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

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f82f01112d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f451b4c12d0>) – 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.

    • @@ -1476,7 +1476,7 @@

    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 0x7f82f01112d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f451b4c12d0>) – 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.

    • @@ -1553,7 +1553,7 @@

    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 0x7f82f01112d0>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f451b4c12d0>) – 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/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html index ff3d32b6..5e5c6485 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html @@ -1425,7 +1425,7 @@

    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 0x7f82f01112d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f451b4c12d0>) – 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/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html index 2f64c34a..a203a48f 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html @@ -1425,7 +1425,7 @@

    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 0x7f82f01112d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f451b4c12d0>) – 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/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html index 24a782fe..0a408464 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html @@ -1422,7 +1422,7 @@

    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 0x7f82f01112d0>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f451b4c12d0>) – 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/docs/helpers/ivy_tests.test_ivy.helpers.globals.html b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html index 0eea9960..22efdc13 100644 --- a/docs/helpers/ivy_tests.test_ivy.helpers.globals.html +++ b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html @@ -1411,7 +1411,7 @@

    Should not be used inside any of the test functions.

    -ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f82e3efdf80>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f450f299fa0>#
    diff --git a/docs/stateful/ivy.stateful.layers.html b/docs/stateful/ivy.stateful.layers.html index 126f8592..7ac719f0 100644 --- a/docs/stateful/ivy.stateful.layers.html +++ b/docs/stateful/ivy.stateful.layers.html @@ -1538,8 +1538,8 @@

  • 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 0x7f82efd41f60>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f82efd41f00>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f451b0e37c0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f451b0e3760>) – 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)

    • @@ -1576,8 +1576,8 @@

  • 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 0x7f82efd41ea0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f82efd41e40>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f451b0e3700>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f451b0e36a0>) – 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”.

    • @@ -1615,8 +1615,8 @@

  • 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 0x7f82efd41de0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f82efd41d80>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f451b0e3640>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f451b0e35e0>) – 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)

    • @@ -1653,8 +1653,8 @@

  • 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 0x7f82efd41d20>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f82efd41cc0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f451b0e3580>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f451b0e3520>) – 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”.

    • @@ -1692,8 +1692,8 @@

  • 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 0x7f82efd41ba0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f82efd41b40>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f451b0e3400>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f451b0e33a0>) – 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)

    • @@ -1730,8 +1730,8 @@

  • 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 0x7f82efd41ae0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f82efd41a80>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f451b0e3340>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f451b0e32e0>) – 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”.

    • @@ -1794,8 +1794,8 @@

  • 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 0x7f82efd41c60>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f82efd41c00>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f451b0e34c0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f451b0e3460>) – 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)

    • @@ -1951,7 +1951,7 @@

    • 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 0x7f82efd41a20>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f451b0e3280>) – 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. @@ -2010,8 +2010,8 @@

