor div(const Tensor & self, const Tensor & other);
static inline Tensor & fmod_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor fmod(const Tensor & self, Scalar other);
static inline Tensor & fmod_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor fmod(const Tensor & self, const Tensor & other);
static inline Tensor & remainder_out(Tensor & result, const Tensor & self, Scalar other);
static inline Tensor remainder(const Tensor & self, Scalar other);
static inline Tensor & remainder_out(Tensor & result, const Tensor & self, const Tensor & other);
static inline Tensor remainder(const Tensor & self, const Tensor & other);
static inline Tensor & clamp_out(Tensor & destination, const Tensor & self, Scalar min, Scalar max);
static inline Tensor clamp(const Tensor & self, Scalar min, Scalar max);
static inline Tensor & clamp_out(Tensor & result, const Tensor & self, Scalar min);
static inline Tensor clamp(const Tensor & self, Scalar min);
static inline Scalar dot(const Tensor & self, const Tensor & tensor);
static inline Tensor & tril_out(Tensor & destination, const Tensor & self, int64_t diagonal=0);
static inline Tensor tril(const Tensor & self, int64_t diagonal=0);
static inline Tensor & triu_out(Tensor & destination, const Tensor & self, int64_t diagonal=0);
static inline Tensor triu(const Tensor & self, int64_t diagonal=0);
static inline Tensor & cross_out(Tensor & destination, const Tensor & self, const Tensor & other, int64_t dim=-1);
static inline Tensor cross(const Tensor & self, const Tensor & other, int64_t dim=-1);
static inline Tensor & eye_out(Tensor & result, int64_t n, int64_t m=1);
static inline Tensor & diag_out(Tensor & result, const Tensor & self, int64_t diagonal=0);
static inline Tensor diag(const Tensor & self, int64_t diagonal=0);
static inline Tensor & addmm_out(Tensor & result, const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1);
static inline Tensor addmm(const Tensor & self, const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & addmv_out(Tensor & result, const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1);
static inline Tensor addmv(const Tensor & self, const Tensor & mat, const Tensor & vec, Scalar beta=1, Scalar alpha=1);
static inline Tensor & addr_out(Tensor & result, const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1);
static inline Tensor addr(const Tensor & self, const Tensor & vec1, const Tensor & vec2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & ger_out(Tensor & result, const Tensor & self, const Tensor & vec2);
static inline Tensor ger(const Tensor & self, const Tensor & vec2);
static inline Tensor & mv_out(Tensor & result, const Tensor & self, const Tensor & vec);
static inline Tensor mv(const Tensor & self, const Tensor & vec);
static inline Tensor & mm_out(Tensor & result, const Tensor & self, const Tensor & mat2);
static inline Tensor mm(const Tensor & self, const Tensor & mat2);
static inline Tensor & bmm_out(Tensor & result, const Tensor & self, const Tensor & mat2);
static inline Tensor bmm(const Tensor & self, const Tensor & mat2);
static inline Tensor & addbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1);
static inline Tensor addbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & baddbmm_out(Tensor & result, const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1);
static inline Tensor baddbmm(const Tensor & self, const Tensor & batch1, const Tensor & batch2, Scalar beta=1, Scalar alpha=1);
static inline Tensor & addcmul_out(Tensor & result, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1);
static inline Tensor addcmul(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1);
static inline Tensor & addcdiv_out(Tensor & result, const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1);
static inline Tensor addcdiv(const Tensor & self, const Tensor & tensor1, const Tensor & tensor2, Scalar value=1);
static inline std::tuple<Tensor &,Tensor &> gesv_out(Tensor & solution, Tensor & lu, const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor,Tensor> gesv(const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor &,Tensor &> gels_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor,Tensor> gels(const Tensor & self, const Tensor & A);
static inline std::tuple<Tensor &,Tensor &> trtrs_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
static inline std::tuple<Tensor,Tensor> trtrs(const Tensor & self, const Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
static inline std::tuple<Tensor &,Tensor &> symeig_out(Tensor & res1, Tensor & res2, const Tensor & self, bool eigenvectors=false, bool upper=true);
static inline std::tuple<Tensor,Tensor> symeig(const Tensor & self, bool eigenvectors=false, bool upper=true);
static inline std::tuple<Tensor &,Tensor &> eig_out(Tensor & res1, Tensor & res2, const Tensor & self, bool eigenvectors=false);
static inline std::tuple<Tensor,Tensor> eig(const Tensor & self, bool eigenvectors=false);
static inline std::tuple<Tensor &,Tensor &,Tensor &> svd_out(Tensor & res1, Tensor & res2, Tensor & res3, const Tensor & self, bool some=true);
static inline std::tuple<Tensor,Tensor,Tensor> svd(const Tensor & self, bool some=true);
static inline Tensor & inverse_out(Tensor & output, const Tensor & self);
static inline Tensor inverse(const Tensor & self);
static inline Tensor & potrf_out(Tensor & output, const Tensor & self, bool upper=true);
static inline Tensor potrf(const Tensor & self, bool upper=true);
static inline Tensor & potrs_out(Tensor & result, const Tensor & self, const Tensor & input2, bool upper=true);
static inline Tensor potrs(const Tensor & self, const Tensor & input2, bool upper=true);
static inline Tensor & potri_out(Tensor & output, const Tensor & self, bool upper=true);
static inline Tensor potri(const Tensor & self, bool upper=true);
static inline std::tuple<Tensor &,Tensor &> pstrf_out(Tensor & res1, Tensor & res2, const Tensor & self, bool upper=true, Scalar tol=-1);
static inline std::tuple<Tensor,Tensor> pstrf(const Tensor & self, bool upper=true, Scalar tol=-1);
static inline std::tuple<Tensor &,Tensor &> qr_out(Tensor & res1, Tensor & res2, const Tensor & self);
static inline std::tuple<Tensor,Tensor> qr(const Tensor & self);
static inline std::tuple<Tensor &,Tensor &> geqrf_out(Tensor & res1, Tensor & res2, const Tensor & self);
static inline std::tuple<Tensor,Tensor> geqrf(const Tensor & self);
static inline Tensor & orgqr_out(Tensor & result, const Tensor & self, const Tensor & input2);
static inline Tensor orgqr(const Tensor & self, const Tensor & input2);
static inline Tensor & ormqr_out(Tensor & result, const Tensor & self, const Tensor & input2, const Tensor & input3, bool left=true, bool transpose=false);
