summaryrefslogtreecommitdiffstats
blob: a6453934cca7b9f59c41bb048d922dcd18e88f9f (plain) (blame)
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
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
/*
 * Copyright (C) 2017 The Android Open Source Project
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package android.hardware.neuralnetworks@1.0;

/**
 * Operand types.
 *
 * The type of an operand in a model.
 *
 * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
 * with at least one dimension). Types not prefaced by TENSOR_* represent
 * scalar values and must have no dimensions.
 */
enum OperandType : int32_t {
    /**
     * The following entries are used to declare scalars.
     */
    FLOAT32             = 0,
    INT32               = 1,
    UINT32              = 2,

    /**
     * The following entries are used to declare tensors.
     */
    TENSOR_FLOAT32      = 3,
    TENSOR_INT32        = 4,

    /**
     * A tensor of 8 bit integers that represent real numbers.
     *
     * Attached to this tensor are two numbers that can be used to convert the
     * 8 bit integer to the real value and vice versa. These two numbers are:
     * - scale: a 32 bit floating point value
     * - zero_value: a 32 bit integer
     *
     * The formula is:
     * real_value = (integer_value - zero_value) * scale.
     */
    TENSOR_QUANT8_ASYMM = 5,

    /**
     * The following entries are OEM specific operand types.
     */
    OEM                 = 10000,
    TENSOR_OEM_BYTE     = 10001,
};

/**
 * Operation types.
 *
 * The type of an operation in a model.
 */
enum OperationType : int32_t {
    /**
     * Adds two tensors, elment-wise.
     *
     * Takes two input tensors of identical type and compatible dimensions.  The output
     * is the sum of both input tensors, optionally modified by an activation function.
     *
     * Two dimensions are compatible when:
     *     1. they are equal, or
     *     2. one of them is 1
     *
     * The size of the output is the maximum size along each dimension of the input operands.
     * It starts with the trailing dimensions, and works its way forward.
     *
     * Example:
     *     input1.dimension =    {4, 1, 2}
     *     input2.dimension = {5, 4, 3, 1}
     *     output.dimension = {5, 4, 3, 2}
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: A tensor.
     * 1: A tensor of the same type, and compatible dimensions as input0.
     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The sum, a tensor of the same type as input0.
     */
    ADD = 0,

    /**
     * Performs a 2-D average pooling operation.
     *
     * The output dimensions are functions of the filter dimensions, stride, and padding.
     *
     * The values in output Tensor is computed as:
     *     output[batch, row, col, channel] =
     *         sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
     * 7: An INT32 value, specifying the filter width.
     * 8: An INT32 value, specifying the filter height.
     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    AVERAGE_POOL_2D = 1,

    /**
     * Concatenates the input tensors along the given dimension.
     *
     * The input tensors must have identical type and the same dimensions except the
     * dimension along the concatenation axis.
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]
     * n+1: An INT32 value, specifying the concatenation axis.
     * n+2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output, a tensor of the same type as the input tensors.
          The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
     */
    CONCATENATION = 2,

    /**
     * Performs an 2-D convolution operation.
     *
     * The CONV_2D op sweeps a 2-D filter that can mix channels together over a batch of
     * images, applying the filter to each window of each image of the appropriate size.
     *
     * The output dimensions are functions of the filter dimensions, stride, and padding.
     *
     * The values in output Tensor is computed as:
     *     output[batch, row, col, channel] =
     *         sum_{i, j} (
     *             input[batch, row + i, col + j, k] *
     *             filter[channel, row + i, col + j, k] +
     *             bias[channel]
     *         )
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
     * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
     *    specifying the filter.
     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
     *    also be of {@link OperandType::TENSOR_FLOAT32}.
     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
     *    should be of {@link OperandType::TENSOR_INT32}.
     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
     */
    CONV_2D = 3,

    /**
     * Performs an depthwise 2-D convolution operation.
     *
     * Given an input tensor of shape [batches, height, width, depth_in] and a filter
     * tensor of shape [depth_out, filter_height, filter_width, depth_in] containing
     * in_channels convolutional filters of depth 1, DEPTHWISE_CONV applies a different
     * filter to each input channel (expanding from 1 channel to channel_multiplier channels
     * for each), then concatenates the results together.
     *
     * The output has depth_out = depth_in * depth_multiplier channels.
     * The output dimensions are functions of the filter dimensions, stride, and padding.
     *
     * The values in output Tensor is computed as:
     *     output[b, i, j, k * channel_multiplier + q] =
     *         sum_{di, dj} (
     *             input[b, strides[1] * i + di, strides[2] * j + dj, k] *
     *             filter[di, dj, k, q]
     *         )
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
     * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
     *    specifying the filter.
     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
     *    also be of {@link OperandType::TENSOR_FLOAT32}.
     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
     *    should be of {@link OperandType::TENSOR_INT32}.
     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
     * 9: An INT32 value, specifying the depthwise multiplier.
     * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
     */
    DEPTHWISE_CONV_2D = 4,