      • 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 0x7f82efd42020>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f82efd41fc0>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f451b0e3880>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f451b0e3820>) – 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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414, 416, 546, 633, 635, 637, 650, 651, 652, 653, 654, 657, 658, 659, 793, 814, 821, 836, 849, 851, 852, 854, 856, 858, 865, 866, 872], "classif": [3, 4, 12, 15, 21, 46, 872], "acceler": [3, 21, 831, 843, 870, 874, 875, 876, 877], "convert": [3, 8, 9, 11, 14, 15, 17, 19, 21, 22, 24, 26, 29, 30, 32, 33, 34, 36, 38, 46, 49, 51, 53, 54, 57, 75, 76, 77, 80, 98, 128, 129, 141, 151, 152, 194, 195, 196, 197, 208, 216, 220, 240, 280, 379, 384, 463, 464, 465, 514, 579, 597, 599, 600, 601, 603, 630, 631, 632, 633, 635, 638, 642, 696, 720, 731, 732, 774, 802, 807, 820, 826, 827, 840, 841, 843, 846, 848, 851, 857, 859, 863, 866, 870, 871, 878], "faster": [3, 4, 9, 11, 14, 15, 21, 32, 33, 49, 51, 58, 63, 81, 86, 377, 450, 638, 688, 816, 819, 828, 859, 874, 877], "infer": [3, 6, 7, 9, 11, 13, 14, 15, 21, 25, 35, 37, 38, 47, 49, 51, 54, 58, 59, 62, 65, 77, 81, 82, 85, 88, 127, 129, 132, 136, 137, 141, 144, 150, 159, 160, 161, 162, 163, 313, 314, 376, 379, 383, 412, 497, 511, 557, 591, 592, 630, 631, 635, 637, 640, 660, 707, 802, 803, 824, 827, 831, 832, 846, 851, 856, 866, 870, 871, 874, 876], "mmpretrain": [3, 21], "segment": [3, 21, 58, 81, 331, 332, 333, 370, 828, 833], "unet": [3, 21], "alexnet": [3, 21], "written": [3, 4, 5, 6, 13, 21, 23, 32, 33, 46, 59, 379, 474, 821, 825, 826, 834, 837, 838, 842, 843, 847, 851, 853, 856, 857, 861, 866, 870, 872, 876, 878, 879], "xgboost": [3, 21], "paddlepaddl": [3, 21, 336, 337, 373, 821], "dinov2": [3, 7, 21], "project": [3, 12, 14, 21, 26, 27, 28, 29, 30, 32, 33, 36, 99, 637, 664, 793, 814, 816, 817, 820, 821, 822, 823, 826, 827, 828, 846, 855, 857, 861, 862, 863, 866, 868, 870, 872, 875, 879, 880], "convnext": [3, 6, 11, 13, 21], "finetun": [3, 21, 46], "video": [4, 8, 11, 12, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 815, 820, 821, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 835, 837, 838, 839, 840, 841, 842, 843, 844, 846, 847, 849, 858, 870], "tutori": [4, 6, 7, 8, 11, 12, 13, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 822, 843, 858], "three": [4, 5, 21, 27, 37, 38, 48, 58, 140, 313, 370, 379, 465, 630, 821, 822, 829, 830, 831, 833, 843, 846, 849, 850, 851, 873, 878], "major": [4, 5, 645, 748, 831, 832, 844, 846, 857, 862, 869, 872], "ml": [4, 5, 6, 13, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 48, 51, 815, 819, 843, 850, 851, 852, 854, 855, 856, 860, 862, 863, 866, 868, 869, 870, 871, 872, 875, 877, 879], "framework": [4, 5, 7, 9, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 39, 46, 48, 50, 53, 59, 171, 193, 203, 206, 217, 544, 560, 564, 596, 599, 631, 632, 635, 642, 721, 772, 774, 778, 785, 790, 797, 802, 803, 817, 818, 820, 821, 824, 825, 826, 827, 828, 830, 831, 832, 833, 835, 836, 838, 839, 840, 842, 843, 846, 847, 849, 850, 851, 853, 856, 857, 858, 859, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 873, 876], "sinc": [4, 8, 12, 13, 29, 30, 32, 33, 46, 48, 58, 81, 99, 373, 816, 821, 822, 825, 826, 827, 828, 829, 830, 831, 832, 835, 842, 843, 857, 862, 872, 878], "automat": [4, 8, 9, 12, 13, 30, 32, 33, 38, 820, 821, 822, 824, 827, 828, 830, 831, 837, 839, 842, 846, 849, 850, 852, 855, 856, 858, 859, 863, 872, 875, 879], "sure": [4, 8, 11, 12, 13, 14, 15, 32, 46, 817, 820, 821, 822, 825, 830, 835, 836, 843, 844, 846, 849, 858], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 15, 27, 28, 30, 47, 58, 63, 75, 86, 104, 376, 378, 399, 457, 581, 635, 638, 681, 795, 812, 814, 821, 822, 823, 826, 829, 831, 839, 840, 841, 842, 843, 846, 847, 850, 852, 854, 856, 857, 859, 862, 865, 870, 871, 872, 873, 874, 875, 878, 879], "dm": [4, 5, 8, 11, 14, 32, 33, 44, 46], "haiku": [4, 5, 8, 11, 14, 30, 32, 33, 44, 46, 50, 790, 814, 856, 863, 866, 872], "exit": [4, 8, 12, 13, 32, 33, 832], "download": [4, 6, 7, 12, 13, 17, 19, 32, 33, 47, 48, 51, 816, 821, 828, 846, 865, 866], "imagenet": [4, 6, 13, 19, 47, 49, 814], "class": [4, 6, 7, 8, 12, 13, 15, 17, 19, 23, 32, 33, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 135, 144, 150, 166, 169, 182, 184, 185, 244, 281, 339, 361, 373, 387, 388, 396, 397, 430, 529, 530, 537, 546, 550, 563, 573, 596, 630, 631, 632, 633, 635, 637, 638, 639, 642, 643, 658, 663, 667, 673, 683, 687, 688, 690, 697, 713, 720, 731, 738, 753, 760, 764, 765, 774, 775, 782, 783, 784, 785, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 801, 802, 805, 807, 812, 814, 820, 827, 828, 829, 831, 832, 833, 834, 838, 840, 841, 844, 845, 846, 849, 851, 852, 854, 855, 856, 859, 865, 866, 870, 872, 873, 879], "wget": [4, 6, 8, 12, 46, 47, 50, 821], "raw": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 46, 49, 50, 75, 814, 834, 866, 873], "githubusercont": [4, 6, 8, 12, 46, 50], "hub": [4, 6, 8, 12, 46, 49, 51], "master": [4, 8, 12, 24, 25, 26, 34, 35, 36, 37, 38, 39, 46, 48, 49, 50, 817, 830, 872, 880], "imagenet_class": [4, 12], "categori": [4, 6, 12, 820, 825, 826, 829, 831, 835, 843, 847, 850], "strip": [4, 12, 25, 35, 862], "readlin": [4, 12, 47], "cat": [4, 7, 12, 47, 844, 849, 851, 856, 865, 866], "jpg": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 48, 49, 814, 866], "filenam": [4, 8, 12, 13, 32, 33, 46, 48, 51, 59, 795, 801, 854], "import": [4, 6, 7, 9, 10, 11, 13, 14, 17, 19, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 47, 49, 50, 51, 58, 69, 73, 77, 81, 96, 195, 196, 200, 212, 308, 388, 523, 558, 574, 632, 635, 641, 646, 717, 718, 753, 785, 802, 803, 814, 819, 820, 821, 822, 823, 825, 826, 827, 828, 829, 831, 832, 833, 834, 837, 840, 841, 842, 843, 844, 845, 846, 847, 851, 853, 854, 856, 857, 858, 862, 865, 866, 867, 868, 870, 872, 875, 876, 878], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 14, 47, 48, 51, 54, 58, 67, 75, 77, 81, 90, 103, 106, 107, 108, 127, 128, 129, 131, 132, 133, 136, 137, 138, 139, 141, 142, 143, 144, 146, 147, 148, 149, 150, 194, 195, 196, 197, 198, 199, 200, 201, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 218, 220, 313, 314, 329, 330, 370, 383, 473, 509, 510, 512, 513, 537, 551, 552, 630, 635, 644, 739, 740, 741, 742, 772, 774, 775, 790, 792, 793, 794, 795, 796, 797, 798, 799, 812, 822, 824, 827, 831, 835, 839, 840, 844, 846, 847, 849, 851, 856, 857, 858, 859, 862, 871, 872, 874, 875, 876, 877], "torchvis": [4, 6, 11, 12, 13, 46, 863], "transform": [4, 5, 6, 7, 11, 12, 13, 14, 29, 32, 33, 46, 47, 49, 58, 62, 81, 85, 376, 377, 398, 399, 404, 405, 408, 409, 410, 420, 421, 424, 441, 637, 661, 777, 780, 793, 814, 840, 846, 856, 859, 865, 866, 870, 872, 873, 874], "pil": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 47, 48, 49, 814, 866], "time": [4, 5, 6, 7, 9, 10, 11, 13, 14, 30, 32, 33, 38, 46, 48, 49, 50, 58, 60, 63, 69, 81, 83, 92, 98, 99, 135, 342, 373, 376, 377, 379, 388, 405, 410, 422, 424, 445, 452, 485, 491, 523, 617, 622, 630, 636, 637, 638, 640, 641, 645, 646, 660, 663, 678, 713, 716, 717, 718, 745, 746, 750, 751, 793, 794, 795, 812, 820, 821, 822, 825, 827, 829, 830, 831, 833, 836, 838, 839, 840, 842, 843, 846, 847, 851, 854, 856, 857, 858, 861, 862, 863, 865, 866, 870, 872, 873, 876, 877, 878], "filterwarn": [4, 5, 13], "ignor": [4, 5, 13, 45, 53, 54, 58, 75, 81, 140, 376, 377, 379, 388, 400, 401, 402, 431, 439, 447, 487, 488, 492, 531, 630, 637, 642, 664, 730, 731, 797, 821, 828, 830, 833, 846, 857, 878], "compos": [4, 6, 7, 11, 12, 13, 32, 33, 46, 58, 81, 376, 390, 391, 392, 393, 821, 829, 843, 846, 865, 867, 872, 879], "resiz": [4, 6, 7, 8, 11, 12, 13, 46, 47, 58, 81, 376, 412, 849], "centercrop": [4, 12, 13], "224": [4, 6, 7, 12, 13, 17, 19, 32, 33, 46, 47, 49, 814, 866], "totensor": [4, 6, 7, 11, 12, 13, 46], "485": [4, 12, 13, 46], "456": [4, 12, 13, 46, 846], "406": [4, 12, 13, 46, 58, 81, 398, 541, 635], "229": [4, 12, 13, 46, 280, 633], "225": [4, 12, 13, 46, 48, 235, 633], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 29, 32, 33, 46, 47, 48, 50, 814, 854, 866], "ipython": [4, 8, 12, 27, 28, 29, 30, 32, 33, 51], "displai": [4, 8, 12, 13, 29, 32, 33, 46, 47, 48, 50, 51, 821, 828, 830, 835, 846, 854], "end": [4, 8, 13, 46, 47, 58, 81, 127, 229, 285, 354, 373, 376, 378, 379, 424, 453, 475, 485, 487, 488, 630, 633, 808, 821, 822, 827, 830, 836, 842, 847, 849, 850, 857, 870, 875], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 14, 218, 632, 832], "ivy_model": [4, 5, 8, 12, 49], "ivy_alexnet": 4, "quick": [4, 21, 33, 822, 824, 844, 855], "trace_graph": [4, 5, 8, 12, 25, 26, 27, 28, 32, 33, 35, 36, 37, 38, 39, 40, 49, 795, 814, 851, 856, 864], "moment": [4, 58, 60, 81, 83, 377, 434, 616, 617, 622, 636, 797, 812, 820, 827, 857, 865, 866], "cost": [4, 60, 83, 616, 617, 620, 622, 623, 624, 636, 641, 716, 717, 718, 808, 831, 849, 870], "arg": [4, 6, 8, 9, 10, 11, 12, 13, 17, 19, 27, 28, 30, 32, 33, 37, 38, 39, 50, 53, 75, 97, 107, 123, 204, 214, 602, 629, 630, 632, 635, 772, 774, 789, 790, 793, 794, 795, 799, 802, 807, 812, 814, 826, 831, 832, 835, 841, 842, 843, 849, 851, 855, 865, 866, 867], "asarrai": [4, 5, 8, 11, 12, 47, 54, 58, 59, 70, 77, 81, 82, 93, 128, 386, 515, 516, 546, 557, 561, 562, 592, 593, 594, 630, 635, 637, 646, 647, 651, 751, 755, 835, 840, 843, 844], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 23, 32, 47, 48, 51, 54, 58, 67, 