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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 0x7efcd04f9240>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fae5c0e5240>) – 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 0x7efcd04f9240>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fae5c0e5240>) – 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 0x7efcd04f9240>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fae5c0e5240>) – 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 3156f0bd..d7954739 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 0x7efcd04f9240>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fae5c0e5240>) – 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 588edff9..67187741 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 0x7efcd04f9240>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fae5c0e5240>) – 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 5f90c0ca..051285d2 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 0x7efcd04f9240>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fae5c0e5240>) – 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 21aa4fa5..2c62b76b 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 0x7efcc43edf40>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7fae4fed1f40>#

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    diff --git a/ivy/docs/stateful/ivy.stateful.layers.html b/ivy/docs/stateful/ivy.stateful.layers.html index 1d2da157..eaa9c8ed 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 0x7efcd00dd210>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7efcd00dffa0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fae5c4fb370>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fae5c4f82b0>) – 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 0x7efcd00dfdc0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7efcd00dfd60>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fae5c4f9e70>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fae5c4fa230>) – 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 0x7efcd00dfd00>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7efcd00df9a0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fae5c4f8400>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fae5c4fb130>) – 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 0x7efcd00df940>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7efcd00df8e0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fae5c4fba90>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fae5c4fbf70>) – 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 0x7efcd00df7c0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7efcd00df160>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fae5bd0f340>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fae5bd0f2e0>) – 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 0x7efcd00df100>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7efcd00df0a0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fae5bd0f280>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fae5bd0f220>) – 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 0x7efcd00df880>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7efcd00df820>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fae5bd0f400>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fae5bd0f3a0>) – 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 0x7efcd00df040>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fae5bd0f1c0>) – 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 0x7efcd00dc070>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7efcd00dcdc0>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fae5c4f8430>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fae5c4fa080>) – 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, 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335, 336, 349, 359, 372, 375, 376, 377, 378, 381, 387, 394, 395, 396, 430, 452, 457, 484, 501, 503, 529, 569, 632, 634, 637, 638, 647, 674, 682, 685, 696, 697, 698, 760, 761, 762, 763, 764, 765, 766, 776, 778, 791, 792, 795, 818, 832, 849, 860, 863], "pictur": [0, 47, 812, 818, 849, 859], "vital": [0, 854, 859], "select": [0, 22, 31, 36, 49, 57, 70, 80, 93, 376, 378, 387, 430, 443, 492, 493, 496, 523, 524, 647, 757, 758, 818, 819, 820, 828, 834, 840, 844, 849, 851, 854, 855, 870, 873, 874], "guid": [0, 16, 29, 812, 813, 818, 819, 820, 826, 835, 841, 843, 876], "recogn": [0, 47, 815, 821], "both": [0, 6, 9, 11, 12, 13, 14, 16, 18, 26, 28, 31, 32, 36, 37, 44, 46, 53, 56, 57, 58, 61, 62, 76, 79, 80, 81, 84, 85, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 155, 171, 175, 178, 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, 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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, 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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, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 