-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathtensors.html
2628 lines (2429 loc) · 323 KB
/
tensors.html
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
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta name="robots" content="noindex">
<meta charset="utf-8">
<meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>torch.Tensor — PyTorch 1.13 documentation</title>
<link rel="canonical" href="https://pytorch.org/docs/stable/tensors.html"/>
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!-- <link rel="stylesheet" href="_static/pygments.css" type="text/css" /> -->
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="_static/katex-math.css" type="text/css" />
<link rel="stylesheet" href="_static/sphinx-dropdown.css" type="text/css" />
<link rel="stylesheet" href="_static/panels-bootstrap.min.css" type="text/css" />
<link rel="stylesheet" href="_static/css/jit.css" type="text/css" />
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="torch.Tensor.new_tensor" href="generated/torch.Tensor.new_tensor.html" />
<link rel="prev" title="torch.nn.functional.torch.nn.parallel.data_parallel" href="generated/torch.nn.functional.torch.nn.parallel.data_parallel.html" />
<!-- Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-117752657-2"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-117752657-2');
</script>
<!-- End Google Analytics -->
<script src="_static/js/modernizr.min.js"></script>
<!-- Preload the theme fonts -->
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-book.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/IBMPlexMono/IBMPlexMono-Medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/FreightSans/freight-sans-medium-italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="_static/fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<!-- Preload the katex fonts -->
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Math-Italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Main-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Main-Bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Size1-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Size4-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Size2-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Size3-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/fonts/KaTeX_Caligraphic-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.15.2/css/all.css" integrity="sha384-vSIIfh2YWi9wW0r9iZe7RJPrKwp6bG+s9QZMoITbCckVJqGCCRhc+ccxNcdpHuYu" crossorigin="anonymous">
</head>
<div class="container-fluid header-holder tutorials-header" id="header-holder">
<div class="container">
<div class="header-container">
<a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
<div class="main-menu">
<ul>
<li>
<a href="https://pytorch.org/get-started">Get Started</a>
</li>
<li>
<a href="https://pytorch.org/ecosystem">Ecosystem</a>
</li>
<li>
<a href="https://pytorch.org/mobile">Mobile</a>
</li>
<li>
<a href="https://pytorch.org/blog/">Blog</a>
</li>
<li>
<a href="https://pytorch.org/tutorials">Tutorials</a>
</li>
<li class="active docs-active">
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="resource-option with-down-orange-arrow">
Docs
</a>
<div class="resources-dropdown-menu">
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/docs/stable/index.html">
<span class="dropdown-title">PyTorch</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/audio/stable/index.html">
<span class="dropdown-title">torchaudio</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/text/stable/index.html">
<span class="dropdown-title">torchtext</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/vision/stable/index.html">
<span class="dropdown-title">torchvision</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/torcharrow">
<span class="dropdown-title">torcharrow</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/data">
<span class="dropdown-title">TorchData</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/torchrec">
<span class="dropdown-title">TorchRec</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/serve/">
<span class="dropdown-title">TorchServe</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/torchx/">
<span class="dropdown-title">TorchX</span>
<p></p>
</a>
<a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/xla">
<span class="dropdown-title">PyTorch on XLA Devices</span>
<p></p>
</a>
</div>
</li>
<li>
<div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
<a class="resource-option with-down-arrow">
Resources
</a>
<div class="resources-dropdown-menu">
<a class="nav-dropdown-item" href="https://pytorch.org/features">
<span class="dropdown-title">About</span>
<p>Learn about PyTorch’s features and capabilities</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/foundation">
<span class="dropdown-title">PyTorch Foundation</span>
<p>Learn about the PyTorch foundation</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/#community-module">
<span class="dropdown-title">Community</span>
<p>Join the PyTorch developer community to contribute, learn, and get your questions answered.</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/community-stories">
<span class="dropdown-title">Community Stories</span>
<p>Learn how our community solves real, everyday machine learning problems with PyTorch.</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/resources">
<span class="dropdown-title">Developer Resources</span>
<p>Find resources and get questions answered</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/events">
<span class="dropdown-title">Events</span>
<p>Find events, webinars, and podcasts</p>
</a>
<a class="nav-dropdown-item" href="https://discuss.pytorch.org/" target="_blank">
<span class="dropdown-title">Forums</span>
<p>A place to discuss PyTorch code, issues, install, research</p>
</a>
<a class="nav-dropdown-item" href="https://pytorch.org/hub">
<span class="dropdown-title">Models (Beta)</span>
<p>Discover, publish, and reuse pre-trained models</p>
</a>
</div>
</div>
</li>
<li>
<a href="https://github.com/pytorch/pytorch">GitHub</a>
</li>
</ul>
</div>
<a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
</div>
</div>
</div>
<body class="pytorch-body">
<div class="table-of-contents-link-wrapper">
<span>Table of Contents</span>
<a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
</div>
<nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
<div class="pytorch-side-scroll">
<div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
<div class="pytorch-left-menu-search">
<div class="version">
<a href='https://pytorch.org/docs/versions.html'>1.13 ▼</a>
</div>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="search.html" method="get">
<input type="text" name="q" placeholder="Search Docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