77, 81, 90, 138, 139, 142, 194, 195, 196, 212, 383, 509, 510, 512, 513, 630, 632, 638, 644, 689, 739, 740, 741, 742, 792, 793, 794, 795, 796, 797, 798, 812, 851, 857, 859, 877], "output": [4, 5, 7, 8, 9, 10, 12, 13, 23, 29, 30, 32, 33, 45, 46, 47, 49, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 149, 150, 153, 155, 180, 214, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 318, 319, 323, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 365, 366, 367, 368, 370, 373, 375, 376, 377, 378, 379, 382, 383, 384, 386, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 408, 409, 410, 412, 413, 414, 415, 418, 420, 421, 422, 424, 425, 427, 428, 429, 431, 433, 436, 437, 439, 442, 443, 444, 445, 447, 448, 451, 453, 454, 455, 456, 457, 458, 459, 460, 461, 468, 469, 470, 473, 475, 476, 477, 478, 479, 482, 483, 484, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 497, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 516, 521, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 540, 541, 542, 546, 547, 548, 550, 554, 563, 570, 577, 578, 579, 603, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 630, 631, 632, 633, 635, 636, 637, 638, 639, 640, 642, 643, 644, 645, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 667, 668, 669, 670, 671, 672, 674, 675, 676, 677, 678, 679, 681, 682, 683, 684, 685, 686, 687, 689, 690, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 710, 711, 712, 713, 715, 732, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 772, 777, 792, 793, 807, 808, 814, 816, 821, 822, 824, 825, 826, 828, 829, 831, 832, 833, 834, 837, 838, 839, 840, 841, 842, 843, 844, 846, 847, 848, 851, 853, 855, 856, 857, 859, 865, 866, 873], 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44, 802, 844], "deepmind": [18, 863], "perceiverio": [18, 863], "backbon": [18, 46, 814, 851, 854], "TO": [18, 20, 31], "efficientnet": 19, "eff_encod": [19, 814], "efficientnet_v2": [19, 814], "efficientnetv2b0": [19, 814], "storag": [19, 46, 47, 854, 862], "googleapi": [19, 46, 47], "efficientnetv2": 19, "b0_notop": 19, "h5": [19, 75], "24274472": 19, "0u": 19, "torch_eff_encod": [19, 814], "modes_to_trac": 19, "1280": [19, 546, 635, 814], "welcom": [21, 47, 814, 815, 821, 822, 823, 845], "varieti": [21, 825, 830, 831, 832, 846, 848, 868, 870, 874, 875, 878, 879], "organ": [21, 826, 829, 839, 843, 845, 847, 859, 862], "main": [21, 33, 54, 58, 63, 81, 86, 133, 146, 147, 148, 314, 329, 330, 370, 377, 379, 428, 474, 630, 638, 671, 672, 692, 814, 817, 820, 821, 822, 823, 825, 828, 829, 836, 840, 842, 870, 872, 873, 878], "exactli": [21, 25, 35, 44, 45, 49, 291, 633, 820, 829, 830, 831, 832, 833, 835, 846, 849, 861, 863], "rush": [21, 863], "jump": [21, 844], "straight": [21, 814, 830, 843, 846, 853], "quickstart": [21, 814], "introduct": [21, 23, 30, 32, 33, 872], "point": [21, 30, 55, 57, 58, 63, 67, 69, 71, 78, 80, 81, 86, 90, 94, 127, 128, 129, 131, 133, 136, 143, 144, 149, 153, 166, 170, 174, 181, 221, 222, 223, 224, 226, 227, 228, 229, 230, 237, 238, 239, 241, 242, 244, 246, 247, 248, 254, 255, 256, 257, 262, 263, 264, 265, 266, 274, 276, 277, 279, 281, 283, 284, 285, 286, 287, 288, 289, 291, 292, 293, 294, 295, 313, 314, 316, 336, 337, 354, 355, 358, 360, 370, 373, 376, 377, 378, 383, 388, 391, 400, 401, 402, 420, 430, 450, 454, 509, 510, 511, 512, 513, 523, 524, 525, 533, 628, 630, 631, 633, 638, 644, 645, 646, 647, 648, 668, 670, 673, 674, 675, 677, 679, 680, 681, 684, 685, 686, 687, 688, 689, 690, 692, 695, 741, 742, 748, 750, 751, 752, 753, 756, 758, 759, 761, 762, 763, 764, 765, 766, 767, 802, 803, 812, 818, 820, 821, 822, 825, 826, 828, 830, 831, 833, 834, 836, 838, 842, 843, 846, 847, 849, 851, 853, 854, 863, 865, 878], "showcas": [21, 814], "real": 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81, 85, 151, 152, 164, 171, 193, 194, 195, 196, 197, 199, 208, 215, 216, 220, 376, 377, 379, 419, 423, 431, 485, 496, 525, 544, 631, 632, 635, 637, 638, 650, 651, 652, 653, 655, 657, 659, 675, 772, 774, 778, 807, 808, 827, 828, 830, 831, 832, 835, 843, 851, 854], "simplest": [23, 821, 833, 846, 849], "interact": [23, 32, 47, 50, 820, 871, 872, 877], "submodul": [23, 32, 46, 48, 103, 104, 627, 628, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 789, 790, 792, 793, 795, 796, 797, 798, 820, 821, 822, 825, 828, 830, 832, 836, 839, 840, 846, 850, 851, 855, 859], "likewis": [23, 28, 32, 39, 822, 829, 831, 834, 838, 839, 843, 849, 854, 865, 866, 878], "nativearrai": [23, 32, 33, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 69, 71, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 103, 107, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123, 124, 126, 128, 129, 130, 132, 137, 138, 139, 140, 141, 142, 144, 146, 147, 150, 153, 154, 155, 156, 159, 160, 161, 162, 163, 164, 166, 169, 172, 173, 174, 176, 178, 180, 181, 187, 197, 198, 214, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 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, 314, 315, 318, 319, 323, 330, 331, 332, 333, 334, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 368, 370, 373, 374, 376, 377, 378, 379, 382, 383, 384, 386, 388, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 408, 409, 410, 412, 413, 414, 415, 416, 418, 419, 420, 423, 424, 425, 426, 427, 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284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 299, 300, 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 326, 335, 336, 337, 338, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 370, 379, 399, 400, 401, 402, 420, 453, 454, 455, 456, 457, 458, 459, 460, 463, 464, 465, 469, 470, 485, 491, 493, 494, 495, 497, 502, 504, 505, 506, 508, 510, 523, 524, 525, 526, 535, 536, 538, 539, 541, 542, 546, 547, 548, 549, 550, 551, 552, 553, 554, 557, 559, 561, 562, 563, 565, 566, 569, 573, 577, 578, 592, 593, 594, 596, 598, 600, 601, 614, 625, 629, 631, 632, 635, 642, 651, 652, 653, 654, 660, 661, 667, 668, 669, 674, 675, 676, 677, 678, 679, 681, 683, 685, 686, 692, 697, 698, 699, 700, 704, 707, 708, 709, 710, 711, 714, 715, 719, 720, 722, 725, 726, 727, 728, 730, 731, 732, 736, 737, 739, 740, 741, 742, 744, 747, 750, 751, 752, 753, 754, 758, 759, 762, 764, 765, 767, 768, 769, 774, 775, 790, 793, 795, 802, 808, 826, 829, 854, 855, 859, 865, 866, 867], "recurs": [23, 32, 33, 46, 48, 53, 75, 76, 167, 168, 200, 201, 377, 449, 551, 552, 558, 631, 632, 635, 642, 719, 720, 723, 729, 730, 731, 772, 821, 825, 828, 829, 836, 839, 842, 855, 857], "fashion": [23, 779, 846, 866], "native_arrai": [23, 32, 33, 54, 55, 57, 77, 79, 80, 81, 82, 86, 93, 111, 114, 137, 140, 142, 144, 150, 153, 154, 155, 156, 164, 169, 176, 198, 207, 215, 231, 235, 240, 241, 242, 244, 248, 252, 260, 261, 269, 274, 277, 280, 283, 288, 336, 337, 364, 373, 378, 379, 459, 485, 491, 495, 535, 538, 565, 566, 569, 600, 627, 630, 631, 632, 633, 635, 637, 638, 639, 640, 644, 645, 648, 649, 651, 652, 659, 667, 670, 674, 675, 680, 681, 685, 689, 690, 692, 695, 697, 699, 700, 707, 739, 748, 757, 763, 766, 768, 774, 784, 802, 818, 836, 844, 846], "data_class": [23, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 396, 397, 546, 550, 688, 713], "low": [23, 32, 35, 51, 58, 62, 67, 81, 85, 90, 376, 419, 423, 637, 644, 650, 651, 652, 653, 655, 657, 659, 740, 742, 779, 829, 835, 842, 843, 849, 851, 868, 870, 872, 873, 874, 876, 878], "c": [23, 32, 38, 47, 48, 54, 58, 59, 60, 62, 65, 71, 77, 78, 80, 81, 82, 83, 85, 86, 88, 92, 94, 98, 99, 117, 128, 129, 139, 142, 166, 169, 224, 235, 241, 242, 262, 263, 265, 274, 277, 285, 292, 376, 377, 379, 382, 388, 390, 391, 392, 393, 404, 409, 425, 427, 429, 430, 432, 444, 463, 464, 465, 475, 493, 497, 502, 503, 504, 507, 525, 538, 546, 547, 548, 549, 557, 561, 562, 592, 601, 616, 617, 620, 622, 623, 624, 627, 630, 631, 633, 635, 636, 637, 638, 640, 642, 645, 646, 648, 651, 652, 653, 654, 655, 656, 658, 673, 675, 677, 707, 711, 719, 722, 726, 727, 728, 730, 731, 736, 737, 748, 753, 759, 760, 765, 767, 796, 807, 808, 815, 821, 824, 827, 828, 829, 833, 839, 841, 850, 851, 852, 854, 857, 859, 860, 862, 863, 866, 868, 872, 876, 877, 879], "fundament": [23, 32, 830, 843, 849, 851, 861, 872], "signatur": [23, 32, 379, 388, 485, 523, 831, 832, 833, 834, 838, 842, 846, 847, 849, 862, 869, 878], "matmul": [23, 32, 33, 49, 63, 86, 377, 447, 615, 635, 638, 688, 827, 846, 847, 851], "to_n": [23, 32, 33, 44, 53, 76, 851], "jaxlib": [23, 29, 47, 802, 821, 826, 831, 832, 838, 847, 851, 853], "xla_extens": [23, 29, 802, 826, 831, 832, 838, 847, 851, 853], "arrayimpl": [23, 29, 802], "disabl": [23, 32, 58, 81, 379, 493, 795, 812, 828], "array_mod": [23, 32, 579, 603, 635, 848], "set_array_mod": [23, 32, 603, 635, 848], "ultim": [23, 32, 865], "sigmoid": [23, 32, 33, 44, 52, 58, 74, 81, 302, 368, 383, 509, 627, 789, 851, 854, 855], "z": [23, 32, 33, 45, 46, 54, 57, 58, 59, 63, 64, 67, 69, 71, 77, 80, 81, 82, 86, 87, 88, 90, 94, 103, 104, 138, 139, 141, 142, 202, 224, 225, 229, 231, 234, 236, 241, 252, 253, 256, 257, 258, 260, 261, 266, 268, 270, 271, 272, 273, 281, 290, 301, 302, 336, 337, 339, 368, 373, 378, 388, 454, 456, 457, 458, 459, 460, 466, 470, 481, 522, 523, 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114, 115, 116, 117, 118, 119, 221, 224, 241, 248, 274, 292, 296, 301, 302, 304, 368, 627, 633, 638, 688, 689, 789, 831], "handle_complex_input": [52, 57, 58, 74, 80, 81, 111, 112, 113, 114, 115, 116, 117, 118, 119, 292, 296, 301, 302, 304, 368, 627, 633, 789, 840], "element": [52, 54, 57, 58, 59, 62, 63, 65, 67, 68, 69, 71, 