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a/ivy/docs/functional/ivy/ivy.functional.ivy.meta.html +++ b/ivy/docs/functional/ivy/ivy.functional.ivy.meta.html @@ -1421,7 +1421,7 @@

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 0x7fc7ad2ed240>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fa9fc08d240>) – 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.

    • @@ -1475,7 +1475,7 @@

    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 0x7fc7ad2ed240>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fa9fc08d240>) – 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.

    • @@ -1552,7 +1552,7 @@

    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 0x7fc7ad2ed240>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fa9fc08d240>) – 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 383ce5c10..036a6f3f0 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 @@ -1424,7 +1424,7 @@

    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 0x7fc7ad2ed240>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fa9fc08d240>) – 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 ff752e7c1..2a22f6ad6 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 @@ -1424,7 +1424,7 @@

    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 0x7fc7ad2ed240>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fa9fc08d240>) – 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 bbf1626bd..c08812212 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 @@ -1421,7 +1421,7 @@

    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 0x7fc7ad2ed240>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fa9fc08d240>) – 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 fa37cb848..c36027c99 100644 --- a/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html +++ b/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html @@ -1410,7 +1410,7 @@

    Should not be used inside any of the test functions.

    -ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7fc7a10d9fb0>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7fa9efe61f80>#

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  • 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 0x7fc7ad1cf490>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fc7ad1cf190>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fa9fbef4670>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fa9fbef4610>) – 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)

    • @@ -1575,8 +1575,8 @@

  • 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 0x7fc7ad1cf100>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fc7ad1cea70>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fa9fbef45b0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fa9fbef4550>) – 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”.

    • @@ -1614,8 +1614,8 @@

  • 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 0x7fc7ad1cfc70>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fc7ad1cfb50>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fa9fbef44f0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fa9fbef4490>) – 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)

    • @@ -1652,8 +1652,8 @@

  • 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 0x7fc7ad1cef50>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fc7ad1cd9c0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fa9fbef4430>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fa9fbef43d0>) – 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”.

    • @@ -1691,8 +1691,8 @@

  • 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 0x7fc7ad1cd930>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fc7ad1ce1a0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fa9fbef4d00>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fa9fbef4790>) – 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)

    • @@ -1729,8 +1729,8 @@

  • 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 0x7fc7ad1ce0e0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fc7ad1ce080>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fa9fbef47f0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fa9fbef4850>) – 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”.

    • @@ -1793,8 +1793,8 @@

  • 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 0x7fc7ad1cd780>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fc7ad1ceb00>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fa9fbef4370>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fa9fbef4ca0>) – 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)

    • @@ -1950,7 +1950,7 @@

    • 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 0x7fc7ad1cdcc0>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fa9fbef5240>) – 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. @@ -2009,8 +2009,8 @@