static inline Tensor ormqr(const Tensor & self, const Tensor & input2, const Tensor & input3, bool left=true, bool transpose=false);
static inline std::tuple<Tensor &,Tensor &> btrifact_out(Tensor & result, Tensor & pivots, const Tensor & self, const Tensor & info={}, bool pivot=true);
static inline std::tuple<Tensor,Tensor> btrifact(const Tensor & self, const Tensor & info={}, bool pivot=true);
static inline Tensor & btrisolve_out(Tensor & result, const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots);
static inline Tensor btrisolve(const Tensor & self, const Tensor & LU_data, const Tensor & LU_pivots);
static inline Tensor & randperm_out(Tensor & result, int64_t n, Generator * generator=nullptr);
static inline Tensor & multinomial_out(Tensor & result, const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr);
static inline Tensor multinomial(const Tensor & self, int64_t num_samples, bool replacement=false, Generator * generator=nullptr);
static inline Tensor & normal_out(Tensor & output, const Tensor & means, double std=1, Generator * generator=nullptr);
static inline Tensor normal(const Tensor & means, double std=1, Generator * generator=nullptr);
static inline Tensor & normal_out(Tensor & output, double mean, const Tensor & std, Generator * generator=nullptr);
static inline Tensor normal(double mean, const Tensor & std, Generator * generator=nullptr);
static inline Tensor & normal_out(Tensor & output, const Tensor & means, const Tensor & std, Generator * generator=nullptr);
static inline Tensor normal(const Tensor & means, const Tensor & std, Generator * generator=nullptr);
static inline Tensor & rand_out(Tensor & result, IntList size, Generator * generator=nullptr);
static inline Tensor & randn_out(Tensor & result, IntList size, Generator * generator=nullptr);
static inline Tensor & select_out(Tensor & result, const Tensor & self, int64_t dim, int64_t sliceIndex);
static inline Tensor select(const Tensor & self, int64_t dim, int64_t sliceIndex);
static inline Tensor & _unnarrow_out(Tensor & result, const Tensor & self, int64_t dimension, int64_t offset, int64_t dimSize);
static inline Tensor _unnarrow(const Tensor & self, int64_t dimension, int64_t offset, int64_t dimSize);
static inline Tensor & cat_out(Tensor & self, TensorList tensors, int64_t dim);
static inline Tensor cat(TensorList tensors, int64_t dim);
static inline Tensor & binary_cross_entropy_out(Tensor & output, const Tensor & input, const Tensor & target, const Tensor & weight={}, bool size_average=true);
static inline Tensor binary_cross_entropy(const Tensor & input, const Tensor & target, const Tensor & weight={}, bool size_average=true);
static inline Tensor & binary_cross_entropy_forward_out(Tensor & output, const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average);
static inline Tensor binary_cross_entropy_forward(const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average);
static inline Tensor & binary_cross_entropy_backward_out(Tensor & grad_input, const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average);
static inline Tensor binary_cross_entropy_backward(const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average);
static inline Tensor & kl_div_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor kl_div(const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor & kl_div_forward_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor kl_div_forward(const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor & kl_div_backward_out(Tensor & grad_input, const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor kl_div_backward(const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor & l1_loss_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor l1_loss(const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor & l1_loss_forward_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor l1_loss_forward(const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor & l1_loss_backward_out(Tensor & grad_input, const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor l1_loss_backward(const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor & mse_loss_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor mse_loss(const Tensor & input, const Tensor & target, bool size_average=true, bool reduce=true);
static inline Tensor & mse_loss_forward_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average, bool reduce);
static inline Tensor mse_loss_forward(const Tensor & input, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & mse_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, const Tensor & target, bool size_average, bool reduce);
static inline Tensor mse_loss_backward(const Tensor & grad_output, const Tensor & input, const Tensor & target, bool size_average, bool reduce);
static inline Tensor & multi_margin_loss_out(Tensor & output, const Tensor & input, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, bool size_average=true);
static inline Tensor multi_margin_loss(const Tensor & input, const Tensor & target, Scalar p=1, Scalar margin=1, const Tensor & weight={}, bool size_average=true);
static inline Tensor & multi_margin_loss_forward_out(Tensor & output, const Tensor & input, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average);
static inline Tensor multi_margin_loss_forward(const Tensor & input, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average);
static inline Tensor & multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & input, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average);
static inline Tensor multi_margin_loss_backward(const Tensor & input, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, bool size_average);
static inline Tensor & multilabel_margin_loss_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor multilabel_margin_loss(const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor & multilabel_margin_loss_forward_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average, const Tensor & is_target);
static inline Tensor multilabel_margin_loss_forward(const Tensor & input, const Tensor & target, bool size_average, const Tensor & is_target);
static inline Tensor & multilabel_margin_loss_backward_out(Tensor & grad_input, const Tensor & input, const Tensor & target, bool size_average, const Tensor & is_target);
static inline Tensor multilabel_margin_loss_backward(const Tensor & input, const Tensor & target, bool size_average, const Tensor & is_target);
static inline Tensor & nll_loss_out(Tensor & output, const Tensor & input, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100);