74, 75, 77, 78, 80, 81, 82, 85, 86, 88, 90, 91, 92, 94, 99, 103, 104, 107, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 130, 136, 137, 146, 147, 148, 164, 166, 169, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 302, 304, 306, 307, 308, 310, 311, 312, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 343, 346, 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"nest": [53, 75, 76, 104, 107, 244, 568, 598, 615, 618, 633, 635, 636, 641, 716, 717, 719, 720, 721, 722, 723, 724, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 797, 826, 828, 829, 839, 841, 847, 854, 855, 857, 859, 872], "unchang": [53, 57, 376, 379, 421, 475, 637, 660], "deriv": [53, 54, 58, 60, 76, 77, 81, 83, 132, 137, 144, 150, 314, 318, 343, 370, 373, 616, 617, 620, 621, 622, 623, 624, 630, 636, 641, 642, 718, 720, 731, 795, 797, 798, 831, 832, 853, 855], "word": [53, 127, 379, 478, 630, 644, 742, 790, 793, 829, 842, 843, 859], "args_to_n": [53, 842], "cont_inplac": 53, "decid": [53, 75, 642, 730, 731, 820, 821, 831, 849], "args_to_new_backend": 53, "shallow": [53, 642, 726, 727, 731, 736, 737], "nativevari": 53, "mutabl": [53, 642, 720, 726, 727, 731, 736, 737, 827], "to_ivi": [53, 76, 642, 732, 842], "leaf": [53, 75, 82, 94, 104, 549, 642, 729, 730, 732, 759, 829, 839, 854], "travers": [53, 76, 642, 723, 731, 829, 831, 835, 851], "lowest": [53, 58, 67, 76, 81, 90, 388, 526, 642, 644, 731, 740, 808, 839, 857, 859, 869, 873, 877], "search": [53, 58, 76, 81, 745, 746, 785, 819, 821, 829, 833, 836, 846, 847, 861], "to_new_backend": 53, "_arraywithcr": [54, 103], "boolean": [54, 55, 57, 58, 59, 65, 68, 71, 75, 77, 78, 80, 81, 82, 88, 91, 94, 103, 104, 124, 126, 128, 129, 130, 136, 153, 169, 171, 173, 174, 177, 193, 203, 211, 217, 231, 232, 233, 234, 235, 236, 268, 269, 270, 271, 336, 337, 352, 373, 377, 379, 435, 446, 452, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 493, 500, 535, 538, 549, 556, 559, 560, 564, 565, 566, 567, 568, 569, 570, 579, 582, 585, 586, 588, 589, 614, 629, 630, 631, 632, 633, 635, 637, 640, 641, 642, 645, 648, 664, 703, 704, 705, 707, 709, 710, 712, 714, 716, 717, 729, 747, 748, 749, 761, 763, 777, 778, 779, 780, 785, 796, 829, 831, 839, 843, 846, 849], "never": [54, 58, 65, 77, 81, 88, 129, 379, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 500, 556, 635, 640, 703, 704, 705, 707, 709, 710, 712, 714, 822, 831, 842, 843, 846], "valueerror": [54, 58, 65, 77, 81, 88, 92, 129, 376, 378, 410, 421, 458, 463, 464, 471, 473, 475, 476, 477, 484, 500, 640, 703, 704, 705, 707, 709, 710, 712, 714, 753, 779, 809, 835], "buffer": [54, 77, 81, 88, 129, 135, 463, 464, 471, 473, 475, 476, 477, 484, 500, 630, 703, 704, 705, 707, 709, 710, 712, 714, 794, 795, 842, 857], "nativedtyp": [54, 55, 58, 62, 63, 67, 68, 71, 77, 81, 86, 90, 91, 94, 127, 128, 129, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 149, 150, 152, 153, 158, 159, 160, 161, 162, 163, 164, 165, 170, 171, 175, 177, 179, 183, 193, 313, 314, 315, 316, 317, 318, 319, 334, 341, 357, 370, 373, 383, 388, 509, 510, 511, 512, 513, 523, 524, 525, 526, 529, 532, 630, 631, 637, 638, 644, 645, 647, 648, 660, 679, 695, 740, 741, 742, 745, 746, 756, 758, 759, 762, 764, 766, 792, 831, 832, 838, 847, 851], "datatyp": [54, 58, 75, 77, 81, 129, 137, 141, 158, 179, 183, 376, 424, 630, 631, 772, 847, 865], "nativedevic": [54, 56, 58, 67, 77, 79, 81, 90, 127, 128, 129, 131, 132, 133, 136, 137, 138, 139, 141, 142, 143, 144, 148, 149, 150, 195, 196, 197, 198, 199, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 220, 313, 314, 329, 370, 383, 509, 510, 512, 513, 630, 632, 644, 739, 740, 741, 742, 792, 797, 798, 831, 832, 835, 838, 847], "39999998": [54, 128, 129, 630, 646, 751], "5999999": [54, 58, 81, 85, 128, 129, 298, 368, 377, 426, 630, 637, 660, 667], "0999999": [54, 71, 128, 129, 298, 308, 311, 354, 368, 373, 630, 762], "10000038": [54, 128, 129, 630], "90786433e": [54, 128, 129, 630], "310": [54, 128, 129, 630], "copy_arrai": [54, 77, 630], "to_ivy_arrai": [54, 77, 130, 630], "empty_lik": [54, 58, 77, 81, 265, 377, 429, 630, 633], "uniniti": [54, 131, 132, 630, 837], "from_dlpack": [54, 77, 630], "full_lik": [54, 77, 630, 847], "fill_valu": [54, 58, 68, 77, 81, 91, 136, 137, 253, 261, 379, 383, 493, 513, 630, 633, 645, 748, 831, 844, 847], "scalar": [54, 57, 58, 59, 63, 74, 77, 80, 81, 82, 86, 98, 113, 137, 142, 224, 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[[185, "set-default-int-dtype"]], "num_gpus": [[206, "num-gpus"]], "unset_soft_device_mode": [[219, "unset-soft-device-mode"]], "num_cpu_cores": [[205, "num-cpu-cores"]], "dev_util": [[199, "dev-util"]], "set_default_device": [[210, "set-default-device"]], "unset_default_int_dtype": [[191, "unset-default-int-dtype"]], "set_default_float_dtype": [[184, "set-default-float-dtype"]], "unset_default_complex_dtype": [[188, "unset-default-complex-dtype"]], "0.2: Transpile": [[36, "0.2:-Transpile"]], "0.0: Unify": [[34, "0.0:-Unify"]], "Write a model using Ivy": [[31, "Write-a-model-using-Ivy"]], "Developing a convolutional network using Ivy": [[20, "Developing-a-convolutional-network-using-Ivy"]], "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"], [13, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[7, "Fine-tuning-the-transpiled-model"], [6, "Fine-tuning-the-transpiled-model"], [13, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[7, "Conclusion"], [6, "Conclusion"], [13, "Conclusion"]], "Accelerating XGBoost with JAX": [[15, "Accelerating-XGBoost-with-JAX"]], "Imports": [[15, "Imports"], [8, "Imports"], [12, "Imports"]], "Tests": [[15, "Tests"]], "Loading the Data": [[15, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[15, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[15, "JAX-backend"]], "Tensorflow backend": [[15, "Tensorflow-backend"]], "PyTorch backend": [[15, "PyTorch-backend"]], "More exhaustive example": [[15, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[15, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[15, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[15, "Comparison-of-Metrics"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "1.2: As a Decorator": [[39, "1.2:-As-a-Decorator"]], "Unify": [[39, "Unify"], [28, "Unify"], [38, "Unify"], [27, "Unify"], [37, "Unify"]], "Compile": [[39, "Compile"], [38, "Compile"], [37, "Compile"]], "Transpile": [[39, "Transpile"], [28, "Transpile"], [38, "Transpile"], [27, "Transpile"], [37, "Transpile"]], "3.0: Perceiver": [[42, "3.0:-Perceiver"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"], [13, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"], [13, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"], [13, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"], [13, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "1.3: Dynamic vs Static": [[40, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[40, "Dynamic"]], "Static": [[40, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[40, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Accelerating PyTorch models with JAX": [[14, "Accelerating-PyTorch-models-with-JAX"]], "Transpiling a Tensorflow model to build on top": [[19, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Installation": [[4, "Installation"], [13, "Installation"], [12, "Installation"]], "Data Preparation": [[4, "Data-Preparation"], [5, "Data-Preparation"], [8, "Data-Preparation"], [12, "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)"]], "Transpile any model": [[30, "Transpile-any-model"]], "Round up": [[30, "Round-up"]], "# 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"]], "Unify code": [[24, "Unify-code"]], "Trace code": [[25, "Trace-code"]], "Quickstart": [[33, "Quickstart"]], "Get familiar with Ivy": [[33, "Get-familiar-with-Ivy"]], "Functional API": [[33, "Functional-API"]], "Stateful API": [[33, "Stateful-API"]], "Tracing code": [[33, "Tracing-code"]], "Any function": [[33, "Any-function"], [32, "Any-function"]], "Any library": [[33, "Any-library"], [32, "Any-library"]], "Any model": [[33, "Any-model"], [32, "Any-model"]], "Transpile code": [[26, "Transpile-code"]], "3.1: Stable Diffusion": [[43, "3.1:-Stable-Diffusion"]], "How to use decorators": [[28, "How-to-use-decorators"]], "Trace": [[28, "Trace"], [27, "Trace"]], "Compilation of a Basic Function": [[45, "Compilation-of-a-Basic-Function"]], "Installs \ud83d\udcbe": [[45, "Installs-\ud83d\udcbe"], [44, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[45, "Imports-\ud83d\udec3"], [44, "Imports-\ud83d\udec3"]], "Import Ivy compiler": [[45, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[45, "Function-compilation-\ud83d\udee0"]], "Set backend": [[45, "Set-backend"]], "Sample input": [[45, "Sample-input"]], "Define function to compile": [[45, "Define-function-to-compile"]], "Compile the function": [[45, "Compile-the-function"]], "Check results": [[45, "Check-results"], [45, "id1"]], "Compiling simple neural network \ud83e\udde0": [[45, "Compiling-simple-neural-network-\ud83e\udde0"]], "Define Model": [[45, "Define-Model"], [44, "Define-Model"]], "Create model": [[45, "Create-model"]], "Define input": [[45, "Define-input"]], "Compile network": [[45, "Compile-network"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "2.0: Kornia": [[41, "2.0:-Kornia"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "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"], [23, "Importing-Ivy"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Transpile any library": [[29, "Transpile-any-library"]], "Training