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variables (Container) – Variables to be optimized during the meta step

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fe544c55120>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f81f3965120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  • inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    • @@ -1474,7 +1474,7 @@

    Meta#

    variables (Container) – Variables to be optimized during the meta step

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fe544c55120>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f81f3965120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  • inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    • @@ -1551,7 +1551,7 @@

    Meta#

    variables (Container) – Variables to be optimized.

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fe544c55120>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f81f3965120>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    • diff --git a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html index 30f925904..a84f1b8e6 100644 --- a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html +++ b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html @@ -1423,7 +1423,7 @@

    fomaml_stepContainer) – Variables to be optimized during the meta step

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fe544c55120>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f81f3965120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  • inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    • diff --git a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html index 5bd51f485..9b2f77aec 100644 --- a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html +++ b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html @@ -1423,7 +1423,7 @@

    maml_stepContainer) – Variables to be optimized during the meta step

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fe544c55120>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f81f3965120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  • inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    • diff --git a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html index 43cd2999d..9b93f1bf5 100644 --- a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html +++ b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html @@ -1420,7 +1420,7 @@

    reptile_stepContainer) – Variables to be optimized.

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fe544c55120>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f81f3965120>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    • diff --git a/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html b/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html index a71ae76ca..c1b8587e9 100644 --- a/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html +++ b/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html @@ -1409,7 +1409,7 @@

    Should not be used inside any of the test functions.

    -ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7fe538a35f30>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f81e7731f40>#
    diff --git a/ivy/docs/stateful/ivy.stateful.layers.html b/ivy/docs/stateful/ivy.stateful.layers.html index 9761ba0f3..e7c48010b 100644 --- a/ivy/docs/stateful/ivy.stateful.layers.html +++ b/ivy/docs/stateful/ivy.stateful.layers.html @@ -1536,8 +1536,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fe544885e40>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fe544885de0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f81f3589fc0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f81f3589f60>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • data_format (default: 'NWC') – NWC” or “NCW”. Defaults to “NWC”.

  • dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    • @@ -1574,8 +1574,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fe544885d80>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fe544885d20>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f81f3589f00>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f81f3589ea0>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • output_shape (default: None) – Shape of the output (Default value = None)

  • data_format (default: 'NWC') – NWC” or “NCW”. Defaults to “NWC”.

    • @@ -1613,8 +1613,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fe544885cc0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fe544885c60>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f81f3589e40>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f81f3589de0>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • data_format (default: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.

  • dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    • @@ -1651,8 +1651,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fe544885c00>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fe544885ba0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f81f3589d80>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f81f3589d20>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • output_shape (default: None) – Shape of the output (Default value = None)

  • data_format (default: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.

    • @@ -1690,8 +1690,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fe544885a80>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fe544885a20>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f81f3589c00>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f81f3589ba0>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • data_format (default: 'NDHWC') – NDHWC” or “NCDHW”. Defaults to “NDHWC”.

  • dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    • @@ -1728,8 +1728,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fe5448859c0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fe544885960>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f81f3589b40>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f81f3589ae0>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • output_shape (default: None) – Shape of the output (Default value = None)

  • data_format (default: 'NDHWC') – NDHWC” or “NCDHW”. Defaults to “NDHWC”.

    • @@ -1792,8 +1792,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fe544885b40>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fe544885ae0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f81f3589cc0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f81f3589c60>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • data_format (default: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.

  • dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    • @@ -1949,7 +1949,7 @@

    • input_channels – Number of input channels for the layer

    • output_channels – Number of output channels for the layer

      • -

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fe544885900>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f81f3589a80>) – Initializer for the weights. Default is GlorotUniform.

    • num_layers (default: 1) – Number of lstm cells in the lstm layer, default is 1.

    • return_sequence (default: True) – Whether or not to return the entire output sequence, or just the latest timestep. @@ -2008,8 +2008,8 @@

      • input_channels – Number of input channels for the layer.

      • output_channels – Number of output channels for the layer.

        • -

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fe544885f00>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fe544885ea0>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f81f358a080>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f81f358a020>) – Initializer for the bias. Default is Zeros.

      • with_bias (default: True) – Whether or not to include a bias term, default is True.

      • device (default: None) – device on which to create the layer’s variables ‘cuda:0’, ‘cuda:1’, ‘cpu’ etc. Default is cpu.