      • 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 0x7fc7ad1cdd50>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fc7ad1ce410>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fa9fbef4730>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fa9fbef46d0>) – 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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336, 369, 372, 375, 378, 387, 419, 492, 522, 615, 616, 629, 630, 632, 635, 637, 639, 647, 685, 686, 714, 764, 813, 815, 820, 821, 824, 827, 829, 830, 833, 835, 857, 865], "github": [2, 4, 5, 8, 11, 12, 13, 31, 45, 46, 47, 48, 49, 813, 815, 816, 818, 821, 822, 824, 827, 829, 830, 832, 833, 835, 836, 844, 845, 857, 860, 879], "com": [2, 4, 5, 6, 7, 8, 11, 12, 13, 18, 31, 45, 46, 47, 48, 49, 813, 815, 820, 821, 824, 827, 829, 830, 835, 857], "unifyai": [2, 4, 8, 12, 31, 45, 46, 47, 48, 49, 813, 815, 820, 821, 827, 835, 857], "model": [2, 3, 4, 9, 14, 15, 20, 21, 22, 48, 50, 240, 273, 377, 453, 632, 789, 793, 794, 811, 813, 853, 854, 858, 864, 865, 869, 870, 871, 872, 873, 874, 875, 877, 878], "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, 821, 829, 853, 854, 855, 857], "repositori": [2, 4, 8, 12, 815, 819, 820, 821, 823, 824, 827, 835, 844, 862], "cd": [2, 4, 8, 12, 31, 48, 813, 815, 820, 821, 835, 857], "resnet": [3, 6, 13, 20, 31, 864, 865], "imag": [3, 4, 6, 7, 11, 13, 16, 20, 28, 31, 32, 45, 46, 47, 48, 49, 50, 57, 61, 79, 80, 84, 102, 220, 221, 222, 223, 226, 229, 238, 241, 243, 245, 254, 255, 256, 261, 263, 276, 283, 284, 286, 287, 291, 375, 394, 395, 411, 412, 413, 415, 545, 632, 634, 636, 649, 650, 651, 652, 653, 656, 657, 658, 792, 813, 820, 835, 848, 850, 851, 853, 855, 857, 864, 865, 871], "classif": [3, 4, 12, 14, 20, 45, 813, 871], "acceler": [3, 20, 813, 830, 842, 869, 873, 874, 875, 876], "convert": [3, 8, 9, 11, 13, 14, 16, 18, 20, 21, 23, 25, 28, 29, 31, 32, 33, 35, 37, 45, 48, 50, 52, 53, 56, 74, 75, 76, 79, 97, 127, 128, 140, 150, 151, 193, 194, 195, 196, 207, 215, 219, 239, 279, 378, 383, 462, 463, 464, 513, 578, 596, 598, 599, 600, 602, 629, 630, 631, 632, 634, 637, 641, 695, 719, 730, 731, 773, 801, 806, 813, 819, 825, 826, 839, 840, 842, 845, 847, 850, 856, 858, 862, 865, 869, 870, 877], "faster": [3, 4, 9, 11, 13, 14, 20, 31, 32, 48, 50, 57, 62, 80, 85, 376, 449, 637, 687, 815, 818, 827, 858, 873, 876], "infer": [3, 6, 7, 9, 11, 13, 14, 20, 24, 34, 36, 37, 46, 48, 50, 53, 57, 58, 61, 64, 76, 80, 81, 84, 87, 126, 128, 131, 135, 136, 140, 143, 149, 158, 159, 160, 161, 162, 312, 313, 375, 378, 382, 411, 496, 510, 556, 590, 591, 629, 630, 634, 636, 639, 659, 706, 801, 802, 823, 826, 830, 831, 845, 850, 855, 865, 869, 870, 873, 875], "mmpretrain": [3, 20], "segment": [3, 20, 57, 80, 330, 331, 332, 369, 827, 832], "unet": [3, 20], "alexnet": [3, 20], "written": [3, 4, 5, 6, 20, 22, 31, 32, 45, 58, 378, 473, 820, 824, 825, 833, 836, 837, 841, 842, 846, 850, 852, 855, 856, 860, 865, 869, 871, 875, 877, 878], "xgboost": [3, 20], "paddlepaddl": [3, 20, 335, 336, 372, 820], "dinov2": [3, 7, 20], "project": [3, 12, 13, 20, 25, 26, 27, 28, 29, 31, 32, 35, 98, 636, 663, 792, 813, 815, 816, 819, 820, 821, 822, 825, 826, 827, 845, 854, 856, 860, 861, 862, 865, 867, 869, 871, 874, 878, 879], "convnext": [3, 6, 11, 20], "video": [4, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 813, 814, 819, 820, 821, 824, 825, 826, 828, 829, 830, 831, 832, 833, 834, 836, 837, 838, 839, 840, 841, 842, 843, 845, 846, 848, 857, 869], "tutori": [4, 6, 7, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 813, 821, 842, 857], "three": [4, 5, 20, 26, 36, 37, 47, 57, 139, 312, 369, 378, 464, 629, 820, 821, 828, 829, 830, 832, 842, 845, 848, 849, 850, 872, 877], "major": [4, 5, 644, 747, 830, 831, 843, 845, 856, 861, 868, 871], "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, 813, 814, 818, 842, 849, 850, 851, 853, 854, 855, 859, 861, 862, 865, 867, 868, 869, 870, 871, 874, 876, 878], "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, 813, 816, 817, 819, 820, 823, 824, 825, 826, 827, 829, 830, 831, 832, 834, 835, 837, 838, 839, 841, 842, 845, 846, 848, 849, 850, 852, 855, 856, 857, 858, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 872, 875], "sinc": [4, 8, 12, 28, 29, 31, 32, 45, 47, 57, 80, 98, 372, 813, 815, 820, 821, 824, 825, 826, 827, 828, 829, 830, 831, 834, 841, 842, 856, 861, 871, 877], "automat": [4, 8, 9, 12, 29, 31, 32, 37, 819, 820, 821, 823, 826, 827, 829, 830, 836, 838, 841, 845, 848, 849, 851, 854, 855, 857, 858, 862, 871, 874, 878], "sure": [4, 8, 11, 12, 13, 14, 31, 45, 816, 819, 820, 821, 824, 829, 834, 835, 842, 843, 845, 848, 857], "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, 811, 813, 820, 821, 822, 825, 828, 830, 838, 839, 840, 841, 842, 845, 846, 849, 851, 853, 855, 856, 858, 861, 864, 869, 870, 871, 872, 873, 874, 877, 878], "dm": [4, 5, 8, 11, 13, 31, 32, 43, 45], "haiku": [4, 5, 8, 11, 13, 29, 31, 32, 43, 45, 49, 789, 813, 855, 862, 865, 871], "exit": [4, 8, 12, 31, 32, 831], "download": [4, 6, 7, 12, 16, 18, 31, 32, 46, 47, 50, 815, 820, 827, 845, 864, 865], "imagenet": [4, 6, 18, 46, 48, 813], "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, 804, 806, 811, 813, 819, 826, 827, 828, 830, 831, 832, 833, 837, 839, 840, 843, 844, 845, 848, 850, 851, 853, 854, 855, 858, 864, 865, 869, 871, 872, 878], "wget": [4, 6, 8, 12, 45, 46, 49, 820], "raw": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 45, 48, 49, 74, 813, 833, 865, 872], "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, 816, 829, 871, 879], "imagenet_class": [4, 12], "categori": [4, 6, 12, 819, 824, 825, 828, 830, 834, 842, 846, 849], "strip": [4, 12, 24, 34, 861], "readlin": [4, 12, 46], "cat": [4, 7, 12, 46, 843, 848, 850, 855, 864, 865], "jpg": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 47, 48, 813, 865], "filenam": [4, 8, 12, 31, 32, 45, 47, 50, 58, 794, 800, 853], "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, 813, 818, 819, 820, 821, 822, 824, 825, 826, 827, 828, 830, 831, 832, 833, 836, 839, 840, 841, 842, 843, 844, 845, 846, 850, 852, 853, 855, 856, 857, 861, 864, 865, 866, 867, 869, 871, 874, 875, 877], "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, 811, 813, 821, 823, 826, 830, 834, 838, 839, 843, 845, 846, 848, 850, 855, 856, 857, 858, 861, 870, 871, 873, 874, 875, 876], "torchvis": [4, 6, 11, 12, 45, 862], "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, 813, 839, 845, 855, 858, 864, 865, 869, 871, 872, 873], "pil": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 46, 47, 48, 813, 865], "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, 811, 819, 820, 821, 824, 826, 828, 829, 830, 832, 835, 837, 838, 839, 841, 842, 845, 846, 850, 853, 855, 856, 857, 860, 861, 862, 864, 865, 869, 871, 872, 875, 876, 877], "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, 820, 827, 829, 832, 845, 856, 877], "compos": [4, 6, 7, 11, 12, 31, 32, 45, 57, 80, 375, 389, 390, 391, 392, 820, 828, 842, 845, 864, 866, 871, 878], "resiz": [4, 6, 7, 8, 11, 12, 45, 46, 57, 80, 375, 411, 848], "centercrop": [4, 12], "224": [4, 6, 7, 12, 16, 18, 31, 32, 45, 46, 48, 813, 865], "totensor": [4, 6, 7, 11, 12, 45], "485": [4, 12, 45], "456": [4, 12, 45, 845], "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, 813, 853, 865], "ipython": [4, 8, 12, 26, 27, 28, 29, 31, 32, 50], "displai": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 50, 820, 827, 829, 834, 845, 853], "end": [4, 8, 45, 46, 57, 80, 126, 228, 284, 353, 372, 375, 377, 378, 423, 452, 474, 484, 486, 487, 629, 632, 807, 813, 820, 821, 826, 829, 835, 841, 846, 848, 849, 856, 869, 874], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 217, 631, 831], "ivy_model": [4, 5, 8, 12, 48], "ivy_alexnet": 4, "quick": [4, 20, 32, 821, 823, 843, 854], "trace_graph": [4, 5, 8, 12, 24, 25, 26, 27, 31, 32, 34, 35, 36, 37, 38, 39, 48, 794, 813, 850, 855, 863], "moment": [4, 57, 59, 80, 82, 376, 433, 615, 616, 621, 635, 796, 811, 819, 826, 856, 864, 865], "cost": [4, 59, 82, 615, 616, 619, 621, 622, 623, 635, 640, 715, 716, 717, 807, 830, 848, 869], "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, 806, 811, 813, 825, 830, 