<p class="caption" role="heading"><span class="caption-text">Community</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="community/build_ci_governance.html">PyTorch Governance | Build + CI</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/contribution_guide.html">PyTorch Contribution Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/design.html">PyTorch Design Philosophy</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/governance.html">PyTorch Governance | Mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="community/persons_of_interest.html">PyTorch Governance | Maintainers</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Developer Notes</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cuda.html">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/ddp.html">Distributed Data Parallel</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/gradcheck.html">Gradcheck mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/hip.html">HIP (ROCm) semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/modules.html">Modules</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/mps.html">MPS backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/numerical_accuracy.html">Numerical accuracy</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/randomness.html">Reproducibility</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/serialization.html">Serialization semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/windows.html">Windows FAQ</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Language Bindings</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="cpp_index.html">C++</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/javadoc/">Javadoc</a></li>
<li class="toctree-l1"><a class="reference internal" href="deploy.html">torch::deploy</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Python API</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="torch.html">torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.html">torch.nn</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.functional.html">torch.nn.functional</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">torch.Tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensor_attributes.html">Tensor Attributes</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensor_view.html">Tensor Views</a></li>
<li class="toctree-l1"><a class="reference internal" href="amp.html">torch.amp</a></li>
<li class="toctree-l1"><a class="reference internal" href="autograd.html">torch.autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="library.html">torch.library</a></li>
<li class="toctree-l1"><a class="reference internal" href="cuda.html">torch.cuda</a></li>
<li class="toctree-l1"><a class="reference internal" href="backends.html">torch.backends</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.algorithms.join.html">torch.distributed.algorithms.join</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.elastic.html">torch.distributed.elastic</a></li>
<li class="toctree-l1"><a class="reference internal" href="fsdp.html">torch.distributed.fsdp</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributed.optim.html">torch.distributed.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="distributions.html">torch.distributions</a></li>
<li class="toctree-l1"><a class="reference internal" href="fft.html">torch.fft</a></li>
<li class="toctree-l1"><a class="reference internal" href="futures.html">torch.futures</a></li>
<li class="toctree-l1"><a class="reference internal" href="fx.html">torch.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="hub.html">torch.hub</a></li>
<li class="toctree-l1"><a class="reference internal" href="jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="linalg.html">torch.linalg</a></li>
<li class="toctree-l1"><a class="reference internal" href="monitor.html">torch.monitor</a></li>
<li class="toctree-l1"><a class="reference internal" href="special.html">torch.special</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch.overrides.html">torch.overrides</a></li>
<li class="toctree-l1"><a class="reference internal" href="package.html">torch.package</a></li>
<li class="toctree-l1"><a class="reference internal" href="profiler.html">torch.profiler</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.init.html">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="onnx.html">torch.onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="optim.html">torch.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="complex_numbers.html">Complex Numbers</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_comm_hooks.html">DDP Communication Hooks</a></li>
<li class="toctree-l1"><a class="reference internal" href="pipeline.html">Pipeline Parallelism</a></li>
<li class="toctree-l1"><a class="reference internal" href="quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc.html">Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="random.html">torch.random</a></li>
<li class="toctree-l1"><a class="reference internal" href="masked.html">torch.masked</a></li>
<li class="toctree-l1"><a class="reference internal" href="nested.html">torch.nested</a></li>
<li class="toctree-l1"><a class="reference internal" href="sparse.html">torch.sparse</a></li>
<li class="toctree-l1"><a class="reference internal" href="storage.html">torch.Storage</a></li>
<li class="toctree-l1"><a class="reference internal" href="testing.html">torch.testing</a></li>
<li class="toctree-l1"><a class="reference internal" href="benchmark_utils.html">torch.utils.benchmark</a></li>
<li class="toctree-l1"><a class="reference internal" href="bottleneck.html">torch.utils.bottleneck</a></li>
<li class="toctree-l1"><a class="reference internal" href="checkpoint.html">torch.utils.checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">torch.utils.cpp_extension</a></li>
<li class="toctree-l1"><a class="reference internal" href="data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="jit_utils.html">torch.utils.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="dlpack.html">torch.utils.dlpack</a></li>
<li class="toctree-l1"><a class="reference internal" href="mobile_optimizer.html">torch.utils.mobile_optimizer</a></li>
<li class="toctree-l1"><a class="reference internal" href="model_zoo.html">torch.utils.model_zoo</a></li>
<li class="toctree-l1"><a class="reference internal" href="tensorboard.html">torch.utils.tensorboard</a></li>
<li class="toctree-l1"><a class="reference internal" href="type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="named_tensor.html">Named Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="name_inference.html">Named Tensors operator coverage</a></li>
<li class="toctree-l1"><a class="reference internal" href="config_mod.html">torch.__config__</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Libraries</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/audio/stable">torchaudio</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/data">TorchData</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/torchrec">TorchRec</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/serve">TorchServe</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/text/stable">torchtext</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/vision/stable">torchvision</a></li>
<li class="toctree-l1"><a class="reference external" href="http://pytorch.org/xla/">PyTorch on XLA Devices</a></li>
</ul>
</div>
</div>
</nav>
<div class="pytorch-container">
<div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
<div class="pytorch-breadcrumbs-wrapper">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="pytorch-breadcrumbs">
<li>
<a href="index.html">
Docs
</a> >
</li>
<li>torch.Tensor</li>
<li class="pytorch-breadcrumbs-aside">
<a href="_sources/tensors.rst.txt" rel="nofollow"><img src="_static/images/view-page-source-icon.svg"></a>
</li>
</ul>
</div>
</div>
<div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
Shortcuts
</div>
</div>
<section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
<div class="pytorch-content-left">
<div class="rst-content">
<div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
<article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
<section id="torch-tensor">
<span id="tensor-doc"></span><h1>torch.Tensor<a class="headerlink" href="#torch-tensor" title="Permalink to this heading">¶</a></h1>