    /**
     * Rearranges data from depth into blocks of spatial data.
     *
     * More specifically, this op outputs a copy of the input tensor where values from
     * the depth dimension are moved in spatial blocks to the height and width dimensions.
     * The value block_size indicates the input block size and how the data is moved.
     *
     * Chunks of data of size block_size * block_size from depth are rearranged into
     * non-overlapping blocks of size block_size x block_size.
     *
     * The width of the output tensor is input_depth * block_size, whereas the height is
     * input_height * block_size.
     * The depth of the input tensor must be divisible by block_size * block_size
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
     *    block_size * block_size must be a divisor of the input depth.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
     *    depth/(block_size*block_size)].
     */
    DEPTH_TO_SPACE = 5,

    /**
     * Dequantizes the input tensor.
     *
     * The formula is:
     *     output = (input - zero_value) * scale.
     *
     * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0, but with type
          {@link OperandType::TENSOR_FLOAT32}.
     */
    DEQUANTIZE = 6,

    /**
     * Looks up items from a given tensor.
     *
     * Each item in the output is a raw copy of the corresponding item in
     * the input “values”. If the the given “lookup” indices are out of bounds,
     * the op will fail and an error will be reported.
     *
     * Inputs:
     * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
     *      then the shape would be [lookup_dimension, values_dimension], where
     *      “lookup_dimension” corresponds to the indexing dimension in the lookup
     *      table, and “values_dimension” to the contents.
     * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
     *      “lookup_size” is the number of elements to look for, and each entry
     *      corresponds to the first dimension of the “values” tensor.
     *
     * Output:
     * * 0: A n-D tensor of type X and the same rank and shape as the “values”
     *      tensor, except for the first dimension which has size “lookup_size”.
     */
    EMBEDDING_LOOKUP = 7,

    /**
     * Computes element-wise floor() on the input tensor.
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: A tensor.
     *
     * Ouputs:
     * 0: The output, a tensor of the same type and dimensions as input0.
     */
    FLOOR = 8,

    /**
     * Denotes a fully (densely) connected layer, which connects all elements in the input
     * tensor with each element in the output tensor.
     *
     * This layer implements the operation:
     *     outputs = activation(inputs * weights’ + bias)
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
     *    a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
     *    [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
     *    and “input_size” is the size of the input.
     * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where “num_units”
     *    corresponds to the number of output nodes.
     * 2: A 1-D tensor, of shape [num_units], specifying the bias.
     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
     *    also be of {@link OperandType::TENSOR_FLOAT32}.
     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
     *    should be of {@link OperandType::TENSOR_INT32}.
     * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output tensor, of shape [batch_size, num_units].
     */
    FULLY_CONNECTED = 9,

    /**
     * Looks up values of a hash table with given keys.
     *
     * Inputs:
     * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
     * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
     *      ascending order.
     * * 2: Values. A tensor with shape [ n … ].
     *
     * Outputs:
     * * 0: Output. A tensor with shape [ k …].
     * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
     *      hits or not.
     */
    HASHTABLE_LOOKUP = 10,

    /**
     * Applies L2 normalization along a the depth dimension.
     *
     * The values in output Tensor is computed as:
     *     output[batch, row, col, channel] =
     *         input[batch, row, col, channel] /
     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
     *
     * For x with more dimensions, independently normalizes each 1-D slice along dimension dim.
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    L2_NORMALIZATION = 11,

    /**
     * Performs an 2-D L2 pooling operation.
     *
     * The output dimensions are functions of the filter dimensions, stride, and padding.
     *
     * The values in output Tensor is computed as:
     *     output[batch, row, col, channel] =
     *         sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
     * 7: An INT32 value, specifying the filter width.
     * 8: An INT32 value, specifying the filter height.
     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    L2_POOL_2D = 12,