static inline Tensor nll_loss(const Tensor & input, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100);
static inline Tensor & nll_loss_forward_out(Tensor & output, const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, const Tensor & total_weight);
static inline Tensor nll_loss_forward(const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, const Tensor & total_weight);
static inline Tensor & nll_loss_backward_out(Tensor & grad_input, const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, const Tensor & total_weight);
static inline Tensor nll_loss_backward(const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, const Tensor & total_weight);
static inline Tensor & nll_loss2d_out(Tensor & output, const Tensor & input, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100);
static inline Tensor nll_loss2d(const Tensor & input, const Tensor & target, const Tensor & weight={}, bool size_average=true, int64_t ignore_index=-100);
static inline Tensor & nll_loss2d_forward_out(Tensor & output, const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, const Tensor & total_weight);
static inline Tensor nll_loss2d_forward(const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, const Tensor & total_weight);
static inline Tensor & nll_loss2d_backward_out(Tensor & grad_input, const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, const Tensor & total_weight);
static inline Tensor nll_loss2d_backward(const Tensor & input, const Tensor & target, const Tensor & weight, bool size_average, int64_t ignore_index, const Tensor & total_weight);
static inline Tensor & smooth_l1_loss_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor smooth_l1_loss(const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor & smooth_l1_loss_forward_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor smooth_l1_loss_forward(const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor & smooth_l1_loss_backward_out(Tensor & grad_input, const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor smooth_l1_loss_backward(const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor & soft_margin_loss_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor soft_margin_loss(const Tensor & input, const Tensor & target, bool size_average=true);
static inline Tensor & soft_margin_loss_forward_out(Tensor & output, const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor soft_margin_loss_forward(const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor & soft_margin_loss_backward_out(Tensor & grad_input, const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor soft_margin_loss_backward(const Tensor & input, const Tensor & target, bool size_average);
static inline Tensor & elu_out(Tensor & output, const Tensor & input, Scalar alpha=1, bool inplace=false);
static inline Tensor elu(const Tensor & input, Scalar alpha=1, bool inplace=false);
static inline Tensor & elu_forward_out(Tensor & output, const Tensor & input, Scalar alpha, bool inplace);
static inline Tensor elu_forward(const Tensor & input, Scalar alpha, bool inplace);
static inline Tensor & elu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, Scalar alpha, bool inplace, const Tensor & output);
static inline Tensor elu_backward(const Tensor & grad_output, const Tensor & input, Scalar alpha, bool inplace, const Tensor & output);
static inline Tensor & glu_out(Tensor & output, const Tensor & input, int64_t dim=-1);
static inline Tensor glu(const Tensor & input, int64_t dim=-1);
static inline Tensor & glu_forward_out(Tensor & output, const Tensor & input, int64_t dim);
static inline Tensor glu_forward(const Tensor & input, int64_t dim);
static inline Tensor & glu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, int64_t dim);
static inline Tensor glu_backward(const Tensor & grad_output, const Tensor & input, int64_t dim);
static inline Tensor & hardshrink_out(Tensor & output, const Tensor & input, Scalar lambd=0.5);
static inline Tensor hardshrink(const Tensor & input, Scalar lambd=0.5);
static inline Tensor & hardshrink_forward_out(Tensor & output, const Tensor & input, Scalar lambd);
static inline Tensor hardshrink_forward(const Tensor & input, Scalar lambd);
static inline Tensor & hardshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, Scalar lambd);
static inline Tensor hardshrink_backward(const Tensor & grad_output, const Tensor & input, Scalar lambd);
static inline Tensor & hardtanh_out(Tensor & output, const Tensor & input, Scalar min_val=-1, Scalar max_val=1, bool inplace=false);
static inline Tensor hardtanh(const Tensor & input, Scalar min_val=-1, Scalar max_val=1, bool inplace=false);
static inline Tensor & hardtanh_forward_out(Tensor & output, const Tensor & input, Scalar min_val, Scalar max_val, bool inplace);
static inline Tensor hardtanh_forward(const Tensor & input, Scalar min_val, Scalar max_val, bool inplace);
static inline Tensor & hardtanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, Scalar min_val, Scalar max_val, bool inplace);
static inline Tensor hardtanh_backward(const Tensor & grad_output, const Tensor & input, Scalar min_val, Scalar max_val, bool inplace);
static inline Tensor & leaky_relu_out(Tensor & output, const Tensor & input, Scalar negative_slope=0.01, bool inplace=false);
static inline Tensor leaky_relu(const Tensor & input, Scalar negative_slope=0.01, bool inplace=false);
static inline Tensor & leaky_relu_forward_out(Tensor & output, const Tensor & input, Scalar negative_slope, bool inplace);
static inline Tensor leaky_relu_forward(const Tensor & input, Scalar negative_slope, bool inplace);
static inline Tensor & leaky_relu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, Scalar negative_slope, bool inplace);
static inline Tensor leaky_relu_backward(const Tensor & grad_output, const Tensor & input, Scalar negative_slope, bool inplace);
static inline Tensor & log_sigmoid_out(Tensor & output, const Tensor & input);
static inline Tensor log_sigmoid(const Tensor & input);
static inline Tensor & log_sigmoid_forward_out(Tensor & output, const Tensor & input, const Tensor & buffer);
static inline Tensor log_sigmoid_forward(const Tensor & input, const Tensor & buffer);
static inline Tensor & log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, const Tensor & buffer);
static inline Tensor log_sigmoid_backward(const Tensor & grad_output, const Tensor & input, const Tensor & buffer);
static inline Tensor & log_softmax_out(Tensor & output, const Tensor & input, int64_t dim);
static inline Tensor log_softmax(const Tensor & input, int64_t dim);
static inline Tensor & log_softmax_forward_out(Tensor & output, const Tensor & input);
static inline Tensor log_softmax_forward(const Tensor & input);