<p>A <a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> is a multi-dimensional matrix containing elements of
a single data type.</p>
<section id="data-types">
<h2>Data types<a class="headerlink" href="#data-types" title="Permalink to this heading">¶</a></h2>
<p>Torch defines 10 tensor types with CPU and GPU variants which are as follows:</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Data type</p></th>
<th class="head"><p>dtype</p></th>
<th class="head"><p>CPU tensor</p></th>
<th class="head"><p>GPU tensor</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>32-bit floating point</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.float32</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.float</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.FloatTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.FloatTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>64-bit floating point</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.float64</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.double</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.DoubleTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.DoubleTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>16-bit floating point <a class="footnote-reference brackets" href="#id4" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.float16</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.half</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.HalfTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.HalfTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>16-bit floating point <a class="footnote-reference brackets" href="#id5" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.bfloat16</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.BFloat16Tensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.BFloat16Tensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>32-bit complex</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.complex32</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.chalf</span></code></p></td>
<td></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>64-bit complex</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.complex64</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.cfloat</span></code></p></td>
<td></td>
<td></td>
</tr>
<tr class="row-even"><td><p>128-bit complex</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.complex128</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.cdouble</span></code></p></td>
<td></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>8-bit integer (unsigned)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.uint8</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ByteTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.ByteTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>8-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int8</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.CharTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.CharTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>16-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int16</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.short</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ShortTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.ShortTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>32-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int32</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.int</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.IntTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.IntTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>64-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int64</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.long</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.LongTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.LongTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>Boolean</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.bool</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.BoolTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.BoolTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>quantized 8-bit integer (unsigned)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.quint8</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ByteTensor</span></code></p></td>
<td><p>/</p></td>
</tr>
<tr class="row-even"><td><p>quantized 8-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.qint8</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.CharTensor</span></code></p></td>
<td><p>/</p></td>
</tr>
<tr class="row-odd"><td><p>quantized 32-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.qint32</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.IntTensor</span></code></p></td>
<td><p>/</p></td>
</tr>
<tr class="row-even"><td><p>quantized 4-bit integer (unsigned) <a class="footnote-reference brackets" href="#id6" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.quint4x2</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ByteTensor</span></code></p></td>
<td><p>/</p></td>
</tr>
</tbody>
</table>
<aside class="footnote brackets" id="id4" role="note">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
<p>Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10
significand bits. Useful when precision is important at the expense of range.</p>
</aside>
<aside class="footnote brackets" id="id5" role="note">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
<p>Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7
significand bits. Useful when range is important, since it has the same
number of exponent bits as <code class="docutils literal notranslate"><span class="pre">float32</span></code></p>
</aside>
<aside class="footnote brackets" id="id6" role="note">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
<p>quantized 4-bit integer is stored as a 8-bit signed integer. Currently it’s only supported in EmbeddingBag operator.</p>
</aside>
<p><a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> is an alias for the default tensor type (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.FloatTensor</span></code>).</p>
</section>
<section id="initializing-and-basic-operations">
<h2>Initializing and basic operations<a class="headerlink" href="#initializing-and-basic-operations" title="Permalink to this heading">¶</a></h2>
<p>A tensor can be constructed from a Python <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a> or sequence using the
<a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a> constructor:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">]])</span>
<span class="go">tensor([[ 1.0000, -1.0000],</span>
<span class="go"> [ 1.0000, -1.0000]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]))</span>
<span class="go">tensor([[ 1, 2, 3],</span>
<span class="go"> [ 4, 5, 6]])</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a> always copies <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>. If you have a Tensor
<code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code> and just want to change its <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> flag, use
<a class="reference internal" href="generated/torch.Tensor.requires_grad_.html#torch.Tensor.requires_grad_" title="torch.Tensor.requires_grad_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">requires_grad_()</span></code></a> or
<a class="reference internal" href="generated/torch.Tensor.detach.html#torch.Tensor.detach" title="torch.Tensor.detach"><code class="xref py py-meth docutils literal notranslate"><span class="pre">detach()</span></code></a> to avoid a copy.