367, 369, 372, 373, 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, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 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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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61, 74, 80, 84, 150, 151, 163, 170, 192, 193, 194, 195, 196, 198, 207, 214, 215, 219, 375, 376, 378, 418, 422, 430, 484, 495, 524, 543, 630, 631, 634, 636, 637, 649, 650, 651, 652, 654, 656, 658, 674, 771, 773, 777, 805, 806, 825, 826, 828, 829, 830, 833, 841, 849, 852], "simplest": [22, 819, 831, 844, 847], "interact": [22, 31, 46, 49, 818, 869, 870, 875], "submodul": [22, 31, 45, 47, 102, 103, 626, 627, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 788, 789, 791, 792, 794, 795, 796, 797, 818, 819, 820, 823, 826, 828, 830, 834, 837, 838, 844, 848, 849, 853, 857], "likewis": [22, 27, 31, 38, 812, 820, 827, 829, 832, 836, 837, 841, 847, 852, 863, 864, 876], "nativearrai": [22, 31, 32, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 68, 70, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 127, 128, 129, 131, 136, 137, 138, 139, 140, 141, 143, 145, 146, 149, 152, 153, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 171, 172, 173, 175, 177, 179, 180, 186, 196, 197, 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, 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, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 313, 314, 317, 318, 322, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 367, 369, 372, 373, 375, 376, 377, 378, 381, 382, 383, 385, 387, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 415, 417, 418, 419, 422, 423, 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701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 719, 720, 721, 725, 726, 727, 730, 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, 797, 824, 827, 831, 833, 836, 837, 838, 840, 841, 845, 846, 849, 851, 857], "alia": [22, 31, 335, 336, 372, 627, 818, 841, 862, 865], "lastli": [22, 31, 824], "subclass": [22, 31, 32, 838, 841, 847, 864], "dict": [22, 31, 32, 45, 49, 52, 58, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 125, 134, 136, 141, 143, 149, 153, 155, 166, 167, 168, 172, 173, 180, 196, 199, 200, 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, 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, 521, 522, 525, 532, 537, 549, 552, 553, 560, 561, 577, 590, 592, 593, 601, 614, 629, 631, 632, 634, 637, 638, 639, 641, 643, 644, 645, 647, 668, 677, 682, 683, 687, 694, 696, 697, 698, 699, 721, 725, 727, 735, 739, 740, 741, 744, 749, 759, 760, 762, 763, 764, 791, 812, 825, 827, 830, 831, 849, 851, 863], "divid": [22, 27, 31, 32, 48, 56, 57, 58, 64, 74, 79, 80, 87, 102, 103, 247, 381, 454, 501, 502, 503, 506, 592, 632, 634, 639, 708, 824, 827, 831, 835, 844], "exp": [22, 31, 32, 56, 57, 79, 80, 116, 118, 245, 265, 278, 301, 367, 375, 377, 403, 408, 457, 626, 632, 637, 685, 839, 841], "entir": [22, 31, 32, 34, 47, 57, 70, 71, 74, 80, 81, 93, 94, 213, 243, 245, 285, 286, 335, 336, 372, 375, 378, 387, 399, 400, 401, 484, 525, 558, 631, 632, 647, 648, 760, 761, 762, 763, 764, 765, 766, 767, 768, 792, 806, 818, 819, 820, 823, 824, 827, 829, 831, 833, 840, 841, 842, 844, 847, 849, 852, 853, 854, 855, 860, 861, 864, 870, 876, 877], "congratul": [22, 28], "independ": [22, 32, 57, 66, 80, 89, 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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, 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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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706, 707, 708, 709, 710, 713, 714, 715, 716, 720, 731, 738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 797, 824, 827, 839, 841, 853, 854, 855, 870], "un": [34, 170, 630, 829, 849], "partial_comp": 34, "time_funct": 34, "express": [34, 56, 57, 79, 80, 98, 221, 225, 227, 228, 237, 239, 279, 285, 290, 359, 372, 632, 798, 806, 832, 841, 849, 854, 870, 871], "maxim": [34, 837, 840, 849, 867, 868, 872, 873, 874], "conclud": [35, 845], "collect": [35, 45, 47, 49, 50, 52, 74, 75, 626, 631, 634, 635, 636, 638, 641, 642, 643, 731, 788, 792, 793, 794, 795, 796, 819, 828, 833, 834, 838, 839, 842, 844, 868, 870, 873], "norm_comp": [36, 37], "global": [36, 37, 47, 58, 74, 81, 103, 158, 159, 160, 161, 162, 211, 212, 213, 582, 583, 586, 592, 593, 605, 606, 609, 630, 631, 634, 784, 795, 801, 819, 824, 825, 828, 829, 830, 833, 837, 841, 849, 870], "b": [37, 51, 56, 57, 58, 61, 62, 70, 73, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 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828, 831, 834, 837, 839, 842, 848, 849, 850, 852, 853, 854, 858, 861, 863, 866], "option": [37, 46, 49, 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, 97, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 152, 153, 154, 155, 157, 158, 159, 160, 161, 162, 168, 170, 180, 192, 196, 208, 211, 212, 