static inline Tensor & log_softmax_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, const Tensor & output);
static inline Tensor log_softmax_backward(const Tensor & grad_output, const Tensor & input, const Tensor & output);
static inline Tensor & prelu_out(Tensor & output, const Tensor & input, const Tensor & weight);
static inline Tensor prelu(const Tensor & input, const Tensor & weight);
static inline Tensor & prelu_forward_out(Tensor & output, const Tensor & input, const Tensor & weight);
static inline Tensor prelu_forward(const Tensor & input, const Tensor & weight);
static inline std::tuple<Tensor &,Tensor &> prelu_backward_out(Tensor & grad_input, Tensor & grad_weight, const Tensor & grad_output, const Tensor & input, const Tensor & weight);
static inline std::tuple<Tensor,Tensor> prelu_backward(const Tensor & grad_output, const Tensor & input, const Tensor & weight, std::array<bool, 2> output_mask={true, true});
static inline Tensor & rrelu_out(Tensor & output, const Tensor & input, Scalar lower=0.125, Scalar upper=0.333333333333, bool training=false, bool inplace=false, Generator * generator=nullptr);
static inline Tensor rrelu(const Tensor & input, Scalar lower=0.125, Scalar upper=0.333333333333, bool training=false, bool inplace=false, Generator * generator=nullptr);
static inline Tensor & rrelu_forward_out(Tensor & output, const Tensor & input, Scalar lower, Scalar upper, bool training, bool inplace, Generator * generator, const Tensor & noise);
static inline Tensor rrelu_forward(const Tensor & input, Scalar lower, Scalar upper, bool training, bool inplace, Generator * generator, const Tensor & noise);
static inline Tensor & rrelu_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, Scalar lower, Scalar upper, bool training, bool inplace, const Tensor & noise);
static inline Tensor rrelu_backward(const Tensor & grad_output, const Tensor & input, Scalar lower, Scalar upper, bool training, bool inplace, const Tensor & noise);
static inline Tensor & softmax_out(Tensor & output, const Tensor & input, int64_t dim);
static inline Tensor softmax(const Tensor & input, int64_t dim);
static inline Tensor & softmax_forward_out(Tensor & output, const Tensor & input);
static inline Tensor softmax_forward(const Tensor & input);
static inline Tensor & softmax_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, const Tensor & output);
static inline Tensor softmax_backward(const Tensor & grad_output, const Tensor & input, const Tensor & output);
static inline Tensor & softplus_out(Tensor & output, const Tensor & input, Scalar beta=1, Scalar threshold=20);
static inline Tensor softplus(const Tensor & input, Scalar beta=1, Scalar threshold=20);
static inline Tensor & softplus_forward_out(Tensor & output, const Tensor & input, Scalar beta, Scalar threshold);
static inline Tensor softplus_forward(const Tensor & input, Scalar beta, Scalar threshold);
static inline Tensor & softplus_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, Scalar beta, Scalar threshold, const Tensor & output);
static inline Tensor softplus_backward(const Tensor & grad_output, const Tensor & input, Scalar beta, Scalar threshold, const Tensor & output);
static inline Tensor & softshrink_out(Tensor & output, const Tensor & input, Scalar lambd=0.5);
static inline Tensor softshrink(const Tensor & input, Scalar lambd=0.5);
static inline Tensor & softshrink_forward_out(Tensor & output, const Tensor & input, Scalar lambd);
static inline Tensor softshrink_forward(const Tensor & input, Scalar lambd);
static inline Tensor & softshrink_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, Scalar lambd);
static inline Tensor softshrink_backward(const Tensor & grad_output, const Tensor & input, Scalar lambd);
static inline Tensor & threshold_out(Tensor & output, const Tensor & input, Scalar threshold, Scalar value, bool inplace=false);
static inline Tensor threshold(const Tensor & input, Scalar threshold, Scalar value, bool inplace=false);
static inline Tensor & threshold_forward_out(Tensor & output, const Tensor & input, Scalar threshold, Scalar value, bool inplace);
static inline Tensor threshold_forward(const Tensor & input, Scalar threshold, Scalar value, bool inplace);
static inline Tensor & threshold_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, Scalar threshold, Scalar value, bool inplace);
static inline Tensor threshold_backward(const Tensor & grad_output, const Tensor & input, Scalar threshold, Scalar value, bool inplace);
static inline std::tuple<Tensor &,Tensor &> adaptive_max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & input, IntList output_size);
static inline std::tuple<Tensor,Tensor> adaptive_max_pool2d(const Tensor & input, IntList output_size);
static inline std::tuple<Tensor &,Tensor &> adaptive_max_pool2d_forward_out(Tensor & output, Tensor & indices, const Tensor & input, IntList output_size);
static inline std::tuple<Tensor,Tensor> adaptive_max_pool2d_forward(const Tensor & input, IntList output_size);
static inline Tensor & adaptive_max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, const Tensor & indices);
static inline Tensor adaptive_max_pool2d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & indices);
static inline Tensor & avg_pool2d_out(Tensor & output, const Tensor & input, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false);
static inline Tensor avg_pool2d(const Tensor & input, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false);
static inline Tensor & avg_pool2d_forward_out(Tensor & output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor avg_pool2d_forward(const Tensor & input, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor & avg_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor avg_pool2d_backward(const Tensor & grad_output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor & avg_pool3d_out(Tensor & output, const Tensor & input, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false);
static inline Tensor avg_pool3d(const Tensor & input, IntList kernel_size, IntList stride={}, IntList padding=0, bool ceil_mode=false, bool count_include_pad=false);
static inline Tensor & avg_pool3d_forward_out(Tensor & output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor avg_pool3d_forward(const Tensor & input, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor & avg_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline Tensor avg_pool3d_backward(const Tensor & grad_output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, bool ceil_mode, bool count_include_pad);
static inline std::tuple<Tensor &,Tensor &> max_pool2d_out(Tensor & output, Tensor & indices, const Tensor & input, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false);