PyTorch ResNet in your TensorFlow Projects": [[13, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Transpiling a PyTorch model to TensorFlow": [[13, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "Load the Data": [[13, "Load-the-Data"]], "Visualize a few images": [[13, "Visualize-a-few-images"]], "Load the pre-trained model": [[13, "Load-the-pre-trained-model"]], "0.1: Compile": [[35, "0.1:-Compile"]], "Transpiling a haiku model to build on top": [[18, "Transpiling-a-haiku-model-to-build-on-top"]], "Write Ivy code": [[23, "Write-Ivy-code"]], "Contents": [[23, "Contents"]], "Installing Ivy": [[23, "Installing-Ivy"]], "Ivy Backend Handler": [[23, "Ivy-Backend-Handler"], [32, "Ivy-Backend-Handler"]], "Data Structures": [[23, "Data-Structures"], [32, "Data-Structures"]], "Ivy Functional API": [[23, "Ivy-Functional-API"], [32, "Ivy-Functional-API"]], "Learn the basics": [[22, "learn-the-basics"], [21, "learn-the-basics"]], "1.1: Framework Selection": [[38, "1.1:-Framework-Selection"]], "Examples and Demos": [[3, "examples-and-demos"], [21, "examples-and-demos"]], "Basic Operations with Ivy": [[44, "Basic-Operations-with-Ivy"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[44, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[44, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[44, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[44, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[44, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[44, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[44, "Set-Backend-Framework"]], "Create Model": [[44, "Create-Model"]], "Create Optimizer": [[44, "Create-Optimizer"]], "Input and Target": [[44, "Input-and-Target"]], "Loss Function": [[44, "Loss-Function"]], "Training Loop": [[44, "Training-Loop"]], "Lazy vs Eager": [[27, "Lazy-vs-Eager"]], "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"]], "ODSC Ivy Demo": [[32, "ODSC-Ivy-Demo"]], "Graph Tracer": [[32, "Graph-Tracer"]], "1.0: Lazy vs Eager": [[37, "1.0:-Lazy-vs-Eager"]], "Guides": [[16, "guides"], [21, "guides"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Transpiling a PyTorch model to build on top": [[17, "Transpiling-a-PyTorch-model-to-build-on-top"]], "Tutorials And Examples": [[21, "tutorials-and-examples"]], "End-to-End Training Pipeline in Ivy": [[48, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[48, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[48, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[48, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[48, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[48, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[48, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[48, "Plotting-the-training-metrics"]], "Save the trained Model": [[48, "Save-the-trained-Model"]], "HuggingFace Tensorflow DeiT": [[49, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[49, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Resnet 18": [[51, "Resnet-18"]], "Image": [[84, "module-ivy.data_classes.container.image"], [61, "module-ivy.data_classes.array.image"]], "Deepmind PerceiverIO on GPU": [[47, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[47, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[47, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[47, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[47, "Run-the-demo..."]], "\u2026with torch backend": [[47, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[47, "....with-tensorflow-backend"]], "\u2026with jax backend": [[47, "...with-jax-backend"]], "\u2026with numpy backend": [[47, "...with-numpy-backend"]], "Conversions": [[76, "module-ivy.data_classes.container.conversions"], [53, "module-ivy.data_classes.array.conversions"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[46, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[46, "Table-of-Contents"]], "Defining the model": [[46, "Defining-the-model"]], "Model construction": [[46, "Model-construction"]], "Some helper functions": [[46, "Some-helper-functions"]], "Transpiling the model": [[46, "Transpiling-the-model"]], "PyTorch pipeline": [[46, "PyTorch-pipeline"]], "Dataset download": [[46, "Dataset-download"]], "DataLoader": [[46, "DataLoader"]], "Training": [[46, "Training"]], "Ivy as a Transpiler Introduction": [[50, 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Transpile Functions \ud83d\udd22": [[50, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[50, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. 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865, 868, 872], "document": [1, 6, 7, 13, 23, 32, 65, 248, 336, 337, 373, 615, 633, 635, 711, 815, 816, 819, 822, 828, 830, 831, 833, 842, 843, 844, 846, 854, 856], "sphinx": [1, 816, 828], "websit": [1, 50, 814, 821, 825, 862], "alreadi": [2, 6, 13, 14, 24, 27, 28, 29, 30, 32, 33, 38, 46, 48, 51, 58, 63, 75, 81, 86, 237, 247, 274, 284, 294, 379, 388, 464, 465, 485, 521, 530, 633, 638, 676, 683, 807, 808, 820, 821, 822, 827, 829, 831, 832, 838, 842, 843, 849, 857, 858, 872, 874, 879], "instal": [2, 7, 8, 9, 10, 11, 14, 15, 17, 19, 24, 25, 26, 27, 28, 29, 30, 32, 33, 46, 48, 49, 50, 51, 816, 821, 822, 827, 828, 836, 837], "skip": [2, 5, 13, 48, 58, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 111, 112, 113, 114, 115, 116, 117, 118, 119, 135, 137, 142, 144, 150, 154, 156, 181, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 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30, 33, 46, 48, 50, 51], "manual": [2, 6, 7, 13, 14, 15, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 642, 719, 729, 730, 820, 821, 822, 831, 837, 846, 855, 858], "mind": [2, 17, 19, 23, 29, 32, 36, 820, 821, 826, 829, 846, 858, 866], "click": [2, 4, 48, 820, 821, 822, 830, 834, 836, 837, 852], "runtim": [2, 4, 5, 8, 11, 12, 13, 14, 25, 32, 35, 46, 47, 824, 839, 846, 849, 872], "restart": [2, 4, 5, 8, 12, 13, 46, 47, 821, 836], "git": [2, 4, 5, 8, 12, 32, 46, 47, 48, 49, 814, 816, 819, 821, 822, 825, 828, 830, 836, 837, 846, 858], "clone": [2, 4, 8, 12, 32, 46, 48, 49, 814, 816, 822, 836, 858], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 14, 19, 27, 28, 29, 30, 32, 33, 46, 47, 48, 49, 50, 51, 57, 58, 80, 81, 83, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 616, 617, 630, 631, 633, 636, 638, 640, 648, 686, 687, 715, 765, 814, 816, 821, 822, 825, 828, 830, 831, 834, 836, 858, 866], "github": [2, 4, 5, 8, 11, 12, 14, 32, 46, 47, 48, 49, 50, 814, 816, 817, 819, 822, 823, 825, 828, 830, 831, 833, 834, 836, 837, 845, 846, 858, 861, 880], "com": [2, 4, 5, 6, 7, 8, 11, 12, 14, 19, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 825, 828, 830, 831, 836, 858], "unifyai": [2, 4, 8, 12, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 828, 836, 858], "model": [2, 3, 4, 9, 15, 16, 21, 22, 23, 49, 51, 241, 274, 378, 454, 633, 790, 794, 795, 812, 854, 855, 859, 865, 866, 870, 871, 872, 873, 874, 875, 876, 878, 879], "depth": [2, 4, 6, 8, 12, 47, 54, 58, 62, 77, 81, 85, 142, 376, 379, 412, 472, 546, 558, 630, 635, 637, 655, 656, 822, 830, 854, 855, 856, 858], "repositori": [2, 4, 8, 12, 816, 820, 821, 822, 824, 825, 828, 836, 845, 863], "cd": [2, 4, 8, 12, 32, 49, 814, 816, 821, 822, 836, 858], "resnet": [3, 6, 14, 21, 32, 865, 866], "imag": [3, 4, 6, 7, 11, 14, 17, 21, 29, 32, 33, 46, 47, 48, 49, 50, 51, 58, 62, 80, 81, 85, 103, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 284, 285, 287, 288, 292, 376, 395, 396, 412, 413, 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635, 637, 640, 660, 707, 802, 803, 824, 827, 831, 832, 846, 851, 856, 866, 870, 871, 874, 876], "mmpretrain": [3, 21], "segment": [3, 21, 58, 81, 331, 332, 333, 370, 828, 833], "unet": [3, 21], "alexnet": [3, 21], "written": [3, 4, 5, 6, 13, 21, 23, 32, 33, 46, 59, 379, 474, 821, 825, 826, 834, 837, 838, 842, 843, 847, 851, 853, 856, 857, 861, 866, 870, 872, 876, 878, 879], "xgboost": [3, 21], "paddlepaddl": [3, 21, 336, 337, 373, 821], "dinov2": [3, 7, 21], "project": [3, 12, 14, 21, 26, 27, 28, 29, 30, 32, 33, 36, 99, 637, 664, 793, 814, 816, 817, 820, 821, 822, 823, 826, 827, 828, 846, 855, 857, 861, 862, 863, 866, 868, 870, 872, 875, 879, 880], "convnext": [3, 6, 11, 13, 21], "finetun": [3, 21, 46], "video": [4, 8, 11, 12, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 815, 820, 821, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 835, 837, 838, 839, 840, 841, 842, 843, 844, 846, 847, 849, 858, 870], "tutori": [4, 6, 7, 8, 11, 12, 13, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 822, 843, 858], "three": [4, 5, 21, 27, 37, 38, 48, 58, 140, 313, 370, 379, 465, 630, 821, 822, 829, 830, 831, 833, 843, 846, 849, 850, 851, 873, 878], "major": [4, 5, 645, 748, 831, 832, 844, 846, 857, 862, 869, 872], "ml": [4, 5, 6, 13, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 48, 51, 815, 819, 843, 850, 851, 852, 854, 855, 856, 860, 862, 863, 866, 868, 869, 870, 871, 872, 875, 877, 879], "framework": [4, 5, 7, 9, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 39, 46, 48, 50, 53, 59, 171, 193, 203, 206, 217, 544, 560, 564, 596, 599, 631, 632, 635, 642, 721, 772, 774, 778, 785, 790, 797, 802, 803, 817, 818, 820, 821, 824, 825, 826, 827, 828, 830, 831, 832, 833, 835, 836, 838, 839, 840, 842, 843, 846, 847, 849, 850, 851, 853, 856, 857, 858, 859, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 873, 876], "sinc": [4, 8, 12, 13, 29, 30, 32, 33, 46, 48, 58, 81, 99, 373, 816, 821, 822, 825, 826, 827, 828, 829, 830, 831, 832, 835, 