If you have a numpy array and want to avoid a copy, use
<a class="reference internal" href="generated/torch.as_tensor.html#torch.as_tensor" title="torch.as_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.as_tensor()</span></code></a>.</p>
</div>
<p>A tensor of specific data type can be constructed by passing a
<a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a> and/or a <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> to a
constructor or tensor creation op:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="go">tensor([[ 0, 0, 0, 0],</span>
<span class="go"> [ 0, 0, 0, 0]], dtype=torch.int32)</span>
<span class="gp">>>> </span><span class="n">cuda0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:0'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda0</span><span class="p">)</span>
<span class="go">tensor([[ 1.0000, 1.0000, 1.0000, 1.0000],</span>
<span class="go"> [ 1.0000, 1.0000, 1.0000, 1.0000]], dtype=torch.float64, device='cuda:0')</span>
</pre></div>
</div>
<p>For more information about building Tensors, see <a class="reference internal" href="torch.html#tensor-creation-ops"><span class="std std-ref">Creation Ops</span></a></p>
<p>The contents of a tensor can be accessed and modified using Python’s indexing
and slicing notation:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
<span class="go">tensor(6)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">8</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="go">tensor([[ 1, 8, 3],</span>
<span class="go"> [ 4, 5, 6]])</span>
</pre></div>
</div>
<p>Use <a class="reference internal" href="generated/torch.Tensor.item.html#torch.Tensor.item" title="torch.Tensor.item"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.item()</span></code></a> to get a Python number from a tensor containing a
single value:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor([[ 1]])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="go">1</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">2.5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor(2.5000)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="go">2.5</span>
</pre></div>
</div>
<p>For more information about indexing, see <a class="reference internal" href="torch.html#indexing-slicing-joining"><span class="std std-ref">Indexing, Slicing, Joining, Mutating Ops</span></a></p>
<p>A tensor can be created with <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad=True</span></code> so that
<a class="reference internal" href="autograd.html#module-torch.autograd" title="torch.autograd"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.autograd</span></code></a> records operations on them for automatic differentiation.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">out</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">grad</span>
<span class="go">tensor([[ 2.0000, -2.0000],</span>
<span class="go"> [ 2.0000, 2.0000]])</span>
</pre></div>
</div>
<p>Each tensor has an associated <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Storage</span></code>, which holds its data.
The tensor class also provides multi-dimensional, <a class="reference external" href="https://en.wikipedia.org/wiki/Stride_of_an_array">strided</a>
view of a storage and defines numeric operations on it.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For more information on tensor views, see <a class="reference internal" href="tensor_view.html#tensor-view-doc"><span class="std std-ref">Tensor Views</span></a>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For more information on the <a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, and
<a class="reference internal" href="tensor_attributes.html#torch.layout" title="torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a> attributes of a <a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>, see
<a class="reference internal" href="tensor_attributes.html#tensor-attributes-doc"><span class="std std-ref">Tensor Attributes</span></a>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Methods which mutate a tensor are marked with an underscore suffix.
For example, <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.FloatTensor.abs_()</span></code> computes the absolute value
in-place and returns the modified tensor, while <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.FloatTensor.abs()</span></code>
computes the result in a new tensor.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To change an existing tensor’s <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> and/or <a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, consider using
<a class="reference internal" href="generated/torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> method on the tensor.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Current implementation of <a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> introduces memory overhead,
thus it might lead to unexpectedly high memory usage in the applications with many tiny tensors.
If this is your case, consider using one large structure.</p>
</div>
</section>
<section id="tensor-class-reference">
<h2>Tensor class reference<a class="headerlink" href="#tensor-class-reference" title="Permalink to this heading">¶</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="torch.Tensor">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">Tensor</span></span><a class="headerlink" href="#torch.Tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>There are a few main ways to create a tensor, depending on your use case.</p>
<ul class="simple">
<li><p>To create a tensor with pre-existing data, use <a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a>.</p></li>
<li><p>To create a tensor with specific size, use <code class="docutils literal notranslate"><span class="pre">torch.*</span></code> tensor creation
ops (see <a class="reference internal" href="torch.html#tensor-creation-ops"><span class="std std-ref">Creation Ops</span></a>).</p></li>
<li><p>To create a tensor with the same size (and similar types) as another tensor,
use <code class="docutils literal notranslate"><span class="pre">torch.*_like</span></code> tensor creation ops
(see <a class="reference internal" href="torch.html#tensor-creation-ops"><span class="std std-ref">Creation Ops</span></a>).</p></li>
<li><p>To create a tensor with similar type but different size as another tensor,
use <code class="docutils literal notranslate"><span class="pre">tensor.new_*</span></code> creation ops.</p></li>
</ul>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.Tensor.T">
<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">T</span></span><a class="headerlink" href="#torch.Tensor.T" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a view of this tensor with its dimensions reversed.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">n</span></code> is the number of dimensions in <code class="docutils literal notranslate"><span class="pre">x</span></code>,
<code class="docutils literal notranslate"><span class="pre">x.T</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.permute(n-1,</span> <span class="pre">n-2,</span> <span class="pre">...,</span> <span class="pre">0)</span></code>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The use of <a class="reference internal" href="#torch.Tensor.T" title="torch.Tensor.T"><code class="xref py py-func docutils literal notranslate"><span class="pre">Tensor.T()</span></code></a> on tensors of dimension other than 2 to reverse their shape
is deprecated and it will throw an error in a future release. Consider <a class="reference internal" href="#torch.Tensor.mT" title="torch.Tensor.mT"><code class="xref py py-attr docutils literal notranslate"><span class="pre">mT</span></code></a>
to transpose batches of matrices or <cite>x.permute(*torch.arange(x.ndim - 1, -1, -1))</cite> to reverse
the dimensions of a tensor.</p>
</div>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.Tensor.H">
<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">H</span></span><a class="headerlink" href="#torch.Tensor.H" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a view of a matrix (2-D tensor) conjugated and transposed.</p>
<p><code class="docutils literal notranslate"><span class="pre">x.H</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.transpose(0,</span> <span class="pre">1).conj()</span></code> for complex matrices and
<code class="docutils literal notranslate"><span class="pre">x.transpose(0,</span> <span class="pre">1)</span></code> for real matrices.</p>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference internal" href="#torch.Tensor.mH" title="torch.Tensor.mH"><code class="xref py py-attr docutils literal notranslate"><span class="pre">mH</span></code></a>: An attribute that also works on batches of matrices.</p>