static inline std::tuple<Tensor,Tensor> max_pool2d(const Tensor & input, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false);
static inline std::tuple<Tensor &,Tensor &> max_pool2d_forward_out(Tensor & output, Tensor & indices, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode);
static inline std::tuple<Tensor,Tensor> max_pool2d_forward(const Tensor & input, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode);
static inline Tensor & max_pool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices);
static inline Tensor max_pool2d_backward(const Tensor & grad_output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices);
static inline std::tuple<Tensor &,Tensor &> max_pool3d_out(Tensor & output, Tensor & indices, const Tensor & input, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false);
static inline std::tuple<Tensor,Tensor> max_pool3d(const Tensor & input, IntList kernel_size, IntList stride={}, IntList padding=0, IntList dilation=1, bool ceil_mode=false);
static inline std::tuple<Tensor &,Tensor &> max_pool3d_forward_out(Tensor & output, Tensor & indices, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode);
static inline std::tuple<Tensor,Tensor> max_pool3d_forward(const Tensor & input, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode);
static inline Tensor & max_pool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices);
static inline Tensor max_pool3d_backward(const Tensor & grad_output, const Tensor & input, IntList kernel_size, IntList stride, IntList padding, IntList dilation, bool ceil_mode, const Tensor & indices);
static inline Tensor & max_unpool2d_out(Tensor & output, const Tensor & input, const Tensor & indices, IntList output_size);
static inline Tensor max_unpool2d(const Tensor & input, const Tensor & indices, IntList output_size);
static inline Tensor & max_unpool2d_forward_out(Tensor & output, const Tensor & input, const Tensor & indices, IntList output_size);
static inline Tensor max_unpool2d_forward(const Tensor & input, const Tensor & indices, IntList output_size);
static inline Tensor & max_unpool2d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, const Tensor & indices, IntList output_size);
static inline Tensor max_unpool2d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & indices, IntList output_size);
static inline Tensor & max_unpool3d_out(Tensor & output, const Tensor & input, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor max_unpool3d(const Tensor & input, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor & max_unpool3d_forward_out(Tensor & output, const Tensor & input, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor max_unpool3d_forward(const Tensor & input, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor & max_unpool3d_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & input, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor max_unpool3d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & indices, IntList output_size, IntList stride, IntList padding);
static inline Tensor & _sigmoid_out(Tensor & output, const Tensor & input);
static inline Tensor _sigmoid(const Tensor & input);
static inline Tensor & _sigmoid_forward_out(Tensor & output, const Tensor & input);
static inline Tensor _sigmoid_forward(const Tensor & input);
static inline Tensor & _sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output);
static inline Tensor _sigmoid_backward(const Tensor & grad_output, const Tensor & output);
static inline Tensor & _tanh_out(Tensor & output, const Tensor & input);
static inline Tensor _tanh(const Tensor & input);
static inline Tensor & _tanh_forward_out(Tensor & output, const Tensor & input);
static inline Tensor _tanh_forward(const Tensor & input);
static inline Tensor & _tanh_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & output);
static inline Tensor _tanh_backward(const Tensor & grad_output, const Tensor & output);
static inline Tensor & batch_norm_out(Tensor & output, const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps);
static inline Tensor batch_norm(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps);
static inline Tensor & batch_norm_forward_out(Tensor & output, const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, const Tensor & save_mean, const Tensor & save_std);
static inline Tensor batch_norm_forward(const Tensor & input, const Tensor & weight, const Tensor & bias, const Tensor & running_mean, const Tensor & running_var, bool training, double momentum, double eps, const Tensor & save_mean, const Tensor & save_std);
static inline std::tuple<Tensor &,Tensor &,Tensor &> batch_norm_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & input, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, bool training, double eps, const Tensor & save_mean, const Tensor & save_std);
static inline std::tuple<Tensor,Tensor,Tensor> batch_norm_backward(const Tensor & grad_output, const Tensor & input, const Tensor & weight, const Tensor & running_mean, const Tensor & running_var, bool training, double eps, const Tensor & save_mean, const Tensor & save_std, std::array<bool, 3> output_mask={true, true, true});
static inline Tensor & conv_transpose2d_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1);
static inline Tensor conv_transpose2d(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1);
static inline Tensor & conv_transpose2d_forward_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline Tensor conv_transpose2d_forward(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline std::tuple<Tensor &,Tensor &,Tensor &> conv_transpose2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline std::tuple<Tensor,Tensor,Tensor> conv_transpose2d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & columns, const Tensor & ones, std::array<bool, 3> output_mask={true, true, true});
static inline Tensor & conv_transpose3d_out(Tensor & output, const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1);
static inline Tensor conv_transpose3d(const Tensor & input, const Tensor & weight, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList output_padding=0, IntList dilation=1);
static inline Tensor & conv_transpose3d_forward_out(Tensor & output, const Tensor & input, const Tensor & weight, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & finput, const Tensor & fgrad_input);
static inline Tensor conv_transpose3d_forward(const Tensor & input, const Tensor & weight, const Tensor & bias, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & finput, const Tensor & fgrad_input);
static inline std::tuple<Tensor &,Tensor &,Tensor &> conv_transpose3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & finput, const Tensor & fgrad_input);
