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-Search.setIndex({"docnames": ["demos/Contributor_demos/Credit Card Fraud Detection/Credit_Card_Fraud_Detection", "demos/README", "demos/assets/01_template", "demos/examples_and_demos", "demos/examples_and_demos/alexnet_demo", "demos/examples_and_demos/bert_demo", "demos/examples_and_demos/convnext_to_torch", "demos/examples_and_demos/dinov2_to_paddle", "demos/examples_and_demos/image_segmentation_with_ivy_unet", "demos/examples_and_demos/lstm_tensorflow_to_torch", "demos/examples_and_demos/lstm_torch_to_tensorflow", "demos/examples_and_demos/mmpretrain_to_jax", "demos/examples_and_demos/resnet_demo", "demos/examples_and_demos/resnet_to_tensorflow", "demos/examples_and_demos/torch_to_jax", "demos/examples_and_demos/xgboost_demo", "demos/guides", "demos/guides/01_transpiling_a_torch_model", "demos/guides/02_transpiling_a_haiku_model", "demos/guides/03_transpiling_a_tf_model", "demos/guides/04_developing_a_convnet_with_ivy", "demos/index", "demos/learn_the_basics", 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345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 370, 373, 374, 375, 376, 377, 378, 379, 382, 383, 384, 386, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 408, 409, 410, 412, 413, 414, 415, 416, 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, 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, 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, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 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, 629, 630, 631, 632, 633, 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, 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, 772, 774, 777, 778, 779, 780, 784, 785, 790, 792, 793, 795, 797, 802, 803, 807, 808, 809, 810, 811, 812, 814, 821, 822, 826, 829, 831, 832, 833, 834, 835, 836, 838, 839, 840, 841, 842, 843, 844, 846, 847, 848, 849, 851, 853, 854, 855, 856, 857, 865, 866, 867, 873], "defin": [0, 24, 30, 32, 33, 34, 54, 58, 59, 63, 77, 81, 82, 86, 101, 117, 142, 146, 147, 148, 224, 241, 248, 274, 275, 283, 285, 288, 301, 305, 309, 315, 318, 319, 320, 329, 330, 331, 332, 333, 336, 337, 339, 368, 370, 373, 376, 377, 379, 388, 412, 429, 485, 491, 526, 561, 562, 582, 627, 630, 633, 635, 637, 638, 648, 662, 669, 674, 675, 687, 761, 762, 763, 765, 820, 821, 826, 827, 830, 831, 834, 838, 841, 843, 844, 846, 847, 853, 855, 857, 859, 867, 869, 870, 871, 872, 873, 876, 878, 879], "proper": [0, 814, 820, 843, 866], "adjust": [0, 46, 71, 94, 377, 448, 648, 765, 767, 802, 812], "comput": [0, 6, 13, 29, 30, 32, 33, 39, 40, 45, 46, 48, 52, 57, 58, 59, 60, 62, 63, 64, 69, 71, 74, 75, 80, 81, 82, 83, 85, 86, 87, 94, 98, 99, 101, 114, 118, 214, 224, 231, 234, 236, 241, 242, 243, 248, 249, 250, 252, 253, 259, 260, 261, 268, 269, 270, 271, 273, 274, 277, 282, 283, 301, 305, 309, 315, 318, 319, 331, 332, 333, 336, 337, 339, 343, 345, 348, 350, 351, 355, 357, 362, 363, 364, 365, 366, 367, 368, 370, 373, 374, 375, 376, 377, 378, 379, 382, 386, 388, 395, 396, 397, 398, 399, 404, 405, 408, 409, 410, 412, 413, 414, 415, 416, 419, 420, 421, 424, 425, 427, 429, 430, 431, 432, 434, 435, 437, 439, 442, 444, 446, 449, 450, 452, 454, 455, 456, 457, 458, 459, 460, 479, 482, 495, 502, 504, 515, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 540, 541, 542, 586, 609, 616, 618, 619, 621, 625, 626, 632, 633, 635, 636, 637, 638, 639, 640, 642, 646, 648, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 661, 668, 669, 673, 674, 675, 678, 679, 681, 683, 685, 687, 688, 690, 692, 694, 695, 697, 698, 699, 703, 725, 750, 751, 752, 753, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 774, 779, 793, 796, 808, 814, 821, 829, 830, 831, 839, 841, 843, 846, 848, 849, 851, 854, 857, 859, 862, 863, 865, 866, 868, 870, 872, 873, 875, 876, 878], "most": [0, 6, 15, 23, 32, 33, 75, 77, 98, 101, 142, 377, 430, 586, 609, 630, 635, 638, 673, 674, 811, 814, 819, 820, 821, 826, 829, 830, 831, 832, 836, 838, 839, 841, 842, 843, 844, 846, 847, 848, 849, 851, 853, 854, 855, 857, 862, 872, 873, 875, 876, 878, 879], "avail": [0, 2, 4, 6, 8, 12, 13, 27, 28, 30, 32, 33, 48, 59, 82, 197, 203, 205, 206, 217, 547, 632, 635, 638, 689, 778, 812, 814, 821, 822, 829, 830, 831, 832, 834, 835, 843, 846, 849, 857, 858, 861, 865, 866, 867, 877, 878], "cpu": [0, 6, 7, 8, 9, 10, 11, 13, 14, 27, 28, 29, 30, 32, 46, 47, 48, 50, 51, 54, 56, 58, 67, 77, 79, 81, 90, 127, 133, 136, 138, 139, 142, 143, 144, 150, 194, 195, 197, 198, 199, 200, 205, 208, 210, 212, 215, 216, 218, 220, 377, 383, 439, 509, 510, 512, 513, 630, 632, 644, 739, 740, 741, 742, 774, 792, 793, 794, 795, 796, 797, 798, 812, 814, 818, 821, 822, 828, 831, 832, 836, 843, 846, 857, 870, 872, 875, 877], "gpu": [0, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 46, 48, 50, 51, 197, 199, 200, 203, 206, 208, 210, 212, 213, 216, 218, 220, 632, 812, 814, 821, 822, 830, 832, 853, 858, 870, 872, 875, 876, 877], "tpu": [0, 46, 195, 201, 210, 212, 217, 632, 812, 832, 872, 875], "explicitli": [0, 638, 674, 675, 690, 774, 793, 794, 795, 818, 825, 826, 827, 829, 831, 834, 835, 836, 839, 840, 841, 842, 844, 846, 851, 857, 866, 872], "hardwar": [0, 4, 46, 103, 107, 821, 849, 862, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878], "mai": [0, 1, 6, 56, 57, 58, 63, 69, 70, 79, 80, 86, 93, 103, 104, 127, 134, 145, 215, 241, 242, 248, 253, 261, 269, 270, 274, 275, 277, 292, 336, 337, 373, 405, 545, 581, 630, 632, 633, 635, 638, 646, 647, 648, 686, 695, 750, 751, 752, 753, 754, 757, 761, 762, 763, 765, 777, 808, 819, 820, 821, 822, 825, 829, 830, 831, 835, 836, 839, 840, 841, 843, 844, 846, 849, 852, 853, 855, 863, 879], "vari": [0, 58, 69, 98, 99, 292, 405, 546, 633, 635, 638, 646, 685, 751, 752, 753, 808, 829, 833, 843, 846, 853], "known": [0, 58, 81, 285, 377, 449, 451, 633, 792, 825, 830, 831, 843, 846], "advanc": [0, 21, 44, 821, 823, 871], "set_soft_device_mod": [0, 4, 15, 19, 219, 632, 832], "section": [0, 1, 2, 6, 7, 13, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 39, 52, 58, 69, 81, 113, 376, 379, 410, 421, 471, 480, 500, 646, 750, 751, 752, 753, 814, 815, 818, 819, 820, 821, 822, 824, 825, 826, 827, 828, 829, 830, 831, 832, 834, 835, 836, 837, 838, 839, 840, 842, 843, 844, 845, 846, 847, 849, 850, 854, 855, 867, 868, 875, 878], "binari": [0, 6, 15, 27, 28, 30, 58, 59, 62, 64, 81, 85, 87, 231, 234, 236, 271, 291, 376, 378, 422, 457, 460, 633, 637, 639, 660, 664, 697], "logist": [0, 15], "gblinear": [0, 15], "booster": [0, 15], "linear": [0, 4, 12, 13, 19, 31, 32, 33, 44, 45, 46, 48, 51, 58, 59, 62, 74, 81, 82, 85, 111, 113, 115, 116, 119, 296, 300, 304, 306, 307, 308, 312, 354, 368, 373, 376, 379, 388, 412, 447, 485, 533, 550, 573, 627, 635, 637, 642, 664, 687, 726, 777, 779, 780, 792, 793, 814, 829, 834, 839, 840, 842, 843, 846, 849, 851, 854, 855, 856, 866, 870, 871, 872, 875], "estim": [0, 58, 81, 350, 373, 388, 523, 812], "rate": [0, 58, 60, 81, 83, 376, 383, 418, 513, 617, 620, 622, 623, 624, 636, 637, 641, 662, 716, 717, 718, 797, 830], "fine": [0, 17, 19, 32, 33, 821, 822, 831, 833, 843, 853, 856, 878], "tune": [0, 17, 19, 32, 33, 877, 878], "regular": [0, 47, 81, 377, 388, 439, 444, 527, 821, 843, 872], "term": [0, 6, 13, 58, 81, 313, 320, 323, 370, 378, 457, 458, 637, 662, 663, 793, 808, 814, 822, 829, 851, 859, 861, 872], "reg_lambda": [0, 15], "reg_alpha": [0, 15], "overfit": [0, 637, 660], "compil": [0, 6, 9, 10, 11, 12, 14, 15, 27, 28, 30, 32, 33, 36, 49, 51, 292, 633, 785, 821, 843, 847, 851, 857, 859, 866, 868, 871, 872, 873, 876, 879], "param": [0, 11, 14, 15, 32, 46, 47, 48, 50, 75, 81, 82, 104, 536, 553, 554, 635, 799, 814, 856, 866], "n_estim": [0, 15], "100": [0, 6, 7, 9, 11, 12, 14, 15, 44, 46, 48, 54, 57, 58, 77, 80, 81, 82, 85, 102, 139, 148, 235, 275, 288, 329, 352, 361, 370, 373, 376, 377, 379, 400, 401, 446, 452, 490, 554, 562, 578, 630, 633, 635, 638, 642, 677, 725, 830, 831, 846, 854, 855, 856, 857, 862, 863, 865], "learning_r": [0, 7, 13, 15], "base_margin": [0, 15], "none": [0, 4, 6, 8, 11, 13, 14, 15, 32, 44, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 98, 102, 103, 104, 107, 108, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 153, 154, 155, 156, 159, 160, 161, 162, 163, 164, 166, 169, 171, 172, 173, 174, 176, 178, 181, 193, 196, 197, 209, 210, 211, 212, 213, 214, 215, 218, 219, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 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858, 872, 874, 879], "instal": [2, 7, 8, 9, 10, 11, 14, 15, 17, 19, 24, 25, 26, 27, 28, 29, 30, 32, 33, 46, 48, 49, 50, 51, 816, 821, 822, 827, 828, 836, 837], "skip": [2, 5, 13, 48, 58, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 111, 112, 113, 114, 115, 116, 117, 118, 119, 135, 137, 142, 144, 150, 154, 156, 181, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 299, 300, 304, 305, 306, 307, 308, 310, 311, 312, 314, 335, 336, 337, 338, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 377, 379, 400, 401, 402, 420, 436, 438, 445, 453, 454, 455, 456, 457, 458, 459, 460, 463, 464, 465, 469, 470, 486, 489, 491, 493, 494, 495, 497, 502, 504, 505, 506, 508, 510, 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47, 48, 49, 814, 816, 819, 821, 822, 825, 828, 830, 836, 837, 846, 858], "clone": [2, 4, 8, 12, 32, 46, 48, 49, 814, 816, 822, 836, 858], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 14, 19, 27, 28, 29, 30, 32, 33, 46, 47, 48, 49, 50, 51, 57, 58, 80, 81, 83, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 616, 617, 630, 631, 633, 636, 638, 640, 648, 686, 687, 715, 765, 814, 816, 821, 822, 825, 828, 830, 831, 834, 836, 858, 866], "github": [2, 4, 5, 8, 11, 12, 14, 32, 46, 47, 48, 49, 50, 814, 816, 817, 819, 822, 823, 825, 828, 830, 831, 833, 834, 836, 837, 845, 846, 858, 861, 880], "com": [2, 4, 5, 6, 7, 8, 11, 12, 14, 19, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 825, 828, 830, 831, 836, 858], "unifyai": [2, 4, 8, 12, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 828, 836, 858], "model": [2, 3, 4, 9, 15, 16, 21, 22, 23, 49, 51, 241, 274, 378, 454, 633, 790, 794, 795, 812, 854, 855, 859, 865, 866, 870, 871, 872, 873, 874, 875, 876, 878, 879], "depth": [2, 4, 6, 8, 12, 47, 54, 58, 62, 77, 81, 85, 142, 376, 379, 412, 472, 546, 558, 630, 635, 637, 655, 656, 822, 830, 854, 855, 856, 858], "repositori": [2, 4, 8, 12, 816, 820, 821, 822, 824, 825, 828, 836, 845, 863], "cd": [2, 4, 8, 12, 32, 49, 814, 816, 821, 822, 836, 858], "resnet": [3, 