842, 843, 857, 862, 872, 878], "automat": [4, 8, 9, 12, 13, 30, 32, 33, 38, 820, 821, 822, 824, 827, 828, 830, 831, 837, 839, 842, 846, 849, 850, 852, 855, 856, 858, 859, 863, 872, 875, 879], "sure": [4, 8, 11, 12, 13, 14, 15, 32, 46, 817, 820, 821, 822, 825, 830, 835, 836, 843, 844, 846, 849, 858], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 15, 27, 28, 30, 47, 58, 63, 75, 86, 104, 376, 378, 399, 457, 581, 635, 638, 681, 795, 812, 814, 821, 822, 823, 826, 829, 831, 839, 840, 841, 842, 843, 846, 847, 850, 852, 854, 856, 857, 859, 862, 865, 870, 871, 872, 873, 874, 875, 878, 879], "dm": [4, 5, 8, 11, 14, 32, 33, 44, 46], "haiku": [4, 5, 8, 11, 14, 30, 32, 33, 44, 46, 50, 790, 814, 856, 863, 866, 872], "exit": [4, 8, 12, 13, 32, 33, 832], "download": [4, 6, 7, 12, 13, 17, 19, 32, 33, 47, 48, 51, 816, 821, 828, 846, 865, 866], "imagenet": [4, 6, 13, 19, 47, 49, 814], "class": [4, 6, 7, 8, 12, 13, 15, 17, 19, 23, 32, 33, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 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795, 812, 820, 821, 822, 825, 827, 829, 830, 831, 833, 836, 838, 839, 840, 842, 843, 846, 847, 851, 854, 856, 857, 858, 861, 862, 863, 865, 866, 870, 872, 873, 876, 877, 878], "filterwarn": [4, 5, 13], "ignor": [4, 5, 13, 45, 53, 54, 58, 75, 81, 140, 376, 377, 379, 388, 400, 401, 402, 431, 439, 447, 487, 488, 492, 531, 630, 637, 642, 664, 730, 731, 797, 821, 828, 830, 833, 846, 857, 878], "compos": [4, 6, 7, 11, 12, 13, 32, 33, 46, 58, 81, 376, 390, 391, 392, 393, 821, 829, 843, 846, 865, 867, 872, 879], "resiz": [4, 6, 7, 8, 11, 12, 13, 46, 47, 58, 81, 376, 412, 849], "centercrop": [4, 12, 13], "224": [4, 6, 7, 12, 13, 17, 19, 32, 33, 46, 47, 49, 814, 866], "totensor": [4, 6, 7, 11, 12, 13, 46], "485": [4, 12, 13, 46], "456": [4, 12, 13, 46, 846], "406": [4, 12, 13, 46, 58, 81, 398, 541, 635], "229": [4, 12, 13, 46, 280, 633], "225": [4, 12, 13, 46, 48, 235, 633], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 29, 32, 33, 46, 47, 48, 50, 814, 854, 866], "ipython": [4, 8, 12, 27, 28, 29, 30, 32, 33, 51], "displai": [4, 8, 12, 13, 29, 32, 33, 46, 47, 48, 50, 51, 821, 828, 830, 835, 846, 854], "end": [4, 8, 13, 46, 47, 58, 81, 127, 229, 285, 354, 373, 376, 378, 379, 424, 453, 475, 485, 487, 488, 630, 633, 808, 821, 822, 827, 830, 836, 842, 847, 849, 850, 857, 870, 875], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 14, 218, 632, 832], "ivy_model": [4, 5, 8, 12, 49], "ivy_alexnet": 4, "quick": [4, 21, 33, 822, 824, 844, 855], "trace_graph": [4, 5, 8, 12, 25, 26, 27, 28, 32, 33, 35, 36, 37, 38, 39, 40, 49, 795, 814, 851, 856, 864], "moment": [4, 58, 60, 81, 83, 377, 434, 616, 617, 622, 636, 797, 812, 820, 827, 857, 865, 866], "cost": [4, 60, 83, 616, 617, 620, 622, 623, 624, 636, 641, 716, 717, 718, 808, 831, 849, 870], "arg": [4, 6, 8, 9, 10, 11, 12, 13, 17, 19, 27, 28, 30, 32, 33, 37, 38, 39, 50, 53, 75, 97, 107, 123, 204, 214, 602, 629, 630, 632, 635, 772, 774, 789, 790, 793, 794, 795, 799, 802, 807, 812, 814, 826, 831, 832, 835, 841, 842, 843, 849, 851, 855, 865, 866, 867], "asarrai": [4, 5, 8, 11, 12, 47, 54, 58, 59, 70, 77, 81, 82, 93, 128, 386, 515, 516, 546, 557, 561, 562, 592, 593, 594, 630, 635, 637, 646, 647, 651, 751, 755, 835, 840, 843, 844], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 23, 32, 47, 48, 51, 54, 58, 67, 77, 81, 90, 138, 139, 142, 194, 195, 196, 212, 383, 509, 510, 512, 513, 630, 632, 638, 644, 689, 739, 740, 741, 742, 792, 793, 794, 795, 796, 797, 798, 812, 851, 857, 859, 877], "output": [4, 5, 7, 8, 9, 10, 12, 13, 23, 29, 30, 32, 33, 45, 46, 47, 49, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 149, 150, 153, 155, 180, 214, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 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731, 732, 736, 737, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 772, 774, 779, 785, 792, 793, 794, 795, 798, 802, 807, 808, 810, 814, 818, 820, 821, 822, 824, 827, 828, 829, 831, 832, 833, 834, 835, 836, 839, 840, 841, 842, 843, 844, 846, 847, 848, 849, 850, 851, 852, 854, 855, 856, 857, 859, 866, 867, 870, 871, 872, 874, 878, 879], "282": [4, 12], "281": [4, 12, 46, 48], "285": [4, 12, 81], "64773697": 4, "29496649": 4, "04526037": 4, "tiger": [4, 12], "tabbi": [4, 7, 12], "egyptian": [4, 12], "torch_alexnet": 4, "alexnet_weight": 4, "imagenet1k_v1": [4, 12, 13], "dropout": [4, 62, 85, 376, 400, 401, 402, 637, 662, 664, 667, 793, 854], "torch_output": [4, 8, 9, 12], "dim": [4, 12, 48, 58, 75, 77, 81, 142, 314, 370, 376, 379, 394, 404, 405, 406, 409, 417, 475, 497, 630, 637, 650, 657, 658, 663, 779, 793, 831, 843, 844, 849], "torch_class": [4, 12], "torch_logit": [4, 12], "tensor": [4, 5, 6, 9, 11, 12, 13, 14, 17, 19, 23, 24, 27, 28, 30, 32, 33, 34, 38, 44, 46, 54, 57, 58, 59, 62, 63, 64, 65, 67, 71, 75, 77, 80, 81, 82, 85, 86, 87, 88, 90, 94, 97, 130, 138, 139, 142, 148, 164, 180, 272, 273, 303, 320, 324, 325, 326, 327, 328, 329, 338, 361, 368, 370, 373, 376, 377, 378, 379, 388, 389, 395, 396, 399, 403, 412, 413, 414, 415, 422, 424, 426, 433, 434, 435, 436, 439, 441, 443, 445, 446, 449, 451, 452, 453, 455, 458, 459, 475, 478, 483, 486, 487, 488, 489, 492, 497, 498, 529, 534, 577, 578, 630, 631, 633, 635, 637, 638, 639, 640, 644, 648, 660, 663, 664, 679, 690, 697, 707, 709, 739, 762, 793, 802, 808, 812, 814, 826, 827, 831, 832, 836, 838, 839, 842, 843, 844, 846, 847, 849, 851, 853, 854, 856, 857, 859, 861, 865, 866, 867, 869, 870, 873, 875, 876, 879], "6477": 4, "2950": 4, "0453": 4, "grad_fn": [4, 12, 30, 44, 619, 626, 636, 854], "takebackward0": [4, 12], "great": [4, 7, 8, 822, 846, 851, 853, 862, 863, 878], "simpl": [4, 7, 17, 21, 22, 24, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 44, 46, 48, 51, 58, 81, 388, 523, 779, 793, 808, 814, 820, 821, 822, 826, 828, 829, 831, 832, 833, 834, 839, 842, 843, 846, 847, 849, 853, 855, 856, 857, 859, 861, 865, 866, 871, 872, 873, 874], "let": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 19, 23, 24, 25, 27, 28, 29, 30, 32, 33, 34, 35, 37, 38, 39, 44, 46, 47, 49, 51, 59, 71, 82, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 285, 287, 288, 292, 553, 554, 633, 635, 638, 648, 692, 762, 764, 765, 766, 767, 814, 820, 823, 826, 828, 829, 830, 831, 832, 833, 834, 835, 836, 843, 844, 846, 847, 848, 849, 851, 853, 854, 855, 856, 863, 865, 866, 879], "ll": [4, 6, 7, 8, 9, 11, 13, 14, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 47, 814, 815, 817, 818, 820, 821, 822, 823, 828, 833, 836, 837, 841, 842, 854, 858, 863, 865, 866], "try": [4, 6, 7, 13, 24, 34, 44, 47, 51, 75, 602, 635, 792, 802, 814, 820, 821, 822, 825, 826, 829, 830, 831, 835, 837, 842, 844, 851, 853, 857, 860, 862, 863, 867], "tf": [4, 6, 8, 9, 10, 13, 14, 17, 19, 24, 27, 28, 30, 32, 33, 34, 35, 37, 39, 44, 49, 50, 790, 814, 826, 831, 832, 838, 842, 843, 846, 847, 849, 851, 856, 857, 859, 865, 866, 867, 872], "onc": [4, 6, 8, 32, 33, 44, 46, 63, 67, 86, 90, 214, 377, 430, 632, 638, 644, 673, 674, 675, 688, 739, 814, 820, 821, 822, 829, 830, 831, 832, 833, 836, 837, 842, 843, 846, 849, 851, 854, 857, 858, 863, 865], "set": [4, 7, 9, 17, 19, 25, 32, 33, 35, 38, 46, 47, 48, 49, 50, 53, 58, 59, 62, 63, 68, 70, 71, 75, 81, 82, 85, 86, 91, 93, 94, 116, 119, 126, 146, 148, 182, 183, 184, 185, 186, 197, 210, 211, 212, 213, 214, 229, 329, 341, 357, 359, 364, 370, 373, 374, 376, 377, 378, 379, 388, 399, 420, 424, 428, 432, 435, 453, 458, 459, 475, 485, 488, 495, 523, 528, 529, 530, 531, 532, 533, 535, 539, 546, 558, 563, 579, 580, 581, 583, 584, 585, 586, 587, 588, 589, 590, 596, 604, 627, 629, 630, 631, 632, 633, 635, 637, 638, 642, 644, 645, 647, 648, 660, 667, 669, 679, 681, 684, 687, 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284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 299, 300, 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 326, 335, 336, 337, 338, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 370, 379, 399, 400, 401, 402, 420, 453, 454, 455, 456, 457, 458, 459, 460, 463, 464, 465, 469, 470, 485, 491, 493, 494, 495, 497, 502, 504, 505, 506, 508, 510, 523, 524, 525, 526, 535, 536, 538, 539, 541, 542, 546, 547, 548, 549, 550, 551, 552, 553, 554, 557, 559, 561, 562, 563, 565, 566, 569, 573, 577, 578, 592, 593, 594, 596, 598, 600, 601, 614, 625, 629, 631, 632, 635, 642, 651, 652, 653, 654, 660, 661, 667, 668, 669, 674, 675, 676, 677, 678, 679, 681, 683, 685, 686, 692, 697, 698, 699, 700, 704, 707, 708, 709, 710, 711, 714, 715, 719, 720, 722, 725, 726, 727, 728, 730, 731, 732, 736, 737, 739, 740, 741, 742, 744, 747, 750, 751, 752, 753, 754, 758, 759, 762, 764, 765, 767, 768, 769, 774, 775, 790, 793, 795, 802, 808, 826, 829, 854, 855, 859, 865, 866, 867], "recurs": [23, 32, 33, 46, 48, 53, 75, 76, 167, 168, 200, 201, 377, 449, 551, 552, 558, 631, 632, 635, 642, 719, 720, 723, 729, 730, 731, 772, 821, 825, 828, 829, 836, 839, 842, 855, 857], "fashion": [23, 779, 846, 866], "native_arrai": [23, 32, 33, 54, 55, 57, 77, 79, 80, 81, 82, 86, 93, 111, 114, 137, 140, 142, 144, 150, 153, 154, 155, 156, 164, 169, 176, 198, 207, 215, 231, 235, 240, 241, 242, 244, 248, 252, 260, 261, 269, 274, 277, 280, 283, 288, 336, 337, 364, 373, 378, 379, 459, 485, 491, 495, 535, 538, 565, 566, 569, 600, 627, 630, 631, 632, 633, 635, 637, 638, 639, 640, 644, 645, 648, 649, 651, 652, 659, 667, 670, 674, 675, 680, 681, 685, 689, 690, 692, 695, 697, 699, 700, 707, 739, 748, 757, 763, 766, 768, 774, 784, 802, 818, 836, 844, 846], "data_class": [23, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 