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, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 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57, 62, 64, 68, 71, 74, 79, 80, 81, 85, 92, 94, 97, 100, 102, 103, 132, 155, 157, 164, 170, 171, 172, 173, 175, 176, 177, 192, 202, 204, 205, 216, 221, 222, 223, 225, 226, 227, 228, 229, 230, 232, 233, 234, 235, 237, 238, 240, 243, 245, 247, 253, 254, 255, 256, 257, 261, 262, 263, 264, 265, 270, 273, 278, 282, 285, 286, 287, 288, 289, 290, 291, 294, 304, 308, 354, 359, 367, 372, 375, 376, 377, 378, 387, 411, 419, 430, 452, 453, 492, 496, 522, 534, 537, 558, 559, 563, 564, 565, 566, 567, 568, 595, 613, 629, 630, 631, 632, 634, 637, 639, 640, 645, 648, 667, 668, 669, 671, 675, 676, 677, 679, 680, 682, 683, 685, 686, 691, 693, 694, 700, 715, 716, 717, 749, 750, 751, 752, 753, 767, 768, 778, 784, 791, 795, 827, 829, 830, 832, 837, 841, 844, 846, 847, 859], "think": [37, 818, 820, 828, 831, 847, 871], "uniqu": [37, 47, 57, 58, 68, 80, 81, 91, 375, 376, 378, 423, 446, 483, 484, 498, 569, 634, 640, 641, 645, 715, 716, 717, 720, 724, 749, 750, 751, 752, 778, 812, 823, 827, 837, 841, 842, 843, 847, 855, 859, 873], "rule": [37, 54, 56, 57, 62, 77, 79, 80, 85, 152, 155, 178, 179, 180, 229, 240, 273, 275, 282, 284, 292, 294, 375, 378, 387, 419, 472, 522, 630, 632, 637, 639, 667, 668, 675, 679, 682, 686, 700, 778, 805, 823, 824, 827, 828, 829, 831, 835, 836, 837, 839, 844, 847, 871], "broadcast": [37, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 97, 102, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 141, 142, 143, 144, 145, 146, 148, 149, 152, 153, 154, 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, 302, 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369, 372, 615, 616, 619, 620, 621, 622, 623, 629, 635, 640, 641, 717, 719, 730, 794, 796, 797, 829, 830, 851, 853], "word": [52, 126, 378, 477, 629, 643, 741, 789, 792, 827, 840, 841, 857], "args_to_n": [52, 840], "cont_inplac": 52, "decid": [52, 74, 641, 729, 730, 812, 818, 819, 829, 847], "args_to_new_backend": 52, "shallow": [52, 641, 725, 726, 730, 735, 736], "nativevari": 52, "mutabl": [52, 641, 719, 725, 726, 730, 735, 736, 825], "to_ivi": [52, 75, 641, 731, 840], "leaf": [52, 74, 81, 93, 103, 548, 641, 728, 729, 731, 758, 827, 837, 852], "travers": [52, 75, 641, 722, 730, 827, 829, 833, 849], "lowest": [52, 57, 66, 75, 80, 89, 387, 525, 641, 643, 730, 739, 806, 837, 855, 857, 867, 871, 875], "search": [52, 57, 75, 80, 744, 745, 784, 817, 819, 827, 831, 834, 844, 845, 859], "to_new_backend": 52, "_arraywithcr": [53, 102], "boolean": [53, 54, 56, 57, 58, 64, 67, 70, 74, 76, 77, 79, 80, 81, 87, 90, 93, 102, 103, 123, 125, 127, 128, 129, 135, 152, 168, 170, 172, 173, 176, 192, 202, 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"angle"]], "set_split_factor": [[211, "set-split-factor"]], "asinh": [[226, "asinh"]], "as_native_dev": [[194, "as-native-dev"]], "valid_dtype": [[192, "valid-dtype"]], "num_ivy_arrays_on_dev": [[206, "num-ivy-arrays-on-dev"]], "print_all_ivy_arrays_on_dev": [[208, "print-all-ivy-arrays-on-dev"]], "unset_default_device": [[217, "unset-default-device"]], "split_factor": [[212, "split-factor"]], "dev_util": [[198, "dev-util"]], "tpu_is_available": [[216, "tpu-is-available"]], "set_default_int_dtype": [[184, "set-default-int-dtype"]], "unset_default_float_dtype": [[189, "unset-default-float-dtype"]], "Conversions": [[75, "module-ivy.data_classes.container.conversions"], [52, "module-ivy.data_classes.array.conversions"]], "Image": [[60, "module-ivy.data_classes.array.image"], [83, "module-ivy.data_classes.container.image"]], "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"]], "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"]], "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"]], "Compilation of a Basic Function": [[44, "Compilation-of-a-Basic-Function"]], "Installs \ud83d\udcbe": [[44, "Installs-\ud83d\udcbe"], [43, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[44, "Imports-\ud83d\udec3"], [43, "Imports-\ud83d\udec3"]], "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"]], "Define Model": [[44, "Define-Model"], [43, "Define-Model"]], "Create model": [[44, "Create-model"]], "Define input": [[44, "Define-input"]], "Compile network": [[44, "Compile-network"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "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"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Unify": [[36, "Unify"], [38, "Unify"], [26, "Unify"], [27, "Unify"], [37, "Unify"]], "Compile": [[36, "Compile"], [38, "Compile"], [37, "Compile"]], "Transpile": [[36, "Transpile"], [38, "Transpile"], [26, "Transpile"], [27, "Transpile"], [37, "Transpile"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "Transpile code": [[25, "Transpile-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 MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[8, "Imports"], [12, "Imports"], [14, "Imports"]], "Data