    /**
     * Applies Local Response Normalization along the depth dimension.
     *
     * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last
     * dimension), and each vector is normalized independently. Within a given vector,
     * each component is divided by the weighted, squared sum of inputs within depth_radius.
     *
     * In details:
     *     sqr_sum[a, b, c, d] =
     *         sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)
     *     output = input / pow((bias + alpha * sqr_sum), beta)
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * 1: An INT32 value, specifying the radius of the normalization window.
     * 2: A FLOAT32 value, specifying the bias, must not be zero.
     * 3: A FLOAT32 value, specifying the scale factor, alpha.
     * 4: A FLOAT32 value, specifying the exponent, beta.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    LOCAL_RESPONSE_NORMALIZATION = 13,

    /**
     * Computes sigmoid activation on the input tensor element-wise.
     *
     * In details:
     *     output = 1 / (1 + exp(-input))
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    LOGISTIC = 14,

    /**
     * Projects an input to a bit vector via locality senstive hashing.
     *
     * Inputs:
     * * 0: Hash functions. Dim.size == 2, DataType: Float.
     *            Tensor[0].Dim[0]: Number of hash functions.
     *            Tensor[0].Dim[1]: Number of seeds per hash functions.
     *            Tensor[0].Dim[1] <= 32 in sparse case.
     *
     * * 1: Input. Dim.size >= 1, no restriction on DataType.
     * * 2: Weight. Optional. Dim.size == 1, DataType: Float.
     *     If not set, each input element is considered to have the same weight of
     *     1.0.
     *     Tensor[1].Dim[0] == Tensor[2].Dim[0]
     * * 3: Type:
     *        Sparse: Value LSHProjectionType_SPARSE(=1).
     *          Computed bit vector is considered to be sparse.
     *          Each output element is an int32 made up of multiple bits computed from
     *          hash functions.
     *
     *        Dense: Value LSHProjectionType_DENSE(=2).
     *          Computed bit vector is considered to be dense. Each output element
     *          represents a bit and can take the value of either 0 or 1.
     *
     * Outputs:
     * * 0: If the projection type is sparse:
     *        Output.Dim == { Tensor[0].Dim[0] }
     *        A tensor of int32 that represents hash signatures.
     *      If the projection type is Dense:
     *        Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
     *        A flattened tensor that represents projected bit vectors.
     */
    LSH_PROJECTION = 15,

    /**
     * Long short-term memory unit (LSTM) recurrent network layer.
     *
     * The default non-peephole implementation is based on:
     * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
     * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
     * Computation, 9(8):1735-1780, 1997.
     *
     * The peephole implementation is based on:
     * https://research.google.com/pubs/archive/43905.pdf
     * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
     * recurrent neural network architectures for large scale acoustic modeling."
     * INTERSPEECH, 2014.
     *
     * The coupling of input and forget gate (CIFG) is based on:
     * http://arxiv.org/pdf/1503.04069.pdf
     * Greff et al. "LSTM: A Search Space Odyssey"
     *
     * The class has the following independently optional inputs:
     * * If input gate (if CIFG): “input_to_forget_weights”,
     *   “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
     * * If no peephole connections: “cell_to_input_weights”,
     *   “cell_to_forget_weights”, “cell_to_output_weights”.
     * * If no projection layer: “projection_weights” and “projection_bias”.
     * * If no projection bias: “projection_bias”.
     *
     * Supported tensor types:
     * * {@link OperandType::TENSOR_FLOAT32}
     *
     * Inputs:
     * * 0: Input.
     *      A 2-D tensor of type T, of shape [batch_size, input_size], where
     *      “batch_size” corresponds to the batching dimension, and “input_size”
     *      is the size of the input.
     * * 1: input_to_input_weights.
     *      A 2-D tensor of type T, of shape [num_units, input_size], where
     *      “num_units” corresponds to the number of cell units.
     * * 2: input_to_forget_weights.
     *      A 2-D tensor of type T, of shape [num_units, input_size].
     * * 3: input_to_cell_weights.
     *      A 2-D tensor of type T, of shape [num_units, input_size].
     * * 4: input_to_output_weights.
     *      A 2-D tensor of type T, of shape [num_units, input_size].
     * * 5: recurrent_to_input_weights.
     *      A 2-D tensor of type T, of shape [num_units, output_size], where
     *      “output_size” corresponds to either the number of cell units (i.e.,
     *      “num_units”), or the second dimension of the “projection_weights”, if
     *      defined.
     * * 6: recurrent_to_forget_weights.
     *      A 2-D tensor of type T, of shape [num_units, output_size].
     * * 7: recurrent_to_cell_weights.
     *      A 2-D tensor of type T, of shape [num_units, output_size].
     * * 8: recurrent_to_output_weights.
     *      A 2-D tensor of type T, of shape [num_units, output_size].
     * * 9: cell_to_input_weights.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 10:cell_to_forget_weights.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 11:cell_to_output_weights.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 12:input_gate_bias.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 13:forget_gate_bias.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 14:cell_bias.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 15:output_gate_bias.
     *      A 1-D tensor of type T, of shape [num_units].
     * * 16:projection_weights.
     *      A 2-D tensor of type T, of shape [output_size, num_units].
     * * 17:projection_bias.
     *      A 1-D tensor of type T, of shape [output_size].
     *
     * Parameters:
     * * 18:fused_activation_function.
     *      An (optional) ActivationFunctionType indicating the activation
     *      function.
     *      If “NONE” is specified then it results in a linear activation.
     * * 19:cell_clip.
     *      A clipping threshold for the cell state, such that values are bound
     *      within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
     *      disabled.
     * * 20:proj_clip.
     *      A clipping threshold for the output from the projection layer, such
     *      that values are bound within [-proj_clip, proj_clip]. If set to 0.0
     *      then clipping is disabled.
     *
     * Outputs:
     * * 0: scratch_buffer.
     *      A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
     * * 1: output_state.
     *      A 2-D tensor of type T, of shape [batch_size, output_size].
     * * 2: cell_state.
     *      A 2-D tensor of type T, of shape [batch_size, num_units].
     * * 3: output.
     *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
     *      effectively the same as the current “output_state” value.
     */
    LSTM = 16,