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349, 369, 372, 418, 532, 547, 560, 592, 626, 629, 632, 634, 637, 641, 643, 650, 676, 682, 726, 741], "050000": 0, "max": [0, 43, 45, 54, 57, 58, 62, 70, 77, 80, 81, 85, 93, 165, 168, 271, 335, 372, 375, 376, 377, 378, 394, 395, 396, 412, 413, 414, 415, 417, 419, 430, 452, 489, 491, 492, 540, 541, 546, 562, 576, 577, 630, 632, 634, 637, 647, 678, 680, 683, 776, 792, 796, 828, 841, 867], "25691": 0, "160000": 0, "reveal": 0, "outlier": [0, 844], "receiv": [0, 6, 45, 49, 97, 536, 572, 634, 640, 715, 716, 717, 792, 810, 815, 819, 820, 829, 830, 844, 847], "anomali": 0, "financi": 0, "behavior": [0, 4, 8, 57, 68, 240, 247, 273, 282, 388, 533, 580, 604, 632, 634, 645, 749, 750, 751, 752, 818, 826, 827, 828, 829, 840, 841, 842, 844, 847, 849, 855, 867], "associ": [0, 12, 57, 62, 80, 85, 223, 273, 378, 387, 461, 525, 632, 637, 680, 683, 695, 773, 820, 829, 837, 838, 841, 842, 844, 855], "122": [0, 13, 54, 168, 238, 632], "211321": 0, "256": [0, 4, 8, 12, 56, 81, 283, 284, 593, 636, 651, 653, 776], "683288": 0, "250000": 0, "105": [0, 62, 84, 636, 637, 659, 660, 675, 682], "890000": 0, "2125": 0, "870000": 0, "deepen": 0, "averag": [0, 6, 7, 45, 47, 57, 59, 63, 80, 82, 86, 375, 377, 381, 387, 389, 390, 394, 395, 396, 454, 455, 456, 457, 458, 459, 506, 522, 615, 616, 621, 635, 636, 638, 640, 663, 696, 715, 716, 791, 792], "across": [0, 1, 12, 13, 14, 26, 27, 28, 29, 43, 57, 67, 74, 80, 81, 90, 102, 211, 212, 240, 247, 273, 291, 377, 381, 452, 503, 506, 537, 558, 594, 631, 632, 634, 636, 641, 644, 659, 663, 724, 744, 745, 792, 818, 823, 829, 831, 833, 836, 837, 839, 844, 847, 868, 870, 875], "all": [0, 1, 2, 4, 5, 6, 7, 8, 12, 13, 16, 17, 18, 19, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 44, 45, 47, 48, 50, 52, 53, 57, 58, 61, 62, 64, 66, 71, 72, 74, 75, 76, 79, 80, 81, 84, 85, 87, 89, 94, 95, 97, 98, 126, 134, 141, 145, 146, 147, 201, 208, 240, 244, 272, 273, 328, 329, 341, 360, 369, 372, 375, 376, 377, 378, 387, 409, 418, 420, 421, 422, 430, 435, 445, 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47, 50, 812, 816, 818, 819, 820, 825, 826, 827, 829, 830, 831, 833, 834, 835, 836, 837, 841, 842, 844, 845, 846, 847, 849, 851, 853, 854, 856, 859, 863, 866, 870], "document": [1, 6, 7, 22, 31, 64, 247, 335, 336, 372, 614, 632, 634, 710, 813, 814, 817, 820, 826, 828, 829, 831, 840, 841, 842, 844, 852, 854], "sphinx": [1, 814, 826], "websit": [1, 49, 819, 823, 860], "alreadi": [2, 6, 13, 23, 26, 27, 28, 29, 31, 32, 37, 45, 47, 50, 57, 62, 74, 80, 85, 236, 246, 273, 283, 293, 378, 387, 463, 464, 484, 520, 529, 632, 637, 675, 682, 805, 806, 812, 818, 819, 820, 825, 827, 829, 830, 836, 840, 841, 847, 855, 856, 870, 872, 877], "instal": [2, 7, 8, 9, 10, 11, 13, 14, 16, 18, 23, 24, 25, 26, 27, 28, 29, 31, 32, 45, 47, 48, 49, 50, 814, 819, 820, 825, 826, 834, 835], "skip": [2, 5, 47, 57, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, 115, 116, 117, 118, 134, 136, 141, 143, 149, 153, 155, 180, 214, 220, 221, 222, 223, 225, 226, 227, 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752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 777, 805, 826, 837, 844], "colab": [2, 5, 13, 14, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 45, 47, 49, 50], "manual": [2, 6, 7, 13, 14, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 641, 718, 728, 729, 818, 819, 820, 829, 835, 844, 853, 856], "mind": [2, 16, 18, 22, 28, 31, 35, 818, 819, 824, 827, 844, 856, 864], "click": [2, 4, 47, 818, 819, 820, 828, 832, 834, 835, 850], "runtim": [2, 4, 5, 8, 11, 12, 13, 24, 31, 34, 45, 46, 822, 837, 844, 847, 870], "restart": [2, 4, 5, 8, 12, 45, 46, 819, 834], "git": [2, 4, 5, 8, 12, 31, 45, 46, 47, 48, 812, 814, 817, 819, 820, 823, 826, 828, 834, 835, 844, 856], "clone": [2, 4, 8, 12, 31, 45, 47, 48, 812, 814, 820, 834, 856], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 18, 26, 27, 28, 29, 31, 32, 45, 46, 47, 48, 49, 50, 56, 57, 79, 80, 82, 147, 155, 243, 253, 254, 269, 328, 335, 336, 369, 372, 375, 378, 387, 419, 492, 522, 615, 616, 629, 630, 632, 635, 637, 639, 647, 685, 686, 714, 764, 812, 814, 819, 820, 823, 826, 828, 829, 832, 834, 856, 864], "github": [2, 4, 5, 8, 11, 12, 13, 31, 45, 46, 47, 48, 49, 812, 814, 815, 817, 820, 821, 823, 826, 828, 829, 831, 832, 834, 835, 843, 844, 856, 859, 878], "com": [2, 4, 5, 6, 7, 8, 11, 12, 13, 18, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 823, 826, 828, 829, 834, 856], "unifyai": [2, 4, 8, 12, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 826, 834, 856], "model": [2, 3, 4, 9, 14, 15, 20, 21, 22, 48, 50, 240, 273, 377, 453, 632, 789, 793, 794, 810, 812, 852, 853, 857, 863, 864, 868, 869, 870, 871, 872, 873, 874, 876, 877], "depth": [2, 4, 6, 8, 12, 46, 53, 57, 61, 76, 80, 84, 141, 375, 378, 411, 471, 545, 557, 629, 634, 636, 654, 655, 820, 828, 852, 853, 854, 856], "repositori": [2, 4, 8, 12, 814, 818, 819, 820, 822, 823, 826, 834, 843, 861], "cd": [2, 4, 8, 12, 31, 48, 812, 814, 819, 820, 834, 856], "resnet": [3, 6, 13, 20, 31, 863, 864], "imag": [3, 4, 6, 7, 11, 13, 16, 20, 28, 31, 32, 45, 46, 47, 48, 49, 50, 57, 61, 79, 80, 84, 102, 220, 221, 222, 223, 226, 229, 238, 241, 243, 245, 254, 255, 256, 261, 263, 276, 283, 284, 286, 287, 291, 375, 394, 395, 411, 412, 413, 415, 545, 632, 634, 636, 649, 650, 651, 652, 653, 656, 657, 658, 792, 812, 819, 834, 847, 849, 850, 852, 854, 856, 863, 864, 870], "classif": [3, 4, 12, 14, 20, 45, 812, 870], "acceler": [3, 20, 812, 829, 841, 868, 872, 873, 874, 875], "convert": [3, 8, 9, 11, 13, 14, 16, 18, 20, 21, 23, 25, 28, 29, 31, 32, 33, 35, 37, 45, 48, 50, 52, 53, 56, 74, 75, 76, 79, 97, 127, 128, 140, 150, 151, 193, 194, 195, 196, 207, 215, 219, 239, 279, 378, 383, 462, 463, 464, 513, 578, 596, 598, 599, 600, 602, 629, 630, 631, 632, 634, 637, 641, 695, 719, 730, 731, 773, 801, 805, 812, 818, 824, 825, 838, 839, 841, 844, 846, 849, 855, 857, 861, 864, 868, 869, 876], "faster": [3, 4, 9, 11, 13, 14, 20, 31, 32, 48, 50, 57, 62, 80, 85, 376, 449, 637, 687, 814, 817, 826, 857, 872, 875], "infer": [3, 6, 7, 9, 11, 13, 14, 20, 24, 34, 36, 37, 46, 48, 50, 53, 57, 58, 61, 64, 76, 80, 81, 84, 87, 126, 128, 131, 135, 136, 140, 143, 149, 158, 159, 160, 161, 162, 312, 313, 375, 378, 382, 411, 496, 510, 556, 590, 591, 629, 630, 634, 636, 639, 659, 706, 801, 802, 822, 825, 829, 830, 844, 849, 854, 864, 868, 869, 872, 874], "mmpretrain": [3, 20], "segment": [3, 20, 57, 80, 330, 331, 332, 369, 826, 831], "unet": [3, 20], "alexnet": [3, 20], "written": [3, 4, 5, 6, 20, 22, 31, 32, 45, 58, 378, 473, 819, 823, 824, 832, 835, 836, 840, 841, 845, 849, 851, 854, 855, 859, 864, 868, 870, 874, 876, 877], "xgboost": [3, 20], "paddlepaddl": [3, 20, 335, 336, 372, 819], "dinov2": [3, 7, 20], "project": [3, 12, 13, 20, 25, 26, 27, 28, 29, 31, 32, 35, 98, 636, 663, 792, 812, 814, 815, 818, 819, 820, 821, 824, 825, 826, 844, 853, 855, 859, 860, 861, 864, 866, 868, 870, 873, 877, 878], "convnext": [3, 6, 11, 20], "video": [4, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 813, 818, 819, 820, 823, 824, 825, 827, 828, 829, 830, 831, 832, 833, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 847, 856, 868], "tutori": [4, 6, 7, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 820, 841, 856], "three": [4, 5, 20, 26, 36, 37, 47, 57, 139, 312, 369, 378, 464, 629, 819, 820, 827, 828, 829, 831, 841, 844, 847, 848, 849, 871, 876], "major": [4, 5, 644, 747, 829, 830, 842, 844, 855, 860, 867, 870], "ml": [4, 5, 6, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 47, 50, 812, 813, 817, 841, 848, 849, 850, 852, 853, 854, 858, 860, 861, 864, 866, 867, 868, 869, 870, 873, 875, 877], "framework": [4, 5, 7, 9, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 35, 36, 38, 45, 47, 49, 52, 58, 170, 192, 202, 205, 216, 543, 559, 563, 595, 598, 630, 631, 634, 641, 720, 771, 773, 777, 784, 789, 796, 801, 802, 812, 815, 816, 818, 819, 822, 823, 824, 825, 826, 828, 829, 830, 831, 833, 834, 836, 837, 838, 840, 841, 844, 845, 847, 848, 849, 851, 854, 855, 856, 857, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 871, 874], "sinc": [4, 8, 12, 28, 29, 31, 32, 45, 47, 57, 80, 98, 372, 812, 814, 819, 820, 823, 824, 825, 826, 827, 828, 829, 830, 833, 840, 841, 855, 860, 870, 876], "automat": [4, 8, 9, 12, 29, 31, 32, 37, 818, 819, 820, 822, 825, 826, 828, 829, 835, 837, 840, 844, 847, 848, 850, 853, 854, 856, 857, 861, 870, 873, 877], "sure": [4, 8, 11, 12, 13, 14, 31, 45, 815, 818, 819, 820, 823, 828, 833, 834, 841, 842, 844, 847, 856], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 26, 27, 29, 46, 57, 62, 74, 85, 103, 375, 377, 398, 456, 580, 634, 637, 680, 794, 810, 812, 819, 820, 821, 824, 827, 829, 837, 838, 839, 840, 841, 844, 845, 848, 850, 852, 854, 855, 857, 860, 863, 868, 869, 870, 871, 872, 873, 876, 877], "dm": [4, 5, 8, 11, 13, 31, 32, 43, 45], "haiku": [4, 5, 8, 11, 13, 29, 31, 32, 43, 45, 49, 789, 812, 854, 861, 864, 870], "exit": [4, 8, 12, 31, 32, 830], "download": [4, 6, 7, 12, 16, 18, 31, 32, 46, 47, 50, 814, 819, 826, 844, 863, 864], "imagenet": [4, 6, 18, 46, 48, 812], "class": [4, 6, 7, 8, 12, 14, 16, 18, 22, 31, 32, 43, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 134, 143, 149, 165, 168, 181, 183, 184, 243, 280, 338, 360, 372, 386, 387, 395, 396, 429, 528, 529, 536, 545, 549, 562, 572, 595, 629, 630, 631, 632, 634, 636, 637, 638, 641, 642, 657, 662, 666, 672, 682, 686, 687, 689, 696, 712, 719, 730, 737, 752, 759, 763, 764, 773, 774, 781, 782, 783, 784, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 800, 801, 805, 810, 812, 818, 825, 826, 827, 829, 830, 831, 832, 836, 838, 839, 842, 843, 844, 847, 849, 850, 852, 853, 854, 857, 863, 864, 868, 870, 871, 877], "wget": [4, 6, 8, 12, 45, 46, 49, 819], "raw": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 45, 48, 49, 74, 812, 832, 864, 871], "githubusercont": [4, 6, 8, 12, 45, 49], "hub": [4, 6, 8, 12, 45, 48, 50], "master": [4, 8, 12, 23, 24, 25, 33, 34, 35, 36, 37, 38, 45, 47, 48, 49, 815, 828, 870, 878], "imagenet_class": [4, 12], "categori": [4, 6, 12, 818, 823, 824, 827, 829, 833, 841, 845, 848], "strip": [4, 12, 24, 34, 860], "readlin": [4, 12, 46], "cat": [4, 7, 12, 46, 842, 847, 849, 854, 863, 864], "jpg": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 47, 48, 812, 864], "filenam": [4, 8, 12, 31, 32, 45, 47, 50, 58, 794, 800, 852], "import": [4, 6, 7, 9, 10, 11, 13, 16, 18, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 46, 48, 49, 50, 57, 68, 72, 76, 80, 95, 194, 195, 199, 211, 307, 387, 522, 557, 573, 631, 634, 640, 645, 716, 717, 752, 784, 801, 802, 812, 817, 818, 819, 820, 821, 823, 824, 825, 826, 827, 829, 830, 831, 832, 835, 838, 839, 840, 841, 842, 843, 844, 845, 849, 851, 852, 854, 855, 856, 860, 863, 864, 865, 866, 868, 870, 873, 874, 876], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 46, 47, 50, 53, 57, 66, 74, 76, 80, 89, 102, 105, 106, 107, 126, 127, 128, 130, 131, 132, 135, 136, 137, 138, 140, 141, 142, 143, 145, 146, 147, 148, 149, 193, 194, 195, 196, 197, 198, 199, 200, 201, 206, 207, 208, 209, 211, 212, 213, 214, 215, 217, 219, 312, 313, 328, 329, 369, 382, 472, 508, 509, 511, 512, 536, 550, 551, 629, 634, 643, 738, 739, 740, 741, 771, 773, 774, 789, 791, 792, 793, 794, 795, 796, 797, 798, 810, 812, 820, 822, 825, 829, 833, 837, 838, 842, 844, 845, 847, 849, 854, 855, 856, 857, 860, 869, 870, 872, 873, 874, 875], "torchvis": [4, 6, 11, 12, 45, 861], "transform": [4, 5, 6, 7, 11, 12, 13, 28, 31, 32, 45, 46, 48, 57, 61, 80, 84, 375, 376, 397, 398, 403, 404, 407, 408, 409, 419, 420, 423, 440, 636, 660, 776, 779, 792, 812, 838, 844, 854, 857, 863, 864, 868, 870, 871, 872], "pil": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 46, 47, 48, 812, 864], "time": [4, 5, 6, 7, 9, 10, 11, 13, 29, 31, 32, 37, 45, 47, 48, 49, 57, 59, 62, 68, 80, 82, 91, 97, 98, 134, 341, 372, 375, 376, 378, 387, 404, 409, 421, 423, 444, 451, 484, 490, 522, 616, 621, 629, 635, 636, 637, 639, 640, 644, 645, 659, 662, 677, 712, 715, 716, 717, 744, 745, 749, 750, 792, 793, 794, 810, 818, 819, 820, 823, 825, 827, 828, 829, 831, 834, 836, 837, 838, 840, 841, 844, 845, 849, 852, 854, 855, 856, 859, 860, 861, 863, 864, 868, 870, 871, 874, 875, 876], "filterwarn": [4, 5], "ignor": [4, 5, 44, 52, 53, 57, 74, 80, 139, 375, 376, 378, 387, 399, 400, 401, 430, 438, 446, 486, 487, 491, 530, 629, 636, 641, 663, 729, 730, 796, 819, 826, 828, 831, 844, 855, 876], "compos": [4, 6, 7, 11, 12, 31, 32, 45, 57, 80, 375, 389, 390, 391, 392, 819, 827, 841, 844, 863, 865, 870, 877], "resiz": [4, 6, 7, 8, 11, 12, 45, 46, 57, 80, 375, 411, 847], "centercrop": [4, 12], "224": [4, 6, 7, 12, 16, 18, 31, 32, 45, 46, 48, 812, 864], "totensor": [4, 6, 7, 11, 12, 45], "485": [4, 12, 45], "456": [4, 12, 45, 844], "406": [4, 12, 45, 57, 80, 397, 540, 634], "229": [4, 12, 45, 279, 632], "225": [4, 12, 45, 47, 234, 632], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 812, 852, 864], "ipython": [4, 8, 12, 26, 27, 28, 29, 31, 32, 50], "displai": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 50, 819, 826, 828, 833, 844, 852], "end": [4, 8, 45, 46, 57, 80, 126, 228, 284, 353, 372, 375, 377, 378, 423, 452, 474, 484, 486, 487, 629, 632, 806, 812, 819, 820, 825, 828, 834, 840, 845, 847, 848, 855, 868, 873], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 217, 631, 830], "ivy_model": [4, 5, 8, 12, 48], "ivy_alexnet": 4, "quick": [4, 20, 32, 820, 822, 842, 853], "trace_graph": [4, 5, 8, 12, 24, 25, 26, 27, 31, 32, 34, 35, 36, 37, 38, 39, 48, 794, 812, 849, 854, 862], "moment": [4, 57, 59, 80, 82, 376, 433, 615, 616, 621, 635, 796, 810, 818, 825, 855, 863, 864], "cost": [4, 59, 82, 615, 616, 619, 621, 622, 623, 635, 640, 715, 716, 717, 806, 829, 847, 868], "arg": [4, 6, 8, 9, 10, 11, 12, 16, 18, 26, 27, 29, 31, 32, 36, 37, 38, 49, 52, 74, 96, 106, 122, 203, 213, 601, 628, 629, 631, 634, 771, 773, 788, 789, 792, 793, 794, 798, 801, 805, 810, 812, 824, 829, 830, 833, 839, 840, 841, 847, 849, 853, 863, 864, 865], "asarrai": [4, 5, 8, 11, 12, 46, 53, 57, 58, 69, 76, 80, 81, 92, 127, 385, 514, 515, 545, 556, 560, 561, 591, 592, 593, 629, 634, 636, 645, 646, 650, 750, 754, 833, 838, 841, 842], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 22, 31, 46, 47, 50, 53, 57, 66, 76, 80, 89, 137, 138, 141, 193, 194, 195, 211, 382, 508, 509, 511, 512, 629, 631, 637, 643, 688, 738, 739, 740, 741, 791, 792, 793, 794, 795, 796, 797, 810, 849, 855, 857, 875], "output": [4, 5, 7, 8, 9, 10, 12, 22, 28, 29, 31, 32, 44, 45, 46, 48, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 152, 154, 179, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 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498, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 