Preparation": [[8, "Data-Preparation"], [12, "Data-Preparation"], [5, "Data-Preparation"], [4, "Data-Preparation"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Load the image example \ud83d\uddbc\ufe0f": [[8, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[8, "Visualise-image"], [12, "Visualise-image"]], "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"]], "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"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Trace": [[26, "Trace"], [27, "Trace"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [4, "Installation"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "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"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Learn the basics": [[20, "learn-the-basics"], [21, "learn-the-basics"]], "Guides": [[20, "guides"], [15, "guides"]], "Examples and Demos": [[20, "examples-and-demos"], [3, "examples-and-demos"]], "How to use decorators": [[27, "How-to-use-decorators"]], "0.0: Unify": [[33, "0.0:-Unify"]], "Trace code": [[24, "Trace-code"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "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"]], "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"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "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)"]], "Unify code": [[23, "Unify-code"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]], "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"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "ODSC Ivy Demo": [[31, "ODSC-Ivy-Demo"]], "Graph Tracer": [[31, "Graph-Tracer"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "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"]], "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"]], "0.1: Compile": [[34, "0.1:-Compile"]], "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"]], "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:"]], "Accelerating PyTorch models with JAX": [[13, 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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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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, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 367, 369, 372, 373, 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, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 537, 538, 540, 541, 544, 545, 546, 547, 548, 549, 552, 553, 554, 555, 556, 558, 559, 560, 561, 562, 564, 565, 566, 567, 568, 569, 571, 572, 574, 575, 576, 577, 578, 580, 581, 587, 588, 590, 591, 592, 593, 594, 595, 597, 598, 599, 600, 601, 602, 610, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 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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, 280, 328, 329, 355, 369, 372, 375, 376, 378, 387, 419, 430, 484, 532, 626, 629, 632, 637, 673, 674, 773, 812, 818, 819, 820, 821, 823, 826, 829, 835, 837, 840, 841, 844, 845, 847, 849, 850, 854, 855, 863, 864, 865, 868, 870, 875, 876, 877], "lazi": [20, 21, 24, 27, 34, 37, 38, 49], "decor": [20, 21, 26, 28, 29, 37, 49, 539, 634, 776, 778, 784, 816, 823, 824, 827, 829, 830, 834, 837, 840, 841, 842, 847], "kornia": [20, 21, 28, 31, 32, 45, 49, 812, 864], "roundup": 22, "indep": [22, 31], "proof": [22, 31], "delv": [22, 32, 812], "theori": [22, 814, 826], "esenti": [22, 31], "abstract": [22, 31, 32, 791, 796, 812, 827, 829, 840, 841, 844, 847, 853, 859, 868, 870, 872, 873, 877], "quirk": [22, 31], "perk": [22, 31, 812, 824, 827], "under": [22, 31, 32, 57, 377, 456, 457, 805, 812, 818, 819, 822, 823, 830, 831, 832, 835, 841, 842, 844, 847, 848, 849, 852, 854, 855, 863, 864, 870, 873, 877], "hood": [22, 31, 32, 812, 822, 830, 831, 835, 841, 844, 847, 848, 849, 852, 854, 863, 864, 877], "appropi": 22, "string": [22, 31, 32, 47, 57, 58, 61, 74, 80, 84, 150, 151, 163, 170, 192, 193, 194, 195, 196, 198, 207, 214, 215, 219, 375, 376, 378, 418, 422, 430, 484, 495, 524, 543, 630, 631, 634, 636, 637, 649, 650, 651, 652, 654, 656, 658, 674, 771, 773, 777, 805, 806, 825, 826, 828, 829, 830, 833, 841, 849, 852], "simplest": [22, 819, 831, 844, 847], "interact": [22, 31, 46, 49, 818, 869, 870, 875], "submodul": [22, 31, 45, 47, 102, 103, 626, 627, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 788, 789, 791, 792, 794, 795, 796, 797, 818, 819, 820, 823, 826, 828, 830, 834, 837, 838, 844, 848, 849, 853, 857], "likewis": [22, 27, 31, 38, 812, 820, 827, 829, 832, 836, 837, 841, 847, 852, 863, 864, 876], "nativearrai": [22, 31, 32, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 68, 70, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 127, 128, 129, 131, 136, 137, 138, 139, 140, 141, 143, 145, 146, 149, 152, 153, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 171, 172, 173, 175, 177, 179, 180, 186, 196, 197, 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, 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, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 