831, 834, 840, 841, 842, 848, 850, 854, 864, 865, 866], "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, 834, 839, 842, 843], "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, 811, 850, 856, 858, 876], "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, 806, 807, 813, 815, 820, 821, 823, 824, 825, 827, 828, 830, 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8, 12, 29, 31, 32, 47, 50, 61, 84, 636, 653, 792, 804, 813], "64": [4, 8, 12, 43, 45, 46, 47, 50, 56, 57, 61, 79, 80, 81, 84, 85, 89, 93, 103, 164, 234, 244, 278, 287, 288, 346, 372, 375, 397, 407, 545, 546, 593, 621, 630, 632, 634, 635, 636, 637, 641, 647, 651, 653, 655, 657, 658, 679, 682, 692, 726, 730, 740, 759, 763, 820, 830, 853, 854, 868, 876], "data_format": [4, 47, 57, 61, 80, 84, 375, 381, 390, 394, 395, 396, 399, 400, 401, 412, 413, 414, 415, 417, 501, 502, 503, 506, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 776, 792, 795, 813], "nchw": [4, 47, 57, 61, 80, 84, 375, 381, 390, 395, 400, 413, 417, 506, 636, 649, 652, 653, 656, 657, 658, 792, 813], "relu": [4, 8, 12, 29, 31, 32, 43, 50, 51, 57, 72, 73, 80, 112, 302, 303, 311, 367, 626, 788, 813, 843, 853, 854], "maxpool2d": [4, 8, 12, 45, 792, 813], "192": [4, 47, 776, 806], "384": [4, 82, 615, 635, 641, 718], "avgpool": [4, 12], "adaptiveavgpool2d": [4, 12, 792], "classifi": [4, 7, 14, 16, 18, 31, 32, 45, 47, 48, 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328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 369, 372, 373, 374, 375, 376, 377, 378, 381, 382, 383, 385, 387, 388, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 412, 413, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 426, 427, 428, 429, 431, 436, 438, 441, 443, 446, 449, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 490, 492, 493, 494, 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, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 571, 572, 573, 574, 576, 577, 581, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 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, 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, 715, 716, 717, 718, 719, 721, 724, 725, 726, 727, 728, 729, 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, 773, 776, 777, 778, 779, 789, 794, 796, 801, 807, 809, 813, 830, 831, 833, 834, 840, 841, 842, 843, 846, 850, 855, 865], "eagertensor": [16, 22, 43, 801, 843], "deepmind": [17, 862], "perceiverio": [17, 862], "backbon": [17, 45, 813, 850, 853], "TO": [17, 19, 30], "replac": [17, 19, 30, 46, 56, 57, 58, 64, 66, 74, 79, 80, 81, 87, 89, 132, 274, 310, 313, 367, 369, 378, 489, 492, 496, 576, 577, 581, 629, 632, 634, 639, 643, 699, 738, 776, 821, 827, 828, 830, 831, 839, 842, 845, 852, 855, 856, 861, 865, 878], "efficientnet": 18, "eff_encod": [18, 813], "efficientnet_v2": [18, 813], "efficientnetv2b0": [18, 813], "storag": [18, 45, 46, 853, 861], "googleapi": [18, 45, 46], "efficientnetv2": 18, "b0_notop": 18, "h5": [18, 74], "24274472": 18, "0u": 18, "torch_eff_encod": [18, 813], "modes_to_trac": 18, "1280": [18, 545, 634, 813], "welcom": [20, 46, 813, 814, 820, 821, 822, 844], "varieti": [20, 824, 829, 830, 831, 845, 847, 867, 869, 873, 874, 877, 878], "organ": [20, 825, 828, 838, 842, 844, 846, 858, 861], "main": [20, 32, 53, 57, 62, 80, 85, 132, 145, 146, 147, 313, 328, 329, 369, 376, 378, 427, 473, 629, 637, 670, 671, 691, 813, 816, 819, 820, 821, 822, 824, 827, 828, 835, 839, 841, 869, 871, 872, 877], "exactli": [20, 24, 34, 43, 44, 48, 290, 632, 819, 828, 829, 830, 831, 832, 834, 845, 848, 860, 862], "rush": [20, 862], "jump": [20, 843], "straight": [20, 813, 829, 842, 845, 852], "quickstart": [20, 813], "introduct": [20, 22, 29, 31, 32, 871], "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, 811, 817, 819, 820, 821, 824, 825, 827, 829, 830, 832, 833, 835, 837, 841, 842, 845, 846, 848, 850, 852, 853, 862, 864, 877], "showcas": [20, 813], "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, 828, 873], "world": [20, 28, 821, 873], "beginn": [20, 814, 871], "got": [20, 43, 834], "cover": [20, 31, 57, 80, 375, 412, 413, 414, 819, 824, 825, 827, 830, 832, 833, 838, 839, 845, 848, 849], "familiar": [20, 21, 22, 819, 820], "concept": [20, 21, 22], "turn": [20, 21, 24, 34, 61, 84, 97, 98, 399, 400, 401, 636, 659, 792, 820, 827, 828, 831, 832, 842, 845, 862], "unus": [20, 21, 24, 832, 841], "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, 813, 819, 820, 821, 822, 824, 827, 830, 836, 838, 841, 842, 845, 846, 848, 850, 851, 855, 856, 864, 865, 866, 869, 871, 876, 877, 878], "lazi": [20, 21, 24, 27, 34, 37, 38, 49], "decor": [20, 21, 26, 28, 29, 37, 49, 539, 634, 776, 778, 784, 817, 824, 825, 828, 830, 831, 835, 838, 841, 842, 843, 848], "kornia": [20, 21, 28, 31, 32, 45, 49, 813, 865], "roundup": 22, "indep": [22, 31], "proof": [22, 31], "delv": [22, 32, 813], "theori": [22, 815, 827], "esenti": [22, 31], "abstract": [22, 31, 32, 791, 796, 813, 828, 830, 841, 842, 845, 848, 854, 860, 869, 871, 873, 874, 878], "quirk": [22, 31], "perk": [22, 31, 813, 825, 828], "under": [22, 31, 32, 57, 377, 456, 457, 806, 813, 819, 820, 823, 824, 831, 832, 833, 836, 842, 843, 845, 848, 849, 850, 853, 855, 856, 864, 865, 871, 874, 878], "hood": [22, 31, 32, 813, 823, 831, 832, 836, 842, 845, 848, 849, 850, 853, 855, 864, 865, 878], "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, 806, 807, 826, 827, 829, 830, 831, 834, 842, 850, 853], "simplest": [22, 820, 832, 845, 848], "interact": [22, 31, 46, 49, 819, 870, 871, 876], "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, 819, 820, 821, 824, 827, 829, 831, 835, 838, 839, 845, 849, 850, 854, 858], "likewis": [22, 27, 31, 38, 813, 821, 828, 830, 833, 837, 838, 842, 848, 853, 864, 865, 877], "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, 825, 828, 832, 834, 837, 838, 839, 841, 842, 846, 847, 850, 852, 858], "alia": [22, 31, 335, 336, 372, 627, 819, 842, 863, 866], "lastli": [22, 31, 825], "subclass": [22, 31, 32, 839, 842, 848, 865], "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, 807, 825, 828, 853, 854, 858, 864, 865, 866], "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, 820, 824, 827, 828, 835, 838, 841, 854, 856], "fashion": [22, 778, 845, 865], "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, 817, 835, 843, 845], "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, 828, 834, 841, 842, 848, 850, 867, 869, 871, 872, 873, 875, 877], "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, 806, 807, 814, 820, 823, 826, 827, 828, 832, 838, 840, 849, 850, 851, 853, 856, 858, 859, 861, 862, 865, 867, 871, 875, 876, 878], "fundament": [22, 31, 829, 842, 848, 850, 860, 871], "signatur": [22, 31, 378, 387, 484, 522, 830, 831, 832, 833, 837, 841, 845, 846, 848, 861, 868, 877], "matmul": [22, 31, 32, 48, 62, 85, 376, 446, 614, 634, 637, 687, 826, 845, 846, 850], "to_n": [22, 31, 32, 43, 52, 75, 850], "jaxlib": [22, 28, 46, 801, 820, 825, 830, 831, 837, 846, 850, 852], "xla_extens": [22, 28, 801, 825, 830, 831, 837, 846, 850, 852], "arrayimpl": [22, 28, 801], "disabl": [22, 31, 57, 80, 378, 492, 794, 811, 827], "array_mod": [22, 31, 578, 602, 634, 847], "set_array_mod": [22, 31, 602, 634, 847], "ultim": [22, 31, 864], "sigmoid": [22, 31, 32, 43, 51, 57, 73, 80, 301, 367, 382, 508, 626, 788, 850, 853, 854], "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, 813, 826, 828, 831, 832, 850, 852, 864], "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, 825, 828, 832, 836, 845], "exp": [22, 31, 32, 56, 57, 79, 80, 116, 118, 245, 265, 278, 301, 367, 375, 377, 403, 408, 457, 626, 632, 637, 685, 840, 842], "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, 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726, 730, 813, 819, 838, 839, 840, 843, 848, 850, 853], "2d": [31, 32, 47, 57, 80, 97, 313, 369, 375, 376, 378, 387, 390, 391, 399, 400, 442, 449, 463, 473, 522, 792, 811, 813, 842, 848], "5f": [31, 32, 813], "nonetheless": [31, 32], "extract": [31, 32, 39, 46, 57, 80, 98, 378, 467, 493, 842, 844, 846, 867, 871, 872, 877], "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, 842, 855], "said": [31, 32, 778, 846, 862, 864], "otherwis": [31, 32, 