515, 520, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 539, 540, 541, 545, 546, 547, 549, 553, 562, 569, 576, 577, 578, 602, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 680, 681, 682, 683, 684, 685, 686, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 709, 710, 711, 712, 714, 731, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 771, 776, 791, 792, 805, 806, 812, 814, 819, 820, 822, 823, 824, 826, 827, 829, 830, 831, 832, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 846, 849, 851, 853, 854, 855, 857, 863, 864, 871], "softmax": [4, 6, 7, 12, 16, 29, 31, 32, 47, 51, 61, 72, 73, 84, 377, 454, 626, 636, 663, 666, 788, 812], "pass": [4, 6, 7, 8, 11, 12, 13, 14, 16, 18, 22, 29, 31, 32, 38, 44, 45, 47, 49, 50, 56, 57, 72, 74, 79, 80, 95, 103, 122, 123, 125, 157, 179, 194, 213, 228, 274, 375, 377, 378, 381, 382, 387, 421, 454, 474, 501, 503, 508, 528, 529, 562, 628, 630, 631, 632, 634, 640, 715, 716, 771, 773, 777, 784, 789, 793, 794, 796, 797, 801, 805, 810, 812, 816, 818, 820, 823, 824, 825, 827, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 844, 847, 855, 863, 864, 865, 868], "argsort": [4, 12, 69, 92, 646, 755, 841], "descend": [4, 12, 69, 92, 637, 646, 687, 688, 753, 756], "top": [4, 12, 15, 20, 29, 31, 32, 45, 46, 57, 64, 80, 319, 369, 377, 378, 452, 494, 545, 634, 700, 812, 819, 820, 829, 834, 841, 843, 844, 847, 852, 853, 870, 874], "logit": [4, 5, 6, 7, 8, 12, 45, 46, 47, 48, 57, 63, 80, 86, 367, 382, 508, 511, 638, 696, 698, 788, 812, 863], "gather": [4, 12, 45, 57, 58, 80, 81, 330, 331, 332, 369, 553, 555, 634, 877], "to_list": [4, 12, 58, 81, 634], "arrai": [4, 5, 6, 7, 9, 10, 12, 13, 14, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 43, 44, 45, 46, 47, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 98, 100, 103, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 152, 153, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 169, 171, 172, 173, 175, 177, 178, 179, 180, 186, 196, 197, 201, 206, 208, 210, 213, 214, 218, 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, 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736, 737, 738, 739, 740, 741, 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, 771, 773, 778, 784, 791, 792, 793, 794, 797, 801, 805, 806, 808, 812, 816, 818, 819, 820, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 837, 838, 839, 840, 841, 842, 844, 845, 846, 847, 848, 849, 850, 852, 853, 854, 855, 857, 864, 865, 868, 869, 870, 872, 876, 877], "282": [4, 12], "281": [4, 12, 45, 47], "285": [4, 12, 80], "64773697": 4, "29496649": 4, "04526037": 4, "tiger": [4, 12], "tabbi": [4, 7, 12], "egyptian": [4, 12], "torch_alexnet": 4, "alexnet_weight": 4, "imagenet1k_v1": [4, 12], "dropout": [4, 61, 84, 375, 399, 400, 401, 636, 661, 663, 666, 792, 852], "torch_output": [4, 8, 9, 12], "dim": [4, 12, 47, 57, 74, 76, 80, 141, 313, 369, 375, 378, 393, 403, 404, 405, 408, 416, 474, 496, 629, 636, 649, 656, 657, 662, 778, 792, 812, 829, 841, 842, 847], "torch_class": [4, 12], "torch_logit": [4, 12], "tensor": [4, 5, 6, 9, 11, 12, 13, 16, 18, 22, 23, 26, 27, 29, 31, 32, 33, 37, 43, 45, 53, 56, 57, 58, 61, 62, 63, 64, 66, 70, 74, 76, 79, 80, 81, 84, 85, 86, 87, 89, 93, 96, 129, 137, 138, 141, 147, 163, 179, 271, 272, 302, 319, 323, 324, 325, 326, 327, 328, 337, 360, 367, 369, 372, 375, 376, 377, 378, 387, 388, 394, 395, 398, 402, 411, 412, 413, 414, 421, 423, 425, 432, 433, 434, 435, 438, 440, 442, 444, 445, 448, 450, 451, 452, 454, 457, 458, 474, 477, 482, 485, 486, 487, 488, 491, 496, 497, 528, 533, 576, 577, 629, 630, 632, 634, 636, 637, 638, 639, 643, 647, 659, 662, 663, 678, 689, 696, 706, 708, 738, 761, 792, 801, 806, 810, 812, 824, 825, 829, 830, 834, 836, 837, 840, 841, 842, 844, 845, 847, 849, 851, 852, 854, 855, 857, 859, 863, 864, 865, 867, 868, 871, 873, 874, 877], "6477": 4, "2950": 4, "0453": 4, "grad_fn": [4, 12, 29, 43, 618, 625, 635, 852], "takebackward0": [4, 12], "great": [4, 7, 8, 812, 820, 844, 849, 851, 860, 861, 876], "simpl": [4, 7, 16, 20, 21, 23, 26, 28, 29, 30, 31, 32, 33, 34, 36, 37, 43, 45, 47, 50, 57, 80, 387, 522, 778, 792, 806, 812, 818, 819, 820, 824, 826, 827, 829, 830, 831, 832, 837, 840, 841, 844, 845, 847, 851, 853, 854, 855, 857, 859, 863, 864, 869, 870, 871, 872], "let": [4, 5, 6, 7, 8, 9, 11, 13, 14, 16, 18, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 43, 45, 46, 48, 50, 58, 70, 81, 220, 221, 222, 223, 226, 229, 238, 241, 243, 245, 254, 255, 256, 261, 263, 276, 284, 286, 287, 291, 552, 553, 632, 634, 637, 647, 691, 761, 763, 764, 765, 766, 812, 818, 821, 824, 826, 827, 828, 829, 830, 831, 832, 833, 834, 841, 842, 844, 845, 846, 847, 849, 851, 852, 853, 854, 861, 863, 864, 877], "ll": [4, 6, 7, 8, 9, 11, 13, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 46, 812, 813, 815, 816, 818, 819, 820, 821, 826, 831, 834, 835, 839, 840, 852, 856, 861, 863, 864], "try": [4, 6, 7, 23, 33, 43, 46, 50, 74, 601, 634, 791, 801, 812, 818, 819, 820, 823, 824, 827, 828, 829, 833, 835, 840, 842, 849, 851, 855, 858, 860, 861, 865], "tf": [4, 6, 8, 9, 10, 13, 16, 18, 23, 26, 27, 29, 31, 32, 33, 34, 36, 38, 43, 48, 49, 789, 812, 824, 829, 830, 836, 840, 841, 844, 845, 847, 849, 854, 855, 857, 863, 864, 865, 870], "onc": [4, 6, 8, 31, 32, 43, 45, 62, 66, 85, 89, 213, 376, 429, 631, 637, 643, 672, 673, 674, 687, 738, 812, 818, 819, 820, 827, 828, 829, 830, 831, 834, 835, 840, 841, 844, 847, 849, 852, 855, 856, 861, 863], "set": [4, 7, 9, 16, 18, 24, 31, 32, 34, 37, 45, 46, 47, 48, 49, 52, 57, 58, 61, 62, 67, 69, 70, 74, 80, 81, 84, 85, 90, 92, 93, 115, 118, 125, 145, 147, 181, 182, 183, 184, 185, 196, 209, 210, 211, 212, 213, 228, 328, 340, 356, 358, 363, 369, 372, 373, 375, 376, 377, 378, 387, 398, 419, 423, 427, 431, 434, 452, 457, 458, 474, 484, 487, 494, 522, 527, 528, 529, 530, 531, 532, 534, 538, 545, 557, 562, 578, 579, 580, 582, 583, 584, 585, 586, 587, 588, 589, 595, 603, 626, 628, 629, 630, 631, 632, 634, 636, 637, 641, 643, 644, 646, 647, 659, 666, 668, 678, 680, 683, 686, 687, 718, 725, 728, 729, 730, 735, 736, 742, 744, 745, 749, 751, 752, 753, 756, 764, 766, 773, 776, 777, 778, 779, 784, 791, 792, 794, 796, 801, 806, 809, 810, 812, 813, 820, 822, 823, 824, 826, 827, 828, 829, 830, 831, 833, 835, 837, 838, 840, 841, 842, 844, 845, 847, 849, 851, 852, 859, 862, 863, 864, 868, 869, 870, 871, 872, 874, 877], "post": [4, 6, 8, 45, 65, 88, 642, 737, 819, 834, 839, 854, 856], "process": [4, 6, 8, 26, 31, 32, 36, 45, 207, 219, 631, 813, 819, 820, 826, 827, 828, 834, 835, 837, 839, 841, 842, 843, 844, 847, 849, 854, 860, 861, 863, 868, 869, 870, 873, 874, 876, 877], "st": [4, 5, 11, 776, 823, 842, 844], "perf_count": [4, 9, 10, 11], "raw_logit": 4, "latenc": [4, 11], "nn": [4, 6, 7, 8, 10, 18, 29, 31, 32, 45, 49, 139, 629, 812, 837, 842, 847, 854, 864, 871], "direct": [4, 57, 80, 341, 348, 352, 357, 361, 372, 375, 378, 409, 420, 475, 476, 490, 646, 756, 818, 824, 826, 841, 847, 853, 854, 866, 870, 871, 874], "tolist": 4, "652289830999962": 4, "int32": [4, 43, 45, 54, 57, 58, 66, 67, 70, 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845, 856, 873, 874], "xlabel": 14, "ylabel": 14, "obviou": [14, 852, 870], "trend": 14, "gap": 14, "train_siz": [14, 45], "widen": 14, "impress": 14, "outcom": [14, 57, 80, 337, 349, 372, 806], "tend": 14, "95933": 14, "9874": 14, "105807": 14, "wrap": [14, 22, 24, 31, 32, 34, 45, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 103, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 128, 129, 131, 133, 134, 136, 138, 139, 140, 141, 143, 145, 146, 149, 153, 154, 155, 168, 172, 173, 180, 197, 214, 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, 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828, 841, 844, 851], "quickstart": [20, 812], "introduct": [20, 22, 29, 31, 32, 870], "point": [20, 29, 54, 56, 57, 62, 66, 68, 70, 77, 79, 80, 85, 89, 93, 126, 127, 128, 130, 132, 135, 142, 143, 148, 152, 165, 169, 173, 180, 220, 221, 222, 223, 225, 226, 227, 228, 229, 236, 237, 238, 240, 241, 243, 245, 246, 247, 253, 254, 255, 256, 261, 262, 263, 264, 265, 273, 275, 276, 278, 280, 282, 283, 284, 285, 286, 287, 288, 290, 291, 292, 293, 294, 312, 313, 315, 335, 336, 353, 354, 357, 359, 369, 372, 375, 376, 377, 382, 387, 390, 399, 400, 401, 419, 429, 449, 453, 508, 509, 510, 511, 512, 522, 523, 524, 532, 627, 629, 630, 632, 637, 643, 644, 645, 646, 647, 667, 669, 672, 673, 674, 676, 678, 679, 680, 683, 684, 685, 686, 687, 688, 689, 691, 694, 740, 741, 747, 749, 750, 751, 752, 755, 757, 758, 760, 761, 762, 763, 764, 765, 766, 801, 802, 810, 816, 818, 819, 820, 823, 824, 826, 828, 829, 831, 832, 834, 836, 840, 841, 844, 845, 847, 849, 851, 852, 861, 863, 876], "showcas": [20, 812], 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280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 302, 303, 304, 305, 306, 307, 309, 310, 311, 313, 325, 334, 335, 336, 337, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 369, 378, 398, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463, 464, 468, 469, 484, 490, 492, 493, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 535, 537, 538, 540, 541, 545, 546, 547, 548, 549, 550, 551, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 572, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 624, 628, 630, 631, 634, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 691, 696, 697, 698, 699, 703, 706, 707, 708, 709, 710, 713, 714, 718, 719, 721, 724, 725, 726, 727, 729, 730, 731, 735, 736, 738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 773, 774, 789, 792, 794, 801, 806, 824, 827, 852, 853, 857, 863, 864, 865], "recurs": [22, 31, 32, 45, 47, 52, 74, 75, 166, 167, 199, 200, 376, 448, 550, 551, 557, 630, 631, 634, 641, 718, 719, 722, 728, 729, 730, 771, 819, 823, 826, 827, 834, 837, 840, 853, 855], "fashion": [22, 778, 844, 864], "native_arrai": [22, 31, 32, 53, 54, 56, 76, 78, 79, 80, 81, 85, 92, 110, 113, 136, 139, 141, 143, 149, 152, 153, 154, 155, 163, 168, 175, 197, 206, 214, 230, 234, 239, 240, 241, 243, 247, 251, 259, 260, 268, 273, 276, 279, 282, 287, 335, 336, 363, 372, 377, 378, 458, 484, 490, 494, 534, 537, 564, 565, 568, 599, 626, 629, 630, 631, 632, 634, 636, 637, 638, 639, 643, 644, 647, 648, 650, 651, 658, 666, 669, 673, 674, 679, 680, 684, 688, 689, 691, 694, 696, 698, 699, 706, 738, 747, 756, 762, 765, 767, 773, 783, 801, 816, 834, 842, 844], "data_class": [22, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 395, 396, 545, 549, 687, 712], "low": [22, 31, 34, 50, 57, 61, 66, 80, 84, 89, 375, 418, 422, 636, 643, 649, 650, 651, 652, 654, 656, 658, 739, 741, 778, 827, 833, 840, 841, 847, 849, 866, 868, 870, 871, 872, 874, 876], "c": [22, 31, 37, 46, 47, 53, 57, 58, 59, 61, 64, 70, 76, 77, 79, 80, 81, 82, 84, 85, 87, 91, 93, 97, 98, 116, 127, 128, 138, 141, 165, 168, 223, 234, 240, 241, 261, 262, 264, 273, 276, 284, 291, 375, 376, 378, 381, 387, 389, 390, 391, 392, 403, 408, 424, 426, 428, 429, 431, 443, 462, 463, 464, 474, 492, 496, 501, 502, 503, 506, 524, 537, 545, 546, 547, 548, 556, 560, 561, 591, 600, 615, 616, 619, 621, 622, 623, 626, 629, 630, 632, 634, 635, 636, 637, 639, 641, 644, 645, 647, 650, 651, 652, 653, 654, 655, 657, 672, 674, 676, 706, 710, 718, 721, 725, 726, 727, 729, 730, 735, 736, 747, 752, 758, 759, 764, 766, 795, 805, 806, 813, 819, 822, 825, 826, 827, 831, 837, 839, 848, 849, 850, 852, 855, 857, 858, 860, 861, 864, 866, 870, 874, 875, 877], "fundament": [22, 31, 828, 841, 847, 849, 859, 870], "signatur": [22, 31, 378, 387, 484, 522, 829, 830, 831, 832, 836, 840, 844, 845, 847, 860, 867, 876], "matmul": [22, 31, 32, 48, 62, 85, 376, 446, 614, 634, 637, 687, 825, 844, 845, 849], "to_n": [22, 31, 32, 43, 52, 75, 849], "jaxlib": [22, 28, 46, 801, 819, 824, 829, 830, 836, 845, 849, 851], "xla_extens": [22, 28, 801, 824, 829, 830, 836, 845, 849, 851], "arrayimpl": [22, 28, 801], "disabl": [22, 31, 57, 80, 378, 492, 794, 810, 826], "array_mod": [22, 31, 578, 602, 634, 846], "set_array_mod": [22, 31, 602, 634, 846], "ultim": [22, 31, 863], "sigmoid": [22, 31, 32, 43, 51, 57, 73, 80, 301, 367, 382, 508, 626, 788, 849, 852, 853], "z": [22, 31, 32, 44, 45, 53, 56, 57, 58, 62, 63, 66, 68, 70, 76, 79, 80, 81, 85, 86, 87, 89, 93, 102, 103, 137, 138, 140, 141, 201, 223, 224, 228, 230, 233, 235, 240, 251, 252, 255, 256, 257, 259, 260, 265, 267, 269, 270, 271, 272, 280, 289, 300, 301, 335, 336, 338, 367, 372, 377, 387, 453, 455, 456, 457, 458, 459, 465, 469, 480, 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846, 847, 870], "act": [25, 35, 57, 80, 298, 363, 373, 820, 831, 846, 855, 877], "shorthand": [25, 35, 37, 844], "pair": [25, 35, 45, 57, 61, 80, 84, 228, 247, 320, 362, 369, 372, 375, 409, 418, 420, 422, 632, 636, 637, 649, 650, 651, 652, 654, 656, 658, 666, 668, 806], "93968587": 25, "26075466": 25, "22723222": 25, "06276492": 25, "47426987": 25, "72835908": 25, "71737559": 25, "50411096": 25, "65419174": 25, "15576624": 25, "implic": [25, 35, 36, 39, 827], "satisfi": [26, 27, 28, 29, 45, 47, 50, 57, 375, 376, 398, 430, 829, 831], "fw": [26, 27, 28, 29, 61, 84, 387, 522, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 773, 819, 844], "mxnet": [26, 27, 28, 29, 209, 631, 801, 818, 819, 860, 877], "einop": [26, 27, 28, 29, 45, 47, 50, 58, 81, 545, 546, 547, 634, 829, 860], "miniconda": [26, 27, 28, 29], "multienv": [26, 27, 28, 29], "site": [26, 27, 28, 29, 871], "psutil": [26, 27, 28, 29, 45, 47, 50], "termcolor": [26, 27, 28, 29, 45, 47, 50, 74, 103], "colorama": [26, 27, 28, 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29], "parso": [26, 27, 28, 29], "ptyprocess": [26, 27, 28, 29], "wcwidth": [26, 27, 28, 29], "asttoken": [26, 27, 28, 29], "pure": [26, 27, 28, 29, 37, 47, 812, 832, 836, 841, 847, 851, 854, 855, 870, 876, 877], "lazili": [26, 27, 28, 31, 32, 36, 38, 49, 812, 863, 864, 865], "actual": [26, 36, 816, 820, 822, 828, 834, 837, 838, 840, 841, 842, 844, 847, 848, 853, 855, 871, 876], "occur": [26, 31, 32, 36, 49, 54, 56, 68, 77, 79, 91, 155, 274, 290, 630, 632, 644, 645, 744, 745, 749, 750, 751, 752, 823, 828, 830, 833, 846], "altern": [26, 36, 46, 57, 80, 85, 97, 98, 334, 342, 343, 344, 348, 350, 351, 352, 353, 355, 356, 357, 361, 362, 372, 818, 819, 826, 840, 852, 873], "assum": [26, 27, 36, 37, 38, 53, 56, 57, 58, 61, 62, 63, 79, 80, 81, 84, 85, 86, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 148, 149, 155, 171, 175, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 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818, 819, 820, 821, 822, 828, 831, 840, 841, 842, 844, 850, 855, 856, 861], "practic": [28, 820, 825, 828, 841, 843, 873], "everi": [28, 31, 32, 37, 45, 53, 57, 58, 80, 81, 135, 136, 301, 335, 336, 349, 367, 372, 375, 378, 412, 413, 414, 421, 498, 