313, 314, 317, 318, 322, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 367, 369, 372, 373, 375, 376, 377, 378, 381, 382, 383, 385, 387, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 415, 417, 418, 419, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 440, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 467, 468, 469, 470, 472, 473, 474, 475, 476, 478, 479, 481, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 522, 523, 524, 525, 526, 534, 537, 538, 540, 541, 545, 546, 547, 549, 552, 553, 554, 555, 556, 558, 560, 561, 562, 565, 568, 569, 571, 576, 577, 578, 581, 590, 591, 592, 593, 594, 595, 597, 599, 600, 602, 613, 615, 616, 617, 619, 621, 622, 623, 624, 626, 628, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 662, 663, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 719, 720, 721, 725, 726, 727, 730, 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, 797, 824, 827, 831, 833, 836, 837, 838, 840, 841, 845, 846, 849, 851, 857], "alia": [22, 31, 335, 336, 372, 627, 818, 841, 862, 865], "lastli": [22, 31, 824], "subclass": [22, 31, 32, 838, 841, 847, 864], "dict": [22, 31, 32, 45, 49, 52, 58, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 125, 134, 136, 141, 143, 149, 153, 155, 166, 167, 168, 172, 173, 180, 196, 199, 200, 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, 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, 521, 522, 525, 532, 537, 549, 552, 553, 560, 561, 577, 590, 592, 593, 601, 614, 629, 631, 632, 634, 637, 638, 639, 641, 643, 644, 645, 647, 668, 677, 682, 683, 687, 694, 696, 697, 698, 699, 721, 725, 727, 735, 739, 740, 741, 744, 749, 759, 760, 762, 763, 764, 791, 812, 825, 827, 830, 831, 849, 851, 863], "divid": [22, 27, 31, 32, 48, 56, 57, 58, 64, 74, 79, 80, 87, 102, 103, 247, 381, 454, 501, 502, 503, 506, 592, 632, 634, 639, 708, 824, 827, 831, 835, 844], "exp": [22, 31, 32, 56, 57, 79, 80, 116, 118, 245, 265, 278, 301, 367, 375, 377, 403, 408, 457, 626, 632, 637, 685, 839, 841], "entir": [22, 31, 32, 34, 47, 57, 70, 71, 74, 80, 81, 93, 94, 213, 243, 245, 285, 286, 335, 336, 372, 375, 378, 387, 399, 400, 401, 484, 525, 558, 631, 632, 647, 648, 760, 761, 762, 763, 764, 765, 766, 767, 768, 792, 806, 818, 819, 820, 823, 824, 827, 829, 831, 833, 840, 841, 842, 844, 847, 849, 852, 853, 854, 855, 860, 861, 864, 870, 876, 877], "congratul": [22, 28], "independ": [22, 32, 57, 66, 80, 89, 223, 240, 273, 283, 381, 382, 506, 508, 632, 637, 643, 668, 686, 738, 812, 823, 829, 831, 838, 849, 854, 864, 868], "div": [23, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 865], "sub": [23, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 57, 62, 64, 74, 75, 79, 80, 81, 85, 87, 103, 272, 376, 378, 387, 430, 470, 479, 499, 528, 529, 557, 634, 637, 639, 640, 671, 691, 708, 715, 716, 717, 818, 820, 822, 827, 833, 841, 842, 844, 851, 852, 853, 865, 866], "with_numpi": 23, "reproduc": [23, 48, 61, 84, 636, 659, 776, 777, 778, 779, 784, 816, 823, 834], "x_": [23, 33, 98, 284, 632, 865], "66391283": 23, "12516928": 23, "38367081": 23, "03102401": 23, "76419425": 23, "52797794": 23, "90346956": 23, "61316347": 23, "27585283": 23, "66309303": 23, "ivy_repo": 23, "sever": [23, 24, 33, 34, 36, 37, 38, 57, 80, 97, 375, 376, 389, 390, 391, 392, 444, 776, 819, 820, 845, 855, 868, 874], "pro": [23, 24, 25, 33, 34, 35, 36, 37, 38], "pick": [24, 34, 791], "trigger": [24, 34, 794, 818, 835], "unif": [24, 26, 27, 34, 36, 813, 851, 860, 866, 876], "55563945": 24, "65538704": 24, "14150524": 24, "46951997": 24, "30220294": 24, "14739668": 24, "57017946": 24, "91962677": 24, "51029003": 24, "59644395": 24, "constitu": [24, 34, 74, 854], "5556394": 24, "655387": 24, "1415051": 24, "4695197": 24, "3022028": 24, "1473966": 24, "5701794": 24, "91962665": 24, "51028997": 24, "5964439": 24, "985": 24, "000": [24, 79, 274, 776, 816, 828, 834], "On": [24, 31, 32, 819, 829, 830, 835, 841, 844, 847, 850, 854], "hand": [24, 56, 376, 446, 776, 812, 823, 829, 830, 835, 837, 844, 855], "learnt": [25, 35], "ivy_norm": 25, "jax_norm": [25, 31, 32], "wider": [25, 35, 585, 608, 634, 829, 846, 876], "avoid": [25, 35, 37, 57, 64, 80, 240, 245, 247, 263, 273, 377, 378, 381, 454, 462, 463, 464, 470, 472, 474, 475, 476, 479, 483, 490, 499, 501, 502, 503, 539, 555, 557, 580, 585, 608, 632, 634, 639, 702, 703, 704, 706, 708, 709, 711, 713, 778, 779, 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, 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80, 81, 85, 96, 97, 375, 376, 397, 398, 402, 403, 404, 407, 408, 409, 419, 420, 426, 430, 504, 505, 507, 540, 541, 562, 634, 637, 678, 694, 737, 792, 796, 845], "slow": [26, 36, 814, 819, 826], "34431235": [26, 27], "51129461": [26, 27], "06686894": [26, 27], "36452447": [26, 27], "98795534": [26, 27], "15493582": [26, 27], "91630631": [26, 27], "41939619": [26, 27], "78909753": [26, 27], "19475674": [26, 27], "norm_trac": 26, "norm_tran": [26, 36], "know": [26, 27, 36, 37, 38, 68, 645, 749, 750, 751, 752, 812, 814, 818, 