    /**
     * Performs an 2-D max pooling operation.
     *
     * The output dimensions are functions of the filter dimensions, stride, and padding.
     *
     * The values in output Tensor is computed as:
     *     output[batch, row, col, channel] =
     *         max_{i, j} (input[batch, row + i, col + j, channel])
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
     * 7: An INT32 value, specifying the filter width.
     * 8: An INT32 value, specifying the filter height.
     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
     */
    MAX_POOL_2D = 17,

    /**
     * Multiplies two tensors, elment-wise.
     *
     * Takes two input tensors of identical type and compatible dimensions.  The output
     * is the product of both input tensors, optionally modified by an activation function.
     *
     * Two dimensions are compatible when:
     *     1. they are equal, or
     *     2. one of them is 1
     *
     * The size of the resulting output is the maximum size along each dimension of the
     * input operands. It starts with the trailing dimensions, and works its way forward.
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: up to 4
     *
     * Inputs:
     * 0: A tensor.
     * 1: A tensor of the same type, and compatible dimensions as input0.
     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
     *    Specifies the activation to invoke on the result of each addition.
     *
     * Ouputs:
     * 0: The product, a tensor of the same type as input0.
     */
    MUL = 18,

    /**
     * Computes rectified linear activation on the input tensor element-wise.
     *
     * In details:
     *     output = max(0, input)
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    RELU = 19,

    /**
     * Computes rectified linear 1 activation on the input tensor element-wise.
     *
     * In details:
     *     output = min(1.f, max(-1.f, input))
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    RELU1 = 20,

    /**
     * Computes rectified linear 6 activation on the input tensor element-wise.
     *
     * In details:
     *     output = min(6, max(0, input))
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    RELU6 = 21,

    /**
     * Reshapes a tensor.
     *
     * Given tensor, this operation returns a tensor that has the same values as tensor,
     * but with a newly specified shape.
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the tensor to be reshaped.
     * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
     *    of the output tensor. The number of elements implied by shape must be the same
     *    as the number of elements in the input tensor.
     *
     * Ouputs:
     * 0: The output tensor, of shape specified by the input shape.
     */
    RESHAPE = 22,

    /**
     * Resizes images to given size using the bilinear interpretation.
     *
     * Resized images will be distorted if their original aspect ratio is not the same as input.
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
     * 1: An INT32 value, specifying the output width of the output tensor.
     * 2: An INT32 value, specifying the output height of the output tensor.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
     */
    RESIZE_BILINEAR = 23,