49, 52, 53, 54, 56, 57, 58, 61, 62, 67, 68, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 126, 128, 129, 134, 136, 137, 138, 141, 143, 149, 152, 153, 155, 156, 158, 159, 160, 161, 162, 171, 175, 179, 180, 196, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 300, 303, 304, 305, 306, 307, 309, 310, 311, 313, 323, 324, 325, 326, 327, 334, 335, 336, 337, 338, 340, 341, 342, 350, 351, 357, 359, 361, 362, 363, 367, 369, 372, 375, 376, 378, 381, 394, 395, 396, 399, 400, 401, 419, 432, 447, 449, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463, 464, 468, 469, 470, 472, 474, 475, 476, 483, 490, 492, 493, 494, 496, 499, 501, 503, 504, 505, 507, 509, 521, 522, 523, 524, 525, 534, 537, 538, 540, 541, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 569, 576, 577, 591, 592, 593, 595, 597, 599, 600, 601, 613, 617, 619, 624, 628, 629, 630, 631, 632, 634, 635, 636, 637, 640, 641, 644, 645, 646, 647, 648, 650, 651, 652, 653, 659, 660, 661, 663, 666, 667, 668, 669, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 687, 691, 693, 694, 696, 697, 698, 699, 702, 703, 704, 706, 707, 708, 709, 710, 711, 713, 714, 715, 716, 731, 738, 739, 740, 741, 743, 744, 745, 746, 748, 749, 750, 751, 752, 753, 755, 757, 758, 760, 761, 762, 763, 764, 765, 766, 767, 768, 771, 776, 777, 792, 794, 795, 801, 813, 821, 825, 828, 830, 831, 832, 838, 839, 841, 845, 850, 857, 864, 865], "x0": [31, 32, 50, 81, 537, 634, 832], "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, 833, 861], "fname": [31, 32, 48, 50, 794, 853], "anticip": [31, 32], "addition": [31, 32, 828, 841, 842, 877], "normalize_native_comp": [31, 32], "return_backend_compiled_fn": 31, "immedi": [31, 32, 811, 819, 820], "built": [31, 32, 37, 45, 47, 50, 126, 629, 792, 793, 794, 813, 820, 821, 827, 828, 845, 851, 857, 864, 870, 871, 875], "eager_graph": [31, 32, 813, 864, 865], "lazy_graph": [31, 32, 813, 864, 865], "thought": [31, 32, 820, 821, 837, 861, 869], "matter": [31, 32, 37, 832, 860], "haven": [31, 32, 37, 857, 871], "jax_out": [31, 32], "ideal": [31, 32, 829, 830, 842, 848, 853], "worth": [31, 32], "differenti": [31, 32, 295, 365, 366, 367, 374, 871], "chosen": [31, 32, 50, 100, 126, 228, 629, 632, 644, 748, 819, 829, 842], "plai": [31, 32, 377, 456, 813, 816, 820, 822, 825, 831, 835, 842, 845, 855, 871, 874], "role": [31, 32, 813, 816, 821, 822, 831, 842, 851, 872, 874, 878], "dl": [31, 32], "effortlessli": [31, 32], "previous": [31, 32, 603, 634, 801, 819, 820, 826, 838, 840, 845, 850], "default_devic": [31, 32, 206, 209, 210, 211, 217, 218, 631, 831, 834, 835], "as_n": [31, 32, 54, 55, 74, 77, 78, 158, 159, 160, 161, 162, 163, 169, 196, 197, 630, 631, 830], "certainli": [31, 32, 813, 861, 877], "upon": [31, 32, 49, 811, 821, 822, 832, 841, 845, 848, 856, 870, 871], "unnecessari": [31, 32, 842], "extend": [31, 32, 57, 80, 378, 387, 484, 525, 826, 827, 830, 833, 834, 837, 842, 846, 856, 868, 871, 877], "infrastructur": [31, 32, 867, 873, 874], "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, 813, 821, 825, 829, 830, 831, 832, 838, 841, 845, 865], "coco": 31, "seamlessli": [32, 845], "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, 819, 821, 824, 825, 828, 829, 830, 831, 832, 833, 834, 837, 838, 839, 841, 842, 843, 845, 846, 848, 850, 852, 854, 856, 860, 868, 871, 877], "wide": [32, 813, 821, 845, 869, 871], "plenti": 32, "resourc": [32, 814, 819, 820, 829], "visit": [32, 819, 820, 821, 829], "page": [32, 813, 819, 820, 821, 827, 829, 835, 851, 852, 855, 857, 866, 879], "newli": [33, 34, 46, 48, 54, 77, 152, 539, 630, 634, 821, 829, 841, 845], "randon": [33, 34, 36, 37, 38], "mean_": 33, "std_": 33, "detect": [33, 37, 56, 74, 79, 255, 632, 641, 718, 729, 819, 820, 826, 828, 829, 836, 845, 853, 854], "inspect": [33, 37, 535, 634], "__": [33, 34, 35, 36, 37, 38, 74, 832, 853], "script": [34, 813, 820, 821, 824, 829, 832, 850, 856, 871], "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, 825, 828, 840, 842, 854, 855, 856, 871], "un": [34, 170, 630, 830, 850], "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, 807, 833, 842, 850, 855, 871, 872], "maxim": [34, 838, 841, 850, 868, 869, 873, 874, 875], "conclud": [35, 846], "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, 820, 829, 834, 835, 839, 840, 843, 845, 869, 871, 874], "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, 820, 825, 826, 829, 830, 831, 834, 838, 842, 850, 871], "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, 443, 446, 451, 452, 453, 455, 456, 457, 458, 462, 463, 464, 465, 468, 469, 470, 471, 474, 475, 476, 478, 479, 480, 481, 483, 484, 490, 492, 493, 494, 495, 496, 499, 500, 505, 507, 509, 510, 512, 513, 515, 522, 523, 524, 525, 527, 529, 532, 534, 537, 538, 540, 541, 544, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 569, 576, 577, 591, 592, 593, 595, 599, 600, 613, 615, 616, 617, 619, 621, 622, 623, 624, 626, 629, 630, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 655, 657, 658, 659, 660, 662, 666, 667, 668, 669, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 696, 697, 698, 699, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 721, 724, 725, 726, 727, 729, 730, 735, 736, 737, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 776, 806, 807, 811, 813, 814, 817, 821, 823, 824, 826, 828, 829, 832, 835, 838, 840, 843, 849, 850, 851, 853, 854, 855, 859, 862, 864, 867], "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, 806, 811, 819, 820, 821, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 841, 842, 843, 845, 846, 848, 850, 855, 856, 864, 865, 866, 871, 877], "prioriti": [37, 74, 801, 816, 819, 821, 822, 831, 841], "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, 828, 830, 831, 833, 838, 842, 845, 847, 848, 860], "think": [37, 819, 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[[280, "real"]], "bitwise_right_shift": [[234, "bitwise-right-shift"]], "log10": [[262, "log10"]], "nan_to_num": [[274, "nan-to-num"]], "cos": [[237, "cos"]], "expm1": [[245, "expm1"]], "cosh": [[238, "cosh"]], "isinf": [[255, "isinf"]], "ceil": [[236, "ceil"]], "bitwise_left_shift": [[232, "bitwise-left-shift"]], "isnan": [[256, "isnan"]], "logical_or": [[269, "logical-or"]], "lcm": [[258, "lcm"]], "bitwise_invert": [[231, "bitwise-invert"]], "exp": [[243, "exp"]], "logical_not": [[268, "logical-not"]], "log": [[261, "log"]], "logical_and": [[267, "logical-and"]], "erf": [[242, "erf"]], "floor": [[246, "floor"]], "floor_divide": [[247, "floor-divide"]], "logical_xor": [[270, "logical-xor"]], "greater_equal": [[252, "greater-equal"]], "bitwise_xor": [[235, "bitwise-xor"]], "equal": [[241, "equal"]], "fmod": [[249, "fmod"]], "multiply": [[273, "multiply"]], "isfinite": [[254, "isfinite"]], "exp2": [[244, "exp2"]], "bitwise_or": [[233, "bitwise-or"]], "negative": [[275, "negative"]], "logaddexp": [[265, "logaddexp"]], "less_equal": [[260, "less-equal"]], "log2": [[264, "log2"]], "maximum": [[271, "maximum"]], "greater": [[251, "greater"]], "divide": [[240, "divide"]], "less": [[259, "less"]], "fmin": [[248, "fmin"]], "gcd": [[250, "gcd"]], "deg2rad": [[239, "deg2rad"]], "logaddexp2": [[266, "logaddexp2"]], "bitwise_and": [[230, "bitwise-and"]], "imag": [[253, "imag"]], "minimum": [[272, "minimum"]], "log1p": [[263, "log1p"]], "isreal": [[257, "isreal"]], "gpu_is_available": [[202, "gpu-is-available"]], "to_device": [[214, "to-device"]], "handle_soft_device_variable": [[203, "handle-soft-device-variable"]], "atan2": [[228, "atan2"]], "unset_default_float_dtype": [[189, "unset-default-float-dtype"]], "dev": [[197, "dev"]], "set_default_device": [[209, "set-default-device"]], "unset_soft_device_mode": [[218, "unset-soft-device-mode"]], "asinh": [[226, "asinh"]], "unset_default_complex_dtype": [[187, "unset-default-complex-dtype"]], "unset_default_uint_dtype": [[191, "unset-default-uint-dtype"]], "unset_default_device": [[217, "unset-default-device"]], "acos": [[221, "acos"]], "set_soft_device_mode": [[210, "set-soft-device-mode"]], "split_func_call": [[213, "split-func-call"]], "dev_util": [[198, "dev-util"]], "set_split_factor": [[211, "set-split-factor"]], "split_factor": [[212, "split-factor"]], "get_all_ivy_arrays_on_dev": [[201