820, 830, 838, 842, 844, 847, 861, 865, 871], "07": [27, 45, 47, 59, 63, 79, 82, 86, 89, 228, 261, 264, 265, 284, 375, 407, 605, 615, 616, 618, 619, 620, 621, 632, 634, 635, 638, 697, 698, 740, 793, 796, 853], "981554": 27, "happen": [27, 31, 32, 292, 632, 812, 819, 820, 821, 830, 840, 844, 852, 861, 863, 864], "wherea": [27, 38, 80, 375, 421, 820, 824, 827, 829, 830, 831, 836, 837, 844, 854, 867], "subtract": [27, 31, 32, 56, 79, 102, 103, 134, 378, 484, 629, 632, 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, 687, 688, 689, 693, 694, 695, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 737, 744, 745, 747, 748, 749, 750, 751, 752, 753, 756, 760, 761, 762, 763, 764, 765, 766, 767, 768, 818, 820, 823, 824, 827, 828, 829, 830, 831, 832, 833, 836, 837, 838, 840, 841, 842, 844, 845, 847, 849, 851, 853, 855, 859, 867, 870, 876], "wide": [32, 812, 820, 844, 868, 870], "plenti": 32, "resourc": [32, 813, 818, 819, 828], "visit": [32, 818, 819, 820, 828], "page": [32, 812, 818, 819, 820, 826, 828, 834, 850, 851, 854, 856, 865, 878], "newli": [33, 34, 46, 48, 54, 77, 152, 539, 630, 634, 820, 828, 840, 844], "randon": [33, 34, 36, 37, 38], "mean_": 33, "std_": 33, "detect": [33, 37, 56, 74, 79, 255, 632, 641, 718, 729, 818, 819, 825, 827, 828, 835, 844, 852, 853], "inspect": [33, 37, 535, 634], "__": [33, 34, 35, 36, 37, 38, 74, 831, 852], "script": [34, 812, 819, 820, 823, 828, 831, 849, 855, 870], "comp": 34, "low_level": 34, "chain": [34, 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, 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, 706, 707, 708, 709, 710, 713, 714, 715, 716, 720, 731, 738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 797, 824, 827, 839, 841, 853, 854, 855, 870], "un": [34, 170, 630, 829, 849], "partial_comp": 34, "time_funct": 34, "express": [34, 56, 57, 79, 80, 98, 221, 225, 227, 228, 237, 239, 279, 285, 290, 359, 372, 632, 798, 806, 832, 841, 849, 854, 870, 871], "maxim": [34, 837, 840, 849, 867, 868, 872, 873, 874], "conclud": [35, 845], "collect": [35, 45, 47, 49, 50, 52, 74, 75, 626, 631, 634, 635, 636, 638, 641, 642, 643, 731, 788, 792, 793, 794, 795, 796, 819, 828, 833, 834, 838, 839, 842, 844, 868, 870, 873], "norm_comp": [36, 37], "global": [36, 37, 47, 58, 74, 81, 103, 158, 159, 160, 161, 162, 211, 212, 213, 582, 583, 586, 592, 593, 605, 606, 609, 630, 631, 634, 784, 795, 801, 819, 824, 825, 828, 829, 830, 833, 837, 841, 849, 870], "b": [37, 51, 56, 57, 58, 61, 62, 70, 73, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 98, 101, 102, 103, 110, 111, 112, 113, 114, 115, 116, 117, 127, 128, 129, 134, 135, 136, 138, 141, 143, 149, 152, 153, 154, 155, 163, 173, 175, 180, 197, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 330, 333, 334, 335, 336, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 355, 356, 357, 358, 359, 361, 362, 363, 367, 369, 372, 375, 376, 377, 378, 382, 385, 387, 394, 395, 396, 397, 399, 400, 403, 407, 408, 409, 412, 413, 414, 418, 419, 422, 425, 428, 430, 432, 436, 439, 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810, 812, 813, 816, 820, 822, 823, 825, 827, 828, 831, 834, 837, 839, 842, 848, 849, 850, 852, 853, 854, 858, 861, 863, 866], "option": [37, 46, 49, 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, 97, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 152, 153, 154, 155, 157, 158, 159, 160, 161, 162, 168, 170, 180, 192, 196, 208, 211, 212, 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, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 319, 323, 324, 325, 326, 327, 328, 329, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 367, 369, 372, 375, 376, 377, 378, 381, 382, 383, 385, 387, 388, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 415, 417, 419, 420, 421, 423, 424, 426, 427, 428, 430, 432, 434, 435, 436, 437, 438, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463, 464, 467, 468, 469, 470, 472, 474, 475, 476, 477, 478, 479, 481, 482, 483, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 515, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 537, 538, 540, 541, 543, 545, 546, 547, 548, 549, 552, 553, 555, 556, 557, 558, 560, 561, 562, 564, 565, 568, 573, 576, 577, 581, 591, 592, 593, 595, 597, 599, 600, 601, 613, 615, 616, 619, 621, 622, 623, 624, 626, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 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, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 724, 725, 729, 730, 735, 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, 777, 784, 788, 789, 791, 792, 794, 796, 797, 805, 810, 818, 819, 820, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 840, 841, 842, 844, 845, 847, 849, 854, 855, 863, 864, 865, 870, 876], "prioriti": [37, 74, 801, 815, 818, 820, 821, 830, 840], "normalize_via_oper": 37, "determin": [37, 56, 57, 62, 64, 68, 71, 74, 79, 80, 81, 85, 92, 94, 97, 100, 102, 103, 132, 155, 157, 164, 170, 171, 172, 173, 175, 176, 177, 192, 202, 204, 205, 216, 221, 222, 223, 225, 226, 