534, 629, 634, 818, 820, 823, 825, 826, 828, 829, 831, 835, 836, 837, 838, 840, 841, 842, 844, 849, 851, 853, 863, 864, 865, 870], "jax_kornia": [28, 31, 32, 812, 864], "though": [28, 817, 818, 820, 829, 830, 832, 837, 840, 841, 847, 852, 855], "000000000034": [28, 31, 32, 812, 864], "raw_img": [28, 31, 32, 812, 864], "sharp": [28, 31, 32, 812], "prefer": [28, 31, 32, 247, 632, 819, 827, 833, 834, 838, 841, 856, 870], "whole": [29, 57, 80, 378, 381, 491, 504, 505, 507, 820, 826, 835], "full": [29, 57, 62, 80, 84, 85, 97, 98, 100, 165, 252, 260, 323, 324, 325, 326, 327, 369, 376, 377, 378, 449, 450, 456, 457, 485, 488, 579, 588, 603, 611, 629, 630, 632, 634, 636, 637, 651, 653, 654, 655, 657, 680, 684, 686, 687, 777, 784, 812, 819, 820, 826, 829, 832, 833, 836, 837, 841, 844, 847, 849, 855, 860, 861, 868, 870, 876], "complex": [29, 31, 32, 45, 51, 56, 57, 62, 70, 73, 77, 79, 80, 85, 93, 110, 111, 112, 113, 114, 115, 116, 117, 118, 142, 143, 158, 172, 181, 187, 220, 221, 222, 223, 224, 225, 226, 229, 237, 238, 240, 241, 243, 245, 253, 254, 255, 256, 257, 261, 262, 263, 264, 273, 275, 276, 278, 280, 283, 284, 285, 286, 287, 290, 291, 295, 300, 301, 303, 338, 343, 344, 367, 372, 375, 376, 387, 398, 409, 419, 420, 424, 429, 430, 431, 442, 444, 530, 531, 592, 593, 626, 629, 630, 632, 634, 637, 644, 647, 672, 673, 674, 678, 685, 687, 689, 691, 694, 747, 762, 763, 765, 777, 788, 806, 815, 818, 821, 826, 829, 831, 838, 841, 844, 845, 847, 852, 853, 854, 855, 857, 864, 866, 868, 870, 872, 876, 877], "neccessari": 29, "set_random_se": [29, 48], "301436": 29, "_c": 29, "0x7f252c392390": 29, "flatten": [29, 31, 32, 45, 47, 50, 57, 58, 62, 64, 67, 68, 80, 81, 85, 87, 90, 91, 340, 356, 372, 376, 378, 387, 427, 473, 483, 487, 492, 493, 496, 498, 520, 527, 528, 529, 530, 531, 532, 545, 549, 634, 637, 639, 644, 645, 675, 682, 694, 700, 705, 707, 744, 745, 749, 750, 751, 752, 771, 773, 812, 840, 847], "keyword": [29, 31, 32, 47, 49, 52, 53, 57, 74, 80, 103, 139, 274, 375, 378, 387, 423, 484, 522, 536, 539, 572, 601, 629, 632, 634, 637, 641, 647, 688, 724, 765, 771, 773, 777, 793, 794, 805, 818, 824, 827, 829, 830, 838, 840, 841, 842, 844, 845, 847, 852, 863, 864, 865], "input_arrai": [29, 31, 32, 840], "torch_model": [29, 31, 32, 49], "159": [29, 73, 110, 626, 636, 660], "thank": [29, 852, 860], "fledg": [29, 819, 849, 850], "output_arrai": [29, 31, 32, 57, 454], "0893": 29, "1504": 29, "1372": 29, "0991": 29, "0867": 29, "0851": 29, "0911": 29, "0804": 29, "0926": 29, "0881": 29, "softmaxbackward0": 29, "furthermor": 29, "relat": [29, 247, 632, 812, 814, 817, 818, 819, 820, 826, 833, 841, 844, 845, 846, 847, 864, 873], "continu": [29, 31, 32, 47, 125, 287, 295, 367, 628, 632, 812, 817, 818, 819, 822, 823, 834, 840, 843, 844, 855, 860, 861, 870], "regress": [30, 870, 877], "checkout": [31, 46, 820, 823, 844], "f705efe7cb5d18df17ce6c1e20f04d0eb4933f48": 31, "theoret": 31, "aspect": [31, 32, 813, 839, 852, 870], "easiest": [31, 812, 814, 819, 856], "defer": [31, 32, 818, 824, 829, 830, 837, 840, 841, 844, 876], "similarli": [31, 44, 139, 147, 223, 328, 335, 336, 369, 372, 629, 632, 825, 829, 841, 847, 851, 876], "essenc": [31, 871, 876], "becom": [31, 57, 80, 97, 346, 372, 378, 464, 639, 699, 801, 820, 821, 827, 829, 831, 833, 840, 855, 859, 861, 863], "slide": [31, 57, 61, 80, 84, 375, 394, 395, 396, 412, 413, 414, 415, 418, 422, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 792], "regressor": [31, 32, 812], "input_dim": [31, 32, 46, 812], "output_dim": [31, 32, 46, 812], "linear0": [31, 32, 43, 812, 852, 853], "linear1": [31, 32, 43, 812, 852, 853], "instanti": [31, 32, 784, 832], "adam": [31, 32, 43, 47, 59, 82, 536, 615, 616, 621, 634, 635, 796, 812, 852, 853, 854, 870], "n_training_exampl": [31, 32, 812], "2000": [31, 32, 80, 314, 369, 812], "random_norm": [31, 32, 61, 62, 66, 84, 85, 89, 545, 634, 636, 637, 643, 651, 653, 654, 655, 657, 658, 662, 687, 812], "linspac": [31, 32, 53, 76, 126, 629, 812, 836, 847, 849, 877], "pred": [31, 32, 46, 47, 57, 63, 80, 86, 377, 453, 456, 638, 696, 697, 698, 812, 827, 837, 840], "gradient": [31, 32, 45, 47, 57, 80, 97, 213, 364, 372, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 631, 640, 715, 716, 717, 773, 784, 796, 812, 822, 845, 852, 853, 855, 870], "grad": [31, 32, 43, 47, 615, 635, 796, 812, 839, 852, 853, 854], "execute_with_gradi": [31, 32, 43, 47, 635, 812, 852, 853, 854, 855], "lambda": [31, 32, 48, 50, 80, 123, 125, 297, 307, 544, 557, 617, 618, 620, 625, 628, 634, 635, 637, 641, 673, 725, 726, 730, 812, 818, 837, 838, 839, 842, 847, 849, 852], "2d": [31, 32, 47, 57, 80, 97, 313, 369, 375, 376, 378, 387, 390, 391, 399, 400, 442, 449, 463, 473, 522, 792, 810, 812, 841, 847], "5f": [31, 32, 812], "nonetheless": [31, 32], "extract": [31, 32, 39, 46, 57, 80, 98, 378, 467, 493, 841, 843, 845, 866, 870, 871, 876], "gc": [31, 32, 557, 634], "decompos": [31, 32, 57, 80, 97, 100, 323, 324, 325, 326, 327, 348, 355, 369, 372, 376, 440, 445, 448, 451, 841, 854], "said": [31, 32, 778, 845, 861, 863], "otherwis": [31, 32, 49, 52, 53, 54, 56, 57, 58, 61, 62, 67, 68, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 126, 128, 129, 134, 136, 137, 138, 141, 143, 149, 152, 153, 155, 156, 158, 159, 160, 161, 162, 171, 175, 179, 180, 196, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 300, 303, 304, 305, 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771, 776, 777, 792, 794, 795, 801, 812, 820, 824, 827, 829, 830, 831, 837, 838, 840, 844, 849, 856, 863, 864], "x0": [31, 32, 50, 81, 537, 634, 831], "normalize_trac": [31, 32], "html": [31, 32, 46, 56, 57, 79, 80, 147, 155, 243, 253, 254, 269, 328, 335, 336, 369, 372, 375, 378, 387, 419, 492, 522, 629, 630, 632, 637, 639, 647, 685, 686, 714, 764, 832, 860], "fname": [31, 32, 48, 50, 794, 852], "anticip": [31, 32], "addition": [31, 32, 827, 840, 841, 876], "normalize_native_comp": [31, 32], "return_backend_compiled_fn": 31, "immedi": [31, 32, 810, 818, 819], "built": [31, 32, 37, 45, 47, 50, 126, 629, 792, 793, 794, 812, 819, 820, 826, 827, 844, 850, 856, 863, 869, 870, 874], "eager_graph": [31, 32, 812, 863, 864], "lazy_graph": [31, 32, 812, 863, 864], "thought": [31, 32, 819, 820, 836, 860, 868], "matter": [31, 32, 37, 831, 859], "haven": [31, 32, 37, 856, 870], "jax_out": [31, 32], "ideal": [31, 32, 828, 829, 841, 847, 852], "worth": [31, 32], "differenti": [31, 32, 295, 365, 366, 367, 374, 870], "chosen": [31, 32, 50, 100, 126, 228, 629, 632, 644, 748, 818, 828, 841], "plai": [31, 32, 377, 456, 812, 815, 819, 821, 824, 830, 834, 841, 844, 854, 870, 873], "role": [31, 32, 812, 815, 820, 821, 830, 841, 850, 871, 873, 877], "dl": [31, 32], "effortlessli": [31, 32], "previous": [31, 32, 603, 634, 801, 818, 819, 825, 837, 839, 844, 849], "default_devic": [31, 32, 206, 209, 210, 211, 217, 218, 631, 830, 833, 834], "as_n": [31, 32, 54, 55, 74, 77, 78, 158, 159, 160, 161, 162, 163, 169, 196, 197, 630, 631, 829], "certainli": [31, 32, 812, 860, 876], "upon": [31, 32, 49, 810, 820, 821, 831, 840, 844, 847, 855, 869, 870], "unnecessari": [31, 32, 841], "extend": [31, 32, 57, 80, 378, 387, 484, 525, 825, 826, 829, 832, 833, 836, 841, 845, 855, 867, 870, 876], "infrastructur": [31, 32, 866, 872, 873], "least": [31, 56, 57, 62, 79, 80, 240, 258, 273, 375, 378, 387, 403, 408, 462, 463, 464, 473, 475, 522, 632, 637, 644, 677, 747, 812, 820, 824, 828, 829, 830, 831, 837, 840, 844, 864], "coco": 31, "seamlessli": [32, 844], "therefor": [32, 37, 53, 56, 57, 62, 79, 80, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 149, 155, 171, 175, 179, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 313, 328, 329, 335, 336, 338, 341, 369, 372, 375, 376, 378, 387, 394, 395, 396, 397, 399, 400, 401, 407, 412, 413, 414, 419, 421, 430, 477, 484, 485, 487, 492, 496, 497, 522, 525, 529, 538, 546, 547, 552, 556, 558, 560, 562, 576, 591, 595, 600, 624, 629, 630, 632, 634, 635, 636, 637, 639, 642, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 658, 659, 660, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 693, 694, 695, 699, 700, 701, 702, 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97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 134, 136, 141, 143, 149, 153, 155, 168, 172, 173, 180, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 299, 303, 304, 305, 306, 307, 309, 310, 311, 313, 334, 335, 336, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 468, 469, 490, 492, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 537, 538, 540, 541, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 619, 624, 640, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 691, 696, 697, 698, 699, 703, 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747], "conserv": [53, 139, 629], "cartesian": [53, 139, 629], "matrix": [53, 57, 58, 61, 62, 80, 81, 84, 85, 97, 98, 100, 102, 139, 145, 146, 147, 328, 329, 369, 376, 378, 387, 426, 429, 430, 433, 434, 435, 437, 440, 441, 442, 443, 444, 445, 446, 447, 450, 451, 482, 522, 534, 540, 629, 634, 636, 637, 660, 667, 669, 671, 672, 673, 674, 676, 677, 678, 679, 680, 681, 683, 684, 685, 686, 687, 688, 689, 691, 692, 695, 776, 778, 791, 792, 806, 810, 818, 829, 841, 868, 870], "ij": [53, 70, 139, 629, 647, 759, 806], "rank": [53, 57, 62, 64, 71, 80, 85, 87, 94, 97, 98, 99, 100, 101, 106, 139, 323, 324, 325, 326, 327, 369, 376, 378, 387, 434, 435, 445, 448, 451, 484, 492, 496, 532, 629, 637, 639, 644, 648, 668, 670, 678, 680, 684, 686, 691, 693, 694, 701, 702, 710, 713, 714, 747, 767, 768, 813, 878], "ni": [53, 139, 629], "xi": [53, 139, 629], "scatter": [53, 58, 76, 81, 141, 576, 577, 629, 634, 826, 840, 847, 877], "j": [53, 56, 57, 58, 62, 70, 76, 79, 80, 85, 97, 125, 141, 221, 222, 223, 224, 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683, 684, 685, 686, 687, 688, 691], "mxn": [53, 57, 62, 85, 145, 146, 329, 369, 629, 637, 671, 678, 680, 681, 683, 684, 688, 691], "matric": [53, 57, 62, 80, 85, 97, 98, 102, 139, 145, 146, 329, 369, 376, 378, 429, 434, 435, 437, 443, 444, 449, 473, 629, 636, 637, 660, 667, 669, 671, 672, 673, 674, 675, 676, 678, 679, 680, 681, 683, 684, 685, 686, 687, 688, 691, 692, 778, 816, 834, 870], "diagon": [53, 57, 62, 80, 85, 98, 132, 145, 146, 147, 313, 328, 329, 369, 376, 378, 427, 430, 440, 446, 473, 629, 637, 670, 691], "triangular": [53, 57, 62, 85, 145, 146, 147, 328, 329, 369, 376, 446, 629, 637, 667, 673, 674, 680, 684], "triu": [53, 76, 629], "upper": [53, 57, 62, 66, 80, 85, 89, 132, 146, 147, 313, 329, 369, 376, 387, 446, 525, 629, 637, 643, 667, 673, 674, 684, 741, 829, 840, 844], "zeros_lik": [53, 57, 76, 152, 269, 378, 492, 615, 616, 619, 621, 622, 623, 629, 630, 632, 635, 637, 639, 684, 699, 841, 847], "data_typ": [54, 57, 77, 80, 182, 630, 826, 829, 844, 845], 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80, 84, 222, 226, 229, 375, 411, 418, 422, 632, 815, 840, 847, 860, 866], "hyperbol": [56, 79, 222, 226, 229, 238, 286, 290, 291, 304, 308, 367, 632], "sector": [56, 79, 222, 226, 229, 632, 860], "multipli": [56, 57, 61, 70, 79, 80, 84, 97, 223, 289, 352, 375, 376, 411, 442, 443, 523, 524, 632, 636, 647, 659, 757, 763, 820, 824, 825, 827, 831], "angl": [56, 79, 228, 238, 286, 291, 350, 372, 632], "deg": [56, 79, 224, 632], "radian": [56, 57, 79, 80, 221, 224, 225, 227, 228, 237, 239, 279, 285, 290, 359, 372, 632, 832], "degre": [56, 57, 70, 79, 80, 93, 224, 239, 279, 322, 369, 378, 490, 632, 647, 764, 766, 869], "1j": [56, 79, 80, 224, 225, 237, 238, 243, 245, 257, 280, 285, 286, 290, 338, 592, 632, 634], "2j": [56, 57, 79, 80, 224, 253, 338, 375, 403, 408, 593, 632, 634], "3j": [56, 57, 79, 80, 224, 257, 280, 338, 372, 632], "35619449": [56, 224, 632], "78539816": [56, 224, 632], "135": [56, 224, 540, 632, 634], "asin": [56, 79, 632], "sine": [56, 79, 225, 226, 285, 286, 632], "927": 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"default-device"]], "total_mem_on_dev": [[215, "total-mem-on-dev"]], "unset_default_complex_dtype": [[187, "unset-default-complex-dtype"]], "set_default_uint_dtype": [[185, "set-default-uint-dtype"]], "split_factor": [[212, "split-factor"]], "function_supported_devices": [[199, "function-supported-devices"]], "used_mem_on_dev": [[219, "used-mem-on-dev"]], "unset_default_device": [[217, "unset-default-device"]], "split_func_call": [[213, "split-func-call"]], "clear_cached_mem_on_dev": [[195, "clear-cached-mem-on-dev"]], "Image": [[83, "module-ivy.data_classes.container.image"], [60, "module-ivy.data_classes.array.image"]], "Conversions": [[52, "module-ivy.data_classes.array.conversions"], [75, "module-ivy.data_classes.container.conversions"]], "End-to-End Training Pipeline in Ivy": [[47, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[47, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[47, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[47, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[47, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[47, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[47, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[47, "Plotting-the-training-metrics"]], "Save the trained Model": [[47, "Save-the-trained-Model"]], "HuggingFace Tensorflow DeiT": [[48, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[48, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Resnet 18": [[50, "Resnet-18"]], "Deepmind PerceiverIO on GPU": [[46, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[46, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[46, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[46, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[46, "Run-the-demo..."]], "\u2026with torch backend": [[46, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[46, "....with-tensorflow-backend"]], "\u2026with jax backend": [[46, "...with-jax-backend"]], "\u2026with numpy backend": [[46, "...with-numpy-backend"]], "Ivy as a Transpiler Introduction": [[49, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[49, "To-use-the-transpiler:"]], "Transpiler Interface": [[49, "Transpiler-Interface"]], "Telemetry": [[49, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[49, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[49, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[49, "3.