static inline std::tuple<Tensor,Tensor,Tensor> conv_transpose3d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList stride, IntList padding, IntList output_padding, IntList dilation, const Tensor & finput, const Tensor & fgrad_input, std::array<bool, 3> output_mask={true, true, true});
static inline Tensor & conv2d_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0);
static inline Tensor conv2d(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0);
static inline Tensor & conv2d_forward_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input);
static inline Tensor conv2d_forward(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input);
static inline std::tuple<Tensor &,Tensor &,Tensor &> conv2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input);
static inline std::tuple<Tensor,Tensor,Tensor> conv2d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input, std::array<bool, 3> output_mask={true, true, true});
static inline Tensor & conv3d_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0);
static inline Tensor conv3d(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0);
static inline Tensor & conv3d_forward_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, const Tensor & finput);
static inline Tensor conv3d_forward(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, const Tensor & finput);
static inline std::tuple<Tensor &,Tensor &,Tensor &> conv3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input);
static inline std::tuple<Tensor,Tensor,Tensor> conv3d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, const Tensor & finput, const Tensor & fgrad_input, std::array<bool, 3> output_mask={true, true, true});
static inline Tensor & conv_dilated2d_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline Tensor conv_dilated2d(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline Tensor & conv_dilated2d_forward_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline Tensor conv_dilated2d_forward(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline std::tuple<Tensor &,Tensor &,Tensor &> conv_dilated2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline std::tuple<Tensor,Tensor,Tensor> conv_dilated2d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones, std::array<bool, 3> output_mask={true, true, true});
static inline Tensor & conv_dilated3d_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline Tensor conv_dilated3d(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias={}, IntList stride=1, IntList padding=0, IntList dilation=1);
static inline Tensor & conv_dilated3d_forward_out(Tensor & output, const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline Tensor conv_dilated3d_forward(const Tensor & input, const Tensor & weight, IntList kernel_size, const Tensor & bias, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline std::tuple<Tensor &,Tensor &,Tensor &> conv_dilated3d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones);
static inline std::tuple<Tensor,Tensor,Tensor> conv_dilated3d_backward(const Tensor & grad_output, const Tensor & input, const Tensor & weight, IntList kernel_size, IntList stride, IntList padding, IntList dilation, const Tensor & columns, const Tensor & ones, std::array<bool, 3> output_mask={true, true, true});
static inline std::vector<Tensor> split(Tensor self, int64_t split_size, int64_t dim=0);
static inline std::vector<Tensor> chunk(Tensor self, int64_t chunks, int64_t dim=0);
static inline Type & infer_type(const Tensor & t) {
AT_ASSERT(t.defined(), "undefined Tensor");
return t.type();
}
static inline Type & infer_type(const TensorList & tl) {
AT_ASSERT(tl.size() > 0, "expected a non-empty list of Tensors");
return tl[0].type();
}
// function definitions are all static inline because
// they are one-line statically dispatched functions that
// invoke the actual dynamic dispatch on the correct argument
static inline Tensor & zeros_out(Tensor & result, IntList size) {
return infer_type(result).zeros_out(result, size);
}
static inline Tensor & zeros_like_out(Tensor & result, const Tensor & input) {
return infer_type(result).zeros_like_out(result, input);
}
static inline Tensor zeros_like(const Tensor & input) {
return infer_type(input).zeros_like(input);
}
static inline Tensor & ones_out(Tensor & result, IntList size) {
return infer_type(result).ones_out(result, size);
}
static inline Tensor & ones_like_out(Tensor & result, const Tensor & input) {
return infer_type(result).ones_like_out(result, input);
}
static inline Tensor ones_like(const Tensor & input) {
return infer_type(input).ones_like(input);
}
static inline int64_t numel(const Tensor & self) {
return infer_type(self).numel(self);
}
static inline Tensor & masked_select_out(Tensor & result, const Tensor & self, const Tensor & mask) {
return infer_type(self).masked_select_out(result, self, mask);
}
static inline Tensor masked_select(const Tensor & self, const Tensor & mask) {
return infer_type(self).masked_select(self, mask);
}
static inline Tensor transpose(const Tensor & self, int64_t dim0, int64_t dim1) {
return infer_type(self).transpose(self, dim0, dim1);
}
static inline Tensor t(const Tensor & self) {
return infer_type(self).t(self);
}
static inline Tensor & squeeze_out(Tensor & result, const Tensor & self, int64_t dim) {
return infer_type(self).squeeze_out(result, self, dim);
}
static inline Tensor squeeze(const Tensor & self, int64_t dim) {
return infer_type(self).squeeze(self, dim);
}
static inline Tensor & squeeze_out(Tensor & result, const Tensor & self) {
return infer_type(self).squeeze_out(result, self);
}
static inline Tensor squeeze(const Tensor & self) {
return infer_type(self).squeeze(self);
}
static inline Tensor & unsqueeze_out(Tensor & result, const Tensor & self, int64_t dim) {
return infer_type(self).unsqueeze_out(result, self, dim);
}
static inline Tensor unsqueeze(const Tensor & self, int64_t dim) {
return infer_type(self).unsqueeze(self, dim);
}
static inline Tensor & nonzero_out(Tensor & result, const Tensor & self) {
return infer_type(self).nonzero_out(result, self);
}
static inline Tensor nonzero(const Tensor & self) {
return infer_type(self).nonzero(self);
}
static inline Tensor & index_select_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index) {
return infer_type(self).index_select_out(result, self, dim, index);
}
static inline Tensor index_select(const Tensor & self, int64_t dim, const Tensor & index) {
return infer_type(self).index_select(self, dim, index);
}
static inline Tensor & range_out(Tensor & result, Scalar start, Scalar end, Scalar step) {
return infer_type(result).range_out(result, start, end, step);