396, 397, 546, 550, 688, 713], "low": [23, 32, 35, 51, 58, 62, 67, 81, 85, 90, 376, 419, 423, 637, 644, 650, 651, 652, 653, 655, 657, 659, 740, 742, 779, 829, 835, 842, 843, 849, 851, 868, 870, 872, 873, 874, 876, 878], "c": [23, 32, 38, 47, 48, 54, 58, 59, 60, 62, 65, 71, 77, 78, 80, 81, 82, 83, 85, 86, 88, 92, 94, 98, 99, 117, 128, 129, 139, 142, 166, 169, 224, 235, 241, 242, 262, 263, 265, 274, 277, 285, 292, 376, 377, 379, 382, 388, 390, 391, 392, 393, 404, 409, 425, 427, 429, 430, 432, 444, 463, 464, 465, 475, 493, 497, 502, 503, 504, 507, 525, 538, 546, 547, 548, 549, 557, 561, 562, 592, 601, 616, 617, 620, 622, 623, 624, 627, 630, 631, 633, 635, 636, 637, 638, 640, 642, 645, 646, 648, 651, 652, 653, 654, 655, 656, 658, 673, 675, 677, 707, 711, 719, 722, 726, 727, 728, 730, 731, 736, 737, 748, 753, 759, 760, 765, 767, 796, 807, 808, 815, 821, 824, 827, 828, 829, 833, 839, 841, 850, 851, 852, 854, 857, 859, 860, 862, 863, 866, 868, 872, 876, 877, 879], "fundament": [23, 32, 830, 843, 849, 851, 861, 872], "signatur": [23, 32, 379, 388, 485, 523, 831, 832, 833, 834, 838, 842, 846, 847, 849, 862, 869, 878], "matmul": [23, 32, 33, 49, 63, 86, 377, 447, 615, 635, 638, 688, 827, 846, 847, 851], "to_n": [23, 32, 33, 44, 53, 76, 851], "jaxlib": [23, 29, 47, 802, 821, 826, 831, 832, 838, 847, 851, 853], "xla_extens": [23, 29, 802, 826, 831, 832, 838, 847, 851, 853], "arrayimpl": [23, 29, 802], "disabl": [23, 32, 58, 81, 379, 493, 795, 812, 828], "array_mod": [23, 32, 579, 603, 635, 848], "set_array_mod": [23, 32, 603, 635, 848], "ultim": [23, 32, 865], "sigmoid": [23, 32, 33, 44, 52, 58, 74, 81, 302, 368, 383, 509, 627, 789, 851, 854, 855], "z": [23, 32, 33, 45, 46, 54, 57, 58, 59, 63, 64, 67, 69, 71, 77, 80, 81, 82, 86, 87, 88, 90, 94, 103, 104, 138, 139, 141, 142, 202, 224, 225, 229, 231, 234, 236, 241, 252, 253, 256, 257, 258, 260, 261, 266, 268, 270, 271, 272, 273, 281, 290, 301, 302, 336, 337, 339, 368, 373, 378, 388, 454, 456, 457, 458, 459, 460, 466, 470, 481, 522, 523, 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241, 274, 284, 382, 383, 507, 509, 633, 638, 644, 669, 687, 739, 814, 825, 831, 833, 840, 851, 856, 866, 870], "div": [24, 25, 26, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 867], "sub": [24, 25, 26, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 58, 63, 65, 75, 76, 80, 81, 82, 86, 88, 104, 273, 377, 379, 388, 431, 471, 480, 500, 529, 530, 558, 635, 638, 640, 641, 672, 692, 709, 716, 717, 718, 820, 822, 824, 829, 835, 843, 844, 846, 853, 854, 855, 867, 868], "with_numpi": 24, "reproduc": [24, 49, 62, 85, 637, 660, 777, 778, 779, 780, 785, 818, 825, 836], "x_": [24, 34, 99, 285, 633, 867], "66391283": 24, "12516928": 24, "38367081": 24, "03102401": 24, "76419425": 24, "52797794": 24, "90346956": 24, "61316347": 24, "27585283": 24, "66309303": 24, "ivy_repo": 24, "sever": [24, 25, 34, 35, 37, 38, 39, 58, 81, 98, 376, 377, 390, 391, 392, 393, 445, 777, 821, 822, 847, 857, 870, 876], "pro": [24, 25, 26, 34, 35, 36, 37, 38, 39], "pick": [25, 35, 792], "trigger": [25, 35, 795, 820, 837], "unif": [25, 27, 28, 35, 37, 815, 853, 862, 868, 878], "55563945": 25, "65538704": 25, "14150524": 25, "46951997": 25, "30220294": 25, "14739668": 25, "57017946": 25, "91962677": 25, "51029003": 25, "59644395": 25, "constitu": [25, 35, 75, 856], "5556394": 25, "655387": 25, "1415051": 25, "4695197": 25, "3022028": 25, "1473966": 25, "5701794": 25, "91962665": 25, "51028997": 25, "5964439": 25, "985": 25, "000": [25, 80, 275, 777, 818, 830, 836], "On": [25, 32, 33, 821, 831, 832, 837, 843, 846, 849, 852, 856], "hand": [25, 57, 377, 447, 777, 825, 831, 832, 837, 839, 846, 857], "learnt": [26, 36], "ivy_norm": 26, "jax_norm": [26, 32, 33], "wider": [26, 36, 586, 609, 635, 831, 848, 878], "avoid": [26, 36, 38, 58, 65, 81, 241, 246, 248, 264, 274, 378, 379, 382, 455, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 500, 502, 503, 504, 540, 556, 558, 581, 586, 609, 633, 635, 640, 703, 704, 705, 707, 709, 710, 712, 714, 779, 780, 821, 822, 827, 828, 829, 830, 831, 835, 840, 843, 846, 847, 848, 849, 872], "act": [26, 36, 58, 81, 299, 364, 374, 822, 833, 848, 857, 879], "shorthand": [26, 36, 38, 846], "pair": [26, 36, 46, 58, 62, 81, 85, 229, 248, 321, 363, 370, 373, 376, 410, 419, 421, 423, 633, 637, 638, 650, 651, 652, 653, 655, 657, 659, 667, 669, 808], "93968587": 26, "26075466": 26, "22723222": 26, "06276492": 26, "47426987": 26, "72835908": 26, "71737559": 26, "50411096": 26, "65419174": 26, "15576624": 26, "implic": [26, 36, 37, 40, 829], "fw": [27, 28, 29, 30, 62, 85, 388, 523, 637, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 774, 821, 846], "mxnet": [27, 28, 29, 30, 210, 632, 802, 820, 821, 862, 879], "einop": [27, 28, 29, 30, 46, 48, 51, 59, 82, 546, 547, 548, 635, 831, 862], "miniconda": [27, 28, 29, 30], "multienv": [27, 28, 29, 30], "site": [27, 28, 29, 30, 873], "psutil": [27, 28, 29, 30, 46, 48, 51], "termcolor": [27, 28, 29, 30, 46, 48, 51, 75, 104], "colorama": [27, 28, 29, 30, 46, 48], "535": [27, 28, 29, 30, 52, 74, 119, 627, 835], "diskcach": [27, 28, 29, 30, 46], "auth": [27, 28, 29, 30], "urllib3": [27, 28, 29, 30, 46], "pyvi": [27, 28, 29, 30, 32, 33], "dill": [27, 28, 29, 30, 46], "astunpars": [27, 28, 29, 30], "cloudpickl": [27, 28, 29, 30], "gast": [27, 28, 29, 30], "wheel": [27, 28, 29, 30, 46, 48, 51, 861], "six": [27, 28, 29, 30, 46, 51, 821, 849], "cachetool": [27, 28, 29, 30], "pyasn1": [27, 28, 29, 30], "rsa": [27, 28, 29, 30], "jsonpickl": [27, 28, 29, 30], "charset": [27, 28, 29, 30, 46], "idna": [27, 28, 29, 30, 46], "certifi": [27, 28, 29, 30, 46], "2017": [27, 28, 29, 30, 46, 637, 664], "jedi": [27, 28, 29, 30], "inlin": [27, 28, 29, 30, 828], "prompt": [27, 28, 29, 30, 820, 822], "toolkit": [27, 28, 29, 30, 872, 873, 879], "pygment": [27, 28, 29, 30], "traitlet": [27, 28, 29, 30], "exceptiongroup": [27, 28, 29, 30], "pexpect": [27, 28, 29, 30], "parso": [27, 28, 29, 30], "ptyprocess": [27, 28, 29, 30], "wcwidth": [27, 28, 29, 30], "asttoken": [27, 28, 29, 30], "pure": [27, 28, 29, 30, 38, 48, 834, 838, 843, 849, 853, 856, 857, 872, 878, 879], "lazili": [27, 28, 29, 32, 33, 37, 39, 50, 814, 865, 866, 867], "actual": [27, 37, 818, 822, 824, 830, 836, 839, 840, 842, 843, 844, 846, 849, 850, 855, 857, 873, 878], "occur": [27, 32, 33, 37, 50, 55, 57, 69, 78, 80, 92, 156, 275, 291, 631, 633, 645, 646, 745, 746, 750, 751, 752, 753, 825, 830, 832, 835, 848], "altern": [27, 37, 47, 58, 81, 86, 98, 99, 335, 343, 344, 345, 349, 351, 352, 353, 354, 356, 357, 358, 362, 363, 373, 820, 821, 828, 842, 854, 875], "assum": [27, 28, 37, 38, 39, 54, 57, 58, 59, 62, 63, 64, 80, 81, 82, 85, 86, 87, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 149, 150, 156, 172, 176, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 235, 236, 237, 238, 239, 241, 242, 244, 246, 247, 248, 252, 253, 254, 255, 256, 257, 258, 261, 263, 264, 265, 266, 268, 269, 270, 271, 274, 276, 277, 278, 279, 281, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 314, 330, 336, 337, 339, 342, 360, 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[27, 28], "36452447": [27, 28], "98795534": [27, 28], "15493582": [27, 28], "91630631": [27, 28], "41939619": [27, 28], "78909753": [27, 28], "19475674": [27, 28], "norm_trac": 27, "norm_tran": [27, 37], "know": [27, 28, 37, 38, 39, 69, 646, 750, 751, 752, 753, 814, 816, 820, 822, 832, 840, 844, 846, 849, 863, 867, 873], "07": [28, 46, 48, 60, 64, 80, 83, 87, 90, 229, 262, 265, 266, 285, 376, 408, 606, 616, 617, 619, 620, 621, 622, 633, 635, 636, 639, 698, 699, 741, 794, 797, 855], "981554": 28, "happen": [28, 32, 33, 293, 633, 814, 821, 822, 823, 832, 842, 846, 854, 863, 865, 866], "wherea": [28, 39, 81, 376, 422, 822, 826, 829, 831, 832, 833, 838, 839, 846, 856, 869], "subtract": [28, 32, 33, 57, 80, 103, 104, 135, 379, 485, 630, 633, 826, 829, 833], "often": [29, 58, 378, 453, 819, 825, 835, 838, 839, 843, 846, 857, 863, 873, 876, 879], "fortun": [29, 30, 825], "everyth": [29, 47, 807, 814, 820, 821, 822, 823, 824, 830, 833, 842, 843, 844, 846, 852, 857, 858, 863], "practic": [29, 822, 827, 830, 843, 845, 875], "jax_kornia": [29, 32, 33, 814, 866], "000000000034": [29, 32, 33, 814, 866], "raw_img": [29, 32, 33, 814, 866], "sharp": [29, 32, 33, 814], "prefer": [29, 32, 33, 248, 633, 821, 829, 835, 836, 840, 843, 858, 872], "whole": [30, 58, 81, 379, 382, 492, 505, 506, 508, 822, 828, 837], "full": [30, 58, 63, 81, 85, 86, 98, 99, 101, 166, 253, 261, 324, 325, 326, 327, 328, 370, 377, 378, 379, 450, 451, 457, 458, 486, 489, 580, 589, 604, 612, 630, 631, 633, 635, 637, 638, 652, 654, 655, 656, 658, 681, 685, 687, 688, 778, 785, 814, 821, 822, 828, 831, 834, 835, 838, 839, 843, 846, 849, 851, 857, 862, 863, 870, 872, 878], "complex": [30, 32, 33, 46, 52, 57, 58, 63, 71, 74, 78, 80, 81, 86, 94, 111, 112, 113, 114, 115, 116, 117, 118, 119, 143, 144, 159, 173, 182, 188, 221, 222, 223, 224, 225, 226, 227, 230, 238, 239, 241, 242, 244, 246, 254, 255, 256, 257, 258, 262, 263, 264, 265, 274, 276, 277, 279, 281, 284, 285, 286, 287, 288, 291, 292, 296, 301, 302, 304, 339, 344, 345, 368, 373, 376, 377, 388, 399, 410, 420, 421, 425, 430, 431, 432, 443, 445, 531, 532, 593, 594, 627, 630, 631, 633, 635, 638, 645, 648, 673, 674, 675, 679, 686, 688, 690, 692, 695, 748, 763, 764, 766, 778, 789, 808, 817, 820, 823, 828, 831, 833, 840, 843, 846, 847, 849, 854, 855, 856, 857, 859, 866, 868, 870, 872, 874, 878, 879], "neccessari": 30, "set_random_se": [30, 49], "301436": 