227, 228, 229, 230, 232, 233, 234, 235, 237, 238, 240, 243, 245, 247, 253, 254, 255, 256, 257, 261, 262, 263, 264, 265, 270, 273, 278, 282, 285, 286, 287, 288, 289, 290, 291, 294, 304, 308, 354, 359, 367, 372, 375, 376, 377, 378, 387, 411, 419, 430, 452, 453, 492, 496, 522, 534, 537, 558, 559, 563, 564, 565, 566, 567, 568, 595, 613, 629, 630, 631, 632, 634, 637, 639, 640, 645, 648, 667, 668, 669, 671, 675, 676, 677, 679, 680, 682, 683, 685, 686, 691, 693, 694, 700, 715, 716, 717, 749, 750, 751, 752, 753, 767, 768, 778, 784, 791, 795, 827, 829, 830, 832, 837, 841, 844, 846, 847, 859], "think": [37, 818, 820, 828, 831, 847, 871], "uniqu": [37, 47, 57, 58, 68, 80, 81, 91, 375, 376, 378, 423, 446, 483, 484, 498, 569, 634, 640, 641, 645, 715, 716, 717, 720, 724, 749, 750, 751, 752, 778, 812, 823, 827, 837, 841, 842, 843, 847, 855, 859, 873], "rule": [37, 54, 56, 57, 62, 77, 79, 80, 85, 152, 155, 178, 179, 180, 229, 240, 273, 275, 282, 284, 292, 294, 375, 378, 387, 419, 472, 522, 630, 632, 637, 639, 667, 668, 675, 679, 682, 686, 700, 778, 805, 823, 824, 827, 828, 829, 831, 835, 836, 837, 839, 844, 847, 871], "broadcast": [37, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 97, 102, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 141, 142, 143, 144, 145, 146, 148, 149, 152, 153, 154, 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, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 329, 335, 336, 337, 338, 339, 340, 343, 344, 346, 348, 350, 352, 353, 354, 355, 359, 367, 369, 372, 375, 376, 377, 378, 381, 382, 387, 394, 395, 396, 398, 399, 400, 401, 402, 403, 404, 408, 409, 411, 412, 413, 414, 417, 419, 424, 426, 427, 435, 436, 441, 442, 444, 453, 454, 455, 456, 458, 459, 465, 469, 472, 477, 485, 486, 487, 488, 490, 492, 494, 496, 497, 501, 504, 505, 507, 508, 509, 511, 512, 522, 523, 524, 525, 528, 529, 530, 531, 532, 540, 541, 545, 546, 547, 552, 553, 562, 576, 577, 615, 616, 619, 621, 622, 623, 624, 626, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 642, 643, 644, 645, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 680, 681, 682, 683, 684, 686, 688, 689, 691, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 709, 710, 711, 712, 714, 737, 738, 739, 740, 741, 743, 744, 745, 746, 748, 752, 753, 757, 758, 760, 761, 762, 763, 764, 765, 766, 767, 768, 776, 778, 805, 827, 829, 831, 832, 833, 844, 845, 849], "elementwis": [37, 57, 65, 80, 88, 300, 302, 362, 367, 637, 642, 692, 737, 837, 845, 849], "taken": [37, 57, 62, 80, 85, 341, 372, 375, 420, 637, 671, 691, 818, 828, 841, 845, 854, 871], "account": [37, 47, 49, 57, 64, 80, 87, 287, 378, 474, 632, 639, 706, 791, 805, 819, 828, 832, 841, 845, 863], "fact": [37, 97, 820, 823, 828, 841, 844, 849, 852], "consum": [37, 773, 827, 828, 836, 842, 844], "thrown": [37, 562, 634, 819, 824, 830, 833, 835, 855], "doesn": [37, 562, 580, 634, 771, 792, 818, 819, 825, 827, 828, 829, 830, 831, 834, 835, 837, 839, 844, 847, 849, 855, 863, 868], "consider": [37, 818, 831, 836, 847, 859, 867, 868], "standalon": [38, 818, 824, 844, 857, 866, 871, 876, 877], "static": [38, 57, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 106, 107, 129, 319, 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375, 378, 420, 474, 636, 659], "deriv": [52, 53, 57, 59, 75, 76, 80, 82, 131, 136, 143, 149, 313, 317, 342, 369, 372, 615, 616, 619, 620, 621, 622, 623, 629, 635, 640, 641, 717, 719, 730, 794, 796, 797, 829, 830, 851, 853], "word": [52, 126, 378, 477, 629, 643, 741, 789, 792, 827, 840, 841, 857], "args_to_n": [52, 840], "cont_inplac": 52, "decid": [52, 74, 641, 729, 730, 812, 818, 819, 829, 847], "args_to_new_backend": 52, "shallow": [52, 641, 725, 726, 730, 735, 736], "nativevari": 52, "mutabl": [52, 641, 719, 725, 726, 730, 735, 736, 825], "to_ivi": [52, 75, 641, 731, 840], "leaf": [52, 74, 81, 93, 103, 548, 641, 728, 729, 731, 758, 827, 837, 852], "travers": [52, 75, 641, 722, 730, 827, 829, 833, 849], "lowest": [52, 57, 66, 75, 80, 89, 387, 525, 641, 643, 730, 739, 806, 837, 855, 857, 867, 871, 875], "search": [52, 57, 75, 80, 744, 745, 784, 817, 819, 827, 831, 834, 844, 845, 859], "to_new_backend": 52, "_arraywithcr": [53, 102], "boolean": [53, 54, 56, 57, 58, 64, 67, 70, 74, 76, 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"atan2"]], "dev": [[197, "dev"]], "as_native_dev": [[194, "as-native-dev"]], "num_ivy_arrays_on_dev": [[206, "num-ivy-arrays-on-dev"]], "abs": [[220, "abs"]], "acos": [[221, "acos"]], "unset_default_dtype": [[188, "unset-default-dtype"]], "clear_cached_mem_on_dev": [[195, "clear-cached-mem-on-dev"]], "atanh": [[229, "atanh"]], "set_default_uint_dtype": [[185, "set-default-uint-dtype"]], "unset_soft_device_mode": [[218, "unset-soft-device-mode"]], "num_cpu_cores": [[204, "num-cpu-cores"]], "gpu_is_available": [[202, "gpu-is-available"]], "function_unsupported_devices": [[200, "function-unsupported-devices"]], "print_all_ivy_arrays_on_dev": [[208, "print-all-ivy-arrays-on-dev"]], "as_ivy_dev": [[193, "as-ivy-dev"]], "get_all_ivy_arrays_on_dev": [[201, "get-all-ivy-arrays-on-dev"]], "handle_soft_device_variable": [[203, "handle-soft-device-variable"]], "to_device": [[214, "to-device"]], "unset_default_uint_dtype": [[191, "unset-default-uint-dtype"]], "tpu_is_available": [[216, "tpu-is-available"]], "total_mem_on_dev": [[215, "total-mem-on-dev"]], "set_soft_device_mode": [[210, "set-soft-device-mode"]], "split_func_call": [[213, "split-func-call"]], "asin": [[225, "asin"]], "percent_used_mem_on_dev": [[207, "percent-used-mem-on-dev"]], "set_default_device": [[209, "set-default-device"]], "unset_default_device": [[217, "unset-default-device"]], "type_promote_arrays": [[186, "type-promote-arrays"]], "unset_default_complex_dtype": [[187, "unset-default-complex-dtype"]], "set_default_int_dtype": [[184, "set-default-int-dtype"]], "function_supported_devices": [[199, "function-supported-devices"]], "used_mem_on_dev": [[219, "used-mem-on-dev"]], "angle": [[224, "angle"]], "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"]], "Conversions": [[52, "module-ivy.data_classes.array.conversions"], [75, "module-ivy.data_classes.container.conversions"]], "Image": [[60, "module-ivy.data_classes.array.image"], [83, "module-ivy.data_classes.container.image"]], "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"]], "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"]], "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"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "Unify": [[37, "Unify"], [38, "Unify"], [27, "Unify"], [36, "Unify"], [26, "Unify"]], "Compile": [[37, "Compile"], [38, "Compile"], [36, "Compile"]], "Transpile": [[37, "Transpile"], [38, "Transpile"], [27, "Transpile"], [36, "Transpile"], [26, "Transpile"]], "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"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "Guides": [[15, "guides"], [20, "guides"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[8, "Imports"], [14, "Imports"], [12, "Imports"]], "Data Preparation": [[8, "Data-Preparation"], [12, "Data-Preparation"], [4, "Data-Preparation"], [5, "Data-Preparation"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Load the image example \ud83d\uddbc\ufe0f": [[8, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[8, "Visualise-image"], [12, "Visualise-image"]], "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"]], "Transpile code": [[25, "Transpile-code"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Learn the basics": [[20, "learn-the-basics"], [21, "learn-the-basics"]], "Examples and Demos": [[20, "examples-and-demos"], [3, "examples-and-demos"]], "0.0: Unify": [[33, "0.0:-Unify"]], "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 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"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "Transpile any library": [[28, "Transpile-any-library"]], "How to use decorators": [[27, "How-to-use-decorators"]], "Trace": [[27, "Trace"], [26, "Trace"]], "Trace code": [[24, "Trace-code"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [4, "Installation"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "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"]], "Compilation of a Basic Function": [[44, "Compilation-of-a-Basic-Function"]], "Installs \ud83d\udcbe": [[44, "Installs-\ud83d\udcbe"], [43, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[44, "Imports-\ud83d\udec3"], [43, "Imports-\ud83d\udec3"]], "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"]], "Define Model": [[44, "Define-Model"], [43, "Define-Model"]], "Create model": [[44, "Create-model"]], "Define input": [[44, "Define-input"]], "Compile network": [[44, "Compile-network"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "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)"]], "0.1: Compile": [[34, "0.1:-Compile"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "# 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"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "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"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Importing Ivy": [[0, "Importing-Ivy"], [22, "Importing-Ivy"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "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"]], "Write Ivy code": [[22, "Write-Ivy-code"]], "Contents": [[22, "Contents"]], "Installing Ivy": [[22, "Installing-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"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "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"]], "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"]], "Unify code": [[23, "Unify-code"]], "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"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]], "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"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]]}, "indexentries": {"_arraywithactivations (class in 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