</div>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.Tensor.mT">
<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">mT</span></span><a class="headerlink" href="#torch.Tensor.mT" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a view of this tensor with the last two dimensions transposed.</p>
<p><code class="docutils literal notranslate"><span class="pre">x.mT</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.transpose(-2,</span> <span class="pre">-1)</span></code>.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.Tensor.mH">
<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">mH</span></span><a class="headerlink" href="#torch.Tensor.mH" title="Permalink to this definition">¶</a></dt>
<dd><p>Accessing this property is equivalent to calling <a class="reference internal" href="generated/torch.adjoint.html#torch.adjoint" title="torch.adjoint"><code class="xref py py-func docutils literal notranslate"><span class="pre">adjoint()</span></code></a>.</p>
</dd></dl>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.new_tensor.html#torch.Tensor.new_tensor" title="torch.Tensor.new_tensor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.new_tensor</span></code></a></p></td>
<td><p>Returns a new Tensor with <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code> as the tensor data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.new_full.html#torch.Tensor.new_full" title="torch.Tensor.new_full"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.new_full</span></code></a></p></td>
<td><p>Returns a Tensor of size <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> filled with <code class="xref py py-attr docutils literal notranslate"><span class="pre">fill_value</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.new_empty.html#torch.Tensor.new_empty" title="torch.Tensor.new_empty"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.new_empty</span></code></a></p></td>
<td><p>Returns a Tensor of size <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> filled with uninitialized data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.new_ones.html#torch.Tensor.new_ones" title="torch.Tensor.new_ones"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.new_ones</span></code></a></p></td>
<td><p>Returns a Tensor of size <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> filled with <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.new_zeros.html#torch.Tensor.new_zeros" title="torch.Tensor.new_zeros"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.new_zeros</span></code></a></p></td>
<td><p>Returns a Tensor of size <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> filled with <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.is_cuda.html#torch.Tensor.is_cuda" title="torch.Tensor.is_cuda"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.is_cuda</span></code></a></p></td>
<td><p>Is <code class="docutils literal notranslate"><span class="pre">True</span></code> if the Tensor is stored on the GPU, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.is_quantized.html#torch.Tensor.is_quantized" title="torch.Tensor.is_quantized"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.is_quantized</span></code></a></p></td>
<td><p>Is <code class="docutils literal notranslate"><span class="pre">True</span></code> if the Tensor is quantized, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.is_meta.html#torch.Tensor.is_meta" title="torch.Tensor.is_meta"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.is_meta</span></code></a></p></td>
<td><p>Is <code class="docutils literal notranslate"><span class="pre">True</span></code> if the Tensor is a meta tensor, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.device.html#torch.Tensor.device" title="torch.Tensor.device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.device</span></code></a></p></td>
<td><p>Is the <a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> where this Tensor is.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.grad.html#torch.Tensor.grad" title="torch.Tensor.grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.grad</span></code></a></p></td>
<td><p>This attribute is <code class="docutils literal notranslate"><span class="pre">None</span></code> by default and becomes a Tensor the first time a call to <code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code> computes gradients for <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.ndim.html#torch.Tensor.ndim" title="torch.Tensor.ndim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.ndim</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.Tensor.dim.html#torch.Tensor.dim" title="torch.Tensor.dim"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dim()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.real.html#torch.Tensor.real" title="torch.Tensor.real"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.real</span></code></a></p></td>
<td><p>Returns a new tensor containing real values of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor for a complex-valued input tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.imag.html#torch.Tensor.imag" title="torch.Tensor.imag"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.imag</span></code></a></p></td>
<td><p>Returns a new tensor containing imaginary values of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.abs.html#torch.Tensor.abs" title="torch.Tensor.abs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.abs</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.abs.html#torch.abs" title="torch.abs"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.abs()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.abs_.html#torch.Tensor.abs_" title="torch.Tensor.abs_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.abs_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.abs.html#torch.Tensor.abs" title="torch.Tensor.abs"><code class="xref py py-meth docutils literal notranslate"><span class="pre">abs()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.absolute.html#torch.Tensor.absolute" title="torch.Tensor.absolute"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.absolute</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.abs.html#torch.abs" title="torch.abs"><code class="xref py py-func docutils literal notranslate"><span class="pre">abs()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.absolute_.html#torch.Tensor.absolute_" title="torch.Tensor.absolute_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.absolute_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.absolute.html#torch.Tensor.absolute" title="torch.Tensor.absolute"><code class="xref py py-meth docutils literal notranslate"><span class="pre">absolute()</span></code></a> Alias for <code class="xref py py-func docutils literal notranslate"><span class="pre">abs_()</span></code></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.acos.html#torch.Tensor.acos" title="torch.Tensor.acos"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.acos</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.acos.html#torch.acos" title="torch.acos"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.acos()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.acos_.html#torch.Tensor.acos_" title="torch.Tensor.acos_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.acos_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.acos.html#torch.Tensor.acos" title="torch.Tensor.acos"><code class="xref py py-meth docutils literal notranslate"><span class="pre">acos()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.arccos.html#torch.Tensor.arccos" title="torch.Tensor.arccos"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.arccos</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.arccos.html#torch.arccos" title="torch.arccos"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.arccos()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.arccos_.html#torch.Tensor.arccos_" title="torch.Tensor.arccos_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.arccos_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.arccos.html#torch.Tensor.arccos" title="torch.Tensor.arccos"><code class="xref py py-meth docutils literal notranslate"><span class="pre">arccos()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.add.html#torch.Tensor.add" title="torch.Tensor.add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.add</span></code></a></p></td>