}
static inline Tensor & arange_out(Tensor & result, Scalar start, Scalar end, Scalar step) {
return infer_type(result).arange_out(result, start, end, step);
}
static inline Tensor & gather_out(Tensor & result, const Tensor & self, int64_t dim, const Tensor & index) {
return infer_type(self).gather_out(result, self, dim, index);
}
static inline Tensor gather(const Tensor & self, int64_t dim, const Tensor & index) {
return infer_type(self).gather(self, dim, index);
}
static inline bool equal(const Tensor & self, const Tensor & other) {
return infer_type(self).equal(self, other);
}
static inline Tensor & __and___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__and___out(result, self, other);
}
static inline Tensor __and__(const Tensor & self, Scalar other) {
return infer_type(self).__and__(self, other);
}
static inline Tensor & __and___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__and___out(result, self, other);
}
static inline Tensor __and__(const Tensor & self, const Tensor & other) {
return infer_type(self).__and__(self, other);
}
static inline Tensor & __iand__(Tensor & self, Scalar other) {
return infer_type(self).__iand__(self, other);
}
static inline Tensor & __iand__(Tensor & self, const Tensor & other) {
return infer_type(self).__iand__(self, other);
}
static inline Tensor & __or___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__or___out(result, self, other);
}
static inline Tensor __or__(const Tensor & self, Scalar other) {
return infer_type(self).__or__(self, other);
}
static inline Tensor & __or___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__or___out(result, self, other);
}
static inline Tensor __or__(const Tensor & self, const Tensor & other) {
return infer_type(self).__or__(self, other);
}
static inline Tensor & __ior__(Tensor & self, Scalar other) {
return infer_type(self).__ior__(self, other);
}
static inline Tensor & __ior__(Tensor & self, const Tensor & other) {
return infer_type(self).__ior__(self, other);
}
static inline Tensor & __xor___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__xor___out(result, self, other);
}
static inline Tensor __xor__(const Tensor & self, Scalar other) {
return infer_type(self).__xor__(self, other);
}
static inline Tensor & __xor___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__xor___out(result, self, other);
}
static inline Tensor __xor__(const Tensor & self, const Tensor & other) {
return infer_type(self).__xor__(self, other);
}
static inline Tensor & __ixor__(Tensor & self, Scalar other) {
return infer_type(self).__ixor__(self, other);
}
static inline Tensor & __ixor__(Tensor & self, const Tensor & other) {
return infer_type(self).__ixor__(self, other);
}
static inline Tensor & __lshift___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__lshift___out(result, self, other);
}
static inline Tensor __lshift__(const Tensor & self, Scalar other) {
return infer_type(self).__lshift__(self, other);
}
static inline Tensor & __lshift___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__lshift___out(result, self, other);
}
static inline Tensor __lshift__(const Tensor & self, const Tensor & other) {
return infer_type(self).__lshift__(self, other);
}
static inline Tensor & __ilshift__(Tensor & self, Scalar other) {
return infer_type(self).__ilshift__(self, other);
}
static inline Tensor & __ilshift__(Tensor & self, const Tensor & other) {
return infer_type(self).__ilshift__(self, other);
}
static inline Tensor & __rshift___out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).__rshift___out(result, self, other);
}
static inline Tensor __rshift__(const Tensor & self, Scalar other) {
return infer_type(self).__rshift__(self, other);
}
static inline Tensor & __rshift___out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).__rshift___out(result, self, other);
}
static inline Tensor __rshift__(const Tensor & self, const Tensor & other) {
return infer_type(self).__rshift__(self, other);
}
static inline Tensor & __irshift__(Tensor & self, Scalar other) {
return infer_type(self).__irshift__(self, other);
}
static inline Tensor & __irshift__(Tensor & self, const Tensor & other) {
return infer_type(self).__irshift__(self, other);
}
static inline Tensor & lt_out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).lt_out(result, self, other);
}
static inline Tensor lt(const Tensor & self, Scalar other) {
return infer_type(self).lt(self, other);
}
static inline Tensor & lt_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).lt_out(result, self, other);
}
static inline Tensor lt(const Tensor & self, const Tensor & other) {
return infer_type(self).lt(self, other);
}
static inline Tensor & gt_out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).gt_out(result, self, other);
}
static inline Tensor gt(const Tensor & self, Scalar other) {
return infer_type(self).gt(self, other);
}
static inline Tensor & gt_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).gt_out(result, self, other);
}
static inline Tensor gt(const Tensor & self, const Tensor & other) {
return infer_type(self).gt(self, other);
}
static inline Tensor & le_out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).le_out(result, self, other);
}
static inline Tensor le(const Tensor & self, Scalar other) {
return infer_type(self).le(self, other);
}
static inline Tensor & le_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).le_out(result, self, other);
}
static inline Tensor le(const Tensor & self, const Tensor & other) {
return infer_type(self).le(self, other);
}
static inline Tensor & ge_out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).ge_out(result, self, other);
}
static inline Tensor ge(const Tensor & self, Scalar other) {
return infer_type(self).ge(self, other);
}
static inline Tensor & ge_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).ge_out(result, self, other);
}
static inline Tensor ge(const Tensor & self, const Tensor & other) {
return infer_type(self).ge(self, other);
}
static inline Tensor & eq_out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).eq_out(result, self, other);
}
static inline Tensor eq(const Tensor & self, Scalar other) {
return infer_type(self).eq(self, other);
}
static inline Tensor & eq_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).eq_out(result, self, other);
}
static inline Tensor eq(const Tensor & self, const Tensor & other) {
return infer_type(self).eq(self, other);
}
static inline Tensor & ne_out(Tensor & result, const Tensor & self, Scalar other) {
return infer_type(self).ne_out(result, self, other);
}
static inline Tensor ne(const Tensor & self, Scalar other) {
return infer_type(self).ne(self, other);
}