, "get-all-ivy-arrays-on-dev"]], "type_promote_arrays": [[186, "type-promote-arrays"]], "percent_used_mem_on_dev": [[207, "percent-used-mem-on-dev"]], "total_mem_on_dev": [[215, "total-mem-on-dev"]], "atanh": [[229, "atanh"]], "asin": [[225, "asin"]], "set_default_uint_dtype": [[185, "set-default-uint-dtype"]], "atan": [[227, "atan"]], "add": [[223, "add"]], "default_device": [[196, "default-device"]], "clear_cached_mem_on_dev": [[195, "clear-cached-mem-on-dev"]], "as_native_dev": [[194, "as-native-dev"]], "function_unsupported_devices": [[200, "function-unsupported-devices"]], "num_cpu_cores": [[204, "num-cpu-cores"]], "set_default_int_dtype": [[184, "set-default-int-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"]], "tpu_is_available": [[216, "tpu-is-available"]], "unset_default_int_dtype": [[190, "unset-default-int-dtype"]], "acosh": [[222, "acosh"]], "unset_default_dtype": [[188, "unset-default-dtype"]], "used_mem_on_dev": [[219, "used-mem-on-dev"]], "abs": [[220, "abs"]], "angle": [[224, "angle"]], "valid_dtype": [[192, "valid-dtype"]], "as_ivy_dev": [[193, "as-ivy-dev"]], "function_supported_devices": [[199, "function-supported-devices"]], "num_gpus": [[205, "num-gpus"]], "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": [[75, "module-ivy.data_classes.container.conversions"], [52, "module-ivy.data_classes.array.conversions"]], "Image": [[83, "module-ivy.data_classes.container.image"], [60, "module-ivy.data_classes.array.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"]], "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"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Transpile any library": [[28, "Transpile-any-library"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Installation": [[4, "Installation"], [12, "Installation"]], "Data Preparation": [[4, "Data-Preparation"], [5, "Data-Preparation"], [12, "Data-Preparation"], [8, "Data-Preparation"]], "Ivy AlexNet inference in Torch": [[4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[4, "TensorFlow-inference"]], "JAX inference": [[4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "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"]], "ODSC Ivy Demo": [[31, "ODSC-Ivy-Demo"]], "Graph Tracer": [[31, "Graph-Tracer"]], "Any function": [[31, "Any-function"], [32, "Any-function"]], "Any library": [[31, "Any-library"], [32, "Any-library"]], "Any model": [[31, "Any-model"], [32, "Any-model"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[43, "Installs-\ud83d\udcbe"], [44, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[43, "Imports-\ud83d\udec3"], [44, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[43, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[43, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[43, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[43, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[43, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[43, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[43, "Set-Backend-Framework"]], "Define Model": [[43, "Define-Model"], [44, "Define-Model"]], "Create Model": [[43, "Create-Model"]], "Create Optimizer": [[43, "Create-Optimizer"]], "Input and Target": [[43, "Input-and-Target"]], "Loss Function": [[43, "Loss-Function"]], "Training Loop": [[43, "Training-Loop"]], "0.0: Unify": [[33, "0.0:-Unify"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "0.1: Compile": [[34, "0.1:-Compile"]], "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."]], "Examples and Demos": [[3, "examples-and-demos"], [20, "examples-and-demos"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "Unify": [[38, "Unify"], [36, "Unify"], [26, "Unify"], [37, "Unify"], [27, "Unify"]], "Compile": [[38, "Compile"], [36, "Compile"], [37, "Compile"]], "Transpile": [[38, "Transpile"], [36, "Transpile"], [26, "Transpile"], [37, "Transpile"], [27, "Transpile"]], "# 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"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "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"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "Trace code": [[24, "Trace-code"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "Unify code": [[23, "Unify-code"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Imports": [[12, "Imports"], [14, "Imports"], [8, "Imports"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Load the image example \ud83d\uddbc\ufe0f": [[12, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [8, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[12, "Visualise-image"], [8, "Visualise-image"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "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"]], "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"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "Guides": [[15, "guides"], [20, "guides"]], "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"]], "Compilation of a Basic Function": [[44, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[44, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[44, "Function-compilation-\ud83d\udee0"]], "Set backend": [[44, "Set-backend"]], "Sample input": [[44, "Sample-input"]], "Define function to compile": [[44, "Define-function-to-compile"]], "Compile the function": [[44, "Compile-the-function"]], "Check results": [[44, "Check-results"], [44, "id1"]], "Compiling simple neural network \ud83e\udde0": [[44, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[44, "Create-model"]], "Define input": [[44, "Define-input"]], "Compile network": [[44, "Compile-network"]], "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"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Learn the basics": [[21, "learn-the-basics"], [20, "learn-the-basics"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Trace": [[26, "Trace"], [27, "Trace"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "How to use decorators": [[27, "How-to-use-decorators"]], "Transpile code": [[25, "Transpile-code"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]]}, "indexentries": {"_arraywithactivations (class in ivy.data_classes.array.activations)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations"]], 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61, 76, 80, 84, 141, 375, 378, 411, 471, 545, 557, 629, 634, 636, 654, 655, 821, 829, 853, 854, 855, 857], "repositori": [2, 4, 8, 12, 815, 819, 820, 821, 823, 824, 827, 835, 844, 862], "cd": [2, 4, 8, 12, 31, 48, 813, 815, 820, 821, 835, 857], "resnet": [3, 6, 13, 20, 31, 864, 865], "imag": [3, 4, 6, 7, 11, 13, 16, 20, 28, 31, 32, 45, 46, 47, 48, 49, 50, 57, 61, 79, 80, 84, 102, 220, 221, 222, 223, 226, 229, 238, 241, 243, 245, 254, 255, 256, 261, 263, 276, 283, 284, 286, 287, 291, 375, 394, 395, 411, 412, 413, 415, 545, 632, 634, 636, 649, 650, 651, 652, 653, 656, 657, 658, 792, 813, 820, 835, 848, 850, 851, 853, 855, 857, 864, 865, 871], "classif": [3, 4, 12, 14, 20, 45, 813, 871], "acceler": [3, 20, 813, 830, 842, 869, 873, 874, 875, 876], "convert": [3, 8, 9, 11, 13, 14, 16, 18, 20, 21, 23, 25, 28, 29, 31, 32, 33, 35, 37, 45, 48, 50, 52, 53, 56, 74, 75, 76, 79, 97, 127, 128, 140, 150, 151, 193, 194, 195, 196, 207, 215, 219, 239, 279, 378, 383, 462, 463, 464, 513, 578, 596, 598, 599, 600, 602, 629, 630, 631, 632, 634, 637, 641, 695, 719, 730, 731, 773, 801, 806, 813, 819, 825, 826, 839, 840, 842, 845, 847, 850, 856, 858, 862, 865, 869, 870, 877], "faster": [3, 4, 9, 11, 13, 14, 20, 31, 32, 48, 50, 57, 62, 80, 85, 376, 449, 637, 687, 815, 818, 827, 858, 873, 876], "infer": [3, 6, 7, 9, 11, 13, 14, 20, 24, 34, 36, 37, 46, 48, 50, 53, 57, 58, 61, 64, 76, 80, 81, 84, 87, 126, 128, 131, 135, 136, 140, 143, 149, 158, 159, 160, 161, 162, 312, 313, 375, 378, 382, 411, 496, 510, 556, 590, 591, 629, 630, 634, 636, 639, 659, 706, 801, 802, 823, 826, 830, 831, 845, 850, 855, 865, 869, 870, 873, 875], "mmpretrain": [3, 20], "segment": [3, 20, 57, 80, 330, 331, 332, 369, 827, 832], "unet": [3, 20], "alexnet": [3, 20], "written": [3, 4, 5, 6, 20, 22, 31, 32, 45, 58, 378, 473, 820, 824, 825, 833, 836, 837, 841, 842, 846, 850, 852, 855, 856, 860, 865, 869, 871, 875, 877, 878], "xgboost": [3, 