<td><p>Add a scalar or tensor to <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.add_.html#torch.Tensor.add_" title="torch.Tensor.add_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.add_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.add.html#torch.Tensor.add" title="torch.Tensor.add"><code class="xref py py-meth docutils literal notranslate"><span class="pre">add()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.addbmm.html#torch.Tensor.addbmm" title="torch.Tensor.addbmm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addbmm</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.addbmm.html#torch.addbmm" title="torch.addbmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addbmm()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.addbmm_.html#torch.Tensor.addbmm_" title="torch.Tensor.addbmm_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addbmm_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.addbmm.html#torch.Tensor.addbmm" title="torch.Tensor.addbmm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addbmm()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.addcdiv.html#torch.Tensor.addcdiv" title="torch.Tensor.addcdiv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addcdiv</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.addcdiv.html#torch.addcdiv" title="torch.addcdiv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addcdiv()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.addcdiv_.html#torch.Tensor.addcdiv_" title="torch.Tensor.addcdiv_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addcdiv_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.addcdiv.html#torch.Tensor.addcdiv" title="torch.Tensor.addcdiv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addcdiv()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.addcmul.html#torch.Tensor.addcmul" title="torch.Tensor.addcmul"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addcmul</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.addcmul.html#torch.addcmul" title="torch.addcmul"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addcmul()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.addcmul_.html#torch.Tensor.addcmul_" title="torch.Tensor.addcmul_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addcmul_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.addcmul.html#torch.Tensor.addcmul" title="torch.Tensor.addcmul"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addcmul()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.addmm.html#torch.Tensor.addmm" title="torch.Tensor.addmm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addmm</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.addmm.html#torch.addmm" title="torch.addmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addmm()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.addmm_.html#torch.Tensor.addmm_" title="torch.Tensor.addmm_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addmm_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.addmm.html#torch.Tensor.addmm" title="torch.Tensor.addmm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addmm()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.sspaddmm.html#torch.Tensor.sspaddmm" title="torch.Tensor.sspaddmm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.sspaddmm</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.sspaddmm.html#torch.sspaddmm" title="torch.sspaddmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sspaddmm()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.addmv.html#torch.Tensor.addmv" title="torch.Tensor.addmv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addmv</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.addmv.html#torch.addmv" title="torch.addmv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addmv()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.addmv_.html#torch.Tensor.addmv_" title="torch.Tensor.addmv_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addmv_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.addmv.html#torch.Tensor.addmv" title="torch.Tensor.addmv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addmv()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.addr.html#torch.Tensor.addr" title="torch.Tensor.addr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addr</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.addr.html#torch.addr" title="torch.addr"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addr()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.addr_.html#torch.Tensor.addr_" title="torch.Tensor.addr_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.addr_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.addr.html#torch.Tensor.addr" title="torch.Tensor.addr"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addr()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.adjoint.html#torch.Tensor.adjoint" title="torch.Tensor.adjoint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.adjoint</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.adjoint.html#torch.adjoint" title="torch.adjoint"><code class="xref py py-func docutils literal notranslate"><span class="pre">adjoint()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.allclose.html#torch.Tensor.allclose" title="torch.Tensor.allclose"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.allclose</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.allclose.html#torch.allclose" title="torch.allclose"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.allclose()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.amax.html#torch.Tensor.amax" title="torch.Tensor.amax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.amax</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.amax.html#torch.amax" title="torch.amax"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.amax()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.amin.html#torch.Tensor.amin" title="torch.Tensor.amin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.amin</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.amin.html#torch.amin" title="torch.amin"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.amin()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.aminmax.html#torch.Tensor.aminmax" title="torch.Tensor.aminmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.aminmax</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.aminmax.html#torch.aminmax" title="torch.aminmax"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.aminmax()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.angle.html#torch.Tensor.angle" title="torch.Tensor.angle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.angle</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.angle.html#torch.angle" title="torch.angle"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.angle()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.apply_.html#torch.Tensor.apply_" title="torch.Tensor.apply_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.apply_</span></code></a></p></td>
<td><p>Applies the function <code class="xref py py-attr docutils literal notranslate"><span class="pre">callable</span></code> to each element in the tensor, replacing each element with the value returned by <code class="xref py py-attr docutils literal notranslate"><span class="pre">callable</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.argmax.html#torch.Tensor.argmax" title="torch.Tensor.argmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.argmax</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.argmax.html#torch.argmax" title="torch.argmax"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.argmax()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.argmin.html#torch.Tensor.argmin" title="torch.Tensor.argmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.argmin</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.argmin.html#torch.argmin" title="torch.argmin"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.argmin()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.argsort.html#torch.Tensor.argsort" title="torch.Tensor.argsort"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.argsort</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.argsort.html#torch.argsort" title="torch.argsort"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.argsort()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.argwhere.html#torch.Tensor.argwhere" title="torch.Tensor.argwhere"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.argwhere</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.argwhere.html#torch.argwhere" title="torch.argwhere"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.argwhere()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.asin.html#torch.Tensor.asin" title="torch.Tensor.asin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.asin</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.asin.html#torch.asin" title="torch.asin"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.asin()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.asin_.html#torch.Tensor.asin_" title="torch.Tensor.asin_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.asin_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.asin.html#torch.Tensor.asin" title="torch.Tensor.asin"><code