static inline Tensor & ne_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).ne_out(result, self, other);
}
static inline Tensor ne(const Tensor & self, const Tensor & other) {
return infer_type(self).ne(self, other);
}
static inline std::tuple<Tensor &,Tensor &> min_out(Tensor & min, Tensor & min_indices, const Tensor & self, int64_t dim, bool keepdim) {
return infer_type(self).min_out(min, min_indices, self, dim, keepdim);
}
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 Tensor & min_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).min_out(result, self, other);
}
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(Tensor & max, Tensor & max_indices, const Tensor & self, int64_t dim, bool keepdim) {
return infer_type(self).max_out(max, max_indices, self, dim, keepdim);
}
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 Tensor & max_out(Tensor & result, const Tensor & self, const Tensor & other) {
return infer_type(self).max_out(result, self, other);
}
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(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool keepdim) {
return infer_type(self).kthvalue_out(values, indices, self, k, dim, keepdim);
}
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 &> mode_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim) {
return infer_type(self).mode_out(values, indices, self, dim, keepdim);
}
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 &> median_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool keepdim) {
return infer_type(self).median_out(values, indices, self, dim, keepdim);
}
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);
}
static inline std::tuple<Tensor &,Tensor &> sort_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t dim, bool descending) {
return infer_type(self).sort_out(values, indices, self, dim, descending);
}
static inline std::tuple<Tensor,Tensor> sort(const Tensor & self, int64_t dim, bool descending) {
return infer_type(self).sort(self, dim, descending);
}
static inline std::tuple<Tensor &,Tensor &> topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) {
return infer_type(self).topk_out(values, indices, self, k, dim, largest, sorted);
}
static inline std::tuple<Tensor,Tensor> topk(const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted) {
return infer_type(self).topk(self, k, dim, largest, sorted);
}
static inline Tensor & abs_out(Tensor & destination, const Tensor & self) {
return infer_type(self).abs_out(destination, self);
}
static inline Tensor abs(const Tensor & self) {
return infer_type(self).abs(self);
}
static inline Tensor & sigmoid_out(Tensor & result, const Tensor & self) {
return infer_type(self).sigmoid_out(result, self);
}
static inline Tensor sigmoid(const Tensor & self) {
return infer_type(self).sigmoid(self);
}
static inline Tensor & log_out(Tensor & result, const Tensor & self) {
return infer_type(self).log_out(result, self);
}
static inline Tensor log(const Tensor & self) {
return infer_type(self).log(self);
}
static inline Tensor & log1p_out(Tensor & result, const Tensor & self) {
return infer_type(self).log1p_out(result, self);
}
static inline Tensor log1p(const Tensor & self) {
return infer_type(self).log1p(self);
}
static inline Tensor & lgamma_out(Tensor & result, const Tensor & self) {
return infer_type(self).lgamma_out(result, self);
}
static inline Tensor lgamma(const Tensor & self) {
return infer_type(self).lgamma(self);
}
static inline Tensor & exp_out(Tensor & result, const Tensor & self) {
return infer_type(self).exp_out(result, self);
}
static inline Tensor exp(const Tensor & self) {
return infer_type(self).exp(self);
}
static inline Tensor & expm1_out(const Tensor & result, const Tensor & self) {
return infer_type(self).exp_out(result, self);
}
static inline Tensor expm1(const Tensor & self) {
return infer_type(self).exp(self);
}
static inline Tensor & cos_out(Tensor & result, const Tensor & self) {
return infer_type(self).cos_out(result, self);
}
static inline Tensor cos(const Tensor & self) {
return infer_type(self).cos(self);
}
static inline Tensor & acos_out(Tensor & result, const Tensor & self) {
return infer_type(self).acos_out(result, self);
}
static inline Tensor acos(const Tensor & self) {
return infer_type(self).acos(self);
}
static inline Tensor & cosh_out(Tensor & result, const Tensor & self) {
return infer_type(self).cosh_out(result, self);
}
static inline Tensor cosh(const Tensor & self) {
return infer_type(self).cosh(self);
}
static inline Tensor & sin_out(Tensor & result, const Tensor & self) {
return infer_type(self).sin_out(result, self);
}
static inline Tensor sin(const Tensor & self) {
return infer_type(self).sin(self);
}
static inline Tensor & asin_out(Tensor & result, const Tensor & self) {
return infer_type(self).asin_out(result, self);
}
static inline Tensor asin(const Tensor & self) {
return infer_type(self).asin(self);
}
static inline Tensor & sinh_out(Tensor & result, const Tensor & self) {
return infer_type(self).sinh_out(result, self);
}
static inline Tensor sinh(const Tensor & self) {
return infer_type(self).sinh(self);
}
static inline Tensor & tan_out(Tensor & result, const Tensor & self) {
return infer_type(self).tan_out(result, self);
}
static inline Tensor tan(const Tensor & self) {
return infer_type(self).tan(self);
}
static inline Tensor & atan_out(Tensor & result, const Tensor & self) {
return infer_type(self).atan_out(result, self);
}
static inline Tensor atan(const Tensor & self) {
return infer_type(self).atan(self);
}
static inline Tensor & tanh_out(Tensor & result, const Tensor & self) {
return infer_type(self).tanh_out(result, self);
}
static inline Tensor tanh(const Tensor & self) {
return infer_type(self).tanh(self);
}
static inline Tensor & erf_out(Tensor & result, const Tensor & self) {
return infer_type(self).erf_out(result, self);
}
static inline Tensor erf(const Tensor & self) {
return infer_type(self).erf(self);
}
static inline Tensor & erfinv_out(Tensor & result, const Tensor & self) {
return infer_type(self).erfinv_out(result, self);
}
static inline Tensor erfinv(const Tensor & self) {
return infer_type(self).erfinv(self);
}
static inline Tensor & sqrt_out(Tensor & result, const Tensor & self) {
return infer_type(self).sqrt_out(result, self);
}
static inline Tensor sqrt(const Tensor & self) {
return infer_type(self).sqrt(self);
}
static inline Tensor & rsqrt_out(Tensor & result, const Tensor & self) {
return infer_type(self).rsqrt_out(result, self);
}
static inline Tensor rsqrt(const Tensor & self) {
return infer_type(self).rsqrt(self);
}
static inline Tensor & ceil_out(Tensor & result, const Tensor & self) {
return infer_type(self).ceil_out(result, self);
}