6, 14, 21, 32, 865, 866], "imag": [3, 4, 6, 7, 11, 14, 17, 21, 29, 32, 33, 46, 47, 48, 49, 50, 51, 58, 62, 80, 81, 85, 103, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 284, 285, 287, 288, 292, 376, 395, 396, 412, 413, 414, 416, 546, 633, 635, 637, 650, 651, 652, 653, 654, 657, 658, 659, 793, 814, 821, 836, 849, 851, 852, 854, 856, 858, 865, 866, 872], "classif": [3, 4, 12, 15, 21, 46, 872], "acceler": [3, 21, 831, 843, 870, 874, 875, 876, 877], "convert": [3, 8, 9, 11, 14, 15, 17, 19, 21, 22, 24, 26, 29, 30, 32, 33, 34, 36, 38, 46, 49, 51, 53, 54, 57, 75, 76, 77, 80, 98, 128, 129, 141, 151, 152, 194, 195, 196, 197, 208, 216, 220, 240, 280, 379, 384, 463, 464, 465, 514, 579, 597, 599, 600, 601, 603, 630, 631, 632, 633, 635, 638, 642, 696, 720, 731, 732, 774, 802, 807, 820, 826, 827, 840, 841, 843, 846, 848, 851, 857, 859, 863, 866, 870, 871, 878], "faster": [3, 4, 9, 11, 14, 15, 21, 32, 33, 49, 51, 58, 63, 81, 86, 377, 450, 638, 688, 816, 819, 828, 859, 874, 877], "infer": [3, 6, 7, 9, 11, 13, 14, 15, 21, 25, 35, 37, 38, 47, 49, 51, 54, 58, 59, 62, 65, 77, 81, 82, 85, 88, 127, 129, 132, 136, 137, 141, 144, 150, 159, 160, 161, 162, 163, 313, 314, 376, 379, 383, 412, 497, 511, 557, 591, 592, 630, 631, 635, 637, 640, 660, 707, 802, 803, 824, 827, 831, 832, 846, 851, 856, 866, 870, 871, 874, 876], "mmpretrain": [3, 21], "segment": [3, 21, 58, 81, 331, 332, 333, 370, 828, 833], "unet": [3, 21], "alexnet": [3, 21], "written": [3, 4, 5, 6, 13, 21, 23, 32, 33, 46, 59, 379, 474, 821, 825, 826, 834, 837, 838, 842, 843, 847, 851, 853, 856, 857, 861, 866, 870, 872, 876, 878, 879], "xgboost": [3, 21], "paddlepaddl": [3, 21, 336, 337, 373, 821], "dinov2": [3, 7, 21], "project": [3, 12, 14, 21, 26, 27, 28, 29, 30, 32, 33, 36, 99, 637, 664, 793, 814, 816, 817, 820, 821, 822, 823, 826, 827, 828, 846, 855, 857, 861, 862, 863, 866, 868, 870, 872, 875, 879, 880], "convnext": [3, 6, 11, 13, 21], "finetun": [3, 21, 46], "video": [4, 8, 11, 12, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 815, 820, 821, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 835, 837, 838, 839, 840, 841, 842, 843, 844, 846, 847, 849, 858, 870], "tutori": [4, 6, 7, 8, 11, 12, 13, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 822, 843, 858], "three": [4, 5, 21, 27, 37, 38, 48, 58, 140, 313, 370, 379, 465, 630, 821, 822, 829, 830, 831, 833, 843, 846, 849, 850, 851, 873, 878], "major": [4, 5, 645, 748, 831, 832, 844, 846, 857, 862, 869, 872], "ml": [4, 5, 6, 13, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 48, 51, 815, 819, 843, 850, 851, 852, 854, 855, 856, 860, 862, 863, 866, 868, 869, 870, 871, 872, 875, 877, 879], "framework": [4, 5, 7, 9, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 39, 46, 48, 50, 53, 59, 171, 193, 203, 206, 217, 544, 560, 564, 596, 599, 631, 632, 635, 642, 721, 772, 774, 778, 785, 790, 797, 802, 803, 817, 818, 820, 821, 824, 825, 826, 827, 828, 830, 831, 832, 833, 835, 836, 838, 839, 840, 842, 843, 846, 847, 849, 850, 851, 853, 856, 857, 858, 859, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 873, 876], "sinc": [4, 8, 12, 13, 29, 30, 32, 33, 46, 48, 58, 81, 99, 373, 816, 821, 822, 825, 826, 827, 828, 829, 830, 831, 832, 835, 842, 843, 857, 862, 872, 878], "automat": [4, 8, 9, 12, 13, 30, 32, 33, 38, 820, 821, 822, 824, 827, 828, 830, 831, 837, 839, 842, 846, 849, 850, 852, 855, 856, 858, 859, 863, 872, 875, 879], "sure": [4, 8, 11, 12, 13, 14, 15, 32, 46, 817, 820, 821, 822, 825, 830, 835, 836, 843, 844, 846, 849, 858], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 15, 27, 28, 30, 47, 58, 63, 75, 86, 104, 376, 378, 399, 457, 581, 635, 638, 681, 795, 812, 814, 821, 822, 823, 826, 829, 831, 839, 840, 841, 842, 843, 846, 847, 850, 852, 854, 856, 857, 859, 862, 865, 870, 871, 872, 873, 874, 875, 878, 879], "dm": [4, 5, 8, 11, 14, 32, 33, 44, 46], "haiku": [4, 5, 8, 11, 14, 30, 32, 33, 44, 46, 50, 790, 814, 856, 863, 866, 872], "exit": [4, 8, 12, 13, 32, 33, 832], "download": [4, 6, 7, 12, 13, 17, 19, 32, 33, 47, 48, 51, 816, 821, 828, 846, 865, 866], "imagenet": [4, 6, 13, 19, 47, 49, 814], "class": [4, 6, 7, 8, 12, 13, 15, 17, 19, 23, 32, 33, 44, 45, 46, 47, 48, 49, 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, 106, 107, 108, 135, 144, 150, 166, 169, 182, 184, 185, 244, 281, 339, 361, 373, 387, 388, 396, 397, 430, 529, 530, 537, 546, 550, 563, 573, 596, 630, 631, 632, 633, 635, 637, 638, 639, 642, 643, 658, 663, 667, 673, 683, 687, 688, 690, 697, 713, 720, 731, 738, 753, 760, 764, 765, 774, 775, 782, 783, 784, 785, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 801, 802, 805, 807, 812, 814, 820, 827, 828, 829, 831, 832, 833, 834, 838, 840, 841, 844, 845, 846, 849, 851, 852, 854, 855, 856, 859, 865, 866, 870, 872, 873, 879], "wget": [4, 6, 8, 12, 46, 47, 50, 821], "raw": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 46, 49, 50, 75, 814, 834, 866, 873], "githubusercont": [4, 6, 8, 12, 46, 50], "hub": [4, 6, 8, 12, 46, 49, 51], "master": [4, 8, 12, 24, 25, 26, 34, 35, 36, 37, 38, 39, 46, 48, 49, 50, 817, 830, 872, 880], "imagenet_class": [4, 12], "categori": [4, 6, 12, 820, 825, 826, 829, 831, 835, 843, 847, 850], "strip": [4, 12, 25, 35, 862], "readlin": [4, 12, 47], "cat": [4, 7, 12, 47, 844, 849, 851, 856, 865, 866], "jpg": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 48, 49, 814, 866], "filenam": [4, 8, 12, 13, 32, 33, 46, 48, 51, 59, 795, 801, 854], "import": [4, 6, 7, 9, 10, 11, 13, 14, 17, 19, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 47, 49, 50, 51, 58, 69, 73, 77, 81, 96, 195, 196, 200, 212, 308, 388, 523, 558, 574, 632, 635, 641, 646, 717, 718, 753, 785, 802, 803, 814, 819, 820, 821, 822, 823, 825, 826, 827, 828, 829, 831, 832, 833, 834, 837, 840, 841, 842, 843, 844, 845, 846, 847, 851, 853, 854, 856, 857, 858, 862, 865, 866, 867, 868, 870, 872, 875, 876, 878], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 14, 47, 48, 51, 54, 58, 67, 75, 77, 81, 90, 103, 106, 107, 108, 127, 128, 129, 131, 132, 133, 136, 137, 138, 139, 141, 142, 143, 144, 146, 147, 148, 149, 150, 194, 195, 196, 197, 198, 199, 200, 201, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 218, 220, 313, 314, 329, 330, 370, 383, 473, 509, 510, 512, 513, 537, 551, 552, 630, 635, 644, 739, 740, 741, 742, 772, 774, 775, 790, 792, 793, 794, 795, 796, 797, 798, 799, 812, 822, 824, 827, 831, 835, 839, 840, 844, 846, 847, 849, 851, 856, 857, 858, 859, 862, 871, 872, 874, 875, 876, 877], "torchvis": [4, 6, 11, 12, 13, 46, 863], "transform": [4, 5, 6, 7, 11, 12, 13, 14, 29, 32, 33, 46, 47, 49, 58, 62, 81, 85, 376, 377, 398, 399, 404, 405, 408, 409, 410, 420, 421, 424, 441, 637, 661, 777, 780, 793, 814, 840, 846, 856, 859, 865, 866, 870, 872, 873, 874], "pil": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 47, 48, 49, 814, 866], "time": [4, 5, 6, 7, 9, 10, 11, 13, 14, 30, 32, 33, 38, 46, 48, 49, 50, 58, 60, 63, 69, 81, 83, 92, 98, 99, 135, 342, 373, 376, 377, 379, 388, 405, 410, 422, 424, 445, 452, 485, 491, 523, 617, 622, 630, 636, 637, 638, 640, 641, 645, 646, 660, 663, 678, 713, 716, 717, 718, 745, 746, 750, 751, 793, 794, 795, 812, 820, 821, 822, 825, 827, 829, 830, 831, 833, 836, 838, 839, 840, 842, 843, 846, 847, 851, 854, 856, 857, 858, 861, 862, 863, 865, 866, 870, 872, 873, 876, 877, 878], "filterwarn": [4, 5, 13], "ignor": [4, 5, 13, 45, 53, 54, 58, 75, 81, 140, 376, 377, 379, 388, 400, 401, 402, 431, 439, 447, 487, 488, 492, 531, 630, 637, 642, 664, 730, 731, 797, 821, 828, 830, 833, 846, 857, 878], "compos": [4, 6, 7, 11, 12, 13, 32, 33, 46, 58, 81, 376, 390, 391, 392, 393, 821, 829, 843, 846, 865, 867, 872, 879], "resiz": [4, 6, 7, 8, 11, 12, 13, 46, 47, 58, 81, 376, 412, 849], "centercrop": [4, 12, 13], "224": [4, 6, 7, 12, 13, 17, 19, 32, 33, 46, 47, 49, 814, 866], "totensor": [4, 6, 7, 11, 12, 13, 46], "485": [4, 12, 13, 46], "456": [4, 12, 13, 46, 846], "406": [4, 12, 13, 46, 58, 81, 398, 541, 635], "229": [4, 12, 13, 46, 280, 633], "225": [4, 12, 13, 46, 48, 235, 633], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 29, 32, 33, 46, 47, 48, 50, 814, 854, 866], "ipython": [4, 8, 12, 27, 28, 29, 30, 32, 33, 51], "displai": [4, 8, 12, 13, 29, 32, 33, 46, 47, 48, 50, 51, 821, 828, 830, 835, 846, 854], "end": [4, 8, 13, 46, 47, 58, 81, 127, 229, 285, 354, 373, 376, 378, 379, 424, 453, 475, 485, 487, 488, 630, 633, 808, 821, 822, 827, 830, 836, 842, 847, 849, 850, 857, 870, 875], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 14, 218, 632, 832], "ivy_model": [4, 5, 8, 12, 49], "ivy_alexnet": 4, "quick": [4, 21, 33, 822, 824, 844, 855], "trace_graph": [4, 5, 8, 12, 25, 26, 27, 28, 32, 33, 35, 36, 37, 38, 39, 40, 49, 795, 814, 851, 856, 864], "moment": [4, 58, 60, 81, 83, 377, 434, 616, 617, 622, 636, 797, 812, 820, 827, 857, 865, 866], "cost": [4, 60, 83, 616, 617, 620, 622, 623, 624, 636, 641, 716, 717, 718, 808, 831, 849, 870], "arg": [4, 6, 8, 9, 10, 11, 12, 13, 17, 19, 27, 28, 30, 32, 33, 37, 38, 39, 50, 53, 75, 97, 107, 123, 204, 214, 602, 629, 630, 632, 635, 772, 774, 789, 790, 793, 794, 795, 799, 802, 807, 812, 814, 826, 831, 832, 835, 841, 842, 843, 849, 851, 855, 865, 866, 867], "asarrai": [4, 5, 8, 11, 12, 47, 54, 58, 59, 70, 77, 81, 82, 93, 128, 386, 515, 516, 546, 557, 561, 562, 592, 593, 594, 630, 635, 637, 646, 647, 651, 751, 755, 835, 840, 843, 844], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 23, 32, 47, 48, 51, 54, 58, 67, 77, 81, 90, 138, 139, 142, 194, 195, 196, 212, 383, 509, 510, 512, 513, 630, 632, 638, 644, 689, 739, 740, 741, 742, 792, 793, 794, 795, 796, 797, 798, 812, 851, 857, 859, 877], "output": [4, 5, 7, 8, 9, 10, 12, 13, 23, 29, 30, 32, 33, 45, 46, 47, 49, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 149, 150, 153, 155, 180, 214, 215, 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, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 318, 319, 323, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 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692, 695, 748, 761, 763, 764, 765, 766, 829, 874], "world": [21, 29, 822, 874], "beginn": [21, 815, 872], "got": [21, 44, 835], "cover": [21, 32, 58, 81, 376, 413, 414, 415, 820, 825, 826, 828, 831, 833, 834, 839, 840, 846, 849, 850], "familiar": [21, 22, 23, 820, 821], "concept": [21, 22, 23], "turn": [21, 22, 25, 35, 62, 85, 98, 99, 400, 401, 402, 637, 660, 793, 821, 828, 829, 832, 833, 843, 846, 863], "unus": [21, 22, 25, 833, 842], "part": [21, 22, 25, 54, 57, 58, 80, 81, 86, 