20], "paddlepaddl": [3, 20, 335, 336, 372, 820], "dinov2": [3, 7, 20], "project": [3, 12, 13, 20, 25, 26, 27, 28, 29, 31, 32, 35, 98, 636, 663, 792, 813, 815, 816, 819, 820, 821, 822, 825, 826, 827, 845, 854, 856, 860, 861, 862, 865, 867, 869, 871, 874, 878, 879], "convnext": [3, 6, 11, 20], "video": [4, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 813, 814, 819, 820, 821, 824, 825, 826, 828, 829, 830, 831, 832, 833, 834, 836, 837, 838, 839, 840, 841, 842, 843, 845, 846, 848, 857, 869], "tutori": [4, 6, 7, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 813, 821, 842, 857], "three": [4, 5, 20, 26, 36, 37, 47, 57, 139, 312, 369, 378, 464, 629, 820, 821, 828, 829, 830, 832, 842, 845, 848, 849, 850, 872, 877], "major": [4, 5, 644, 747, 830, 831, 843, 845, 856, 861, 868, 871], "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, 813, 814, 818, 842, 849, 850, 851, 853, 854, 855, 859, 861, 862, 865, 867, 868, 869, 870, 871, 874, 876, 878], "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, 813, 816, 817, 819, 820, 823, 824, 825, 826, 827, 829, 830, 831, 832, 834, 835, 837, 838, 839, 841, 842, 845, 846, 848, 849, 850, 852, 855, 856, 857, 858, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 872, 875], "sinc": [4, 8, 12, 28, 29, 31, 32, 45, 47, 57, 80, 98, 372, 813, 815, 820, 821, 824, 825, 826, 827, 828, 829, 830, 831, 834, 841, 842, 856, 861, 871, 877], "automat": [4, 8, 9, 12, 29, 31, 32, 37, 819, 820, 821, 823, 826, 827, 829, 830, 836, 838, 841, 845, 848, 849, 851, 854, 855, 857, 858, 862, 871, 874, 878], "sure": [4, 8, 11, 12, 13, 14, 31, 45, 816, 819, 820, 821, 824, 829, 834, 835, 842, 843, 845, 848, 857], "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, 811, 813, 820, 821, 822, 825, 828, 830, 838, 839, 840, 841, 842, 845, 846, 849, 851, 853, 855, 856, 858, 861, 864, 869, 870, 871, 872, 873, 874, 877, 878], "dm": [4, 5, 8, 11, 13, 31, 32, 43, 45], "haiku": [4, 5, 8, 11, 13, 29, 31, 32, 43, 45, 49, 789, 813, 855, 862, 865, 871], "exit": [4, 8, 12, 31, 32, 831], "download": [4, 6, 7, 12, 16, 18, 31, 32, 46, 47, 50, 815, 820, 827, 845, 864, 865], "imagenet": [4, 6, 18, 46, 48, 813], "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, 804, 806, 811, 813, 819, 826, 827, 828, 830, 831, 832, 833, 837, 839, 840, 843, 844, 845, 848, 850, 851, 853, 854, 855, 858, 864, 865, 869, 871, 872, 878], "wget": [4, 6, 8, 12, 45, 46, 49, 820], "raw": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 45, 48, 49, 74, 813, 833, 865, 872], "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, 816, 829, 871, 879], "imagenet_class": [4, 12], "categori": [4, 6, 12, 819, 824, 825, 828, 830, 834, 842, 846, 849], "strip": [4, 12, 24, 34, 861], "readlin": [4, 12, 46], "cat": [4, 7, 12, 46, 843, 848, 850, 855, 864, 865], "jpg": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 47, 48, 813, 865], "filenam": [4, 8, 12, 31, 32, 45, 47, 50, 58, 794, 800, 853], "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, 813, 818, 819, 820, 821, 822, 824, 825, 826, 827, 828, 830, 831, 832, 833, 836, 839, 840, 841, 842, 843, 844, 845, 846, 850, 852, 853, 855, 856, 857, 861, 864, 865, 866, 867, 869, 871, 874, 875, 877], "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, 811, 813, 821, 823, 826, 830, 834, 838, 839, 843, 845, 846, 848, 850, 855, 856, 857, 858, 861, 870, 871, 873, 874, 875, 876], "torchvis": [4, 6, 11, 12, 45, 862], "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, 813, 839, 845, 855, 858, 864, 865, 869, 871, 872, 873], "pil": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 46, 47, 48, 813, 865], "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, 811, 819, 820, 821, 824, 826, 828, 829, 830, 832, 835, 837, 838, 839, 841, 842, 845, 846, 850, 853, 855, 856, 857, 860, 861, 862, 864, 865, 869, 871, 872, 875, 876, 877], "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, 820, 827, 829, 832, 845, 856, 877], "compos": [4, 6, 7, 11, 12, 31, 32, 45, 57, 80, 375, 389, 390, 391, 392, 820, 828, 842, 845, 864, 866, 871, 878], "resiz": [4, 6, 7, 8, 11, 12, 45, 46, 57, 80, 375, 411, 848], "centercrop": [4, 12], "224": [4, 6, 7, 12, 16, 18, 31, 32, 45, 46, 48, 813, 865], "totensor": [4, 6, 7, 11, 12, 45], "485": [4, 12, 45], "456": [4, 12, 45, 845], "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, 813, 853, 865], "ipython": [4, 8, 12, 26, 27, 28, 29, 31, 32, 50], "displai": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 50, 820, 827, 829, 834, 845, 853], "end": [4, 8, 45, 46, 57, 80, 126, 228, 284, 353, 372, 375, 377, 378, 423, 452, 474, 484, 486, 487, 629, 632, 807, 813, 820, 821, 826, 829, 835, 841, 846, 848, 849, 856, 869, 874], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 217, 631, 831], "ivy_model": [4, 5, 8, 12, 48], "ivy_alexnet": 4, "quick": [4, 20, 32, 821, 823, 843, 854], "trace_graph": [4, 5, 8, 12, 24, 25, 26, 27, 31, 32, 34, 35, 36, 37, 38, 39, 48, 794, 813, 850, 855, 863], "moment": [4, 57, 59, 80, 82, 376, 433, 615, 616, 621, 635, 796, 811, 819, 826, 856, 864, 865], "cost": [4, 59, 82, 615, 616, 619, 621, 622, 623, 635, 640, 715, 716, 717, 807, 830, 848, 869], "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, 806, 811, 813, 825, 830, 831, 834, 840, 841, 842, 848, 850, 854, 864, 865, 866], "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, 834, 839, 842, 843], "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, 811, 850, 856, 858, 876], "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, 806, 807, 813, 815, 820, 821, 823, 824, 825, 827, 828, 830, 831, 832, 833, 836, 837, 838, 839, 840, 841, 842, 843, 845, 846, 847, 850, 852, 854, 855, 856, 858, 864, 865, 872], "softmax": [4, 6, 7, 12, 16, 29, 31, 32, 47, 51, 61, 72, 73, 84, 377, 454, 626, 636, 663, 666, 788, 813], "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, 806, 811, 813, 817, 819, 821, 824, 825, 826, 828, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 845, 848, 856, 864, 865, 866, 869], "argsort": [4, 12, 69, 92, 646, 755, 842], "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, 813, 820, 821, 830, 835, 842, 844, 845, 848, 853, 854, 871, 875], "logit": [4, 5, 6, 7, 8, 12, 45, 46, 47, 48, 57, 63, 80, 86, 367, 382, 508, 511, 638, 696, 698, 788, 813, 864], "gather": [4, 12, 45, 57, 58, 80, 81, 330, 331, 332, 369, 553, 555, 634, 878], "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, 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763, 764, 765, 766, 767, 768, 773, 776, 777, 778, 779, 789, 794, 796, 801, 807, 809, 813, 830, 831, 833, 834, 840, 841, 842, 843, 846, 850, 855, 865], "eagertensor": [16, 22, 43, 801, 843], "deepmind": [17, 862], "perceiverio": [17, 862], "backbon": [17, 45, 813, 850, 853], "TO": [17, 19, 30], "replac": [17, 19, 30, 46, 56, 57, 58, 64, 66, 74, 79, 80, 81, 87, 89, 132, 274, 310, 313, 367, 369, 378, 489, 492, 496, 576, 577, 581, 629, 632, 634, 639, 643, 699, 738, 776, 821, 827, 828, 830, 831, 839, 842, 845, 852, 855, 856, 861, 865, 878], "efficientnet": 18, "eff_encod": [18, 813], "efficientnet_v2": [18, 813], "efficientnetv2b0": [18, 813], "storag": [18, 45, 46, 853, 861], "googleapi": [18, 45, 46], "efficientnetv2": 18, "b0_notop": 18, "h5": [18, 74], "24274472": 18, "0u": 18, "torch_eff_encod": [18, 813], "modes_to_trac": 18, "1280": [18, 545, 634, 813], "welcom": [20, 46, 813, 814, 820, 821, 822, 844], "varieti": [20, 824, 829, 830, 831, 845, 847, 867, 869, 873, 874, 877, 878], "organ": [20, 825, 828, 838, 842, 844, 846, 858, 861], "main": [20, 32, 53, 57, 62, 80, 85, 132, 145, 146, 147, 313, 328, 329, 369, 376, 378, 427, 473, 629, 637, 670, 671, 691, 813, 816, 819, 820, 821, 822, 824, 827, 828, 835, 839, 841, 869, 871, 872, 877], "exactli": [20, 24, 34, 43, 44, 48, 290, 632, 819, 828, 829, 830, 831, 832, 834, 845, 848, 860, 862], "rush": [20, 862], "jump": [20, 843], "straight": [20, 813, 829, 842, 845, 852], "quickstart": [20, 813], "introduct": [20, 22, 29, 31, 32, 871], "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, 811, 817, 819, 820, 821, 824, 825, 827, 829, 830, 832, 833, 835, 837, 841, 842, 845, 846, 848, 850, 852, 853, 862, 864, 877], "showcas": [20, 813], "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, 828, 