-Transpile-Models-\ud83c\udf10"]], "Quickstart": [[32, "Quickstart"]], "Get familiar with Ivy": [[32, "Get-familiar-with-Ivy"]], "Functional API": [[32, "Functional-API"]], "Stateful API": [[32, "Stateful-API"]], "Tracing code": [[32, "Tracing-code"]], "Any function": [[32, "Any-function"], [31, "Any-function"]], "Any library": [[32, "Any-library"], [31, "Any-library"]], "Any model": [[32, "Any-model"], [31, "Any-model"]], "0.1: Compile": [[34, "0.1:-Compile"]], "Learn the basics": [[21, "learn-the-basics"], [20, "learn-the-basics"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "Trace code": [[24, "Trace-code"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]], "Write Ivy code": [[22, "Write-Ivy-code"]], "Contents": [[22, "Contents"]], "Installing Ivy": [[22, "Installing-Ivy"]], "Importing Ivy": [[22, "Importing-Ivy"], [0, "Importing-Ivy"]], "Ivy Backend Handler": [[22, "Ivy-Backend-Handler"], [31, "Ivy-Backend-Handler"]], "Data Structures": [[22, "Data-Structures"], [31, "Data-Structures"]], "Ivy Functional API": [[22, "Ivy-Functional-API"], [31, "Ivy-Functional-API"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[45, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[45, "Table-of-Contents"]], "Defining the model": [[45, "Defining-the-model"]], "Model construction": [[45, "Model-construction"]], "Some helper functions": [[45, "Some-helper-functions"]], "Transpiling the model": [[45, "Transpiling-the-model"]], "PyTorch pipeline": [[45, "PyTorch-pipeline"]], "Dataset download": [[45, "Dataset-download"]], "DataLoader": [[45, "DataLoader"]], "Training": [[45, "Training"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[6, "Comparing-the-results"], [7, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[6, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[6, "Conclusion"], [7, "Conclusion"]], "Transpile any library": [[28, "Transpile-any-library"]], "Unify code": [[23, "Unify-code"]], "1.3: Dynamic vs Static": [[39, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[39, "Dynamic"]], "Static": [[39, "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.": [[39, "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."]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Unify": [[26, "Unify"], [27, "Unify"], [37, "Unify"], [38, "Unify"], [36, "Unify"]], "Trace": [[26, "Trace"], [27, "Trace"]], "Transpile": [[26, "Transpile"], [27, "Transpile"], [37, "Transpile"], [38, "Transpile"], [36, "Transpile"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [4, "Installation"]], "Imports": [[12, "Imports"], [14, "Imports"], [8, "Imports"]], "Data Preparation": [[12, "Data-Preparation"], [5, "Data-Preparation"], [8, "Data-Preparation"], [4, "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"]], "How to use decorators": [[27, "How-to-use-decorators"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "0.0: Unify": [[33, "0.0:-Unify"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "Compile": [[37, "Compile"], [38, "Compile"], [36, "Compile"]], "Accelerating XGBoost with JAX": [[14, "Accelerating-XGBoost-with-JAX"]], "Tests": [[14, "Tests"]], "Loading the Data": [[14, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[14, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[14, "JAX-backend"]], "Tensorflow backend": [[14, "Tensorflow-backend"]], "PyTorch backend": [[14, "PyTorch-backend"]], "More exhaustive example": [[14, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[14, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[14, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[14, "Comparison-of-Metrics"]], "Guides": [[15, "guides"], [20, "guides"]], "ODSC Ivy Demo": [[31, "ODSC-Ivy-Demo"]], "Graph Tracer": [[31, "Graph-Tracer"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Examples and Demos": [[20, "examples-and-demos"], [3, "examples-and-demos"]], "Transpile code": [[25, "Transpile-code"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-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"]], "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:"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[43, "Installs-\ud83d\udcbe"], [44, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[43, "Imports-\ud83d\udec3"], [44, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[43, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[43, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[43, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[43, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[43, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[43, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[43, "Set-Backend-Framework"]], "Define Model": [[43, "Define-Model"], [44, "Define-Model"]], "Create Model": [[43, "Create-Model"]], "Create Optimizer": [[43, "Create-Optimizer"]], "Input and Target": [[43, "Input-and-Target"]], "Loss Function": [[43, "Loss-Function"]], "Training Loop": [[43, "Training-Loop"]], "# 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"]], "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"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "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)"]], "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"]], "Compilation of a Basic Function": [[44, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[44, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[44, "Function-compilation-\ud83d\udee0"]], "Set backend": [[44, "Set-backend"]], "Sample input": [[44, "Sample-input"]], "Define function to compile": [[44, "Define-function-to-compile"]], "Compile the function": [[44, "Compile-the-function"]], "Check results": [[44, "Check-results"], [44, "id1"]], "Compiling simple neural network \ud83e\udde0": [[44, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[44, "Create-model"]], "Define input": [[44, "Define-input"]], "Compile network": [[44, "Compile-network"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "2.0: Kornia": [[40, "2.0:-Kornia"]]}, 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8, 43, 56, 57, 79, 80, 81, 84, 89, 119, 137, 226, 228, 240, 242, 253, 315, 348, 349, 369, 372, 418, 532, 547, 560, 592, 626, 629, 632, 634, 637, 641, 643, 650, 676, 682, 726, 741], "050000": 0, "max": [0, 43, 45, 54, 57, 58, 62, 70, 77, 80, 81, 85, 93, 165, 168, 271, 335, 372, 375, 376, 377, 378, 394, 395, 396, 412, 413, 414, 415, 417, 419, 430, 452, 489, 491, 492, 540, 541, 546, 562, 576, 577, 630, 632, 634, 637, 647, 678, 680, 683, 776, 792, 796, 828, 841, 867], "25691": 0, "160000": 0, "reveal": 0, "outlier": [0, 844], "receiv": [0, 6, 45, 49, 97, 536, 572, 634, 640, 715, 716, 717, 792, 810, 815, 819, 820, 829, 830, 844, 847], "anomali": 0, "financi": 0, "behavior": [0, 4, 8, 57, 68, 240, 247, 273, 282, 388, 533, 580, 604, 632, 634, 645, 749, 750, 751, 752, 818, 826, 827, 828, 829, 840, 841, 842, 844, 847, 849, 855, 867], "associ": [0, 12, 57, 62, 80, 85, 223, 273, 378, 387, 461, 525, 632, 637, 680, 683, 695, 773, 820, 829, 837, 838, 841, 842, 844, 855], "122": [0, 13, 54, 168, 238, 632], "211321": 0, "256": [0, 4, 8, 12, 56, 81, 283, 284, 593, 636, 651, 653, 776], "683288": 0, "250000": 0, "105": [0, 62, 84, 636, 637, 659, 660, 675, 682], "890000": 0, "2125": 0, "870000": 0, "deepen": 0, "averag": [0, 6, 7, 45, 47, 57, 59, 63, 80, 82, 86, 375, 377, 381, 387, 389, 390, 394, 395, 396, 454, 455, 456, 457, 458, 459, 506, 522, 615, 616, 621, 635, 636, 638, 640, 663, 696, 715, 716, 791, 792], "across": [0, 1, 12, 13, 14, 26, 27, 28, 29, 43, 57, 67, 74, 80, 81, 90, 102, 211, 212, 240, 247, 273, 291, 377, 381, 452, 503, 506, 537, 558, 594, 631, 632, 634, 636, 641, 644, 659, 663, 724, 744, 745, 792, 818, 823, 829, 831, 833, 836, 837, 839, 844, 847, 868, 870, 875], "all": [0, 1, 2, 4, 5, 6, 7, 8, 12, 13, 16, 17, 18, 19, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 44, 45, 47, 48, 50, 52, 53, 57, 58, 61, 62, 64, 66, 71, 72, 74, 75, 76, 79, 80, 81, 84, 85, 87, 89, 94, 95, 97, 98, 126, 134, 141, 145, 146, 147, 201, 208, 240, 244, 272, 273, 328, 329, 341, 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863, 864, 870, 873, 875, 876, 877], "groupbi": 0, "94838": 0, "202258": 0, "008258": 0, "006271": 0, "012171": 0, "007860": 0, "005453": 0, "002419": 0, "009637": 0, "000987": 0, "004467": 0, "000644": 0, "001235": [0, 47], "000024": 0, "000070": 0, "000182": 0, "000072": 0, "000089": 0, "000295": 0, "000131": 0, "80746": 0, "806911": 0, "771948": 0, "623778": 0, "033281": 0, "542029": 0, "151225": 0, "397737": 0, "568731": 0, "570636": 0, "581123": 0, "372319": 0, "713588": 0, "014049": 0, "040308": 0, "105130": 0, "041449": 0, "051648": 0, "170575": 0, "075667": 0, "In": [0, 3, 4, 5, 6, 16, 18, 20, 22, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 43, 45, 50, 55, 57, 58, 64, 78, 80, 81, 87, 97, 98, 207, 214, 215, 219, 223, 240, 241, 247, 255, 256, 273, 276, 282, 284, 375, 378, 381, 399, 400, 401, 421, 462, 463, 464, 470, 472, 474, 475, 476, 477, 479, 483, 489, 490, 499, 501, 503, 535, 555, 562, 580, 631, 632, 634, 637, 639, 643, 685, 702, 703, 704, 706, 708, 709, 711, 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391, 392, 397, 398, 403, 404, 407, 408, 409, 417, 418, 419, 420, 422, 430, 431, 438, 442, 444, 446, 451, 452, 464, 470, 473, 477, 479, 490, 499, 501, 502, 503, 506, 508, 509, 510, 511, 512, 515, 522, 532, 629, 632, 636, 637, 639, 641, 643, 644, 647, 648, 649, 650, 651, 652, 654, 656, 658, 663, 668, 671, 675, 677, 678, 679, 680, 681, 682, 683, 684, 687, 688, 691, 692, 693, 694, 701, 702, 704, 710, 714, 726, 739, 740, 741, 747, 761, 763, 764, 765, 766, 767, 768, 792, 795, 805, 812, 822, 826, 828, 844, 856, 864], "after": [0, 4, 5, 8, 9, 11, 12, 13, 31, 32, 46, 57, 58, 59, 61, 65, 74, 80, 81, 82, 84, 88, 186, 287, 304, 308, 357, 367, 372, 375, 376, 378, 398, 399, 400, 401, 418, 422, 443, 473, 484, 562, 616, 619, 621, 622, 623, 630, 632, 634, 635, 636, 641, 642, 649, 650, 651, 652, 654, 656, 658, 659, 729, 737, 796, 801, 812, 818, 819, 820, 823, 825, 826, 828, 829, 831, 833, 836, 839, 842, 844, 848, 856, 863, 864, 870], "combin": [0, 14, 37, 57, 74, 80, 103, 375, 387, 409, 420, 522, 550, 551, 634, 637, 668, 677, 820, 824, 827, 828, 829, 831, 833, 837, 844, 854, 870], "them": [0, 3, 4, 11, 13, 16, 18, 20, 31, 32, 37, 376, 446, 539, 575, 634, 776, 792, 812, 814, 818, 820, 821, 823, 824, 825, 826, 827, 828, 829, 833, 835, 838, 840, 841, 842, 844, 846, 849, 851, 852, 853, 855, 857, 858, 859, 860, 861, 862, 863, 864, 865, 867, 868, 870, 872, 876], "achiev": [0, 11, 13, 14, 31, 812, 813, 815, 821, 828, 829, 837, 838, 844, 847, 852, 854, 857], "concaten": [0, 43, 57, 58, 64, 80, 85, 378, 469, 545, 549, 634, 636, 639, 663, 682, 700, 776, 842, 847, 849, 852], "along": [0, 46, 51, 53, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 73, 74, 76, 79, 80, 81, 85, 86, 87, 89, 90, 92, 93, 94, 97, 98, 100, 113, 117, 122, 137, 138, 213, 287, 290, 292, 330, 331, 332, 335, 336, 340, 341, 356, 363, 369, 372, 373, 375, 376, 377, 378, 381, 387, 397, 403, 404, 407, 408, 409, 419, 420, 445, 456, 469, 470, 471, 473, 475, 476, 484, 489, 492, 494, 496, 504, 505, 506, 507, 523, 524, 525, 527, 528, 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639, 647, 685, 686, 714, 764, 812, 814, 819, 820, 823, 826, 828, 829, 832, 834, 856, 864], "github": [2, 4, 5, 8, 11, 12, 13, 31, 45, 46, 47, 48, 49, 812, 814, 815, 817, 820, 821, 823, 826, 828, 829, 831, 832, 834, 835, 843, 844, 856, 859, 878], "com": [2, 4, 5, 6, 7, 8, 11, 12, 13, 18, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 823, 826, 828, 829, 834, 856], "unifyai": [2, 4, 8, 12, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 826, 834, 856], "model": [2, 3, 4, 9, 14, 15, 20, 21, 22, 48, 50, 240, 273, 377, 453, 632, 789, 793, 794, 810, 812, 852, 853, 857, 863, 864, 868, 869, 870, 871, 872, 873, 874, 876, 877], "depth": [2, 4, 6, 8, 12, 46, 53, 57, 61, 76, 80, 84, 141, 375, 378, 411, 471, 545, 557, 629, 634, 636, 654, 655, 820, 828, 852, 853, 854, 856], "repositori": [2, 4, 8, 12, 814, 818, 819, 820, 822, 823, 826, 834, 843, 861], "cd": [2, 4, 8, 12, 31, 48, 812, 814, 819, 820, 834, 856], "resnet": [3, 6, 13, 20, 31, 863, 864], "imag": [3, 4, 6, 7, 11, 13, 16, 20, 28, 31, 32, 45, 46, 47, 48, 49, 50, 57, 61, 79, 80, 84, 102, 220, 221, 222, 223, 226, 229, 238, 241, 243, 245, 254, 255, 256, 261, 263, 276, 283, 284, 286, 287, 291, 375, 394, 395, 411, 412, 413, 415, 545, 632, 634, 636, 649, 650, 651, 652, 653, 656, 657, 658, 792, 812, 819, 834, 847, 849, 850, 852, 854, 856, 863, 864, 870], "classif": [3, 4, 12, 14, 20, 45, 812, 870], "acceler": [3, 20, 812, 829, 841, 868, 872, 873, 874, 875], "convert": [3, 8, 9, 11, 13, 14, 16, 18, 20, 21, 23, 25, 28, 29, 31, 32, 33, 35, 37, 45, 48, 50, 52, 53, 56, 74, 75, 76, 79, 97, 127, 128, 140, 150, 151, 193, 194, 195, 196, 207, 215, 219, 239, 279, 378, 383, 462, 463, 464, 513, 578, 596, 598, 599, 600, 602, 629, 630, 631, 632, 634, 637, 641, 695, 719, 730, 731, 773, 801, 805, 812, 818, 824, 825, 838, 839, 841, 844, 846, 849, 855, 857, 861, 864, 868, 869, 876], "faster": [3, 4, 9, 11, 13, 14, 20, 31, 32, 48, 50, 57, 62, 80, 85, 376, 449, 637, 687, 814, 817, 826, 857, 872, 875], "infer": [3, 6, 7, 9, 11, 13, 14, 20, 24, 34, 36, 37, 46, 48, 50, 53, 57, 58, 61, 64, 76, 80, 81, 84, 87, 126, 128, 131, 135, 136, 140, 143, 149, 158, 159, 160, 161, 162, 312, 313, 375, 378, 382, 411, 496, 510, 556, 590, 591, 629, 630, 634, 636, 639, 659, 706, 801, 802, 822, 825, 829, 830, 844, 849, 854, 864, 868, 869, 872, 874], "mmpretrain": [3, 20], "segment": [3, 20, 57, 80, 330, 331, 332, 369, 826, 831], "unet": [3, 20], "alexnet": [3, 20], "written": [3, 4, 5, 6, 20, 22, 31, 32, 45, 58, 378, 473, 819, 823, 824, 832, 835, 836, 840, 841, 845, 849, 851, 854, 855, 859, 864, 868, 870, 874, 876, 877], "xgboost": [3, 20], "paddlepaddl": [3, 20, 335, 336, 372, 819], "dinov2": [3, 7, 20], "project": [3, 12, 13, 20, 25, 26, 27, 28, 29, 31, 32, 35, 98, 636, 663, 792, 812, 814, 815, 818, 819, 820, 821, 824, 825, 826, 844, 853, 855, 859, 860, 861, 864, 866, 868, 870, 873, 877, 878], "convnext": [3, 6, 11, 20], "video": [4, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 813, 818, 819, 820, 823, 824, 825, 827, 828, 829, 830, 831, 832, 833, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 847, 856, 868], "tutori": [4, 6, 7, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 820, 841, 856], "three": [4, 5, 20, 26, 36, 37, 47, 57, 139, 312, 369, 378, 464, 629, 819, 820, 827, 828, 829, 831, 841, 844, 847, 848, 849, 871, 876], "major": [4, 5, 644, 747, 829, 830, 842, 844, 855, 860, 867, 870], "ml": [4, 5, 6, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 47, 50, 812, 813, 817, 841, 848, 849, 850, 852, 853, 854, 858, 860, 861, 864, 866, 867, 868, 869, 870, 873, 875, 877], "framework": [4, 5, 7, 9, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 35, 36, 38, 45, 47, 49, 52, 58, 170, 192, 202, 205, 216, 543, 559, 563, 595, 598, 630, 631, 634, 641, 720, 771, 773, 777, 784, 789, 796, 801, 802, 812, 815, 816, 818, 819, 822, 823, 824, 825, 826, 828, 829, 830, 831, 833, 834, 836, 837, 838, 840, 841, 844, 845, 847, 848, 849, 851, 854, 855, 856, 857, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 871, 874], "sinc": [4, 8, 12, 28, 29, 31, 