103, 113, 116, 119, 146, 147, 148, 254, 258, 281, 329, 330, 356, 370, 373, 376, 377, 379, 388, 420, 431, 485, 533, 627, 630, 633, 638, 674, 675, 774, 820, 821, 822, 823, 825, 828, 831, 837, 839, 842, 843, 846, 847, 849, 851, 852, 856, 857, 865, 866, 867, 870, 872, 877, 878, 879], "lazi": [21, 22, 25, 28, 35, 38, 39, 50], "decor": [21, 22, 27, 29, 30, 38, 50, 540, 635, 777, 779, 785, 818, 825, 826, 829, 831, 832, 836, 839, 842, 843, 844, 849], "kornia": [21, 22, 29, 32, 33, 46, 50, 814, 866], "roundup": 23, 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826], "subclass": [23, 32, 33, 840, 843, 849, 866], "dict": [23, 32, 33, 46, 50, 53, 59, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 104, 111, 112, 113, 114, 115, 116, 117, 118, 119, 124, 126, 135, 137, 142, 144, 150, 154, 156, 167, 168, 169, 173, 174, 181, 197, 200, 201, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 299, 300, 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 326, 335, 336, 337, 338, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 370, 379, 399, 400, 401, 402, 420, 453, 454, 455, 456, 457, 458, 459, 460, 463, 464, 465, 469, 470, 485, 491, 493, 494, 495, 497, 502, 504, 505, 506, 508, 510, 523, 524, 525, 526, 535, 536, 538, 539, 541, 542, 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"return_backend_compiled_fn": 32, "immedi": [32, 33, 812, 814, 820, 821], "built": [32, 33, 38, 46, 48, 51, 127, 630, 793, 794, 795, 821, 822, 828, 829, 846, 852, 858, 865, 871, 872, 876], "eager_graph": [32, 33, 814, 865, 866], "lazy_graph": [32, 33, 814, 865, 866], "thought": [32, 33, 821, 822, 838, 862, 870], "matter": [32, 33, 38, 833, 861], "haven": [32, 33, 38, 858, 872], "jax_out": [32, 33], "ideal": [32, 33, 830, 831, 843, 849, 854], "worth": [32, 33], "differenti": [32, 33, 296, 366, 367, 368, 375, 872], "chosen": [32, 33, 51, 101, 127, 229, 630, 633, 645, 749, 820, 830, 843], "plai": [32, 33, 378, 457, 814, 817, 821, 823, 826, 832, 836, 843, 846, 856, 872, 875], "role": [32, 33, 814, 817, 822, 823, 832, 843, 852, 873, 875, 879], "dl": [32, 33], "effortlessli": [32, 33], "previous": [32, 33, 604, 635, 802, 820, 821, 827, 839, 841, 846, 851], "default_devic": [32, 33, 207, 210, 211, 212, 218, 219, 632, 832, 835, 836], "as_n": [32, 33, 55, 56, 75, 78, 79, 159, 160, 161, 162, 163, 164, 170, 197, 198, 631, 632, 831], "certainli": [32, 33, 862, 878], "unnecessari": [32, 33, 843], "extend": [32, 33, 58, 81, 379, 388, 485, 526, 827, 828, 831, 834, 835, 838, 843, 847, 857, 869, 872, 878], "infrastructur": [32, 33, 868, 874, 875], "least": [32, 57, 58, 63, 80, 81, 241, 259, 274, 376, 379, 388, 404, 409, 463, 464, 465, 474, 476, 523, 633, 638, 645, 678, 748, 822, 826, 830, 831, 832, 833, 839, 842, 846, 866], "coco": 32, "seamlessli": [33, 846], "therefor": [33, 38, 54, 57, 58, 63, 80, 81, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 148, 149, 150, 156, 172, 176, 180, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 235, 236, 237, 238, 239, 241, 242, 244, 246, 247, 248, 252, 253, 254, 255, 256, 257, 261, 263, 264, 265, 266, 268, 269, 270, 271, 274, 276, 277, 278, 279, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 314, 329, 330, 336, 337, 339, 342, 370, 373, 376, 377, 379, 388, 395, 396, 397, 398, 400, 401, 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446, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 469, 470, 471, 472, 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, 508, 510, 511, 512, 513, 514, 515, 516, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 538, 539, 541, 542, 545, 546, 547, 548, 549, 550, 553, 554, 557, 559, 561, 562, 563, 565, 566, 567, 569, 570, 572, 577, 578, 592, 593, 594, 595, 596, 598, 600, 601, 614, 616, 617, 620, 622, 623, 624, 625, 651, 652, 653, 654, 655, 656, 659, 660, 661, 663, 667, 668, 669, 671, 672, 673, 674, 675, 676, 677, 678, 679, 681, 684, 685, 686, 688, 692, 693, 695, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 767, 768, 769, 826, 833, 834, 849], "docstr": [52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 154, 155, 156, 166, 169, 173, 174, 181, 198, 215, 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, 300, 301, 302, 304, 305, 306, 307, 308, 310, 311, 312, 313, 314, 315, 316, 318, 319, 320, 323, 330, 332, 333, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 373, 376, 379, 388, 395, 396, 397, 398, 400, 401, 402, 404, 408, 409, 410, 413, 414, 415, 419, 420, 423, 424, 425, 426, 427, 428, 430, 431, 432, 433, 434, 435, 437, 441, 442, 443, 444, 445, 446, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 469, 470, 471, 472, 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, 508, 510, 511, 512, 513, 514, 515, 516, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 538, 539, 541, 542, 545, 546, 547, 548, 549, 550, 553, 554, 557, 559, 561, 562, 563, 565, 566, 567, 569, 570, 572, 577, 578, 592, 593, 594, 595, 596, 598, 600, 601, 614, 615, 616, 617, 620, 622, 623, 624, 625, 630, 631, 633, 635, 638, 640, 645, 646, 647, 648, 649, 651, 652, 653, 654, 655, 656, 659, 660, 661, 663, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 694, 695, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 738, 739, 740, 741, 742, 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, 819, 820, 824, 828, 837, 838, 839, 840, 843, 845, 847], "liter": [52, 57, 58, 63, 74, 80, 81, 86, 111, 112, 113, 114, 115, 116, 117, 118, 119, 292, 296, 301, 302, 304, 368, 376, 377, 379, 382, 398, 408, 412, 420, 435, 441, 446, 449, 452, 485, 507, 627, 633, 638, 647, 679, 695, 756, 789, 849], "magnitud": [52, 57, 58, 74, 80, 81, 111, 112, 113, 114, 115, 116, 117, 118, 119, 221, 224, 241, 248, 274, 292, 296, 301, 302, 304, 368, 627, 633, 638, 688, 689, 789, 831], "handle_complex_input": [52, 57, 58, 74, 80, 81, 111, 112, 113, 114, 115, 116, 117, 118, 119, 292, 296, 301, 302, 304, 368, 627, 633, 789, 840], "element": [52, 54, 57, 58, 59, 62, 63, 65, 67, 68, 69, 71, 74, 75, 77, 78, 80, 81, 82, 85, 86, 88, 90, 91, 92, 94, 99, 103, 104, 107, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 130, 136, 137, 146, 147, 148, 164, 166, 169, 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, 302, 304, 306, 307, 308, 310, 311, 312, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 343, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 368, 370, 373, 376, 377, 378, 379, 388, 389, 400, 401, 402, 405, 410, 413, 414, 415, 419, 421, 422, 423, 429, 430, 431, 453, 463, 464, 465, 475, 476, 477, 479, 482, 492, 493, 495, 497, 499, 521, 522, 524, 525, 526, 527, 528, 529, 531, 532, 534, 538, 541, 542, 553, 554, 570, 572, 592, 593, 594, 596, 600, 601, 627, 630, 633, 635, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 660, 669, 671, 673, 674, 678, 683, 685, 686, 688, 692, 700, 703, 704, 705, 706, 707, 708, 709, 710, 719, 722, 728, 739, 747, 748, 749, 750, 751, 752, 753, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 772, 774, 777, 779, 793, 808, 834, 844, 846, 849, 851, 876], "138": [52, 111, 627], "165": [52, 111, 627, 637, 661], "hardswish": [52, 58, 74, 81, 299, 368, 627, 789], "leaky_relu": [52, 74, 81, 296, 627, 778], "alpha": [52, 57, 58, 74, 80, 81, 108, 113, 224, 290, 296, 297, 305, 309, 315, 368, 370, 377, 382, 383, 431, 507, 510, 511, 512, 627, 633, 789, 838, 843, 844], "slope": [52, 58, 74, 81, 113, 296, 297, 303, 305, 309, 368, 627, 789], "leaki": [52, 74, 113, 627, 789], "log_softmax": [52, 74, 627, 789], "0719": [52, 74, 114], "mish": [52, 74, 627, 789], "30340147": [52, 115, 627], "86509842": [52, 74, 115, 627], "269": [52, 117], "881": [52, 57, 80, 117, 227, 240, 280, 633], "422": [52, 118, 627], "155": [52, 85, 118, 627, 637, 661], "softplu": [52, 74, 627, 789, 849], "beta": [52, 58, 66, 74, 81, 89, 119, 305, 309, 315, 318, 319, 368, 370, 377, 378, 382, 383, 431, 459, 507, 511, 512, 627, 643, 738, 789, 814, 849], "threshold": [52, 57, 58, 74, 80, 81, 119, 272, 273, 312, 338, 368, 373, 378, 379, 454, 459, 492, 627, 633, 789, 849], "union": [52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123, 124, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 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, 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, 368, 370, 373, 374, 376, 377, 378, 379, 382, 383, 384, 386, 388, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 408, 409, 410, 412, 413, 414, 415, 416, 418, 419, 420, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 441, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 468, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 484, 485, 486, 487, 488, 489, 491, 492, 493, 494, 495, 497, 498, 499, 500, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 535, 538, 539, 541, 542, 546, 547, 548, 549, 550, 553, 554, 555, 556, 557, 559, 561, 562, 563, 565, 566, 569, 570, 572, 573, 577, 578, 582, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 614, 615, 616, 617, 618, 619, 620, 622, 623, 624, 625, 627, 629, 630, 631, 632, 633, 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, 663, 664, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 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, 726, 727, 728, 730, 731, 736, 737, 738, 739, 740, 741, 742, 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, 774, 777, 792, 797, 798, 826, 829, 831, 832, 833, 835, 838, 839, 842, 847, 849, 851, 856, 865, 866, 867], "3461": [52, 74, 119, 627], "6491": [52, 74, 119, 627], "_array_to_new_backend": 53, "_to_ivi": 53, "_to_n": 53, "to_ignor": [53, 73, 96, 642, 730, 731], "_to_new_backend": 53, "args_to_ivi": 53, "include_deriv": [53, 76, 642, 720, 731, 774], "nest": [53, 75, 76, 104, 107, 244, 568, 598, 615, 618, 633, 635, 636, 641, 716, 717, 719, 720, 721, 722, 723, 724, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 797, 826, 828, 829, 839, 841, 847, 854, 855, 857, 859, 872], "unchang": [53, 57, 376, 379, 421, 475, 637, 660], "deriv": [53, 54, 58, 60, 76, 77, 81, 83, 132, 137, 144, 150, 314, 318, 343, 370, 373, 616, 617, 620, 621, 622, 623, 624, 630, 636, 641, 642, 718, 720, 731, 795, 797, 798, 831, 832, 853, 855], "word": [53, 127, 379, 478, 630, 644, 742, 790, 793, 829, 842, 843, 859], "args_to_n": [53, 842], "cont_inplac": 53, "decid": [53, 75, 642, 730, 731, 820, 821, 831, 849], "args_to_new_backend": 53, "shallow": [53, 642, 726, 727, 731, 736, 737], "nativevari": 53, "mutabl": [53, 642, 720, 726, 727, 731, 736, 737, 827], "to_ivi": [53, 76, 642, 732, 842], "leaf": [53, 75, 82, 94, 104, 549, 642, 729, 730, 732, 