873], "world": [20, 28, 821, 873], "beginn": [20, 814, 871], "got": [20, 43, 834], "cover": [20, 31, 57, 80, 375, 412, 413, 414, 819, 824, 825, 827, 830, 832, 833, 838, 839, 845, 848, 849], "familiar": [20, 21, 22, 819, 820], "concept": [20, 21, 22], "turn": [20, 21, 24, 34, 61, 84, 97, 98, 399, 400, 401, 636, 659, 792, 820, 827, 828, 831, 832, 842, 845, 862], "unus": [20, 21, 24, 832, 841], "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, 813, 819, 820, 821, 822, 824, 827, 830, 836, 838, 841, 842, 845, 846, 848, 850, 851, 855, 856, 864, 865, 866, 869, 871, 876, 877, 878], "lazi": [20, 21, 24, 27, 34, 37, 38, 49], "decor": [20, 21, 26, 28, 29, 37, 49, 539, 634, 776, 778, 784, 817, 824, 825, 828, 830, 831, 835, 838, 841, 842, 843, 848], "kornia": [20, 21, 28, 31, 32, 45, 49, 813, 865], "roundup": 22, "indep": [22, 31], "proof": [22, 31], "delv": [22, 32, 813], "theori": [22, 815, 827], "esenti": [22, 31], "abstract": [22, 31, 32, 791, 796, 813, 828, 830, 841, 842, 845, 848, 854, 860, 869, 871, 873, 874, 878], "quirk": [22, 31], "perk": [22, 31, 813, 825, 828], "under": [22, 31, 32, 57, 377, 456, 457, 806, 813, 819, 820, 823, 824, 831, 832, 833, 836, 842, 843, 845, 848, 849, 850, 853, 855, 856, 864, 865, 871, 874, 878], "hood": [22, 31, 32, 813, 823, 831, 832, 836, 842, 845, 848, 849, 850, 853, 855, 864, 865, 878], "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, 806, 807, 826, 827, 829, 830, 831, 834, 842, 850, 853], "simplest": [22, 820, 832, 845, 848], "interact": [22, 31, 46, 49, 819, 870, 871, 876], "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, 819, 820, 821, 824, 827, 829, 831, 835, 838, 839, 845, 849, 850, 854, 858], "likewis": [22, 27, 31, 38, 813, 821, 828, 830, 833, 837, 838, 842, 848, 853, 864, 865, 877], "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, 825, 828, 832, 834, 837, 838, 839, 841, 842, 846, 847, 850, 852, 858], "alia": [22, 31, 335, 336, 372, 627, 819, 842, 863, 866], "lastli": [22, 31, 825], "subclass": [22, 31, 32, 839, 842, 848, 865], "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, 807, 825, 828, 853, 854, 858, 864, 865, 866], "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, 820, 824, 827, 828, 835, 838, 841, 854, 856], "fashion": [22, 778, 845, 865], "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, 817, 835, 843, 845], "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, 828, 834, 841, 842, 848, 850, 867, 869, 871, 872, 873, 875, 877], "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, 806, 807, 814, 820, 823, 826, 827, 828, 832, 838, 840, 849, 850, 851, 853, 856, 858, 859, 861, 862, 865, 867, 871, 875, 876, 878], "fundament": [22, 31, 829, 842, 848, 850, 860, 871], "signatur": [22, 31, 378, 387, 484, 522, 830, 831, 832, 833, 837, 841, 845, 846, 848, 861, 868, 877], "matmul": [22, 31, 32, 48, 62, 85, 376, 446, 614, 634, 637, 687, 826, 845, 846, 850], "to_n": [22, 31, 32, 43, 52, 75, 850], "jaxlib": [22, 28, 46, 801, 820, 825, 830, 831, 837, 846, 850, 852], "xla_extens": [22, 28, 801, 825, 830, 831, 837, 846, 850, 852], "arrayimpl": [22, 28, 801], "disabl": [22, 31, 57, 80, 378, 492, 794, 811, 827], "array_mod": [22, 31, 578, 602, 634, 847], "set_array_mod": [22, 31, 602, 634, 847], "ultim": [22, 31, 864], "sigmoid": [22, 31, 32, 43, 51, 57, 73, 80, 301, 367, 382, 508, 626, 788, 850, 853, 854], "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, 813, 826, 828, 831, 832, 850, 852, 864], "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, 825, 828, 832, 836, 845], "exp": [22, 31, 32, 56, 57, 79, 80, 116, 118, 245, 265, 278, 301, 367, 375, 377, 403, 408, 457, 626, 632, 637, 685, 840, 842], "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, 807, 819, 820, 821, 824, 825, 828, 830, 832, 834, 841, 842, 843, 845, 848, 850, 853, 854, 855, 856, 861, 862, 865, 871, 877, 878], "congratul": [22, 28], "independ": [22, 32, 57, 66, 80, 89, 223, 240, 273, 283, 381, 382, 506, 508, 632, 637, 643, 668, 686, 738, 813, 824, 830, 832, 839, 850, 855, 865, 869], "div": [23, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 866], "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, 819, 821, 823, 828, 834, 842, 843, 845, 852, 853, 854, 866, 867], "with_numpi": 23, "reproduc": [23, 48, 61, 84, 636, 659, 776, 777, 778, 779, 784, 817, 824, 835], "x_": [23, 33, 98, 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854, 855], "execute_with_gradi": [31, 32, 43, 47, 635, 813, 853, 854, 855, 856], "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, 813, 819, 838, 839, 840, 843, 848, 850, 853], "2d": [31, 32, 47, 57, 80, 97, 313, 369, 375, 376, 378, 387, 390, 391, 399, 400, 442, 449, 463, 473, 522, 792, 811, 813, 842, 848], "5f": [31, 32, 813], "nonetheless": [31, 32], "extract": [31, 32, 39, 46, 57, 80, 98, 378, 467, 493, 842, 844, 846, 867, 871, 872, 877], "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, 842, 855], "said": [31, 32, 778, 846, 862, 864], "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, 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632, 634, 635, 636, 637, 640, 641, 644, 645, 646, 647, 648, 650, 651, 652, 653, 659, 660, 661, 663, 666, 667, 668, 669, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 687, 691, 693, 694, 696, 697, 698, 699, 702, 703, 704, 706, 707, 708, 709, 710, 711, 713, 714, 715, 716, 731, 738, 739, 740, 741, 743, 744, 745, 746, 748, 749, 750, 751, 752, 753, 755, 757, 758, 760, 761, 762, 763, 764, 765, 766, 767, 768, 771, 776, 777, 792, 794, 795, 801, 813, 821, 825, 828, 830, 831, 832, 838, 839, 841, 845, 850, 857, 864, 865], "x0": [31, 32, 50, 81, 537, 634, 832], "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, 833, 861], "fname": [31, 32, 48, 50, 794, 853], "anticip": [31, 32], "addition": [31, 32, 828, 841, 842, 877], "normalize_native_comp": [31, 32], "return_backend_compiled_fn": 31, "immedi": [31, 32, 811, 819, 820], "built": [31, 32, 37, 45, 47, 50, 126, 629, 792, 793, 794, 813, 820, 821, 827, 828, 845, 851, 857, 864, 870, 871, 875], "eager_graph": [31, 32, 813, 864, 865], "lazy_graph": [31, 32, 813, 864, 865], "thought": [31, 32, 820, 821, 837, 861, 869], "matter": [31, 32, 37, 832, 860], "haven": [31, 32, 37, 857, 871], "jax_out": [31, 32], "ideal": [31, 32, 829, 830, 842, 848, 853], "worth": [31, 32], "differenti": [31, 32, 295, 365, 366, 367, 374, 871], "chosen": [31, 32, 50, 100, 126, 228, 629, 632, 644, 748, 819, 829, 842], "plai": [31, 32, 377, 456, 813, 816, 820, 822, 825, 831, 835, 842, 845, 855, 871, 874], "role": [31, 32, 813, 816, 821, 822, 831, 842, 851, 872, 874, 878], "dl": [31, 32], "effortlessli": [31, 32], "previous": [31, 32, 603, 634, 801, 819, 820, 826, 838, 840, 845, 850], "default_devic": [31, 32, 206, 209, 210, 211, 217, 218, 631, 831, 834, 835], "as_n": [31, 32, 54, 55, 74, 77, 78, 158, 159, 160, 161, 162, 163, 169, 196, 197, 630, 631, 830], "certainli": [31, 32, 813, 861, 877], "upon": [31, 32, 49, 811, 821, 822, 832, 841, 845, 848, 856, 870, 871], "unnecessari": [31, 32, 842], "extend": [31, 32, 57, 80, 378, 387, 484, 525, 826, 827, 830, 833, 834, 837, 842, 846, 856, 868, 871, 877], "infrastructur": [31, 32, 867, 873, 874], "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, 813, 821, 825, 829, 830, 831, 832, 838, 841, 845, 865], "coco": 31, "seamlessli": [32, 845], "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, 819, 821, 824, 825, 828, 829, 830, 831, 832, 833, 834, 837, 838, 839, 841, 842, 843, 845, 846, 848, 850, 852, 854, 856, 860, 868, 871, 877], "wide": [32, 813, 821, 845, 869, 871], "plenti": 32, "resourc": [32, 814, 819, 820, 829], "visit": [32, 819, 820, 821, 829], "page": [32, 813, 819, 820, 821, 827, 829, 835, 851, 852, 855, 857, 866, 879], "newli": [33, 34, 46, 48, 54, 77, 152, 539, 630, 634, 821, 829, 841, 845], "randon": [33, 34, 36, 37, 38], "mean_": 33, "std_": 33, "detect": [33, 37, 56, 74, 79, 255, 632, 641, 718, 729, 819, 820, 826, 828, 829, 836, 845, 853, 854], "inspect": [33, 37, 535, 634], "__": [33, 34, 35, 36, 37, 38, 74, 832, 853], "script": [34, 813, 820, 821, 824, 829, 832, 850, 856, 871], "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, 825, 828, 840, 842, 854, 855, 856, 871], "un": [34, 170, 