32, 45, 47, 57, 80, 98, 372, 812, 814, 819, 820, 823, 824, 825, 826, 827, 828, 829, 830, 833, 840, 841, 855, 860, 870, 876], "automat": [4, 8, 9, 12, 29, 31, 32, 37, 818, 819, 820, 822, 825, 826, 828, 829, 835, 837, 840, 844, 847, 848, 850, 853, 854, 856, 857, 861, 870, 873, 877], "sure": [4, 8, 11, 12, 13, 14, 31, 45, 815, 818, 819, 820, 823, 828, 833, 834, 841, 842, 844, 847, 856], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 26, 27, 29, 46, 57, 62, 74, 85, 103, 375, 377, 398, 456, 580, 634, 637, 680, 794, 810, 812, 819, 820, 821, 824, 827, 829, 837, 838, 839, 840, 841, 844, 845, 848, 850, 852, 854, 855, 857, 860, 863, 868, 869, 870, 871, 872, 873, 876, 877], "dm": [4, 5, 8, 11, 13, 31, 32, 43, 45], "haiku": [4, 5, 8, 11, 13, 29, 31, 32, 43, 45, 49, 789, 812, 854, 861, 864, 870], "exit": [4, 8, 12, 31, 32, 830], "download": [4, 6, 7, 12, 16, 18, 31, 32, 46, 47, 50, 814, 819, 826, 844, 863, 864], "imagenet": [4, 6, 18, 46, 48, 812], "class": [4, 6, 7, 8, 12, 14, 16, 18, 22, 31, 32, 43, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 134, 143, 149, 165, 168, 181, 183, 184, 243, 280, 338, 360, 372, 386, 387, 395, 396, 429, 528, 529, 536, 545, 549, 562, 572, 595, 629, 630, 631, 632, 634, 636, 637, 638, 641, 642, 657, 662, 666, 672, 682, 686, 687, 689, 696, 712, 719, 730, 737, 752, 759, 763, 764, 773, 774, 781, 782, 783, 784, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 800, 801, 805, 810, 812, 818, 825, 826, 827, 829, 830, 831, 832, 836, 838, 839, 842, 843, 844, 847, 849, 850, 852, 853, 854, 857, 863, 864, 868, 870, 871, 877], "wget": [4, 6, 8, 12, 45, 46, 49, 819], "raw": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 45, 48, 49, 74, 812, 832, 864, 871], "githubusercont": [4, 6, 8, 12, 45, 49], "hub": [4, 6, 8, 12, 45, 48, 50], "master": [4, 8, 12, 23, 24, 25, 33, 34, 35, 36, 37, 38, 45, 47, 48, 49, 815, 828, 870, 878], "imagenet_class": [4, 12], "categori": [4, 6, 12, 818, 823, 824, 827, 829, 833, 841, 845, 848], "strip": [4, 12, 24, 34, 860], "readlin": [4, 12, 46], "cat": [4, 7, 12, 46, 842, 847, 849, 854, 863, 864], "jpg": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 47, 48, 812, 864], "filenam": [4, 8, 12, 31, 32, 45, 47, 50, 58, 794, 800, 852], "import": [4, 6, 7, 9, 10, 11, 13, 16, 18, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 46, 48, 49, 50, 57, 68, 72, 76, 80, 95, 194, 195, 199, 211, 307, 387, 522, 557, 573, 631, 634, 640, 645, 716, 717, 752, 784, 801, 802, 812, 817, 818, 819, 820, 821, 823, 824, 825, 826, 827, 829, 830, 831, 832, 835, 838, 839, 840, 841, 842, 843, 844, 845, 849, 851, 852, 854, 855, 856, 860, 863, 864, 865, 866, 868, 870, 873, 874, 876], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 46, 47, 50, 53, 57, 66, 74, 76, 80, 89, 102, 105, 106, 107, 126, 127, 128, 130, 131, 132, 135, 136, 137, 138, 140, 141, 142, 143, 145, 146, 147, 148, 149, 193, 194, 195, 196, 197, 198, 199, 200, 201, 206, 207, 208, 209, 211, 212, 213, 214, 215, 217, 219, 312, 313, 328, 329, 369, 382, 472, 508, 509, 511, 512, 536, 550, 551, 629, 634, 643, 738, 739, 740, 741, 771, 773, 774, 789, 791, 792, 793, 794, 795, 796, 797, 798, 810, 812, 820, 822, 825, 829, 833, 837, 838, 842, 844, 845, 847, 849, 854, 855, 856, 857, 860, 869, 870, 872, 873, 874, 875], "torchvis": [4, 6, 11, 12, 45, 861], "transform": [4, 5, 6, 7, 11, 12, 13, 28, 31, 32, 45, 46, 48, 57, 61, 80, 84, 375, 376, 397, 398, 403, 404, 407, 408, 409, 419, 420, 423, 440, 636, 660, 776, 779, 792, 812, 838, 844, 854, 857, 863, 864, 868, 870, 871, 872], "pil": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 46, 47, 48, 812, 864], "time": [4, 5, 6, 7, 9, 10, 11, 13, 29, 31, 32, 37, 45, 47, 48, 49, 57, 59, 62, 68, 80, 82, 91, 97, 98, 134, 341, 372, 375, 376, 378, 387, 404, 409, 421, 423, 444, 451, 484, 490, 522, 616, 621, 629, 635, 636, 637, 639, 640, 644, 645, 659, 662, 677, 712, 715, 716, 717, 744, 745, 749, 750, 792, 793, 794, 810, 818, 819, 820, 823, 825, 827, 828, 829, 831, 834, 836, 837, 838, 840, 841, 844, 845, 849, 852, 854, 855, 856, 859, 860, 861, 863, 864, 868, 870, 871, 874, 875, 876], "filterwarn": [4, 5], "ignor": [4, 5, 44, 52, 53, 57, 74, 80, 139, 375, 376, 378, 387, 399, 400, 401, 430, 438, 446, 486, 487, 491, 530, 629, 636, 641, 663, 729, 730, 796, 819, 826, 828, 831, 844, 855, 876], "compos": [4, 6, 7, 11, 12, 31, 32, 45, 57, 80, 375, 389, 390, 391, 392, 819, 827, 841, 844, 863, 865, 870, 877], "resiz": [4, 6, 7, 8, 11, 12, 45, 46, 57, 80, 375, 411, 847], "centercrop": [4, 12], "224": [4, 6, 7, 12, 16, 18, 31, 32, 45, 46, 48, 812, 864], "totensor": [4, 6, 7, 11, 12, 45], "485": [4, 12, 45], "456": [4, 12, 45, 844], "406": [4, 12, 45, 57, 80, 397, 540, 634], "229": [4, 12, 45, 279, 632], "225": [4, 12, 45, 47, 234, 632], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 812, 852, 864], "ipython": [4, 8, 12, 26, 27, 28, 29, 31, 32, 50], "displai": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 50, 819, 826, 828, 833, 844, 852], "end": [4, 8, 45, 46, 57, 80, 126, 228, 284, 353, 372, 375, 377, 378, 423, 452, 474, 484, 486, 487, 629, 632, 806, 812, 819, 820, 825, 828, 834, 840, 845, 847, 848, 855, 868, 873], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 217, 631, 830], "ivy_model": [4, 5, 8, 12, 48], "ivy_alexnet": 4, "quick": [4, 20, 32, 820, 822, 842, 853], "trace_graph": [4, 5, 8, 12, 24, 25, 26, 27, 31, 32, 34, 35, 36, 37, 38, 39, 48, 794, 812, 849, 854, 862], "moment": [4, 57, 59, 80, 82, 376, 433, 615, 616, 621, 635, 796, 810, 818, 825, 855, 863, 864], "cost": [4, 59, 82, 615, 616, 619, 621, 622, 623, 635, 640, 715, 716, 717, 806, 829, 847, 868], "arg": [4, 6, 8, 9, 10, 11, 12, 16, 18, 26, 27, 29, 31, 32, 36, 37, 38, 49, 52, 74, 96, 106, 122, 203, 213, 601, 628, 629, 631, 634, 771, 773, 788, 789, 792, 793, 794, 798, 801, 805, 810, 812, 824, 829, 830, 833, 839, 840, 841, 847, 849, 853, 863, 864, 865], "asarrai": [4, 5, 8, 11, 12, 46, 53, 57, 58, 69, 76, 80, 81, 92, 127, 385, 514, 515, 545, 556, 560, 561, 591, 592, 593, 629, 634, 636, 645, 646, 650, 750, 754, 833, 838, 841, 842], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 22, 31, 46, 47, 50, 53, 57, 66, 76, 80, 89, 137, 138, 141, 193, 194, 195, 211, 382, 508, 509, 511, 512, 629, 631, 637, 643, 688, 738, 739, 740, 741, 791, 792, 793, 794, 795, 796, 797, 810, 849, 855, 857, 875], "output": [4, 5, 7, 8, 9, 10, 12, 22, 28, 29, 31, 32, 44, 45, 46, 48, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 152, 154, 179, 213, 214, 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, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 322, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 364, 365, 366, 367, 369, 372, 374, 375, 376, 377, 378, 381, 382, 383, 385, 387, 388, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 417, 419, 420, 421, 423, 424, 426, 427, 428, 430, 432, 435, 436, 438, 441, 442, 443, 444, 446, 447, 450, 452, 453, 454, 455, 456, 457, 458, 459, 460, 467, 468, 469, 472, 474, 475, 476, 477, 478, 481, 482, 483, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 496, 497, 498, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 515, 520, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 539, 540, 541, 545, 546, 547, 549, 553, 562, 569, 576, 577, 578, 602, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 680, 681, 682, 683, 684, 685, 686, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 709, 710, 711, 712, 714, 731, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 771, 776, 791, 792, 805, 806, 812, 814, 819, 820, 822, 823, 824, 826, 827, 829, 830, 831, 832, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 846, 849, 851, 853, 854, 855, 857, 863, 864, 871], "softmax": [4, 6, 7, 12, 16, 29, 31, 32, 47, 51, 61, 72, 73, 84, 377, 454, 626, 636, 663, 666, 788, 812], "pass": [4, 6, 7, 8, 11, 12, 13, 14, 16, 18, 22, 29, 31, 32, 38, 44, 45, 47, 49, 50, 56, 57, 72, 74, 79, 80, 95, 103, 122, 123, 125, 157, 179, 194, 213, 228, 274, 375, 377, 378, 381, 382, 387, 421, 454, 474, 501, 503, 508, 528, 529, 562, 628, 630, 631, 632, 634, 640, 715, 716, 771, 773, 777, 784, 789, 793, 794, 796, 797, 801, 805, 810, 812, 816, 818, 820, 823, 824, 825, 827, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 844, 847, 855, 863, 864, 865, 868], "argsort": [4, 12, 69, 92, 646, 755, 841], "descend": [4, 12, 69, 92, 637, 646, 687, 688, 753, 756], "top": [4, 12, 15, 20, 29, 31, 32, 45, 46, 57, 64, 80, 319, 369, 377, 378, 452, 494, 545, 634, 700, 812, 819, 820, 829, 834, 841, 843, 844, 847, 852, 853, 870, 874], "logit": [4, 5, 6, 7, 8, 12, 45, 46, 47, 48, 57, 63, 80, 86, 367, 382, 508, 511, 638, 696, 698, 788, 812, 863], "gather": [4, 12, 45, 57, 58, 80, 81, 330, 331, 332, 369, 553, 555, 634, 877], "to_list": [4, 12, 58, 81, 634], "arrai": [4, 5, 6, 7, 9, 10, 12, 13, 14, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 43, 44, 45, 46, 47, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 98, 100, 103, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 152, 153, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 169, 171, 172, 173, 175, 177, 178, 179, 180, 186, 196, 197, 201, 206, 208, 210, 213, 214, 218, 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, 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720, 721, 724, 725, 726, 727, 730, 731, 735, 736, 737, 738, 739, 740, 741, 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, 771, 773, 778, 784, 791, 792, 793, 794, 797, 801, 805, 806, 808, 812, 816, 818, 819, 820, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 837, 838, 839, 840, 841, 842, 844, 845, 846, 847, 848, 849, 850, 852, 853, 854, 855, 857, 864, 865, 868, 869, 870, 872, 876, 877], "282": [4, 12], "281": [4, 12, 45, 47], "285": [4, 12, 80], "64773697": 4, "29496649": 4, "04526037": 4, "tiger": [4, 12], "tabbi": [4, 7, 12], "egyptian": [4, 12], "torch_alexnet": 4, "alexnet_weight": 4, "imagenet1k_v1": [4, 12], "dropout": [4, 61, 84, 375, 399, 400, 401, 636, 661, 663, 666, 792, 852], "torch_output": [4, 8, 9, 12], "dim": [4, 12, 47, 57, 74, 76, 80, 141, 313, 369, 375, 378, 393, 403, 404, 405, 408, 416, 474, 496, 629, 636, 649, 656, 657, 662, 778, 792, 812, 829, 841, 842, 847], "torch_class": [4, 12], "torch_logit": [4, 12], "tensor": [4, 5, 6, 9, 11, 12, 13, 16, 18, 22, 23, 26, 27, 29, 31, 32, 33, 37, 43, 45, 53, 56, 57, 58, 61, 62, 63, 64, 66, 70, 74, 76, 79, 80, 81, 84, 85, 86, 87, 89, 93, 96, 129, 137, 138, 141, 147, 163, 179, 271, 272, 302, 319, 323, 324, 325, 326, 327, 328, 337, 360, 367, 369, 372, 375, 376, 377, 378, 387, 388, 394, 395, 398, 402, 411, 412, 413, 414, 421, 423, 425, 432, 433, 434, 435, 438, 440, 442, 444, 445, 448, 450, 451, 452, 454, 457, 458, 474, 477, 482, 485, 486, 487, 488, 491, 496, 497, 528, 533, 576, 577, 629, 630, 632, 634, 636, 637, 638, 639, 643, 647, 659, 662, 663, 678, 689, 696, 706, 708, 738, 761, 792, 801, 806, 810, 812, 824, 825, 829, 830, 834, 836, 837, 840, 841, 842, 844, 845, 847, 849, 851, 852, 854, 855, 857, 859, 863, 864, 865, 867, 868, 871, 873, 874, 877], "6477": 4, "2950": 4, "0453": 4, "grad_fn": [4, 12, 29, 43, 618, 625, 635, 852], "takebackward0": [4, 12], "great": [4, 7, 8, 812, 820, 844, 849, 851, 860, 861, 876], "simpl": [4, 7, 16, 20, 21, 23, 26, 28, 29, 30, 31, 32, 33, 34, 36, 37, 43, 45, 47, 50, 57, 80, 387, 522, 778, 792, 806, 812, 818, 819, 820, 824, 826, 827, 829, 830, 831, 832, 837, 840, 841, 844, 845, 847, 851, 853, 854, 855, 857, 859, 863, 864, 869, 870, 871, 872], "let": [4, 5, 6, 7, 8, 9, 11, 13, 14, 16, 18, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 43, 45, 46, 48, 50, 58, 70, 81, 220, 221, 222, 223, 226, 229, 238, 241, 243, 245, 254, 255, 256, 261, 263, 276, 284, 286, 287, 291, 552, 553, 632, 634, 637, 647, 691, 761, 763, 764, 765, 766, 812, 818, 821, 824, 826, 827, 828, 829, 830, 831, 832, 833, 834, 841, 842, 844, 845, 846, 847, 849, 851, 852, 853, 854, 861, 863, 864, 877], "ll": [4, 6, 7, 8, 9, 11, 13, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 46, 812, 813, 815, 816, 818, 819, 820, 821, 826, 831, 834, 835, 839, 840, 852, 856, 861, 863, 864], "try": [4, 6, 7, 23, 33, 43, 46, 50, 74, 601, 634, 791, 801, 812, 818, 819, 820, 823, 824, 827, 828, 829, 833, 835, 840, 842, 849, 851, 855, 858, 860, 861, 865], "tf": [4, 6, 8, 9, 10, 13, 16, 18, 23, 26, 27, 29, 31, 32, 33, 34, 36, 38, 43, 48, 49, 789, 812, 824, 829, 830, 836, 840, 841, 844, 845, 847, 849, 854, 855, 857, 863, 864, 865, 870], "onc": [4, 6, 8, 31, 32, 43, 45, 62, 66, 85, 89, 213, 376, 429, 631, 637, 643, 672, 673, 674, 687, 738, 812, 818, 819, 820, 827, 828, 829, 830, 831, 834, 835, 840, 841, 844, 847, 849, 852, 855, 856, 861, 863], "set": [4, 7, 9, 16, 18, 24, 31, 32, 34, 37, 45, 46, 47, 48, 49, 52, 57, 58, 61, 62, 67, 69, 70, 74, 80, 81, 84, 85, 90, 92, 93, 115, 118, 125, 145, 147, 181, 182, 183, 184, 185, 196, 209, 210, 211, 212, 213, 228, 328, 340, 356, 358, 363, 369, 372, 373, 375, 376, 377, 378, 387, 398, 419, 423, 427, 431, 434, 452, 457, 458, 474, 484, 487, 494, 522, 527, 528, 529, 530, 531, 532, 534, 538, 545, 557, 562, 578, 579, 580, 582, 583, 584, 585, 586, 587, 588, 589, 595, 603, 626, 628, 629, 630, 631, 632, 634, 636, 637, 641, 643, 644, 646, 647, 659, 666, 668, 678, 680, 683, 686, 687, 718, 725, 728, 729, 730, 735, 736, 742, 744, 745, 749, 751, 752, 753, 756, 764, 766, 773, 776, 777, 778, 779, 784, 791, 792, 794, 796, 801, 806, 809, 810, 812, 813, 820, 822, 823, 824, 826, 827, 828, 829, 830, 831, 833, 835, 837, 838, 840, 841, 842, 844, 845, 847, 849, 851, 852, 859, 862, 863, 864, 868, 869, 870, 871, 872, 874, 877], "post": [4, 6, 8, 45, 65, 88, 642, 737, 819, 834, 839, 854, 856], "process": [4, 6, 8, 26, 31, 32, 36, 45, 207, 219, 631, 813, 819, 820, 826, 827, 828, 834, 835, 837, 839, 841, 842, 843, 844, 847, 849, 854, 860, 861, 863, 868, 869, 870, 873, 874, 876, 877], "st": [4, 5, 11, 776, 823, 842, 844], "perf_count": [4, 9, 10, 11], "raw_logit": 4, "latenc": [4, 11], "nn": [4, 6, 7, 8, 10, 18, 29, 31, 32, 45, 49, 139, 629, 812, 837, 842, 847, 854, 864, 871], "direct": [4, 57, 80, 341, 348, 352, 357, 361, 372, 375, 378, 409, 420, 475, 476, 490, 646, 756, 818, 824, 826, 841, 847, 853, 854, 866, 870, 871, 874], "tolist": 4, "652289830999962": 4, "int32": [4, 43, 45, 54, 57, 58, 66, 67, 70, 77, 80, 81, 89, 90, 132, 137, 141, 143, 149, 152, 155, 157, 159, 161, 163, 166, 168, 169, 173, 176, 180, 184, 188, 190, 208, 235, 271, 272, 383, 387, 513, 523, 524, 525, 553, 562, 599, 629, 630, 631, 632, 634, 643, 644, 647, 739, 740, 741, 745, 757, 758, 763, 765, 776, 777, 829, 841, 844, 849], "6477362": 4, "29496726": 4, "04526032": 4, "As": [4, 6, 7, 8, 11, 13, 14, 16, 18, 24, 28, 29, 31, 32, 34, 37, 43, 44, 68, 72, 95, 637, 645, 685, 749, 750, 751, 752, 812, 816, 818, 819, 820, 821, 824, 826, 827, 828, 829, 830, 833, 834, 835, 836, 837, 840, 841, 842, 843, 844, 847, 