    /**
     * A basic recurrent neural network layer.
     *
     * This layer implements the operation:
     * outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias)
     *
     * Where:
     * * “input_weights” is a weight matrix that multiplies the inputs;
     * * “recurrent_weights” is a weight matrix that multiplies the current
     *    “state” which itself is the output from the previous time step
     *    computation;
     * * “bias” is a bias vector (added to each output vector in the batch);
     * * “activation” is the function passed as the “fused_activation_function”
     *   argument (if not “NONE”).
     *
     * Supported tensor types:
     * * {@link OperandType::TENSOR_FLOAT32}
     *
     * Inputs:
     * * 0: input.
     *      A 2-D tensor of type T, of shape [batch_size, input_size], where
     *      “batch_size” corresponds to the batching dimension, and “input_size” is
     *      the size of the input.
     * * 1: weights.
     *      A 2-D tensor of type T, of shape [num_units, input_size], where
     *      “num_units” corresponds to the number of units.
     * * 2: recurrent_weights.
     *      A 2-D tensor of type T, of shape [num_units, num_units], with columns
     *      corresponding to the weights from each unit.
     * * 3: bias.
     *      A 1-D tensor of type T, of shape [num_units].
     *
     *    For FLOAT32 input tensor, bias must also be FLOAT32.
     *    For UINT8 input tensor, bias must be INT32.
     *
     * Parameters
     * * 4: fused_activation_function.
     *      An (optional) ActivationFunctionType indicating the activation
     *      function. If “NONE” is specified then it results in a linear
     *      activation.
     *
     * * 5: Hidden state.
     *      A 2-D tensor of type T, of shape [batch_size, num_units].
     *
     * Outputs:
     * * 0: output.
     *      A 2-D tensor of type T, of shape [batch_size, num_units]. This is
     *      effectively the same as the current state value.
     */
    RNN = 24,

    /**
     * Computes the softmax activation on the input tensor element-wise, per batch, by
     * normalizing the input vector so the maximum coefficient is zero.
     *
     * In details:
     *     output[batch, i] =
     *         exp((input[batch, i] - max(input[batch, :])) * beta) /
     *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: 2 or 4.
     *
     * Inputs:
     * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
     * 1: A FLOAT32 value, specifying the scaling factor for the exponent, beta.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    SOFTMAX = 25,

    /**
     * Rearranges blocks of spatial data, into depth.
     *
     * More specifically, this op outputs a copy of the input tensor where values from
     * the height and width dimensions are moved to the depth dimension.
     * The value block_size indicates the input block size and how the data is moved.
     *
     * Chunks of data of size block_size * block_size from depth are rearranged into
     * non-overlapping blocks of size block_size x block_size.
     *
     * The depth of the output tensor is input_depth * block_size * block_size.
     * The input tensor's height and width must be divisible by block_size.
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
     * Supported tensor rank: 4, with "NHWC" data layout.
     *
     * Inputs:
     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
     *    block_size must be a divisor of both the input height and width.
     *
     * Ouputs:
     * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
     *    depth*block_size*block_size].
     */
    SPACE_TO_DEPTH = 26,

    /**
     * SVDF op is a kind of stateful layer derived from the notion that a
     * densely connected layer that's processing a sequence of input frames can
     * be approximated by using a singular value decomposition of each of its
     * nodes. The implementation is based on:
     *
     * https://research.google.com/pubs/archive/43813.pdf
     *
     * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
     * “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
     * INTERSPEECH, 2015.
     *
     * It processes the incoming input using a 2-stage filtering mechanism:
     * * stage 1 performs filtering on the "features" dimension, whose outputs get
     *   pushed into a memory of fixed-size memory_size.
     * * stage 2 performs filtering on the "time" dimension of the memory_size
     *   memoized outputs of stage 1.
     *
     * Specifically, for rank 1, this layer implements the operation:
     *
     *    memory = push(conv1d(inputs, weights_feature, feature_dim, "VALID"));
     *    outputs = activation(memory * weights_time + bias);
     *
     * Where:
     * * “weights_feature” is a weights matrix that processes the inputs (by
     *   convolving the input with every “feature filter”), and whose outputs get
     *   pushed, stacked in order, into the fixed-size “memory” (the oldest entry
     *   gets dropped);
     * * “weights_time” is a weights matrix that processes the “memory” (by a
     *   batched matrix multiplication on the num_units);
     * * “bias” is an optional bias vector (added to each output vector in the
     *   batch); and
     * * “activation” is the function passed as the “fused_activation_function”
     *   argument (if not “NONE”).
     *
     * Each rank adds a dimension to the weights matrices by means of stacking
     * the filters.
     *
     * Supported tensor types:
     * * {@link OperandType::TENSOR_FLOAT32}
     *
     * Inputs:
     * * 0: input.
     *      A 2-D tensor of type T, of shape [batch_size, input_size], where
     *      “batch_size” corresponds to the batching dimension, and “input_size” is
     *      the size of the input.
     * * 1: weights_feature.
     *      A 2-D tensor of type T, of shape [num_units, input_size], where
     *      “num_units” corresponds to the number of units.
     * * 2: weights_time.
     *      A 2-D tensor of type T, of shape [num_units, memory_size], where
     *      “memory_size” corresponds to the fixed-size of the memory.
     * * 3: bias.
     *      A optional 1-D tensor of type T, of shape [num_units].
     *
     *    For FLOAT32 input tensor, bias must also be FLOAT32.
     *    For UINT8 input tensor, bias must be INT32.
     *
     * Parameters:
     * * 4: rank.
     *      The rank of the SVD approximation.
     * * 5: fused_activation_function.
     *      An (optional) ActivationFunctionType indicating the activation function.
     *      If “NONE” is specified then it results in a linear activation.
     *
     * Outputs:
     * * 0: state.
     *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
     * * 1: output.
     *      A 2-D tensor of type T, of shape [batch_size, num_units].
     */
    SVDF = 27,