class="xref py py-meth docutils literal notranslate"><span class="pre">asin()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.arcsin.html#torch.Tensor.arcsin" title="torch.Tensor.arcsin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.arcsin</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.arcsin.html#torch.arcsin" title="torch.arcsin"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.arcsin()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.arcsin_.html#torch.Tensor.arcsin_" title="torch.Tensor.arcsin_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.arcsin_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.arcsin.html#torch.Tensor.arcsin" title="torch.Tensor.arcsin"><code class="xref py py-meth docutils literal notranslate"><span class="pre">arcsin()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.as_strided.html#torch.Tensor.as_strided" title="torch.Tensor.as_strided"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.as_strided</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.as_strided.html#torch.as_strided" title="torch.as_strided"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.as_strided()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.atan.html#torch.Tensor.atan" title="torch.Tensor.atan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.atan</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.atan.html#torch.atan" title="torch.atan"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atan()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.atan_.html#torch.Tensor.atan_" title="torch.Tensor.atan_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.atan_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.atan.html#torch.Tensor.atan" title="torch.Tensor.atan"><code class="xref py py-meth docutils literal notranslate"><span class="pre">atan()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.arctan.html#torch.Tensor.arctan" title="torch.Tensor.arctan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.arctan</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.arctan.html#torch.arctan" title="torch.arctan"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.arctan()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.arctan_.html#torch.Tensor.arctan_" title="torch.Tensor.arctan_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.arctan_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.arctan.html#torch.Tensor.arctan" title="torch.Tensor.arctan"><code class="xref py py-meth docutils literal notranslate"><span class="pre">arctan()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.atan2.html#torch.Tensor.atan2" title="torch.Tensor.atan2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.atan2</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.atan2.html#torch.atan2" title="torch.atan2"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atan2()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.atan2_.html#torch.Tensor.atan2_" title="torch.Tensor.atan2_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.atan2_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.atan2.html#torch.Tensor.atan2" title="torch.Tensor.atan2"><code class="xref py py-meth docutils literal notranslate"><span class="pre">atan2()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.arctan2.html#torch.Tensor.arctan2" title="torch.Tensor.arctan2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.arctan2</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.arctan2.html#torch.arctan2" title="torch.arctan2"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.arctan2()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.arctan2_.html#torch.Tensor.arctan2_" title="torch.Tensor.arctan2_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.arctan2_</span></code></a></p></td>
<td><p>atan2_(other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.all.html#torch.Tensor.all" title="torch.Tensor.all"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.all</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.all.html#torch.all" title="torch.all"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.all()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.any.html#torch.Tensor.any" title="torch.Tensor.any"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.any</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.any.html#torch.any" title="torch.any"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.any()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.backward.html#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.backward</span></code></a></p></td>
<td><p>Computes the gradient of current tensor w.r.t.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.baddbmm.html#torch.Tensor.baddbmm" title="torch.Tensor.baddbmm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.baddbmm</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.baddbmm.html#torch.baddbmm" title="torch.baddbmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.baddbmm()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.baddbmm_.html#torch.Tensor.baddbmm_" title="torch.Tensor.baddbmm_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.baddbmm_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.baddbmm.html#torch.Tensor.baddbmm" title="torch.Tensor.baddbmm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">baddbmm()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bernoulli.html#torch.Tensor.bernoulli" title="torch.Tensor.bernoulli"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bernoulli</span></code></a></p></td>
<td><p>Returns a result tensor where each <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="monospace">result[i]</mtext></mrow><annotation encoding="application/x-tex">\texttt{result[i]}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord text"><span class="mord texttt">result[i]</span></span></span></span></span></span> is independently sampled from <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Bernoulli</mtext><mo stretchy="false">(</mo><mtext mathvariant="monospace">self[i]</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{self[i]})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Bernoulli</span></span><span class="mopen">(</span><span class="mord text"><span class="mord texttt">self[i]</span></span><span class="mclose">)</span></span></span></span></span>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bernoulli_.html#torch.Tensor.bernoulli_" title="torch.Tensor.bernoulli_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bernoulli_</span></code></a></p></td>
<td><p>Fills each location of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> with an independent sample from <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Bernoulli</mtext><mo stretchy="false">(</mo><mtext mathvariant="monospace">p</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{p})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Bernoulli</span></span><span class="mopen">(</span><span class="mord text"><span class="mord texttt">p</span></span><span class="mclose">)</span></span></span></span></span>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bfloat16.html#torch.Tensor.bfloat16" title="torch.Tensor.bfloat16"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bfloat16</span></code></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">self.bfloat16()</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">self.to(torch.bfloat16)</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bincount.html#torch.Tensor.bincount" title="torch.Tensor.bincount"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bincount</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.bincount.html#torch.bincount" title="torch.bincount"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bincount()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_not.html#torch.Tensor.bitwise_not" title="torch.Tensor.bitwise_not"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_not</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.bitwise_not.html#torch.bitwise_not" title="torch.bitwise_not"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bitwise_not()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_not_.html#torch.Tensor.bitwise_not_" title="torch.Tensor.bitwise_not_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_not_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.bitwise_not.html#torch.Tensor.bitwise_not" title="torch.Tensor.bitwise_not"><code