759, 829, 839, 854], "travers": [53, 76, 642, 723, 731, 829, 831, 835, 851], "lowest": [53, 58, 67, 76, 81, 90, 388, 526, 642, 644, 731, 740, 808, 839, 857, 859, 869, 873, 877], "search": [53, 58, 76, 81, 745, 746, 785, 819, 821, 829, 833, 836, 846, 847, 861], "to_new_backend": 53, "_arraywithcr": [54, 103], "boolean": [54, 55, 57, 58, 59, 65, 68, 71, 75, 77, 78, 80, 81, 82, 88, 91, 94, 103, 104, 124, 126, 128, 129, 130, 136, 153, 169, 171, 173, 174, 177, 193, 203, 211, 217, 231, 232, 233, 234, 235, 236, 268, 269, 270, 271, 336, 337, 352, 373, 377, 379, 435, 446, 452, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 493, 500, 535, 538, 549, 556, 559, 560, 564, 565, 566, 567, 568, 569, 570, 579, 582, 585, 586, 588, 589, 614, 629, 630, 631, 632, 633, 635, 637, 640, 641, 642, 645, 648, 664, 703, 704, 705, 707, 709, 710, 712, 714, 716, 717, 729, 747, 748, 749, 761, 763, 777, 778, 779, 780, 785, 796, 829, 831, 839, 843, 846, 849], "never": [54, 58, 65, 77, 81, 88, 129, 379, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 500, 556, 635, 640, 703, 704, 705, 707, 709, 710, 712, 714, 822, 831, 842, 843, 846], "valueerror": [54, 58, 65, 77, 81, 88, 92, 129, 376, 378, 410, 421, 458, 463, 464, 471, 473, 475, 476, 477, 484, 500, 640, 703, 704, 705, 707, 709, 710, 712, 714, 753, 779, 809, 835], "buffer": [54, 77, 81, 88, 129, 135, 463, 464, 471, 473, 475, 476, 477, 484, 500, 630, 703, 704, 705, 707, 709, 710, 712, 714, 794, 795, 842, 857], "nativedtyp": [54, 55, 58, 62, 63, 67, 68, 71, 77, 81, 86, 90, 91, 94, 127, 128, 129, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 149, 150, 152, 153, 158, 159, 160, 161, 162, 163, 164, 165, 170, 171, 175, 177, 179, 183, 193, 313, 314, 315, 316, 317, 318, 319, 334, 341, 357, 370, 373, 383, 388, 509, 510, 511, 512, 513, 523, 524, 525, 526, 529, 532, 630, 631, 637, 638, 644, 645, 647, 648, 660, 679, 695, 740, 741, 742, 745, 746, 756, 758, 759, 762, 764, 766, 792, 831, 832, 838, 847, 851], "datatyp": [54, 58, 75, 77, 81, 129, 137, 141, 158, 179, 183, 376, 424, 630, 631, 772, 847, 865], "nativedevic": [54, 56, 58, 67, 77, 79, 81, 90, 127, 128, 129, 131, 132, 133, 136, 137, 138, 139, 141, 142, 143, 144, 148, 149, 150, 195, 196, 197, 198, 199, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 220, 313, 314, 329, 370, 383, 509, 510, 512, 513, 630, 632, 644, 739, 740, 741, 742, 792, 797, 798, 831, 832, 835, 838, 847], "39999998": [54, 128, 129, 630, 646, 751], "5999999": [54, 58, 81, 85, 128, 129, 298, 368, 377, 426, 630, 637, 660, 667], "0999999": [54, 71, 128, 129, 298, 308, 311, 354, 368, 373, 630, 762], "10000038": [54, 128, 129, 630], "90786433e": [54, 128, 129, 630], "310": [54, 128, 129, 630], "copy_arrai": [54, 77, 630], "to_ivy_arrai": [54, 77, 130, 630], "empty_lik": [54, 58, 77, 81, 265, 377, 429, 630, 633], "uniniti": [54, 131, 132, 630, 837], "from_dlpack": [54, 77, 630], "full_lik": [54, 77, 630, 847], "fill_valu": [54, 58, 68, 77, 81, 91, 136, 137, 253, 261, 379, 383, 493, 513, 630, 633, 645, 748, 831, 844, 847], "scalar": [54, 57, 58, 59, 63, 74, 77, 80, 81, 82, 86, 98, 113, 137, 142, 224, 245, 290, 296, 339, 340, 342, 347, 350, 352, 354, 359, 373, 376, 377, 378, 379, 424, 431, 453, 463, 464, 465, 474, 479, 601, 614, 630, 633, 635, 638, 695, 831, 841, 843, 857, 872], "fill": [54, 57, 58, 67, 68, 75, 77, 80, 81, 90, 91, 131, 136, 137, 139, 142, 143, 144, 149, 150, 275, 314, 370, 377, 379, 383, 435, 441, 446, 452, 474, 493, 494, 510, 512, 513, 630, 633, 644, 645, 740, 748, 792, 820, 844], "000123": [54, 137, 630], "stop": [54, 58, 60, 77, 81, 83, 127, 138, 139, 214, 377, 446, 452, 579, 617, 620, 622, 623, 624, 625, 630, 632, 635, 636, 641, 642, 716, 717, 718, 730, 797, 812, 838, 841, 849, 851, 857, 872], "num": [54, 77, 138, 139, 630, 777, 822, 838, 851], "endpoint": [54, 77, 138, 139, 630, 792, 838], "logspac": [54, 77, 630, 851], "sequenc": [54, 58, 62, 63, 65, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 104, 111, 112, 113, 114, 115, 116, 117, 118, 119, 133, 135, 137, 139, 142, 144, 150, 154, 156, 169, 173, 174, 181, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 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329, 350, 370, 384, 514, 630, 633, 645, 748], "conserv": [54, 140, 630], "cartesian": [54, 140, 630], "matrix": [54, 58, 59, 62, 63, 81, 82, 85, 86, 98, 99, 101, 103, 140, 146, 147, 148, 329, 330, 370, 377, 379, 388, 427, 430, 431, 434, 435, 436, 438, 441, 442, 443, 444, 445, 446, 447, 448, 451, 452, 483, 523, 535, 541, 630, 635, 637, 638, 661, 668, 670, 672, 673, 674, 675, 677, 678, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 690, 692, 693, 696, 777, 779, 792, 793, 808, 812, 820, 831, 843, 870, 872], "ij": [54, 71, 140, 630, 648, 760, 808], "rank": [54, 58, 63, 65, 72, 81, 86, 88, 95, 98, 99, 100, 101, 102, 107, 140, 324, 325, 326, 327, 328, 370, 377, 379, 388, 435, 436, 446, 449, 452, 485, 493, 497, 533, 630, 638, 640, 645, 649, 669, 671, 679, 681, 685, 687, 692, 694, 695, 702, 703, 711, 714, 715, 748, 768, 769, 815, 880], "ni": [54, 140, 630], "xi": [54, 140, 630], "scatter": [54, 59, 77, 82, 142, 577, 578, 630, 635, 828, 842, 849, 879], "unless": [54, 58, 63, 77, 81, 142, 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672, 673, 674, 675, 676, 677, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 692, 693, 779, 818, 836, 872], "diagon": [54, 58, 63, 81, 86, 99, 133, 146, 147, 148, 314, 329, 330, 370, 377, 379, 428, 431, 441, 447, 474, 630, 638, 671, 692], "triangular": [54, 58, 63, 86, 146, 147, 148, 329, 330, 370, 377, 447, 630, 638, 668, 674, 675, 681, 685], "triu": [54, 77, 630], "upper": [54, 58, 63, 67, 81, 86, 90, 133, 147, 148, 314, 330, 370, 377, 388, 447, 526, 630, 638, 644, 668, 674, 675, 685, 742, 831, 842, 846], "zeros_lik": [54, 58, 77, 153, 270, 379, 493, 616, 617, 620, 622, 623, 624, 630, 631, 633, 636, 638, 640, 685, 700, 843, 849], "data_typ": [55, 58, 78, 81, 183, 631, 828, 831, 846, 847], "_arraywithdatatyp": [55, 103], "irrespect": [55, 63, 78, 86, 153, 631, 638, 688, 829, 842, 853, 879], "promot": [55, 57, 58, 63, 78, 80, 81, 86, 93, 103, 104, 153, 156, 179, 180, 181, 187, 222, 223, 224, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 238, 239, 241, 244, 246, 248, 262, 263, 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284, 286, 287, 288, 291, 292, 294, 336, 337, 360, 373, 559, 628, 631, 633, 635, 638, 648, 649, 686, 695, 761, 763, 768, 769, 825, 834], "desir": [55, 56, 58, 68, 71, 75, 78, 79, 81, 91, 94, 98, 153, 155, 156, 215, 320, 361, 370, 373, 379, 388, 483, 529, 532, 533, 631, 632, 638, 645, 648, 690, 747, 762, 792, 793, 822, 827, 830, 831, 832, 843, 851, 861, 865, 872], "broadcast_arrai": [55, 78, 631], "mix": [55, 57, 78, 80, 81, 82, 87, 90, 103, 104, 154, 167, 168, 181, 200, 201, 231, 234, 235, 236, 241, 242, 248, 252, 260, 261, 271, 274, 277, 283, 378, 388, 459, 530, 549, 551, 552, 553, 554, 563, 598, 601, 631, 632, 633, 635, 637, 638, 639, 640, 643, 648, 651, 653, 656, 658, 659, 661, 667, 668, 690, 697, 699, 700, 738, 760, 762, 765, 778, 780, 820, 824, 831, 832, 833, 842, 849, 851, 859, 872, 876, 878], "broadcast_to": [55, 78, 631, 831], "can_cast": [55, 78, 631, 831, 839, 843], "accord": [55, 58, 59, 65, 71, 78, 88, 94, 156, 166, 224, 235, 241, 248, 274, 285, 320, 370, 376, 379, 421, 485, 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169], "1220": [55, 169], "int16": [55, 58, 67, 71, 78, 90, 156, 160, 162, 167, 169, 176, 191, 388, 524, 525, 631, 648, 740, 758, 759, 764, 766, 777, 778, 831, 843, 846, 851], "32768": [55, 78, 169, 594, 635], "32767": [55, 78, 169], "is_bool_dtyp": [55, 78, 631], "is_float_dtyp": [55, 78, 631, 847], "is_int_dtyp": [55, 78, 631, 844, 847], "is_uint_dtyp": [55, 78, 631, 844, 847], "result_typ": [55, 78, 631, 831], "arrays_and_dtyp": [55, 78, 181, 631], "_arraywithdevic": [56, 103], "move": [56, 58, 79, 81, 148, 211, 215, 219, 329, 370, 379, 484, 630, 632, 795, 822, 832, 847], "addit": [56, 58, 59, 66, 79, 81, 82, 89, 124, 126, 215, 224, 284, 378, 382, 388, 453, 507, 522, 527, 546, 547, 548, 615, 629, 632, 633, 635, 637, 641, 643, 664, 718, 738, 793, 808, 820, 821, 822, 827, 831, 833, 834, 837, 839, 841, 842, 843, 846, 847, 849, 853, 854, 856, 865, 872, 873, 874, 878], "__dlpack__": [56, 79, 134, 215, 630, 632], "caveat": [56, 79, 215, 378, 457, 632], "portabl": [56, 79, 215, 632, 814, 870], "_arraywithelementwis": [57, 103], "ab": [57, 63, 73, 80, 96, 103, 104, 279, 335, 352, 373, 379, 492, 633, 638, 642, 679, 689, 695, 727, 730, 774, 807, 808, 818, 826, 831, 836, 840, 843, 846, 869], "absolut": [57, 58, 63, 73, 75, 80, 81, 86, 103, 221, 285, 335, 352, 355, 361, 373, 377, 378, 431, 448, 454, 456, 633, 638, 679, 680, 681, 686, 772, 774, 777, 779, 780, 815, 821], "aco": [57, 80, 633], "invers": [57, 58, 63, 80, 81, 86, 222, 223, 226, 227, 228, 229, 230, 345, 373, 376, 386, 399, 408, 410, 420, 515, 633, 638, 677, 680, 684, 799, 831], "cosin": [57, 80, 222, 223, 238, 239, 313, 316, 370, 376, 398, 408, 633, 793], "acosh": [57, 80, 167, 168, 631, 633, 818, 836], "area": [57, 58, 80, 81, 85, 223, 227, 230, 376, 412, 419, 423, 633, 817, 842, 849, 862, 868], "hyperbol": [57, 80, 223, 227, 230, 239, 287, 291, 292, 305, 309, 368, 633], "sector": [57, 80, 223, 227, 230, 633, 862], "multipli": [57, 58, 62, 71, 80, 81, 85, 98, 224, 290, 353, 376, 377, 412, 443, 444, 524, 525, 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frameworks by one line of code \u261d": [[44, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[44, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[44, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[44, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[44, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[44, "Set-Backend-Framework"]], "Define Model": [[44, "Define-Model"], [45, "Define-Model"]], "Create Model": [[44, "Create-Model"]], "Create Optimizer": [[44, "Create-Optimizer"]], "Input and Target": [[44, "Input-and-Target"]], "Loss Function": [[44, "Loss-Function"]], "Training Loop": [[44, "Training-Loop"]], "1.1: Framework Selection": [[38, "1.1:-Framework-Selection"]], "Accelerating XGBoost with JAX": [[15, "Accelerating-XGBoost-with-JAX"]], "Imports": [[15, "Imports"], [12, "Imports"], [8, "Imports"]], "Tests": [[15, "Tests"]], "Loading the Data": [[15, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[15, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[15, "JAX-backend"]], "Tensorflow backend": [[15, "Tensorflow-backend"]], "PyTorch