    /**
     * Computes hyperbolic tangent of input tensor element-wise.
     *
     * In details:
     *     output = tanh(input)
     *
     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
     * Supported tensor rank: up to 4.
     *
     * Inputs:
     * 0: A tensor, specifying the input.
     *
     * Ouputs:
     * 0: The output tensor of same shape as input0.
     */
    TANH = 28,

    /**
     * OEM specific operation.
     *
     * This operation is OEM specific. It should only be used for OEM applications.
     */
    OEM_OPERATION = 10000,
};

/**
 * Fused activation function types.
 */
enum FusedActivationFunc : int32_t {
    NONE  = 0,
    RELU  = 1,
    RELU1 = 2,
    RELU6 = 3,
};

/**
 * How an operand is used.
 */
enum OperandLifeTime : int32_t {
    /**
     * The operand is internal to the model.  It's created by an operation
     * and consumed by other operations.
     */
    TEMPORARY_VARIABLE,

    /**
     * The operand is an input of the model. An operand can't be both
     * input and output of a model.
     */
    MODEL_INPUT,

    /**
     * The operand is an output of the model.
     */
    MODEL_OUTPUT,

    /**
     * The operand is a constant found in Model.operandValues.
     */
    CONSTANT_COPY,

    /**
     * The operand is a constant that was specified via a Memory object.
     */
    CONSTANT_REFERENCE,

    /**
     * The operand does not have a value. This is valid only for optional arguments
     * of operations.
     */
    NO_VALUE,
};

/**
 * Status of a device.
 */
enum DeviceStatus : int32_t {
    AVAILABLE,
    BUSY,
    OFFLINE,
    UNKNOWN,
};

/**
 * Performance information for the reference workload.
 *
 * Used by a driver to report its performance characteristics.
 */
struct PerformanceInfo {
    /**
     * Execution time in nanoseconds.
     */
    float execTime;

    /**
     * Power usage in picoJoules.
     */
    float powerUsage;
};

/**
 * The capabilities of a driver.
 */
struct Capabilities {
    /**
     * Driver performance when operating on float32 data.
     */
    PerformanceInfo float32Performance;

    /**
     * Driver performance when operating on asymmetric 8-bit quantized data.
     */
    PerformanceInfo quantized8Performance;
};

/**
 * Describes the location of a data object.
 */
struct DataLocation {
    /**
     * The index of the memory pool where this location is found.
     */
    uint32_t poolIndex;

    /**
     * Offset in bytes from the start of the pool.
     */
    uint32_t offset;

    /**
     * The length of the data in bytes.
     */
    uint32_t length;
};

/**
 * Describes one operand of the model's graph.
 */
struct Operand {
    /**
     * Data type of the operand.
     */
    OperandType type;

    /**
     * Dimensions of the operand.
     */
    vec<uint32_t> dimensions;

    /**
     * The number of operations that use this operand as input.
     */
    uint32_t numberOfConsumers;

    /**
     * Quantized scale of the operand.
     *
     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
     * TENSOR_INT32.
     */
    float scale;