630, 830, 850], "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, 807, 833, 842, 850, 855, 871, 872], "maxim": [34, 838, 841, 850, 868, 869, 873, 874, 875], "conclud": [35, 846], "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, 820, 829, 834, 835, 839, 840, 843, 845, 869, 871, 874], "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, 820, 825, 826, 829, 830, 831, 834, 838, 842, 850, 871], "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, 443, 446, 451, 452, 453, 455, 456, 457, 458, 462, 463, 464, 465, 468, 469, 470, 471, 474, 475, 476, 478, 479, 480, 481, 483, 484, 490, 492, 493, 494, 495, 496, 499, 500, 505, 507, 509, 510, 512, 513, 515, 522, 523, 524, 525, 527, 529, 532, 534, 537, 538, 540, 541, 544, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 569, 576, 577, 591, 592, 593, 595, 599, 600, 613, 615, 616, 617, 619, 621, 622, 623, 624, 626, 629, 630, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 655, 657, 658, 659, 660, 662, 666, 667, 668, 669, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 696, 697, 698, 699, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 721, 724, 725, 726, 727, 729, 730, 735, 736, 737, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 776, 806, 807, 811, 813, 814, 817, 821, 823, 824, 826, 828, 829, 832, 835, 838, 840, 843, 849, 850, 851, 853, 854, 855, 859, 862, 864, 867], "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, 806, 811, 819, 820, 821, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 841, 842, 843, 845, 846, 848, 850, 855, 856, 864, 865, 866, 871, 877], "prioriti": [37, 74, 801, 816, 819, 821, 822, 831, 841], "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, 828, 830, 831, 833, 838, 842, 845, 847, 848, 860], "think": [37, 819, 821, 829, 832, 848, 872], "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, 813, 824, 828, 838, 842, 843, 844, 848, 856, 860, 874], "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, 806, 824, 825, 828, 829, 830, 832, 836, 837, 838, 840, 845, 848, 872], "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, 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"args_to_ivi": 52, "include_deriv": [52, 75, 641, 719, 730, 773], "nest": [52, 74, 75, 103, 106, 243, 567, 597, 614, 617, 632, 634, 635, 640, 715, 716, 718, 719, 720, 721, 722, 723, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 796, 825, 827, 828, 838, 840, 846, 853, 854, 856, 858, 871], "unchang": [52, 56, 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, 830, 831, 852, 854], "word": [52, 126, 378, 477, 629, 643, 741, 789, 792, 828, 841, 842, 858], "args_to_n": [52, 841], "cont_inplac": 52, "decid": [52, 74, 641, 729, 730, 813, 819, 820, 830, 848], "args_to_new_backend": 52, "shallow": [52, 641, 725, 726, 730, 735, 736], "nativevari": 52, "mutabl": [52, 641, 719, 725, 726, 730, 735, 736, 826], "to_ivi": [52, 75, 641, 731, 841], "leaf": [52, 74, 81, 93, 103, 548, 641, 728, 729, 731, 758, 828, 838, 853], "travers": [52, 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[[224, "angle"]], "num_gpus": [[205, "num-gpus"]], "acosh": [[222, "acosh"]], "print_all_ivy_arrays_on_dev": [[208, "print-all-ivy-arrays-on-dev"]], "handle_soft_device_variable": [[203, "handle-soft-device-variable"]], "tpu_is_available": [[216, "tpu-is-available"]], "split_factor": [[212, "split-factor"]], "unset_default_device": [[217, "unset-default-device"]], "acos": [[221, "acos"]], "abs": [[220, "abs"]], "function_unsupported_devices": [[200, "function-unsupported-devices"]], "unset_soft_device_mode": [[218, "unset-soft-device-mode"]], "add": [[223, "add"]], "atan2": [[228, "atan2"]], "function_supported_devices": [[199, "function-supported-devices"]], "dev_util": [[198, "dev-util"]], "type_promote_arrays": [[186, "type-promote-arrays"]], "unset_default_uint_dtype": [[191, "unset-default-uint-dtype"]], "dev": [[197, "dev"]], "gpu_is_available": [[202, "gpu-is-available"]], "asin": [[225, "asin"]], "as_native_dev": [[194, "as-native-dev"]], "to_device": [[214, "to-device"]], "used_mem_on_dev": [[219, "used-mem-on-dev"]], "clear_cached_mem_on_dev": [[195, "clear-cached-mem-on-dev"]], "total_mem_on_dev": [[215, "total-mem-on-dev"]], "num_cpu_cores": [[204, "num-cpu-cores"]], "atanh": [[229, "atanh"]], "split_func_call": [[213, "split-func-call"]], "unset_default_complex_dtype": [[187, "unset-default-complex-dtype"]], "set_default_device": [[209, "set-default-device"]], "atan": [[227, "atan"]], "set_default_uint_dtype": [[185, "set-default-uint-dtype"]], "unset_default_float_dtype": [[189, "unset-default-float-dtype"]], "set_soft_device_mode": [[210, "set-soft-device-mode"]], "set_split_factor": [[211, "set-split-factor"]], "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"]], "Conversions": [[75, "module-ivy.data_classes.container.conversions"], [52, "module-ivy.data_classes.array.conversions"]], "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"]], "Image": [[60, "module-ivy.data_classes.array.image"], [83, "module-ivy.data_classes.container.image"]], "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"]], "Resnet 18": [[50, "Resnet-18"]], "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"]], "Guides": [[15, "guides"], [20, "guides"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Unify": [[36, "Unify"], [37, "Unify"], [38, "Unify"], [26, "Unify"], [27, "Unify"]], "Compile": [[36, "Compile"], [37, "Compile"], [38, "Compile"]], "Transpile": [[36, "Transpile"], [37, "Transpile"], [38, "Transpile"], [26, "Transpile"], [27, "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"]], "How To Convert Models from PyTorch to PaddlePaddle": [[7, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[7, "About-the-Model"]], "Transpiling the Model": [[7, "Transpiling-the-Model"]], "Comparing the results": [[7, "Comparing-the-results"], [6, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[7, "Fine-tuning-the-transpiled-model"], [6, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[7, "Conclusion"], [6, "Conclusion"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[8, "Imports"], [14, "Imports"], [12, "Imports"]], "Data Preparation": [[8, "Data-Preparation"], [4, "Data-Preparation"], [5, "Data-Preparation"], [12, "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"]], "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:"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "1.3: Dynamic vs Static": [[39, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[39, "Dynamic"]], "Static": [[39, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[39, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "Trace code": [[24, "Trace-code"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "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"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Installation": [[4, "Installation"], [12, "Installation"]], "Ivy AlexNet inference in Torch": [[4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[4, "TensorFlow-inference"]], "JAX inference": [[4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "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"]], "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"]], "# 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"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Trace": [[26, "Trace"], [27, "Trace"]], "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"]], "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"]], "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"]], "Transpile any library": [[28, "Transpile-any-library"]], "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"]], "Unify code": [[23, "Unify-code"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "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"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "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"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "How to use decorators": [[27, "How-to-use-decorators"]], "0.1: Compile": [[34, "0.1:-Compile"]], "Transpile code": [[25, "Transpile-code"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, 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