851, 852, 853, 855, 859, 863, 864, 865, 870, 875], "ident": [4, 6, 9, 14, 29, 46, 48, 62, 74, 132, 201, 555, 581, 629, 631, 634, 637, 641, 675, 679, 731, 792, 827, 837, 838, 841, 842, 845, 847, 851, 852, 855, 857, 859, 861], "had": [4, 827, 828, 840, 845, 849, 870, 871], "postprocess": 4, "routin": [4, 828, 840, 841, 847, 855, 870], "feed": [4, 213, 631, 863, 870, 871], "carefulli": [4, 278, 632, 791, 841, 868, 873], "rewrit": 4, "easili": [4, 28, 31, 32, 43, 812, 819, 824, 828, 834, 841, 844, 847, 852, 853, 854, 855, 860, 870, 876, 877], "quickest": 4, "particular": [4, 31, 32, 268, 632, 777, 819, 820, 823, 825, 828, 829, 831, 838, 840, 841, 844, 845, 866, 870, 876], "again": [4, 8, 25, 26, 34, 35, 36, 37, 637, 685, 820, 824, 825, 826, 827, 831, 833, 835, 840, 841, 844, 845, 847, 852, 854, 855, 860, 861, 875, 876], "speed": [4, 11, 13, 14, 31, 32, 45, 50, 58, 81, 569, 634, 844, 859, 873], "repeat": [4, 5, 25, 35, 57, 58, 64, 80, 81, 87, 375, 378, 387, 404, 409, 473, 522, 547, 634, 639, 640, 712, 716, 717, 805, 820, 824, 825, 831, 832, 840, 844], "previou": [4, 14, 24, 25, 26, 28, 34, 35, 36, 38, 59, 80, 82, 187, 188, 189, 190, 191, 364, 374, 375, 421, 602, 604, 605, 606, 607, 609, 610, 612, 616, 621, 630, 634, 635, 791, 809, 819, 820, 823, 825, 828, 830, 836, 841, 844, 847, 854, 855, 873], "trace": [4, 5, 6, 8, 11, 12, 13, 20, 21, 25, 28, 31, 34, 36, 37, 49, 58, 62, 74, 81, 85, 564, 565, 568, 579, 588, 603, 611, 634, 637, 773, 784, 794, 796, 810, 812, 823, 827, 829, 841, 846, 847, 849, 854, 855, 862, 863, 864, 871, 876], "026875037000081647": 4, "overrid": [4, 8, 37, 46, 53, 57, 76, 80, 141, 387, 522, 629, 824, 826], "prealloc": [4, 8], "temporari": [4, 8, 589, 612, 634, 806, 829, 846], "fix": [4, 8, 47, 57, 80, 97, 98, 372, 375, 376, 421, 451, 636, 663, 812, 816, 819, 820, 823, 829, 835, 844, 845], "until": [4, 8, 806, 820, 840, 849, 855, 860, 863, 877], "o": [4, 8, 44, 45, 46, 47, 49, 572, 634, 636, 663, 812, 819, 822, 828, 849, 856], "environ": [4, 8, 13, 26, 27, 28, 29, 46, 49, 812, 813, 820, 856, 870, 872], "xla_python_client_alloc": [4, 8], "platform": [4, 6, 8, 14, 26, 27, 29, 814, 817, 819, 826, 868, 872, 874], "jit": [4, 11, 13, 31, 34, 849, 855, 863, 870], "img_jax": [4, 8], "device_put": [4, 11], "warm": 4, "_": [4, 9, 10, 11, 13, 14, 31, 44, 45, 56, 57, 74, 79, 80, 82, 98, 155, 243, 245, 253, 254, 269, 335, 336, 372, 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[4, 636, 651, 653], "104": [4, 70, 637, 647, 682, 759], "259": 4, "72447652": 4, "13937832": 4, "05874982": 4, "samoi": 4, "wallabi": 4, "pomeranian": 4, "incorrect": [4, 828], "predict": [4, 6, 7, 8, 12, 14, 45, 46, 47, 48, 57, 63, 80, 86, 377, 453, 456, 459, 638, 696, 697, 698, 812, 829], "down": [4, 24, 34, 48, 57, 80, 375, 378, 411, 476, 812, 819, 844, 857, 870, 876], "itself": [4, 7, 26, 36, 56, 97, 274, 535, 601, 632, 634, 641, 730, 806, 816, 819, 820, 823, 826, 827, 828, 829, 830, 833, 834, 835, 840, 841, 853, 855, 859, 863, 869, 870, 871, 876], "version": [4, 6, 9, 14, 28, 29, 34, 45, 46, 47, 50, 51, 57, 80, 97, 110, 291, 340, 342, 372, 387, 527, 532, 614, 632, 634, 637, 673, 674, 773, 801, 802, 812, 819, 820, 826, 828, 829, 832, 840, 842, 849, 859, 860, 861, 864, 876, 877], "004749261999904775": 4, "7245": 4, "1394": 4, "0587": 4, "promis": [4, 7, 860], "sourc": [4, 7, 9, 10, 12, 18, 23, 24, 25, 26, 27, 28, 29, 31, 32, 37, 38, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 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840, 841, 843, 844, 849, 850, 852, 854, 855, 861, 863, 865, 870, 871, 873], "__init__": [4, 8, 16, 18, 31, 32, 43, 44, 45, 47, 74, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 774, 781, 782, 783, 788, 791, 792, 793, 794, 795, 796, 797, 800, 801, 805, 807, 810, 812, 818, 824, 825, 829, 833, 841, 845, 849, 851, 852, 853, 854, 864], "self": [4, 6, 7, 8, 16, 18, 31, 32, 43, 44, 45, 47, 49, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 103, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 128, 129, 131, 133, 134, 136, 137, 138, 139, 140, 141, 143, 145, 146, 147, 149, 152, 153, 154, 155, 163, 165, 168, 171, 172, 173, 175, 177, 180, 197, 214, 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, 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818, 819, 820, 822, 828, 829, 833, 834, 843, 844, 845, 856, 873, 874], "xlabel": 14, "ylabel": 14, "obviou": [14, 852, 870], "trend": 14, "gap": 14, "train_siz": [14, 45], "widen": 14, "impress": 14, "outcom": [14, 57, 80, 337, 349, 372, 806], "tend": 14, "95933": 14, "9874": 14, "105807": 14, "wrap": [14, 22, 24, 31, 32, 34, 45, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 103, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 128, 129, 131, 133, 134, 136, 138, 139, 140, 141, 143, 145, 146, 149, 153, 154, 155, 168, 172, 173, 180, 197, 214, 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, 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861], "jump": [20, 842], "straight": [20, 812, 828, 841, 844, 851], "quickstart": [20, 812], "introduct": [20, 22, 29, 31, 32, 870], "point": [20, 29, 54, 56, 57, 62, 66, 68, 70, 77, 79, 80, 85, 89, 93, 126, 127, 128, 130, 132, 135, 142, 143, 148, 152, 165, 169, 173, 180, 220, 221, 222, 223, 225, 226, 227, 228, 229, 236, 237, 238, 240, 241, 243, 245, 246, 247, 253, 254, 255, 256, 261, 262, 263, 264, 265, 273, 275, 276, 278, 280, 282, 283, 284, 285, 286, 287, 288, 290, 291, 292, 293, 294, 312, 313, 315, 335, 336, 353, 354, 357, 359, 369, 372, 375, 376, 377, 382, 387, 390, 399, 400, 401, 419, 429, 449, 453, 508, 509, 510, 511, 512, 522, 523, 524, 532, 627, 629, 630, 632, 637, 643, 644, 645, 646, 647, 667, 669, 672, 673, 674, 676, 678, 679, 680, 683, 684, 685, 686, 687, 688, 689, 691, 694, 740, 741, 747, 749, 750, 751, 752, 755, 757, 758, 760, 761, 762, 763, 764, 765, 766, 801, 802, 810, 816, 818, 819, 820, 823, 824, 826, 828, 829, 831, 832, 834, 836, 840, 841, 844, 845, 847, 849, 851, 852, 861, 863, 876], "showcas": [20, 812], "real": [20, 28, 56, 57, 70, 79, 80, 93, 102, 112, 115, 118, 142, 143, 220, 221, 222, 223, 225, 226, 227, 228, 229, 238, 240, 241, 243, 245, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 270, 273, 275, 276, 278, 282, 283, 284, 286, 287, 288, 289, 290, 291, 293, 294, 335, 336, 342, 343, 344, 354, 372, 375, 376, 398, 419, 420, 429, 430, 626, 629, 632, 637, 644, 647, 672, 673, 674, 678, 685, 687, 688, 691, 694, 747, 760, 762, 763, 764, 765, 827, 872], "world": [20, 28, 820, 872], "beginn": [20, 813, 870], "got": [20, 43, 833], "cover": [20, 31, 57, 80, 375, 412, 413, 414, 818, 823, 824, 826, 829, 831, 832, 837, 838, 844, 847, 848], "familiar": [20, 21, 22, 818, 819], "concept": [20, 21, 22], "turn": [20, 21, 24, 34, 61, 84, 97, 98, 399, 400, 401, 636, 659, 792, 819, 826, 827, 830, 831, 841, 844, 861], "unus": [20, 21, 24, 831, 840], "part": [20, 21, 24, 53, 56, 57, 79, 80, 85, 102, 112, 115, 118, 145, 146, 147, 253, 257, 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824, 827, 852, 853, 857, 863, 864, 865], "recurs": [22, 31, 32, 45, 47, 52, 74, 75, 166, 167, 199, 200, 376, 448, 550, 551, 557, 630, 631, 634, 641, 718, 719, 722, 728, 729, 730, 771, 819, 823, 826, 827, 834, 837, 840, 853, 855], "fashion": [22, 778, 844, 864], "native_arrai": [22, 31, 32, 53, 54, 56, 76, 78, 79, 80, 81, 85, 92, 110, 113, 136, 139, 141, 143, 149, 152, 153, 154, 155, 163, 168, 175, 197, 206, 214, 230, 234, 239, 240, 241, 243, 247, 251, 259, 260, 268, 273, 276, 279, 282, 287, 335, 336, 363, 372, 377, 378, 458, 484, 490, 494, 534, 537, 564, 565, 568, 599, 626, 629, 630, 631, 632, 634, 636, 637, 638, 639, 643, 644, 647, 648, 650, 651, 658, 666, 669, 673, 674, 679, 680, 684, 688, 689, 691, 694, 696, 698, 699, 706, 738, 747, 756, 762, 765, 767, 773, 783, 801, 816, 834, 842, 844], "data_class": [22, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 395, 396, 545, 549, 687, 712], "low": [22, 31, 34, 50, 57, 61, 66, 80, 84, 89, 375, 418, 422, 636, 643, 649, 650, 651, 652, 654, 656, 658, 739, 741, 778, 827, 833, 840, 841, 847, 849, 866, 868, 870, 871, 872, 874, 876], "c": [22, 31, 37, 46, 47, 53, 57, 58, 59, 61, 64, 70, 76, 77, 79, 80, 81, 82, 84, 85, 87, 91, 93, 97, 98, 116, 127, 128, 138, 141, 165, 168, 223, 234, 240, 241, 261, 262, 264, 273, 276, 284, 291, 375, 376, 378, 381, 387, 389, 390, 391, 392, 403, 408, 424, 426, 428, 429, 431, 443, 462, 463, 464, 474, 492, 496, 501, 502, 503, 506, 524, 537, 545, 546, 547, 548, 556, 560, 561, 591, 600, 615, 616, 619, 621, 622, 623, 626, 629, 630, 632, 634, 635, 636, 637, 639, 641, 644, 645, 647, 650, 651, 652, 653, 654, 655, 657, 672, 674, 676, 706, 710, 718, 721, 725, 726, 727, 729, 730, 735, 736, 747, 752, 758, 759, 764, 766, 795, 805, 806, 813, 819, 822, 825, 826, 827, 831, 837, 839, 848, 849, 850, 852, 855, 857, 858, 860, 861, 864, 866, 870, 874, 875, 877], "fundament": [22, 31, 828, 841, 847, 849, 859, 870], "signatur": [22, 31, 378, 387, 484, 522, 829, 830, 831, 832, 836, 840, 844, 845, 847, 860, 867, 876], "matmul": [22, 31, 32, 48, 62, 85, 376, 446, 614, 634, 637, 687, 825, 844, 845, 849], "to_n": [22, 31, 32, 43, 52, 75, 849], "jaxlib": [22, 28, 46, 801, 819, 824, 829, 830, 836, 845, 849, 851], "xla_extens": [22, 28, 801, 824, 829, 830, 836, 845, 849, 851], "arrayimpl": [22, 28, 801], "disabl": [22, 31, 57, 80, 378, 492, 794, 810, 826], "array_mod": [22, 31, 578, 602, 634, 846], "set_array_mod": [22, 31, 602, 634, 846], "ultim": [22, 31, 863], "sigmoid": [22, 31, 32, 43, 51, 57, 73, 80, 301, 367, 382, 508, 626, 788, 849, 852, 853], "z": [22, 31, 32, 44, 45, 53, 56, 57, 58, 62, 63, 66, 68, 70, 76, 79, 80, 81, 85, 86, 87, 89, 93, 102, 103, 137, 138, 140, 141, 201, 223, 224, 228, 230, 233, 235, 240, 251, 252, 255, 256, 257, 259, 260, 265, 267, 269, 270, 271, 272, 280, 289, 300, 301, 335, 336, 338, 367, 372, 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819, 820, 825, 826, 827, 828, 829, 833, 838, 841, 844, 845, 846, 847, 870], "act": [25, 35, 57, 80, 298, 363, 373, 820, 831, 846, 855, 877], "shorthand": [25, 35, 37, 844], "pair": [25, 35, 45, 57, 61, 80, 84, 228, 247, 320, 362, 369, 372, 375, 409, 418, 420, 422, 632, 636, 637, 649, 650, 651, 652, 654, 656, 658, 666, 668, 806], "93968587": 25, "26075466": 25, "22723222": 25, "06276492": 25, "47426987": 25, "72835908": 25, "71737559": 25, "50411096": 25, "65419174": 25, "15576624": 25, "implic": [25, 35, 36, 39, 827], "satisfi": [26, 27, 28, 29, 45, 47, 50, 57, 375, 376, 398, 430, 829, 831], "fw": [26, 27, 28, 29, 61, 84, 387, 522, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 773, 819, 844], "mxnet": [26, 27, 28, 29, 209, 631, 801, 818, 819, 860, 877], "einop": [26, 27, 28, 29, 45, 47, 50, 58, 81, 545, 546, 547, 634, 829, 860], "miniconda": [26, 27, 28, 29], "multienv": [26, 27, 28, 29], "site": [26, 27, 28, 29, 871], "psutil": [26, 27, 28, 29, 45, 47, 50], "termcolor": [26, 27, 28, 29, 45, 47, 50, 74, 103], "colorama": [26, 27, 28, 29, 45, 47], "535": [26, 27, 28, 29, 51, 73, 118, 626, 833], "diskcach": [26, 27, 28, 29, 45], "auth": [26, 27, 28, 29], "urllib3": [26, 27, 28, 29, 45], "pyvi": [26, 27, 28, 29, 31, 32], "dill": [26, 27, 28, 29, 45], "astunpars": [26, 27, 28, 29], "cloudpickl": [26, 27, 28, 29], "gast": [26, 27, 28, 29], "wheel": [26, 27, 28, 29, 45, 47, 50, 859], "six": [26, 27, 28, 29, 45, 50, 819, 847], "cachetool": [26, 27, 28, 29], "pyasn1": [26, 27, 28, 29], "rsa": [26, 27, 28, 29], "jinja2": [26, 27, 28, 29], "jsonpickl": [26, 27, 28, 29], "networkx": [26, 27, 28, 29, 50], "charset": [26, 27, 28, 29, 45], "idna": [26, 27, 28, 29, 45], "certifi": [26, 27, 28, 29, 45], "2017": [26, 27, 28, 29, 45, 636, 663], "jedi": [26, 27, 28, 29], "inlin": [26, 27, 28, 29, 826], "prompt": [26, 27, 28, 29, 818, 820], "toolkit": [26, 27, 28, 29, 870, 871, 877], "pygment": [26, 27, 28, 29], "traitlet": [26, 27, 28, 29], "exceptiongroup": [26, 27, 28, 29], 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824, 827, 831], "filelock": [28, 45], "extens": [28, 45, 56, 62, 79, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 139, 142, 143, 144, 145, 146, 148, 149, 155, 165, 168, 180, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 335, 336, 338, 372, 375, 378, 387, 419, 492, 496, 522, 629, 630, 632, 637, 639, 644, 645, 646, 647, 648, 667, 668, 669, 670, 671, 673, 674, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 693, 694, 700, 702, 703, 704, 706, 707, 709, 710, 714, 744, 745, 747, 748, 749, 750, 751, 752, 753, 756, 760, 761, 762, 763, 764, 765, 766, 767, 768, 817, 819, 820, 832, 834, 835, 844, 867, 870, 877], "sympi": [28, 860], "fsspec": [28, 45], "mpmath": 28, "often": [28, 57, 377, 452, 817, 823, 833, 836, 837, 841, 844, 855, 861, 871, 874, 877], "fortun": [28, 29, 823], "everyth": [28, 46, 805, 812, 818, 819, 820, 821, 822, 828, 831, 840, 841, 842, 844, 850, 855, 856, 861], "practic": [28, 820, 825, 828, 841, 843, 873], "everi": [28, 31, 32, 37, 45, 53, 57, 58, 80, 81, 135, 136, 301, 335, 336, 349, 367, 372, 375, 378, 412, 413, 414, 421, 498, 534, 629, 634, 818, 820, 823, 825, 826, 828, 829, 831, 835, 836, 837, 838, 840, 841, 842, 844, 849, 851, 853, 863, 864, 865, 870], "jax_kornia": [28, 31, 32, 812, 864], "though": [28, 817, 818, 820, 829, 830, 832, 837, 840, 841, 847, 852, 855], "000000000034": [28, 31, 32, 812, 864], "raw_img": [28, 31, 32, 812, 864], "sharp": [28, 31, 32, 812], "prefer": [28, 31, 32, 247, 632, 819, 827, 833, 834, 838, 841, 856, 870], "whole": [29, 57, 80, 378, 381, 491, 504, 505, 507, 820, 826, 835], "full": [29, 57, 62, 80, 84, 85, 97, 98, 100, 165, 252, 260, 323, 324, 325, 326, 327, 369, 376, 377, 378, 449, 450, 456, 457, 485, 488, 579, 588, 603, 611, 629, 630, 632, 634, 636, 637, 651, 653, 654, 655, 657, 680, 684, 686, 687, 777, 784, 812, 819, 820, 826, 829, 832, 833, 836, 837, 841, 844, 847, 849, 855, 860, 861, 868, 870, 876], "complex": [29, 31, 32, 45, 51, 56, 57, 62, 70, 73, 77, 79, 80, 85, 93, 110, 111, 112, 113, 114, 115, 116, 117, 118, 142, 143, 158, 172, 181, 187, 220, 221, 222, 223, 224, 225, 226, 229, 237, 238, 240, 241, 243, 245, 253, 254, 255, 256, 257, 261, 262, 263, 264, 273, 275, 276, 278, 280, 283, 284, 285, 286, 287, 290, 291, 295, 300, 301, 303, 338, 343, 344, 367, 372, 375, 376, 387, 398, 409, 419, 420, 424, 429, 430, 431, 442, 444, 530, 531, 592, 593, 626, 629, 630, 632, 634, 637, 644, 647, 672, 673, 674, 678, 685, 687, 689, 691, 694, 747, 762, 763, 765, 777, 788, 806, 815, 818, 821, 826, 829, 831, 838, 841, 844, 845, 847, 852, 853, 854, 855, 857, 864, 866, 868, 870, 872, 876, 877], "neccessari": 29, "set_random_se": [29, 48], "301436": 29, "_c": 29, "0x7f252c392390": 29, "flatten": [29, 31, 32, 45, 47, 50, 57, 58, 62, 64, 67, 68, 80, 81, 85, 87, 90, 91, 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295, 367, 628, 632, 812, 817, 