    /**
     * Quantized zero-point offset of the operand.
     *
     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
     */
    int32_t zeroPoint;

    /**
     * How the operand is used.
     */
    OperandLifeTime lifetime;

    /**
     * Where to find the data for this operand.
     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or NO_VALUE:
     * - All the fields will be 0.
     * If the lifetime is CONSTANT_COPY:
     * - location.poolIndex is 0.
     * - location.offset is the offset in bytes into Model.operandValues.
     * - location.length is set.
     * If the lifetime is CONSTANT_REFERENCE:
     * - location.poolIndex is set.
     * - location.offset is the offset in bytes into the specified pool.
     * - location.length is set.
     */
    DataLocation location;
};

/**
 * Describes one operation of the model's graph.
 */
struct Operation {
    /**
     * The operation type.
     */
    OperationType type;

    /**
     * Describes the table that contains the indexes of the inputs of the
     * operation. The offset is the index in the operandIndexes table.
     */
    vec<uint32_t> inputs;

    /**
     * Describes the table that contains the indexes of the outputs of the
     * operation. The offset is the index in the operandIndexes table.
     */
    vec<uint32_t> outputs;
};

/**
 * A Neural Network Model.
 *
 * This includes not only the execution graph, but also constant data such as
 * weights or scalars added at construction time. The only information that
 * might not be known is the shape of the input tensors.
 */
struct Model {
    /**
     * All operands included in the model.
     */
    vec<Operand> operands;

    /**
     * All operations included in the model.
     *
     * The operations are sorted into execution order.
     */
    vec<Operation> operations;

    /**
     * Input indexes of the model.
     *
     * Each value corresponds to the index of the operand in "operands".
     */
    vec<uint32_t> inputIndexes;

    /**
     * Output indexes of the model.
     *
     * Each value corresponds to the index of the operand in "operands".
     */
    vec<uint32_t> outputIndexes;

    /**
     * A byte buffer containing operand data that were copied into the model.
     *
     * An operand's value must be located here if and only if Operand::lifetime
     * equals OperandLifeTime::CONSTANT_COPY.
     */
    vec<uint8_t> operandValues;

    /**
     * A collection of shared memory pools containing operand data that were
     * registered by the model.
     *
     * An operand's value must be located here if and only if Operand::lifetime
     * equals OperandLifeTime::CONSTANT_REFERENCE.
     */
    vec<memory> pools;
};

/**
 * Metadata information specifying the location of the input or output data and
 * any updates to the input or output operand.
 */
struct RequestArgument {
    /**
     * If true, the argument does not have a value. This can be used for operations
     * that take optional arguments. If true, the fields of location are set to 0 and
     * the dimensions vector is left empty.
     */
    bool hasNoValue;

    /**
     * The location within one of the memory pools passed in the Request.
     */
    DataLocation location;

    /**
     * Updated dimension information.
     *
     * If dimensions.size() > 0, dimension information was provided along with the
     * argument.  This can be the case for models that accept inputs of varying size.
     * This can't change the rank, just the value of the dimensions that were
     * unspecified in the model.
     */
    vec<uint32_t> dimensions;
};

/**
 * Inputs to be sent to and outputs to be retrieved from a prepared model.
 *
 * A Request serves two primary tasks:
 * 1) Provides the input and output data to be used when executing the model.
 * 2) Specifies any updates to the input operand metadata that were left
 *    unspecified at model preparation time.
 */
struct Request {
    /**
     * Input data and information to be used in the execution of a prepared
     * model.
     *
     * The index of the input corresponds to the index in Model.inputIndexes.
     *   E.g., input[i] corresponds to Model.inputIndexes[i].
     */
    vec<RequestArgument> inputs;

    /**
     * Output data and information to be used in the execution of a prepared
     * model.
     *
     * The index of the output corresponds to the index in Model.outputIndexes.
     *   E.g., output[i] corresponds to Model.outputIndexes[i].
     */
    vec<RequestArgument> outputs;

    /**
     * A collection of shared memory pools containing operand data for both the
     * inputs and the outputs to a model.
     */
    vec<memory> pools;
};

/**
 * Return status of a function.
 */
enum ErrorStatus : int32_t {
    NONE,
    DEVICE_UNAVAILABLE,
    GENERAL_FAILURE,
    OUTPUT_INSUFFICIENT_SIZE,
    INVALID_ARGUMENT,
};