backend": [[15, "PyTorch-backend"]], "More exhaustive example": [[15, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[15, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[15, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[15, "Comparison-of-Metrics"]], "Transpile code": [[26, "Transpile-code"]], "Transpiling a haiku model to build on top": [[18, "Transpiling-a-haiku-model-to-build-on-top"]], "Accelerating PyTorch models with JAX": [[14, "Accelerating-PyTorch-models-with-JAX"]], "Transpiling a Tensorflow model to build on top": [[19, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Transpile any model": [[30, "Transpile-any-model"]], "Round up": [[30, "Round-up"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Installation": [[4, "Installation"], [13, "Installation"], [12, "Installation"]], "Data Preparation": [[4, "Data-Preparation"], [12, "Data-Preparation"], [5, "Data-Preparation"], [8, "Data-Preparation"]], "Ivy AlexNet inference in Torch": [[4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[4, "TensorFlow-inference"]], "JAX inference": [[4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "Training PyTorch ResNet in your TensorFlow Projects": [[13, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Framework Incompatibility": [[13, "Framework-Incompatibility"], [6, "Framework-Incompatibility"]], "Transpiling a PyTorch model to TensorFlow": [[13, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "About the transpiled model": [[13, "About-the-transpiled-model"], [6, "About-the-transpiled-model"]], "Setting-up the source model": [[13, "Setting-up-the-source-model"], [6, "Setting-up-the-source-model"]], "Load the Data": [[13, "Load-the-Data"]], "Visualize a few images": [[13, "Visualize-a-few-images"]], "Load the pre-trained model": [[13, "Load-the-pre-trained-model"]], "Converting the model from TensorFlow to PyTorch": [[13, "Converting-the-model-from-TensorFlow-to-PyTorch"], [6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[13, "Comparing-the-results"], [6, "Comparing-the-results"], [7, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[13, "Fine-tuning-the-transpiled-model"], [6, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[13, "Conclusion"], [6, "Conclusion"], [7, "Conclusion"]], "ODSC Ivy Demo": [[32, "ODSC-Ivy-Demo"]], "Ivy Backend Handler": [[32, "Ivy-Backend-Handler"], [23, "Ivy-Backend-Handler"]], "Data Structures": [[32, "Data-Structures"], [23, "Data-Structures"]], "Ivy Functional API": [[32, "Ivy-Functional-API"], [23, "Ivy-Functional-API"]], "Graph Tracer": [[32, "Graph-Tracer"]], "Any function": [[32, "Any-function"], [33, "Any-function"]], "Any library": [[32, "Any-library"], [33, "Any-library"]], "Any model": [[32, "Any-model"], [33, "Any-model"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "1.3: Dynamic vs Static": [[40, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[40, "Dynamic"]], "Static": [[40, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[40, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "0.2: Transpile": [[36, "0.2:-Transpile"]], "3.0: Perceiver": [[42, "3.0:-Perceiver"]], "2.0: Kornia": [[41, "2.0:-Kornia"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Compilation of a Basic Function": [[45, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[45, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[45, "Function-compilation-\ud83d\udee0"]], "Set backend": [[45, "Set-backend"]], "Sample input": [[45, "Sample-input"]], "Define function to compile": [[45, "Define-function-to-compile"]], "Compile the function": [[45, "Compile-the-function"]], "Check results": [[45, "Check-results"], [45, "id1"]], "Compiling simple neural network \ud83e\udde0": [[45, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[45, "Create-model"]], "Define input": [[45, "Define-input"]], "Compile network": [[45, "Compile-network"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Load the image example \ud83d\uddbc\ufe0f": [[12, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [8, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[12, "Visualise-image"], [8, "Visualise-image"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "Write a model using Ivy": [[31, "Write-a-model-using-Ivy"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Importing Ivy": [[0, "Importing-Ivy"], [23, "Importing-Ivy"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Transpile any library": [[29, "Transpile-any-library"]], "Unify code": [[24, "Unify-code"]], "How to use decorators": [[28, "How-to-use-decorators"]], "Trace": [[28, "Trace"], [27, "Trace"]], "Lazy vs Eager": [[27, "Lazy-vs-Eager"]], "Quickstart": [[33, "Quickstart"]], "Get familiar with Ivy": [[33, "Get-familiar-with-Ivy"]], "Functional API": [[33, "Functional-API"]], "Stateful API": [[33, "Stateful-API"]], "Tracing code": [[33, "Tracing-code"]], "How To Convert Models from PyTorch to PaddlePaddle": [[7, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[7, "About-the-Model"]], "Transpiling the Model": [[7, "Transpiling-the-Model"]], "0.0: Unify": [[34, "0.0:-Unify"]], "3.1: Stable Diffusion": [[43, "3.1:-Stable-Diffusion"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Transpiling a PyTorch model to build on top": [[17, "Transpiling-a-PyTorch-model-to-build-on-top"]], "Developing a convolutional network using Ivy": [[20, "Developing-a-convolutional-network-using-Ivy"]], "Write Ivy code": [[23, "Write-Ivy-code"]], "Contents": [[23, "Contents"]], "Installing Ivy": [[23, "Installing-Ivy"]], "0.1: Compile": [[35, "0.1:-Compile"]], "1.2: As a Decorator": [[39, "1.2:-As-a-Decorator"]], "Trace code": [[25, "Trace-code"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[46, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[46, "Table-of-Contents"]], "Defining the model": [[46, "Defining-the-model"]], "Model construction": [[46, "Model-construction"]], "Some helper functions": [[46, "Some-helper-functions"]], "Transpiling the model": [[46, "Transpiling-the-model"]], "PyTorch pipeline": [[46, "PyTorch-pipeline"]], "Dataset download": [[46, "Dataset-download"]], "DataLoader": [[46, "DataLoader"]], "Training": [[46, "Training"]], "Resnet 18": [[51, "Resnet-18"]], "Ivy as a Transpiler Introduction": [[50, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[50, "To-use-the-transpiler:"]], "Transpiler Interface": [[50, "Transpiler-Interface"]], "Telemetry": [[50, "Telemetry"]], "1. 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844, 846, 857], "122": [0, 14, 55, 169, 239, 633], "211321": 0, "256": [0, 4, 8, 12, 13, 57, 82, 284, 285, 594, 637, 652, 654, 777], "683288": 0, "250000": 0, "105": [0, 13, 63, 85, 637, 638, 660, 661, 676, 683], "890000": 0, "2125": 0, "870000": 0, "deepen": 0, "averag": [0, 6, 7, 46, 48, 58, 60, 64, 81, 83, 87, 376, 378, 382, 388, 390, 391, 395, 396, 397, 455, 456, 457, 458, 459, 460, 507, 523, 616, 617, 622, 636, 637, 639, 641, 664, 697, 716, 717, 792, 793], "across": [0, 1, 12, 14, 15, 27, 28, 29, 30, 44, 58, 68, 75, 81, 82, 91, 103, 212, 213, 241, 248, 274, 292, 378, 382, 453, 504, 507, 538, 559, 595, 632, 633, 635, 637, 642, 645, 660, 664, 725, 745, 746, 793, 820, 825, 831, 833, 835, 838, 839, 841, 846, 849, 870, 872, 877], "all": [0, 1, 2, 4, 5, 6, 7, 8, 12, 13, 14, 17, 18, 19, 20, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 45, 46, 48, 49, 51, 53, 54, 58, 59, 62, 63, 65, 67, 72, 73, 75, 76, 77, 80, 81, 82, 85, 86, 88, 90, 95, 96, 98, 99, 127, 135, 142, 146, 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75, 77, 80, 81, 86, 88, 94, 95, 103, 127, 133, 135, 140, 148, 293, 329, 336, 337, 370, 373, 376, 377, 379, 388, 404, 405, 409, 410, 420, 421, 428, 463, 464, 465, 469, 474, 475, 521, 533, 630, 633, 638, 640, 645, 648, 649, 669, 670, 676, 678, 681, 683, 684, 694, 695, 709, 745, 746, 748, 761, 762, 763, 764, 765, 766, 767, 768, 769, 839, 841, 846, 849, 851, 869, 872, 879], "reshap": [0, 4, 32, 33, 48, 49, 58, 62, 63, 65, 75, 81, 85, 86, 88, 361, 373, 376, 377, 379, 395, 396, 397, 400, 413, 414, 415, 418, 427, 444, 469, 475, 615, 635, 637, 638, 640, 653, 655, 659, 679, 695, 842, 843, 846, 849, 851, 853, 856, 869], "float32": [0, 4, 8, 12, 15, 17, 19, 24, 25, 44, 46, 47, 48, 54, 55, 58, 59, 62, 77, 78, 81, 82, 85, 94, 139, 142, 144, 150, 151, 152, 156, 160, 161, 164, 165, 166, 167, 170, 173, 174, 176, 181, 184, 190, 240, 254, 281, 334, 347, 370, 373, 376, 377, 378, 388, 398, 408, 421, 447, 453, 458, 526, 563, 600, 630, 631, 633, 635, 637, 638, 641, 653, 655, 656, 659, 686, 688, 689, 695, 717, 718, 774, 777, 778, 814, 831, 833, 844, 846, 847, 866, 867], "def": [0, 4, 8, 11, 13, 14, 15, 17, 19, 23, 24, 25, 26, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 44, 45, 46, 47, 48, 50, 57, 80, 123, 225, 540, 629, 635, 641, 642, 717, 718, 725, 807, 814, 818, 820, 821, 825, 826, 829, 831, 832, 833, 835, 836, 838, 839, 841, 842, 843, 844, 846, 847, 849, 851, 853, 854, 855, 856, 865, 866, 867], "isinst": [0, 8, 15, 30, 32, 33, 835, 843, 846, 847, 855, 856], "rang": [0, 4, 6, 7, 9, 10, 13, 15, 32, 33, 44, 45, 46, 48, 54, 58, 71, 77, 81, 127, 138, 139, 288, 300, 308, 320, 368, 370, 377, 379, 388, 431, 443, 478, 486, 488, 493, 498, 524, 525, 526, 546, 615, 630, 633, 635, 646, 648, 750, 758, 759, 764, 766, 777, 779, 780, 792, 814, 817, 820, 831, 835, 839, 846, 851, 854, 855, 856, 872, 878], "len": [0, 6, 7, 8, 13, 15, 46, 48, 54, 58, 63, 81, 86, 140, 317, 326, 327, 370, 376, 377, 388, 410, 421, 433, 436, 446, 452, 533, 630, 638, 674, 693, 829, 830, 835, 842, 843, 846, 853, 856, 865], 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836, 843, 846, 857, 870, 872, 875, 877], "gpu": [0, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 46, 48, 50, 51, 197, 199, 200, 203, 206, 208, 210, 212, 213, 216, 218, 220, 632, 812, 814, 821, 822, 830, 832, 853, 858, 870, 872, 875, 876, 877], "tpu": [0, 46, 195, 201, 210, 212, 217, 632, 812, 832, 872, 875], "explicitli": [0, 638, 674, 675, 690, 774, 793, 794, 795, 818, 825, 826, 827, 829, 831, 834, 835, 836, 839, 840, 841, 842, 844, 846, 851, 857, 866, 872], "hardwar": [0, 4, 46, 103, 107, 821, 849, 862, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878], "mai": [0, 1, 6, 56, 57, 58, 63, 69, 70, 79, 80, 86, 93, 103, 104, 127, 134, 145, 215, 241, 242, 248, 253, 261, 269, 270, 274, 275, 277, 292, 336, 337, 373, 405, 545, 581, 630, 632, 633, 635, 638, 646, 647, 648, 686, 695, 750, 751, 752, 753, 754, 757, 761, 762, 763, 765, 777, 808, 819, 820, 821, 822, 825, 829, 830, 831, 835, 836, 839, 840, 841, 843, 844, 846, 849, 852, 853, 855, 863, 879], "vari": [0, 58, 69, 98, 99, 292, 405, 546, 633, 635, 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47, 48, 49, 814, 816, 819, 821, 822, 825, 828, 830, 836, 837, 846, 858], "clone": [2, 4, 8, 12, 32, 46, 48, 49, 814, 816, 822, 836, 858], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 14, 19, 27, 28, 29, 30, 32, 33, 46, 47, 48, 49, 50, 51, 57, 58, 80, 81, 83, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 616, 617, 630, 631, 633, 636, 638, 640, 648, 686, 687, 715, 765, 814, 816, 821, 822, 825, 828, 830, 831, 834, 836, 858, 866], "github": [2, 4, 5, 8, 11, 12, 14, 32, 46, 47, 48, 49, 50, 814, 816, 817, 819, 822, 823, 825, 828, 830, 831, 833, 834, 836, 837, 845, 846, 858, 861, 880], "com": [2, 4, 5, 6, 7, 8, 11, 12, 14, 19, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 825, 828, 830, 831, 836, 858], "unifyai": [2, 4, 8, 12, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 828, 836, 858], "model": [2, 3, 4, 9, 15, 16, 21, 22, 23, 49, 51, 241, 274, 378, 454, 633, 790, 794, 795, 812, 854, 855, 859, 865, 866, 870, 871, 872, 873, 874, 875, 876, 878, 879], "depth": [2, 4, 6, 8, 12, 47, 54, 58, 62, 77, 81, 85, 142, 376, 379, 412, 472, 546, 558, 630, 635, 637, 655, 656, 822, 830, 854, 855, 856, 858], "repositori": [2, 4, 8, 12, 816, 820, 821, 822, 824, 825, 828, 836, 845, 863], "cd": [2, 4, 8, 12, 32, 49, 814, 816, 821, 822, 836, 858], "resnet": [3, 6, 14, 21, 32, 865, 866], "imag": [3, 4, 6, 7, 11, 14, 17, 21, 29, 32, 33, 46, 47, 48, 49, 50, 51, 58, 62, 80, 81, 85, 103, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 284, 285, 287, 288, 292, 376, 395, 396, 412, 413, 414, 416, 546, 633, 635, 637, 650, 651, 652, 653, 654, 657, 658, 659, 793, 814, 821, 836, 849, 851, 852, 854, 856, 858, 865, 866, 872], "classif": [3, 4, 12, 15, 21, 46, 872], "acceler": [3, 21, 831, 843, 870, 874, 875, 876, 877], "convert": [3, 8, 9, 11, 14, 15, 17, 19, 21, 22, 24, 26, 29, 30, 32, 33, 34, 36, 38, 46, 49, 51, 53, 54, 57, 75, 76, 77, 80, 98, 128, 129, 141, 151, 152, 194, 195, 196, 197, 208, 216, 220, 240, 280, 379, 384, 463, 464, 465, 514, 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21], "project": [3, 12, 14, 21, 26, 27, 28, 29, 30, 32, 33, 36, 99, 637, 664, 793, 814, 816, 817, 820, 821, 822, 823, 826, 827, 828, 846, 855, 857, 861, 862, 863, 866, 868, 870, 872, 875, 879, 880], "convnext": [3, 6, 11, 13, 21], "finetun": [3, 21, 46], "video": [4, 8, 11, 12, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 815, 820, 821, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 835, 837, 838, 839, 840, 841, 842, 843, 844, 846, 847, 849, 858, 870], "tutori": [4, 6, 7, 8, 11, 12, 13, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 822, 843, 858], "three": [4, 5, 21, 27, 37, 38, 48, 58, 140, 313, 370, 379, 465, 630, 821, 822, 829, 830, 831, 833, 843, 846, 849, 850, 851, 873, 878], "major": [4, 5, 645, 748, 831, 832, 844, 846, 857, 862, 869, 872], "ml": [4, 5, 6, 13, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 48, 51, 815, 819, 843, 850, 851, 852, 854, 855, 856, 860, 862, 863, 866, 868, 869, 870, 871, 872, 875, 877, 879], "framework": [4, 5, 7, 9, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 39, 46, 48, 50, 53, 59, 171, 193, 203, 206, 217, 544, 560, 564, 596, 599, 631, 632, 635, 642, 721, 772, 774, 778, 785, 790, 797, 802, 803, 817, 818, 820, 821, 824, 825, 826, 827, 828, 830, 831, 832, 833, 835, 836, 838, 839, 840, 842, 843, 846, 847, 849, 850, 851, 853, 856, 857, 858, 859, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 873, 876], "sinc": [4, 8, 12, 13, 29, 30, 32, 33, 46, 48, 58, 81, 99, 373, 816, 821, 822, 825, 826, 827, 828, 829, 830, 831, 832, 835, 842, 843, 857, 862, 872, 878], "automat": [4, 8, 9, 12, 13, 30, 32, 33, 38, 820, 821, 822, 824, 827, 828, 830, 831, 837, 839, 842, 846, 849, 850, 852, 855, 856, 858, 859, 863, 872, 875, 879], "sure": [4, 8, 11, 12, 13, 14, 15, 32, 46, 817, 820, 821, 822, 825, 830, 835, 836, 843, 844, 846, 849, 858], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 15, 27, 28, 30, 47, 58, 63, 75, 86, 104, 376, 378, 399, 457, 581, 635, 638, 681, 795, 812, 814, 821, 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58, 69, 73, 77, 81, 96, 195, 196, 200, 212, 308, 388, 523, 558, 574, 632, 635, 641, 646, 717, 718, 753, 785, 802, 803, 814, 819, 820, 821, 822, 823, 825, 826, 827, 828, 829, 831, 832, 833, 834, 837, 840, 841, 842, 843, 844, 845, 846, 847, 851, 853, 854, 856, 857, 858, 862, 865, 866, 867, 868, 870, 872, 875, 876, 878], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 14, 47, 48, 51, 54, 58, 67, 75, 77, 81, 90, 103, 106, 107, 108, 127, 128, 129, 131, 132, 133, 136, 137, 138, 139, 141, 142, 143, 144, 146, 147, 148, 149, 150, 194, 195, 196, 197, 198, 199, 200, 201, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 218, 220, 313, 314, 329, 330, 370, 383, 473, 509, 510, 512, 513, 537, 551, 552, 630, 635, 644, 739, 740, 741, 742, 772, 774, 775, 790, 792, 793, 794, 795, 796, 797, 798, 799, 812, 822, 824, 827, 831, 835, 839, 840, 844, 846, 847, 849, 851, 856, 857, 858, 859, 862, 871, 872, 874, 875, 876, 877], "torchvis": [4, 6, 11, 12, 13, 46, 863], "transform": [4, 5, 6, 7, 11, 12, 13, 14, 29, 32, 33, 46, 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392, 393, 821, 829, 843, 846, 865, 867, 872, 879], "resiz": [4, 6, 7, 8, 11, 12, 13, 46, 47, 58, 81, 376, 412, 849], "centercrop": [4, 12, 13], "224": [4, 6, 7, 12, 13, 17, 19, 32, 33, 46, 47, 49, 814, 866], "totensor": [4, 6, 7, 11, 12, 13, 46], "485": [4, 12, 13, 46], "456": [4, 12, 13, 46, 846], "406": [4, 12, 13, 46, 58, 81, 398, 541, 635], "229": [4, 12, 13, 46, 280, 633], "225": [4, 12, 13, 46, 48, 235, 633], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 29, 32, 33, 46, 47, 48, 50, 814, 854, 866], "ipython": [4, 8, 12, 27, 28, 29, 30, 32, 33, 51], "displai": [4, 8, 12, 13, 29, 32, 33, 46, 47, 48, 50, 51, 821, 828, 830, 835, 846, 854], "end": [4, 8, 13, 46, 47, 58, 81, 127, 229, 285, 354, 373, 376, 378, 379, 424, 453, 475, 485, 487, 488, 630, 633, 808, 821, 822, 827, 830, 836, 842, 847, 849, 850, 857, 870, 875], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 14, 218, 632, 832], "ivy_model": [4, 5, 8, 12, 49], "ivy_alexnet": 4, "quick": [4, 21, 33, 822, 824, 844, 855], "trace_graph": [4, 5, 8, 12, 25, 26, 27, 28, 32, 33, 35, 36, 37, 38, 39, 40, 49, 795, 814, 851, 856, 864], "moment": [4, 58, 60, 81, 83, 377, 434, 616, 617, 622, 636, 797, 812, 820, 827, 857, 865, 866], "cost": [4, 60, 83, 616, 617, 620, 622, 623, 624, 636, 641, 716, 717, 718, 808, 831, 849, 870], "arg": [4, 6, 8, 9, 10, 11, 12, 13, 17, 19, 27, 28, 30, 32, 33, 37, 38, 39, 50, 53, 75, 97, 107, 123, 204, 214, 602, 629, 630, 632, 635, 772, 774, 789, 790, 793, 794, 795, 799, 802, 807, 812, 814, 826, 831, 832, 835, 841, 842, 843, 849, 851, 855, 865, 866, 867], "asarrai": [4, 5, 8, 11, 12, 47, 54, 58, 59, 70, 77, 81, 82, 93, 128, 386, 515, 516, 546, 557, 561, 562, 592, 593, 594, 630, 635, 637, 646, 647, 651, 751, 755, 835, 840, 843, 844], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 23, 32, 47, 48, 51, 54, 58, 67, 77, 81, 90, 138, 139, 142, 194, 195, 196, 212, 383, 509, 510, 512, 513, 630, 632, 638, 644, 689, 739, 740, 741, 742, 792, 793, 794, 795, 796, 797, 798, 812, 851, 857, 859, 877], "output": [4, 5, 7, 8, 9, 10, 12, 13, 23, 29, 30, 32, 33, 45, 46, 47, 49, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 149, 150, 153, 155, 180, 214, 215, 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, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 318, 319, 323, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 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[23, 32, 58, 81, 379, 493, 795, 812, 828], "array_mod": [23, 32, 579, 603, 635, 848], "set_array_mod": [23, 32, 603, 635, 848], "ultim": [23, 32, 865], "sigmoid": [23, 32, 33, 44, 52, 58, 74, 81, 302, 368, 383, 509, 627, 789, 851, 854, 855], "z": [23, 32, 33, 45, 46, 54, 57, 58, 59, 63, 64, 67, 69, 71, 77, 80, 81, 82, 86, 87, 88, 90, 94, 103, 104, 138, 139, 141, 142, 202, 224, 225, 229, 231, 234, 236, 241, 252, 253, 256, 257, 258, 260, 261, 266, 268, 270, 271, 272, 273, 281, 290, 301, 302, 336, 337, 339, 368, 373, 378, 388, 454, 456, 457, 458, 459, 460, 466, 470, 481, 522, 523, 526, 533, 538, 550, 553, 554, 561, 562, 578, 591, 593, 594, 602, 615, 630, 632, 633, 635, 638, 639, 640, 642, 644, 645, 646, 648, 669, 678, 683, 684, 688, 695, 697, 698, 699, 700, 722, 726, 728, 736, 740, 741, 742, 745, 750, 760, 761, 763, 764, 765, 792, 814, 827, 829, 832, 833, 851, 853, 865], "divid": [23, 28, 32, 33, 49, 57, 58, 59, 65, 75, 80, 81, 88, 103, 104, 248, 382, 455, 502, 503, 504, 507, 593, 633, 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843, 844, 846, 853, 854, 855, 867, 868], "with_numpi": 24, "reproduc": [24, 49, 62, 85, 637, 660, 777, 778, 779, 780, 785, 818, 825, 836], "x_": [24, 34, 99, 285, 633, 867], "66391283": 24, "12516928": 24, "38367081": 24, "03102401": 24, "76419425": 24, "52797794": 24, "90346956": 24, "61316347": 24, "27585283": 24, "66309303": 24, "ivy_repo": 24, "sever": [24, 25, 34, 35, 37, 38, 39, 58, 81, 98, 376, 377, 390, 391, 392, 393, 445, 777, 821, 822, 847, 857, 870, 876], "pro": [24, 25, 26, 34, 35, 36, 37, 38, 39], "pick": [25, 35, 792], "trigger": [25, 35, 795, 820, 837], "unif": [25, 27, 28, 35, 37, 815, 853, 862, 868, 878], "55563945": 25, "65538704": 25, "14150524": 25, "46951997": 25, "30220294": 25, "14739668": 25, "57017946": 25, "91962677": 25, "51029003": 25, "59644395": 25, "constitu": [25, 35, 75, 856], "5556394": 25, "655387": 25, "1415051": 25, "4695197": 25, "3022028": 25, "1473966": 25, "5701794": 25, "91962665": 25, "51028997": 25, "5964439": 25, "985": 25, "000": [25, 80, 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"15576624": 26, "implic": [26, 36, 37, 40, 829], "fw": [27, 28, 29, 30, 62, 85, 388, 523, 637, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 774, 821, 846], "mxnet": [27, 28, 29, 30, 210, 632, 802, 820, 821, 862, 879], "einop": [27, 28, 29, 30, 46, 48, 51, 59, 82, 546, 547, 548, 635, 831, 862], "miniconda": [27, 28, 29, 30], "multienv": [27, 28, 29, 30], "site": [27, 28, 29, 30, 873], "psutil": [27, 28, 29, 30, 46, 48, 51], "termcolor": [27, 28, 29, 30, 46, 48, 51, 75, 104], "colorama": [27, 28, 29, 30, 46, 48], "535": [27, 28, 29, 30, 52, 74, 119, 627, 835], "diskcach": [27, 28, 29, 30, 46], "auth": [27, 28, 