818, 819, 822, 823, 834, 840, 843, 844, 855, 860, 861, 870], "regress": [30, 870, 877], "checkout": [31, 46, 820, 823, 844], "f705efe7cb5d18df17ce6c1e20f04d0eb4933f48": 31, "theoret": 31, "aspect": [31, 32, 813, 839, 852, 870], "easiest": [31, 812, 814, 819, 856], "defer": [31, 32, 818, 824, 829, 830, 837, 840, 841, 844, 876], "similarli": [31, 44, 139, 147, 223, 328, 335, 336, 369, 372, 629, 632, 825, 829, 841, 847, 851, 876], "essenc": [31, 871, 876], "becom": [31, 57, 80, 97, 346, 372, 378, 464, 639, 699, 801, 820, 821, 827, 829, 831, 833, 840, 855, 859, 861, 863], "slide": [31, 57, 61, 80, 84, 375, 394, 395, 396, 412, 413, 414, 415, 418, 422, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 792], "regressor": [31, 32, 812], "input_dim": [31, 32, 46, 812], "output_dim": [31, 32, 46, 812], "linear0": [31, 32, 43, 812, 852, 853], "linear1": [31, 32, 43, 812, 852, 853], "instanti": [31, 32, 784, 832], "adam": [31, 32, 43, 47, 59, 82, 536, 615, 616, 621, 634, 635, 796, 812, 852, 853, 854, 870], "n_training_exampl": [31, 32, 812], "2000": [31, 32, 80, 314, 369, 812], "random_norm": [31, 32, 61, 62, 66, 84, 85, 89, 545, 634, 636, 637, 643, 651, 653, 654, 655, 657, 658, 662, 687, 812], "linspac": [31, 32, 53, 76, 126, 629, 812, 836, 847, 849, 877], "pred": [31, 32, 46, 47, 57, 63, 80, 86, 377, 453, 456, 638, 696, 697, 698, 812, 827, 837, 840], "gradient": [31, 32, 45, 47, 57, 80, 97, 213, 364, 372, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 631, 640, 715, 716, 717, 773, 784, 796, 812, 822, 845, 852, 853, 855, 870], "grad": [31, 32, 43, 47, 615, 635, 796, 812, 839, 852, 853, 854], "execute_with_gradi": [31, 32, 43, 47, 635, 812, 852, 853, 854, 855], "lambda": [31, 32, 48, 50, 80, 123, 125, 297, 307, 544, 557, 617, 618, 620, 625, 628, 634, 635, 637, 641, 673, 725, 726, 730, 812, 818, 837, 838, 839, 842, 847, 849, 852], "2d": [31, 32, 47, 57, 80, 97, 313, 369, 375, 376, 378, 387, 390, 391, 399, 400, 442, 449, 463, 473, 522, 792, 810, 812, 841, 847], "5f": [31, 32, 812], "nonetheless": [31, 32], "extract": [31, 32, 39, 46, 57, 80, 98, 378, 467, 493, 841, 843, 845, 866, 870, 871, 876], "gc": [31, 32, 557, 634], "decompos": [31, 32, 57, 80, 97, 100, 323, 324, 325, 326, 327, 348, 355, 369, 372, 376, 440, 445, 448, 451, 841, 854], "said": [31, 32, 778, 845, 861, 863], "otherwis": [31, 32, 49, 52, 53, 54, 56, 57, 58, 61, 62, 67, 68, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 126, 128, 129, 134, 136, 137, 138, 141, 143, 149, 152, 153, 155, 156, 158, 159, 160, 161, 162, 171, 175, 179, 180, 196, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 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[31, 32], "differenti": [31, 32, 295, 365, 366, 367, 374, 870], "chosen": [31, 32, 50, 100, 126, 228, 629, 632, 644, 748, 818, 828, 841], "plai": [31, 32, 377, 456, 812, 815, 819, 821, 824, 830, 834, 841, 844, 854, 870, 873], "role": [31, 32, 812, 815, 820, 821, 830, 841, 850, 871, 873, 877], "dl": [31, 32], "effortlessli": [31, 32], "previous": [31, 32, 603, 634, 801, 818, 819, 825, 837, 839, 844, 849], "default_devic": [31, 32, 206, 209, 210, 211, 217, 218, 631, 830, 833, 834], "as_n": [31, 32, 54, 55, 74, 77, 78, 158, 159, 160, 161, 162, 163, 169, 196, 197, 630, 631, 829], "certainli": [31, 32, 812, 860, 876], "upon": [31, 32, 49, 810, 820, 821, 831, 840, 844, 847, 855, 869, 870], "unnecessari": [31, 32, 841], "extend": [31, 32, 57, 80, 378, 387, 484, 525, 825, 826, 829, 832, 833, 836, 841, 845, 855, 867, 870, 876], "infrastructur": [31, 32, 866, 872, 873], "least": [31, 56, 57, 62, 79, 80, 240, 258, 273, 375, 378, 387, 403, 408, 462, 463, 464, 473, 475, 522, 632, 637, 644, 677, 747, 812, 820, 824, 828, 829, 830, 831, 837, 840, 844, 864], "coco": 31, "seamlessli": [32, 844], "therefor": [32, 37, 53, 56, 57, 62, 79, 80, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 149, 155, 171, 175, 179, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 313, 328, 329, 335, 336, 338, 341, 369, 372, 375, 376, 378, 387, 394, 395, 396, 397, 399, 400, 401, 407, 412, 413, 414, 419, 421, 430, 477, 484, 485, 487, 492, 496, 497, 522, 525, 529, 538, 546, 547, 552, 556, 558, 560, 562, 576, 591, 595, 600, 624, 629, 630, 632, 634, 635, 636, 637, 639, 642, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 658, 659, 660, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 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80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 134, 136, 141, 143, 149, 153, 155, 168, 172, 173, 180, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 299, 303, 304, 305, 306, 307, 309, 310, 311, 313, 334, 335, 336, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 468, 469, 490, 492, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 537, 538, 540, 541, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 619, 624, 640, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 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639, 684, 699, 841, 847], "data_typ": [54, 57, 77, 80, 182, 630, 826, 829, 844, 845], "_arraywithdatatyp": [54, 102], "irrespect": [54, 62, 77, 85, 152, 630, 637, 687, 827, 840, 851, 877], "promot": [54, 56, 57, 62, 77, 79, 80, 85, 92, 102, 103, 152, 155, 178, 179, 180, 186, 221, 222, 223, 225, 226, 227, 228, 229, 230, 232, 233, 234, 235, 237, 238, 240, 243, 245, 247, 261, 262, 263, 264, 265, 270, 273, 278, 282, 285, 286, 287, 288, 289, 290, 291, 294, 346, 354, 359, 372, 375, 387, 419, 522, 585, 608, 630, 632, 634, 637, 639, 647, 667, 668, 675, 676, 677, 678, 679, 680, 682, 683, 685, 686, 693, 694, 700, 710, 753, 761, 764, 776, 777, 821, 823, 832, 833, 837, 846], "nan": [54, 56, 57, 58, 68, 70, 77, 79, 80, 81, 152, 220, 221, 222, 223, 225, 226, 227, 228, 229, 236, 237, 238, 239, 240, 241, 243, 245, 246, 247, 248, 249, 254, 255, 256, 261, 262, 263, 264, 265, 268, 273, 274, 276, 278, 279, 282, 283, 284, 285, 286, 287, 290, 291, 293, 300, 334, 335, 336, 347, 351, 356, 359, 367, 372, 378, 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397, 407, 632, 792], "acosh": [56, 79, 166, 167, 630, 632, 816, 834], "area": [56, 57, 79, 80, 84, 222, 226, 229, 375, 411, 418, 422, 632, 815, 840, 847, 860, 866], "hyperbol": [56, 79, 222, 226, 229, 238, 286, 290, 291, 304, 308, 367, 632], "sector": [56, 79, 222, 226, 229, 632, 860], "multipli": [56, 57, 61, 70, 79, 80, 84, 97, 223, 289, 352, 375, 376, 411, 442, 443, 523, 524, 632, 636, 647, 659, 757, 763, 820, 824, 825, 827, 831], "angl": [56, 79, 228, 238, 286, 291, 350, 372, 632], "deg": [56, 79, 224, 632], "radian": [56, 57, 79, 80, 221, 224, 225, 227, 228, 237, 239, 279, 285, 290, 359, 372, 632, 832], "degre": [56, 57, 70, 79, 80, 93, 224, 239, 279, 322, 369, 378, 490, 632, 647, 764, 766, 869], "1j": [56, 79, 80, 224, 225, 237, 238, 243, 245, 257, 280, 285, 286, 290, 338, 592, 632, 634], "2j": [56, 57, 79, 80, 224, 253, 338, 375, 403, 408, 593, 632, 634], "3j": [56, 57, 79, 80, 224, 257, 280, 338, 372, 632], "35619449": [56, 224, 632], "78539816": [56, 224, 632], "135": [56, 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Dataset and Dynamic loader": [[47, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[47, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[47, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[47, "Plotting-the-training-metrics"]], "Save the trained Model": [[47, "Save-the-trained-Model"]], "Image": [[83, "module-ivy.data_classes.container.image"], [60, "module-ivy.data_classes.array.image"]], "Ivy as a Transpiler Introduction": [[49, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[49, "To-use-the-transpiler:"]], "Transpiler Interface": [[49, "Transpiler-Interface"]], "Telemetry": [[49, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[49, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[49, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[49, "3.-Transpile-Models-\ud83c\udf10"]], "Resnet 18": [[50, "Resnet-18"]], "HuggingFace Tensorflow DeiT": [[48, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[48, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Deepmind PerceiverIO on GPU": [[46, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[46, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[46, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[46, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[46, "Run-the-demo..."]], "\u2026with torch backend": [[46, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[46, "....with-tensorflow-backend"]], "\u2026with jax backend": [[46, "...with-jax-backend"]], "\u2026with numpy backend": [[46, "...with-numpy-backend"]], "0.0: Unify": [[33, "0.0:-Unify"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "Write Ivy code": [[22, "Write-Ivy-code"]], "Contents": [[22, "Contents"]], "Installing Ivy": [[22, "Installing-Ivy"]], "Importing Ivy": [[22, "Importing-Ivy"], [0, "Importing-Ivy"]], "Ivy Backend Handler": [[22, "Ivy-Backend-Handler"], [31, "Ivy-Backend-Handler"]], "Data Structures": [[22, "Data-Structures"], [31, "Data-Structures"]], "Ivy Functional API": [[22, "Ivy-Functional-API"], [31, "Ivy-Functional-API"]], "Examples and Demos": [[3, "examples-and-demos"], [20, "examples-and-demos"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Data Preparation": [[5, "Data-Preparation"], [4, "Data-Preparation"], [12, "Data-Preparation"], [8, "Data-Preparation"]], "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"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]], "Trace code": [[24, "Trace-code"]], "0.1: Compile": [[34, "0.1:-Compile"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[6, "Comparing-the-results"], [7, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[6, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[6, "Conclusion"], [7, "Conclusion"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "Unify": [[37, "Unify"], [27, "Unify"], [26, "Unify"], [36, "Unify"], [38, "Unify"]], "Compile": [[37, "Compile"], [36, "Compile"], [38, "Compile"]], "Transpile": [[37, "Transpile"], [27, "Transpile"], [26, "Transpile"], [36, "Transpile"], [38, "Transpile"]], "Accelerating XGBoost with JAX": [[14, "Accelerating-XGBoost-with-JAX"]], "Imports": [[14, "Imports"], [12, "Imports"], [8, "Imports"]], "Tests": [[14, "Tests"]], "Loading the Data": [[14, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[14, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[14, "JAX-backend"]], "Tensorflow backend": [[14, "Tensorflow-backend"]], "PyTorch backend": [[14, "PyTorch-backend"]], "More exhaustive example": [[14, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[14, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[14, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[14, "Comparison-of-Metrics"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[45, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[45, "Table-of-Contents"]], "Defining the model": [[45, "Defining-the-model"]], "Model construction": [[45, "Model-construction"]], "Some helper functions": [[45, "Some-helper-functions"]], "Transpiling the model": [[45, "Transpiling-the-model"]], "PyTorch pipeline": [[45, "PyTorch-pipeline"]], "Dataset download": [[45, "Dataset-download"]], "DataLoader": [[45, "DataLoader"]], "Training": [[45, "Training"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "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"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "How to use decorators": [[27, "How-to-use-decorators"]], "Trace": [[27, "Trace"], [26, "Trace"]], "Transpile code": [[25, "Transpile-code"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]], "1.3: Dynamic vs Static": [[39, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[39, "Dynamic"]], "Static": [[39, "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.": [[39, "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."]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[43, "Installs-\ud83d\udcbe"], [44, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[43, "Imports-\ud83d\udec3"], [44, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[43, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[43, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[43, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[43, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[43, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[43, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[43, "Set-Backend-Framework"]], "Define Model": [[43, "Define-Model"], [44, "Define-Model"]], "Create Model": [[43, "Create-Model"]], "Create Optimizer": [[43, "Create-Optimizer"]], "Input and Target": [[43, "Input-and-Target"]], "Loss Function": [[43, "Loss-Function"]], "Training Loop": [[43, "Training-Loop"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "ODSC Ivy Demo": [[31, "ODSC-Ivy-Demo"]], "Graph Tracer": [[31, "Graph-Tracer"]], "Any function": [[31, "Any-function"], [32, "Any-function"]], "Any library": [[31, "Any-library"], [32, "Any-library"]], "Any model": [[31, "Any-model"], [32, "Any-model"]], "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:"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Installation": [[4, "Installation"], [12, "Installation"]], "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)"]], "Unify code": [[23, "Unify-code"]], "Transpile any library": [[28, "Transpile-any-library"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Quickstart": [[32, "Quickstart"]], "Get familiar with Ivy": [[32, "Get-familiar-with-Ivy"]], "Functional API": [[32, "Functional-API"]], "Stateful API": [[32, "Stateful-API"]], "Tracing code": [[32, "Tracing-code"]], "Guides": [[15, "guides"], [20, "guides"]], "Compilation of a Basic Function": [[44, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[44, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[44, "Function-compilation-\ud83d\udee0"]], "Set backend": [[44, "Set-backend"]], "Sample input": [[44, "Sample-input"]], "Define function to compile": [[44, "Define-function-to-compile"]], "Compile the function": [[44, "Compile-the-function"]], "Check results": [[44, "Check-results"], [44, "id1"]], "Compiling simple neural network \ud83e\udde0": [[44, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[44, "Create-model"]], "Define input": [[44, "Define-input"]], "Compile network": [[44, "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"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "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"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Learn the basics": [[20, "learn-the-basics"], [21, "learn-the-basics"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "Demos": [[1, "demos"]], "Creating a Notebook for 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