class="xref py py-meth docutils literal notranslate"><span class="pre">bitwise_not()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_and.html#torch.Tensor.bitwise_and" title="torch.Tensor.bitwise_and"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_and</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.bitwise_and.html#torch.bitwise_and" title="torch.bitwise_and"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bitwise_and()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_and_.html#torch.Tensor.bitwise_and_" title="torch.Tensor.bitwise_and_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_and_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.bitwise_and.html#torch.Tensor.bitwise_and" title="torch.Tensor.bitwise_and"><code class="xref py py-meth docutils literal notranslate"><span class="pre">bitwise_and()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_or.html#torch.Tensor.bitwise_or" title="torch.Tensor.bitwise_or"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_or</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.bitwise_or.html#torch.bitwise_or" title="torch.bitwise_or"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bitwise_or()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_or_.html#torch.Tensor.bitwise_or_" title="torch.Tensor.bitwise_or_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_or_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.bitwise_or.html#torch.Tensor.bitwise_or" title="torch.Tensor.bitwise_or"><code class="xref py py-meth docutils literal notranslate"><span class="pre">bitwise_or()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_xor.html#torch.Tensor.bitwise_xor" title="torch.Tensor.bitwise_xor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_xor</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.bitwise_xor.html#torch.bitwise_xor" title="torch.bitwise_xor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bitwise_xor()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_xor_.html#torch.Tensor.bitwise_xor_" title="torch.Tensor.bitwise_xor_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_xor_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.bitwise_xor.html#torch.Tensor.bitwise_xor" title="torch.Tensor.bitwise_xor"><code class="xref py py-meth docutils literal notranslate"><span class="pre">bitwise_xor()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_left_shift.html#torch.Tensor.bitwise_left_shift" title="torch.Tensor.bitwise_left_shift"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_left_shift</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.bitwise_left_shift.html#torch.bitwise_left_shift" title="torch.bitwise_left_shift"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bitwise_left_shift()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_left_shift_.html#torch.Tensor.bitwise_left_shift_" title="torch.Tensor.bitwise_left_shift_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_left_shift_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.bitwise_left_shift.html#torch.Tensor.bitwise_left_shift" title="torch.Tensor.bitwise_left_shift"><code class="xref py py-meth docutils literal notranslate"><span class="pre">bitwise_left_shift()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_right_shift.html#torch.Tensor.bitwise_right_shift" title="torch.Tensor.bitwise_right_shift"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_right_shift</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.bitwise_right_shift.html#torch.bitwise_right_shift" title="torch.bitwise_right_shift"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bitwise_right_shift()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bitwise_right_shift_.html#torch.Tensor.bitwise_right_shift_" title="torch.Tensor.bitwise_right_shift_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bitwise_right_shift_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.bitwise_right_shift.html#torch.Tensor.bitwise_right_shift" title="torch.Tensor.bitwise_right_shift"><code class="xref py py-meth docutils literal notranslate"><span class="pre">bitwise_right_shift()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.bmm.html#torch.Tensor.bmm" title="torch.Tensor.bmm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bmm</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.bmm.html#torch.bmm" title="torch.bmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bmm()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.bool.html#torch.Tensor.bool" title="torch.Tensor.bool"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.bool</span></code></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">self.bool()</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">self.to(torch.bool)</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.byte.html#torch.Tensor.byte" title="torch.Tensor.byte"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.byte</span></code></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">self.byte()</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">self.to(torch.uint8)</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.broadcast_to.html#torch.Tensor.broadcast_to" title="torch.Tensor.broadcast_to"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.broadcast_to</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.broadcast_to.html#torch.broadcast_to" title="torch.broadcast_to"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.broadcast_to()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.cauchy_.html#torch.Tensor.cauchy_" title="torch.Tensor.cauchy_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.cauchy_</span></code></a></p></td>
<td><p>Fills the tensor with numbers drawn from the Cauchy distribution:</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.ceil.html#torch.Tensor.ceil" title="torch.Tensor.ceil"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.ceil</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.ceil.html#torch.ceil" title="torch.ceil"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.ceil()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.ceil_.html#torch.Tensor.ceil_" title="torch.Tensor.ceil_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.ceil_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.Tensor.ceil.html#torch.Tensor.ceil" title="torch.Tensor.ceil"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ceil()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.char.html#torch.Tensor.char" title="torch.Tensor.char"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.char</span></code></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">self.char()</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">self.to(torch.int8)</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.cholesky.html#torch.Tensor.cholesky" title="torch.Tensor.cholesky"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.cholesky</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.cholesky.html#torch.cholesky" title="torch.cholesky"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cholesky()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.cholesky_inverse.html#torch.Tensor.cholesky_inverse" title="torch.Tensor.cholesky_inverse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.cholesky_inverse</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.cholesky_inverse.html#torch.cholesky_inverse" title="torch.cholesky_inverse"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cholesky_inverse()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.cholesky_solve.html#torch.Tensor.cholesky_solve" title="torch.Tensor.cholesky_solve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.cholesky_solve</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.cholesky_solve.html#torch.cholesky_solve" title="torch.cholesky_solve"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cholesky_solve()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.Tensor.chunk.html#torch.Tensor.chunk" title="torch.Tensor.chunk"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.chunk</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.chunk.html#torch.chunk" title="torch.chunk"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.chunk()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.Tensor.clamp.html#torch.Tensor.clamp" title="torch.Tensor.clamp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tensor.clamp</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.clamp.html#torch.clamp" title="torch.clamp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.clamp()</span></code></a></p></td>
</tr>