29, 30], "urllib3": [27, 28, 29, 30, 46], "pyvi": [27, 28, 29, 30, 32, 33], "dill": [27, 28, 29, 30, 46], "astunpars": [27, 28, 29, 30], "cloudpickl": [27, 28, 29, 30], "gast": [27, 28, 29, 30], "wheel": [27, 28, 29, 30, 46, 48, 51, 861], "six": [27, 28, 29, 30, 46, 51, 821, 849], "cachetool": [27, 28, 29, 30], "pyasn1": [27, 28, 29, 30], "rsa": [27, 28, 29, 30], "jsonpickl": 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606, 616, 617, 619, 620, 621, 622, 633, 635, 636, 639, 698, 699, 741, 794, 797, 855], "981554": 28, "happen": [28, 32, 33, 293, 633, 814, 821, 822, 823, 832, 842, 846, 854, 863, 865, 866], "wherea": [28, 39, 81, 376, 422, 822, 826, 829, 831, 832, 833, 838, 839, 846, 856, 869], "subtract": [28, 32, 33, 57, 80, 103, 104, 135, 379, 485, 630, 633, 826, 829, 833], "often": [29, 58, 378, 453, 819, 825, 835, 838, 839, 843, 846, 857, 863, 873, 876, 879], "fortun": [29, 30, 825], "everyth": [29, 47, 807, 814, 820, 821, 822, 823, 824, 830, 833, 842, 843, 844, 846, 852, 857, 858, 863], "practic": [29, 822, 827, 830, 843, 845, 875], "jax_kornia": [29, 32, 33, 814, 866], "000000000034": [29, 32, 33, 814, 866], "raw_img": [29, 32, 33, 814, 866], "sharp": [29, 32, 33, 814], "prefer": [29, 32, 33, 248, 633, 821, 829, 835, 836, 840, 843, 858, 872], "whole": [30, 58, 81, 379, 382, 492, 505, 506, 508, 822, 828, 837], "full": [30, 58, 63, 81, 85, 86, 98, 99, 101, 166, 253, 261, 324, 325, 326, 327, 328, 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"_c": 30, "0x7f252c392390": 30, "flatten": [30, 32, 33, 46, 48, 51, 58, 59, 63, 65, 68, 69, 81, 82, 86, 88, 91, 92, 341, 357, 373, 377, 379, 388, 428, 474, 484, 488, 493, 494, 497, 499, 521, 528, 529, 530, 531, 532, 533, 546, 550, 635, 638, 640, 645, 646, 676, 683, 695, 701, 706, 708, 745, 746, 750, 751, 752, 753, 772, 774, 814, 842, 849], "keyword": [30, 32, 33, 48, 50, 53, 54, 58, 75, 81, 104, 140, 275, 376, 379, 388, 424, 485, 523, 537, 540, 573, 602, 630, 633, 635, 638, 642, 648, 689, 725, 766, 772, 774, 778, 794, 795, 807, 820, 826, 829, 831, 832, 840, 842, 843, 844, 846, 847, 849, 854, 865, 866, 867], "input_arrai": [30, 32, 33, 842], "torch_model": [30, 32, 33, 50], "159": [30, 74, 111, 627, 637, 661], "thank": [30, 854, 862], "fledg": [30, 821, 851, 852], "output_arrai": [30, 32, 33, 58, 455], "0893": 30, "1504": 30, "1372": 30, "0991": 30, "0867": 30, "0851": 30, "0911": 30, "0804": 30, "0926": 30, "0881": 30, "softmaxbackward0": 30, "furthermor": 30, "relat": [30, 248, 633, 814, 816, 819, 820, 821, 822, 828, 835, 843, 846, 847, 848, 849, 866, 875], "regress": [31, 872, 879], "checkout": [32, 47, 822, 825, 846], "f705efe7cb5d18df17ce6c1e20f04d0eb4933f48": 32, "theoret": 32, "aspect": [32, 33, 815, 841, 854, 872], "easiest": [32, 814, 816, 821, 858], "defer": [32, 33, 820, 826, 831, 832, 839, 842, 843, 846, 878], "similarli": [32, 45, 140, 148, 224, 329, 336, 337, 370, 373, 630, 633, 827, 831, 843, 849, 853, 878], "essenc": [32, 873, 878], "becom": [32, 58, 81, 98, 347, 373, 379, 465, 640, 700, 802, 822, 823, 829, 831, 833, 835, 842, 857, 861, 863, 865], "slide": [32, 58, 62, 81, 85, 376, 395, 396, 397, 413, 414, 415, 416, 419, 423, 637, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 793], "regressor": [32, 33], "input_dim": [32, 33, 47], "output_dim": [32, 33, 47], "linear0": [32, 33, 44, 854, 855], "linear1": [32, 33, 44, 854, 855], "adam": [32, 33, 44, 48, 60, 83, 537, 616, 617, 622, 635, 636, 797, 854, 855, 856, 872], "n_training_exampl": [32, 33], "2000": [32, 33, 81, 315, 370], "random_norm": [32, 33, 62, 63, 67, 85, 86, 90, 546, 635, 637, 638, 644, 652, 654, 655, 656, 658, 659, 663, 688], "linspac": [32, 33, 54, 77, 127, 630, 838, 849, 851, 879], "execute_with_gradi": [32, 33, 44, 48, 636, 854, 855, 856, 857], "lambda": [32, 33, 49, 51, 81, 124, 126, 298, 308, 545, 558, 618, 619, 621, 626, 629, 635, 636, 638, 642, 674, 726, 727, 731, 820, 839, 840, 841, 844, 849, 851, 854], "2d": [32, 33, 48, 58, 81, 98, 314, 370, 376, 377, 379, 388, 391, 392, 400, 401, 443, 450, 464, 474, 523, 793, 812, 843, 849], "5f": [32, 33], "nonetheless": [32, 33], "gc": [32, 33, 558, 635], "decompos": [32, 33, 58, 81, 98, 101, 324, 325, 326, 327, 328, 349, 356, 370, 373, 377, 441, 446, 449, 452, 843, 856], "said": [32, 33, 779, 847, 863, 865], "otherwis": [32, 33, 50, 53, 54, 55, 57, 58, 59, 62, 63, 68, 69, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 98, 111, 112, 113, 114, 115, 116, 117, 118, 119, 124, 127, 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"return_backend_compiled_fn": 32, "immedi": [32, 33, 812, 814, 820, 821], "built": [32, 33, 38, 46, 48, 51, 127, 630, 793, 794, 795, 821, 822, 828, 829, 846, 852, 858, 865, 871, 872, 876], "eager_graph": [32, 33, 814, 865, 866], "lazy_graph": [32, 33, 814, 865, 866], "thought": [32, 33, 821, 822, 838, 862, 870], "matter": [32, 33, 38, 833, 861], "haven": [32, 33, 38, 858, 872], "jax_out": [32, 33], "ideal": [32, 33, 830, 831, 843, 849, 854], "worth": [32, 33], "differenti": [32, 33, 296, 366, 367, 368, 375, 872], "chosen": [32, 33, 51, 101, 127, 229, 630, 633, 645, 749, 820, 830, 843], "plai": [32, 33, 378, 457, 814, 817, 821, 823, 826, 832, 836, 843, 846, 856, 872, 875], "role": [32, 33, 814, 817, 822, 823, 832, 843, 852, 873, 875, 879], "dl": [32, 33], "effortlessli": [32, 33], "previous": [32, 33, 604, 635, 802, 820, 821, 827, 839, 841, 846, 851], "default_devic": [32, 33, 207, 210, 211, 212, 218, 219, 632, 832, 835, 836], "as_n": [32, 33, 55, 56, 75, 78, 79, 159, 160, 161, 162, 163, 164, 170, 197, 198, 631, 632, 831], "certainli": [32, 33, 862, 878], "unnecessari": [32, 33, 843], "extend": [32, 33, 58, 81, 379, 388, 485, 526, 827, 828, 831, 834, 835, 838, 843, 847, 857, 869, 872, 878], "infrastructur": [32, 33, 868, 874, 875], "least": [32, 57, 58, 63, 80, 81, 241, 259, 274, 376, 379, 388, 404, 409, 463, 464, 465, 474, 476, 523, 633, 638, 645, 678, 748, 822, 826, 830, 831, 832, 833, 839, 842, 846, 866], "coco": 32, "seamlessli": [33, 846], "therefor": [33, 38, 54, 57, 58, 63, 80, 81, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 148, 149, 150, 156, 172, 176, 180, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 235, 236, 237, 238, 239, 241, 242, 244, 246, 247, 248, 252, 253, 254, 255, 256, 257, 261, 263, 264, 265, 266, 268, 269, 270, 271, 274, 276, 277, 278, 279, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 314, 329, 330, 336, 337, 339, 342, 370, 373, 376, 377, 379, 388, 395, 396, 397, 398, 400, 401, 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"randon": [34, 35, 37, 38, 39], "mean_": 34, "std_": 34, "detect": [34, 38, 57, 75, 80, 256, 633, 642, 719, 730, 820, 821, 827, 829, 830, 837, 846, 854, 855], "inspect": [34, 38, 536, 635], "__": [34, 35, 36, 37, 38, 39, 75, 833, 854], "script": [35, 814, 821, 822, 825, 830, 833, 851, 857, 872], "comp": 35, "low_level": 35, "chain": [35, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 98, 111, 112, 113, 114, 115, 116, 117, 118, 119, 135, 137, 142, 144, 150, 154, 156, 169, 173, 174, 181, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 300, 304, 305, 306, 307, 308, 310, 311, 312, 314, 335, 336, 337, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 400, 401, 402, 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214, 583, 584, 587, 593, 594, 606, 607, 610, 631, 632, 635, 785, 796, 802, 821, 826, 827, 830, 831, 832, 835, 839, 843, 851, 872], "b": [38, 52, 57, 58, 59, 62, 63, 71, 74, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 98, 99, 102, 103, 104, 111, 112, 113, 114, 115, 116, 117, 118, 128, 129, 130, 135, 136, 137, 139, 142, 144, 150, 153, 154, 155, 156, 164, 174, 176, 181, 198, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 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, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 318, 319, 331, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 356, 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725, 726, 727, 728, 730, 731, 736, 737, 738, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 777, 807, 808, 812, 814, 815, 818, 822, 824, 825, 827, 829, 830, 833, 836, 839, 841, 844, 850, 851, 852, 854, 855, 856, 860, 863, 865, 868], "prioriti": [38, 75, 802, 817, 820, 822, 823, 832, 842], "normalize_via_oper": 38, "determin": [38, 57, 58, 63, 65, 69, 72, 75, 80, 81, 82, 86, 93, 95, 98, 101, 103, 104, 133, 156, 158, 165, 171, 172, 173, 174, 176, 177, 178, 193, 203, 205, 206, 217, 222, 223, 224, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 238, 239, 241, 244, 246, 248, 254, 255, 256, 257, 258, 262, 263, 264, 265, 266, 271, 274, 279, 283, 286, 287, 288, 289, 290, 291, 292, 295, 305, 309, 355, 360, 368, 373, 376, 377, 378, 379, 388, 412, 420, 431, 453, 454, 493, 497, 523, 535, 538, 559, 560, 564, 565, 566, 567, 568, 569, 596, 614, 630, 631, 632, 633, 635, 638, 640, 641, 646, 649, 668, 669, 670, 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[[269, "logical-not"]], "bitwise_invert": [[232, "bitwise-invert"]], "deg2rad": [[240, "deg2rad"]], "bitwise_or": [[234, "bitwise-or"]], "isnan": [[257, "isnan"]], "erf": [[243, "erf"]], "expm1": [[246, "expm1"]], "greater_equal": [[253, "greater-equal"]], "exp2": [[245, "exp2"]], "logical_and": [[268, "logical-and"]], "logical_xor": [[271, "logical-xor"]], "lcm": [[259, "lcm"]], "gcd": [[251, "gcd"]], "imag": [[254, "imag"]], "log10": [[263, "log10"]], "exp": [[244, "exp"]], "nan_to_num": [[275, "nan-to-num"]], "less_equal": [[261, "less-equal"]], "logaddexp": [[266, "logaddexp"]], "log": [[262, "log"]], "minimum": [[273, "minimum"]], "maximum": [[272, "maximum"]], "multiply": [[274, "multiply"]], "isinf": [[256, "isinf"]], "logaddexp2": [[267, "logaddexp2"]], "fmod": [[250, "fmod"]], "less": [[260, "less"]], "greater": [[252, "greater"]], "floor_divide": [[248, "floor-divide"]], "cosh": [[239, "cosh"]], "log2": [[265, "log2"]], "divide": [[241, "divide"]], "How To Convert Models from PyTorch to PaddlePaddle": [[7, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[7, "About-the-Model"]], "Transpiling the Model": [[7, "Transpiling-the-Model"]], "Comparing the results": [[7, "Comparing-the-results"], [13, "Comparing-the-results"], [6, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[7, "Fine-tuning-the-transpiled-model"], [13, "Fine-tuning-the-transpiled-model"], [6, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[7, "Conclusion"], [13, "Conclusion"], [6, "Conclusion"]], "Quickstart": [[33, "Quickstart"]], "Get familiar with Ivy": [[33, "Get-familiar-with-Ivy"]], "Functional API": [[33, "Functional-API"]], "Stateful API": [[33, "Stateful-API"]], "Tracing code": [[33, "Tracing-code"]], "Any function": [[33, "Any-function"], [32, "Any-function"]], "Any library": [[33, "Any-library"], [32, "Any-library"]], "Any model": [[33, "Any-model"], [32, "Any-model"]], "Transpile any library": [[29, "Transpile-any-library"]], "1.3: Dynamic vs Static": [[40, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[40, "Dynamic"]], "Static": [[40, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[40, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Compilation of a Basic Function": [[45, "Compilation-of-a-Basic-Function"]], "Installs \ud83d\udcbe": [[45, "Installs-\ud83d\udcbe"], [44, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[45, "Imports-\ud83d\udec3"], [44, "Imports-\ud83d\udec3"]], "Import Ivy compiler": [[45, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[45, "Function-compilation-\ud83d\udee0"]], "Set backend": [[45, "Set-backend"]], "Sample input": [[45, "Sample-input"]], "Define function to compile": [[45, "Define-function-to-compile"]], "Compile the function": [[45, "Compile-the-function"]], "Check results": [[45, "Check-results"], [45, "id1"]], "Compiling simple neural network \ud83e\udde0": [[45, "Compiling-simple-neural-network-\ud83e\udde0"]], "Define Model": [[45, "Define-Model"], [44, "Define-Model"]], "Create model": [[45, "Create-model"]], "Define input": [[45, "Define-input"]], "Compile network": [[45, "Compile-network"]], "Write a model using Ivy": [[31, "Write-a-model-using-Ivy"]], "Unify code": [[24, "Unify-code"]], "Developing a convolutional network using Ivy": [[20, "Developing-a-convolutional-network-using-Ivy"]], "How to use decorators": [[28, "How-to-use-decorators"]], "Unify": [[28, "Unify"], [27, "Unify"], [37, "Unify"], [39, "Unify"], [38, "Unify"]], "Trace": [[28, "Trace"], [27, "Trace"]], "Transpile": [[28, "Transpile"], [27, "Transpile"], [37, "Transpile"], [39, "Transpile"], [38, "Transpile"]], "0.2: Transpile": [[36, "0.2:-Transpile"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [13, "Installation"], [4, "Installation"]], "Imports": [[12, "Imports"], [15, "Imports"], [8, "Imports"]], "Data Preparation": [[12, "Data-Preparation"], [4, "Data-Preparation"], [8, "Data-Preparation"], [5, "Data-Preparation"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Load the image example \ud83d\uddbc\ufe0f": [[12, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [8, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[12, "Visualise-image"], [8, "Visualise-image"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "0.1: Compile": [[35, "0.1:-Compile"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Accelerating XGBoost with JAX": [[15, "Accelerating-XGBoost-with-JAX"]], "Tests": [[15, "Tests"]], "Loading the Data": [[15, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[15, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[15, "JAX-backend"]], "Tensorflow backend": [[15, "Tensorflow-backend"]], "PyTorch backend": [[15, "PyTorch-backend"]], "More exhaustive example": [[15, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[15, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[15, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[15, "Comparison-of-Metrics"]], "Training PyTorch ResNet in your TensorFlow Projects": [[13, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Framework Incompatibility": [[13, "Framework-Incompatibility"], [6, "Framework-Incompatibility"]], "Transpiling a PyTorch model to TensorFlow": [[13, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "About the transpiled model": [[13, "About-the-transpiled-model"], [6, "About-the-transpiled-model"]], "Setting-up the source model": [[13, "Setting-up-the-source-model"], [6, "Setting-up-the-source-model"]], "Load the Data": [[13, "Load-the-Data"]], "Visualize a few images": [[13, "Visualize-a-few-images"]], "Load the pre-trained model": [[13, "Load-the-pre-trained-model"]], "Converting the model from TensorFlow to PyTorch": [[13, "Converting-the-model-from-TensorFlow-to-PyTorch"], [6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "2.0: Kornia": [[41, "2.0:-Kornia"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Ivy AlexNet inference in Torch": [[4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[4, "TensorFlow-inference"]], "JAX inference": [[4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Transpiling a PyTorch model to build on top": [[17, "Transpiling-a-PyTorch-model-to-build-on-top"]], "ODSC Ivy Demo": [[32, "ODSC-Ivy-Demo"]], "Ivy Backend Handler": [[32, "Ivy-Backend-Handler"], [23, "Ivy-Backend-Handler"]], "Data Structures": [[32, "Data-Structures"], [23, "Data-Structures"]], "Ivy Functional API": [[32, "Ivy-Functional-API"], [23, "Ivy-Functional-API"]], "Graph Tracer": [[32, "Graph-Tracer"]], "Transpiling a haiku model to build on top": [[18, "Transpiling-a-haiku-model-to-build-on-top"]], "Lazy vs Eager": [[27, "Lazy-vs-Eager"]], "Transpiling a Tensorflow model to build on top": [[19, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "1.0: Lazy vs Eager": [[37, "1.0:-Lazy-vs-Eager"]], "Compile": [[37, "Compile"], [39, "Compile"], [38, "Compile"]], "Trace code": [[25, "Trace-code"]], "3.0: Perceiver": [[42, "3.0:-Perceiver"]], "1.2: As a Decorator": [[39, "1.2:-As-a-Decorator"]], "Transpile code": [[26, "Transpile-code"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "Write Ivy code": [[23, "Write-Ivy-code"]], "Contents": [[23, "Contents"]], "Installing Ivy": [[23, "Installing-Ivy"]], "Importing Ivy": [[23, "Importing-Ivy"], [0, "Importing-Ivy"]], "Learn the basics": [[22, "learn-the-basics"], [21, "learn-the-basics"]], "Examples and Demos": [[3, "examples-and-demos"], [21, "examples-and-demos"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "3.1: Stable Diffusion": [[43, "3.1:-Stable-Diffusion"]], "Transpile any model": [[30, "Transpile-any-model"]], "Round up": [[30, "Round-up"]], "Tutorials And Examples": [[21, "tutorials-and-examples"]], "Guides": [[21, "guides"], [16, "guides"]], "Basic Operations with Ivy": [[44, "Basic-Operations-with-Ivy"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[44, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[44, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[44, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[44, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[44, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[44, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[44, "Set-Backend-Framework"]], "Create Model": [[44, "Create-Model"]], "Create Optimizer": [[44, "Create-Optimizer"]], "Input and Target": [[44, "Input-and-Target"]], "Loss Function": [[44, "Loss-Function"]], "Training Loop": [[44, "Training-Loop"]], "1.1: Framework Selection": [[38, "1.1:-Framework-Selection"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "Accelerating PyTorch models with JAX": [[14, "Accelerating-PyTorch-models-with-JAX"]], "0.0: Unify": [[34, "0.0:-Unify"]], "Resnet 18": [[51, "Resnet-18"]], "Image": [[84, "module-ivy.data_classes.container.image"], [61, "module-ivy.data_classes.array.image"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[46, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[46, "Table-of-Contents"]], "Defining the model": [[46, "Defining-the-model"]], "Model construction": [[46, "Model-construction"]], "Some helper functions": [[46, "Some-helper-functions"]], "Transpiling the model": [[46, "Transpiling-the-model"]], "PyTorch pipeline": [[46, "PyTorch-pipeline"]], "Dataset download": [[46, "Dataset-download"]], "DataLoader": [[46, "DataLoader"]], "Training": [[46, "Training"]], "Conversions": [[53, "module-ivy.data_classes.array.conversions"], [76, "module-ivy.data_classes.container.conversions"]], "HuggingFace Tensorflow DeiT": [[49, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[49, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Deepmind PerceiverIO on GPU": [[47, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[47, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[47, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[47, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[47, "Run-the-demo..."]], "\u2026with torch backend": [[47, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[47, "....with-tensorflow-backend"]], "\u2026with jax backend": [[47, "...with-jax-backend"]], "\u2026with numpy backend": [[47, "...with-numpy-backend"]], "End-to-End Training Pipeline in Ivy": [[48, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[48, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[48, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[48, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[48, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[48, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[48, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[48, "Plotting-the-training-metrics"]], "Save the trained Model": [[48, "Save-the-trained-Model"]], "Ivy as a Transpiler Introduction": [[50, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[50, "To-use-the-transpiler:"]], "Transpiler Interface": [[50, "Transpiler-Interface"]], "Telemetry": [[50, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[50, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[50, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[50, "3.-Transpile-Models-\ud83c\udf10"]]}, "indexentries": {"_arraywithactivations (class in ivy.data_classes.array.activations)": [[52, "ivy.data_classes.array.activations._ArrayWithActivations"]], "_abc_impl (ivy.data_classes.array.activations._arraywithactivations attribute)": [[52, "ivy.data_classes.array.activations._ArrayWithActivations._abc_impl"]], "gelu() (ivy.data_classes.array.activations._arraywithactivations method)": [[52, "ivy.data_classes.array.activations._ArrayWithActivations.gelu"]], "hardswish() (ivy.data_classes.array.activations._arraywithactivations method)": [[52, "ivy.data_classes.array.activations._ArrayWithActivations.hardswish"]], "ivy.data_classes.array.activations": [[52, "module-ivy.data_classes.array.activations"]], "leaky_relu() (ivy.data_classes.array.activations._arraywithactivations method)": [[52, "ivy.data_classes.array.activations._ArrayWithActivations.leaky_relu"]], "log_softmax() 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\ No newline at end of file