30, "_c": 30, "0x7f252c392390": 30, "flatten": [30, 32, 33, 46, 48, 51, 58, 59, 63, 65, 68, 69, 81, 82, 86, 88, 91, 92, 341, 357, 373, 377, 379, 388, 428, 474, 484, 488, 493, 494, 497, 499, 521, 528, 529, 530, 531, 532, 533, 546, 550, 635, 638, 640, 645, 646, 676, 683, 695, 701, 706, 708, 745, 746, 750, 751, 752, 753, 772, 774, 814, 842, 849], "keyword": [30, 32, 33, 48, 50, 53, 54, 58, 75, 81, 104, 140, 275, 376, 379, 388, 424, 485, 523, 537, 540, 573, 602, 630, 633, 635, 638, 642, 648, 689, 725, 766, 772, 774, 778, 794, 795, 807, 820, 826, 829, 831, 832, 840, 842, 843, 844, 846, 847, 849, 854, 865, 866, 867], "input_arrai": [30, 32, 33, 842], "torch_model": [30, 32, 33, 50], "159": [30, 74, 111, 627, 637, 661], "thank": [30, 854, 862], "fledg": [30, 821, 851, 852], "output_arrai": [30, 32, 33, 58, 455], "0893": 30, "1504": 30, "1372": 30, "0991": 30, "0867": 30, "0851": 30, "0911": 30, "0804": 30, "0926": 30, "0881": 30, "softmaxbackward0": 30, "furthermor": 30, "relat": [30, 248, 633, 814, 816, 819, 820, 821, 822, 828, 835, 843, 846, 847, 848, 849, 866, 875], "regress": [31, 872, 879], "checkout": [32, 47, 822, 825, 846], "f705efe7cb5d18df17ce6c1e20f04d0eb4933f48": 32, "theoret": 32, "aspect": [32, 33, 815, 841, 854, 872], "easiest": [32, 814, 816, 821, 858], "defer": [32, 33, 820, 826, 831, 832, 839, 842, 843, 846, 878], "similarli": [32, 45, 140, 148, 224, 329, 336, 337, 370, 373, 630, 633, 827, 831, 843, 849, 853, 878], "essenc": [32, 873, 878], "becom": [32, 58, 81, 98, 347, 373, 379, 465, 640, 700, 802, 822, 823, 829, 831, 833, 835, 842, 857, 861, 863, 865], "slide": [32, 58, 62, 81, 85, 376, 395, 396, 397, 413, 414, 415, 416, 419, 423, 637, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 793], "regressor": [32, 33], "input_dim": [32, 33, 47], "output_dim": [32, 33, 47], "linear0": [32, 33, 44, 854, 855], "linear1": [32, 33, 44, 854, 855], "adam": [32, 33, 44, 48, 60, 83, 537, 616, 617, 622, 635, 636, 797, 854, 855, 856, 872], "n_training_exampl": [32, 33], "2000": [32, 33, 81, 315, 370], "random_norm": [32, 33, 62, 63, 67, 85, 86, 90, 546, 635, 637, 638, 644, 652, 654, 655, 656, 658, 659, 663, 688], "linspac": [32, 33, 54, 77, 127, 630, 838, 849, 851, 879], "execute_with_gradi": [32, 33, 44, 48, 636, 854, 855, 856, 857], "lambda": [32, 33, 49, 51, 81, 124, 126, 298, 308, 545, 558, 618, 619, 621, 626, 629, 635, 636, 638, 642, 674, 726, 727, 731, 820, 839, 840, 841, 844, 849, 851, 854], "2d": [32, 33, 48, 58, 81, 98, 314, 370, 376, 377, 379, 388, 391, 392, 400, 401, 443, 450, 464, 474, 523, 793, 812, 843, 849], "5f": [32, 33], "nonetheless": [32, 33], "gc": [32, 33, 558, 635], "decompos": [32, 33, 58, 81, 98, 101, 324, 325, 326, 327, 328, 349, 356, 370, 373, 377, 441, 446, 449, 452, 843, 856], "said": [32, 33, 779, 847, 863, 865], "otherwis": [32, 33, 50, 53, 54, 55, 57, 58, 59, 62, 63, 68, 69, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 98, 111, 112, 113, 114, 115, 116, 117, 118, 119, 124, 127, 129, 130, 135, 137, 138, 139, 142, 144, 150, 153, 154, 156, 157, 159, 160, 161, 162, 163, 172, 176, 180, 181, 197, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 299, 300, 301, 304, 305, 306, 307, 308, 310, 311, 312, 314, 324, 325, 326, 327, 328, 335, 336, 337, 338, 339, 341, 342, 343, 351, 352, 358, 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"x0": [32, 33, 51, 82, 538, 635, 833], "normalize_trac": [32, 33], "html": [32, 33, 47, 57, 58, 80, 81, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 630, 631, 633, 638, 640, 648, 686, 687, 715, 765, 834, 862], "fname": [32, 33, 49, 51, 795, 854], "anticip": [32, 33], "addition": [32, 33, 829, 842, 843, 878], "normalize_native_comp": [32, 33], "return_backend_compiled_fn": 32, "immedi": [32, 33, 812, 814, 820, 821], "built": [32, 33, 38, 46, 48, 51, 127, 630, 793, 794, 795, 821, 822, 828, 829, 846, 852, 858, 865, 871, 872, 876], "eager_graph": [32, 33, 814, 865, 866], "lazy_graph": [32, 33, 814, 865, 866], "thought": [32, 33, 821, 822, 838, 862, 870], "matter": [32, 33, 38, 833, 861], "haven": [32, 33, 38, 858, 872], "jax_out": [32, 33], "ideal": [32, 33, 830, 831, 843, 849, 854], "worth": [32, 33], "differenti": [32, 33, 296, 366, 367, 368, 375, 872], "chosen": [32, 33, 51, 101, 127, 229, 630, 633, 645, 749, 820, 830, 843], "plai": [32, 33, 378, 457, 814, 817, 821, 823, 826, 832, 836, 843, 846, 856, 872, 875], "role": [32, 33, 814, 817, 822, 823, 832, 843, 852, 873, 875, 879], "dl": [32, 33], "effortlessli": [32, 33], "previous": [32, 33, 604, 635, 802, 820, 821, 827, 839, 841, 846, 851], "default_devic": [32, 33, 207, 210, 211, 212, 218, 219, 632, 832, 835, 836], "as_n": [32, 33, 55, 56, 75, 78, 79, 159, 160, 161, 162, 163, 164, 170, 197, 198, 631, 632, 831], "certainli": [32, 33, 862, 878], "unnecessari": [32, 33, 843], "extend": [32, 33, 58, 81, 379, 388, 485, 526, 827, 828, 831, 834, 835, 838, 843, 847, 857, 869, 872, 878], "infrastructur": [32, 33, 868, 874, 875], "least": [32, 57, 58, 63, 80, 81, 241, 259, 274, 376, 379, 388, 404, 409, 463, 464, 465, 474, 476, 523, 633, 638, 645, 678, 748, 822, 826, 830, 831, 832, 833, 839, 842, 846, 866], "coco": 32, "seamlessli": [33, 846], "therefor": [33, 38, 54, 57, 58, 63, 80, 81, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 148, 149, 150, 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832, 833, 834, 835, 838, 839, 840, 842, 843, 844, 846, 847, 849, 851, 853, 855, 857, 861, 869, 872, 878], "wide": [33, 814, 822, 846, 870, 872], "plenti": 33, "resourc": [33, 815, 820, 821, 830], "visit": [33, 820, 821, 822, 830], "page": [33, 814, 820, 821, 822, 828, 830, 836, 852, 853, 856, 858, 867, 880], "newli": [34, 35, 47, 49, 55, 78, 153, 540, 631, 635, 822, 830, 842, 846], "randon": [34, 35, 37, 38, 39], "mean_": 34, "std_": 34, "detect": [34, 38, 57, 75, 80, 256, 633, 642, 719, 730, 820, 821, 827, 829, 830, 837, 846, 854, 855], "inspect": [34, 38, 536, 635], "__": [34, 35, 36, 37, 38, 39, 75, 833, 854], "script": [35, 814, 821, 822, 825, 830, 833, 851, 857, 872], "comp": 35, "low_level": 35, "chain": [35, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 98, 111, 112, 113, 114, 115, 116, 117, 118, 119, 135, 137, 142, 144, 150, 154, 156, 169, 173, 174, 181, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 300, 304, 305, 306, 307, 308, 310, 311, 312, 314, 335, 336, 337, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 400, 401, 402, 420, 453, 454, 455, 456, 457, 458, 459, 460, 469, 470, 491, 493, 495, 497, 502, 504, 505, 506, 508, 510, 523, 524, 525, 526, 535, 538, 539, 541, 542, 546, 547, 548, 549, 550, 553, 554, 557, 559, 561, 562, 563, 565, 566, 569, 577, 578, 592, 593, 594, 596, 598, 600, 601, 614, 620, 625, 641, 642, 651, 652, 653, 654, 660, 661, 667, 668, 669, 674, 675, 676, 677, 678, 679, 681, 683, 685, 686, 692, 697, 698, 699, 700, 704, 707, 708, 709, 710, 711, 714, 715, 716, 717, 721, 732, 739, 740, 741, 742, 744, 747, 750, 751, 752, 753, 754, 758, 759, 762, 764, 765, 767, 768, 769, 798, 826, 829, 841, 843, 855, 856, 857, 872], "un": [35, 171, 631, 831, 851], "partial_comp": 35, "time_funct": 35, "express": [35, 57, 58, 80, 81, 99, 222, 226, 228, 229, 238, 240, 280, 286, 291, 360, 373, 633, 799, 808, 834, 843, 851, 856, 872, 873], "maxim": [35, 839, 842, 851, 869, 870, 874, 875, 876], "conclud": [36, 847], "norm_comp": [37, 38], "global": [37, 38, 48, 59, 75, 82, 104, 159, 160, 161, 162, 163, 212, 213, 214, 583, 584, 587, 593, 594, 606, 607, 610, 631, 632, 635, 785, 796, 802, 821, 826, 827, 830, 831, 832, 835, 839, 843, 851, 872], "b": [38, 52, 57, 58, 59, 62, 63, 71, 74, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 98, 99, 102, 103, 104, 111, 112, 113, 114, 115, 116, 117, 118, 128, 129, 130, 135, 136, 137, 139, 142, 144, 150, 153, 154, 155, 156, 164, 174, 176, 181, 198, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 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233, 234, 235, 236, 238, 239, 241, 244, 246, 248, 254, 255, 256, 257, 258, 262, 263, 264, 265, 266, 271, 274, 279, 283, 286, 287, 288, 289, 290, 291, 292, 295, 305, 309, 355, 360, 368, 373, 376, 377, 378, 379, 388, 412, 420, 431, 453, 454, 493, 497, 523, 535, 538, 559, 560, 564, 565, 566, 567, 568, 569, 596, 614, 630, 631, 632, 633, 635, 638, 640, 641, 646, 649, 668, 669, 670, 672, 676, 677, 678, 680, 681, 683, 684, 686, 687, 692, 694, 695, 701, 716, 717, 718, 750, 751, 752, 753, 754, 768, 769, 779, 785, 792, 796, 829, 831, 832, 834, 839, 843, 846, 848, 849, 861], "think": [38, 820, 822, 830, 833, 849, 873], "uniqu": [38, 48, 58, 59, 69, 81, 82, 92, 376, 377, 379, 424, 447, 484, 485, 499, 570, 635, 641, 642, 646, 716, 717, 718, 721, 725, 750, 751, 752, 753, 779, 814, 825, 829, 839, 843, 844, 845, 849, 857, 861, 875], "rule": [38, 55, 57, 58, 63, 78, 80, 81, 86, 153, 156, 179, 180, 181, 230, 241, 274, 276, 283, 285, 293, 295, 376, 379, 388, 420, 473, 523, 631, 633, 638, 640, 668, 669, 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822, 831, 842, 843, 846], "valueerror": [54, 58, 65, 77, 81, 88, 92, 129, 376, 378, 410, 421, 458, 463, 464, 471, 473, 475, 476, 477, 484, 500, 640, 703, 704, 705, 707, 709, 710, 712, 714, 753, 779, 809, 835], "buffer": [54, 77, 81, 88, 129, 135, 463, 464, 471, 473, 475, 476, 477, 484, 500, 630, 703, 704, 705, 707, 709, 710, 712, 714, 794, 795, 842, 857], "nativedtyp": [54, 55, 58, 62, 63, 67, 68, 71, 77, 81, 86, 90, 91, 94, 127, 128, 129, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 149, 150, 152, 153, 158, 159, 160, 161, 162, 163, 164, 165, 170, 171, 175, 177, 179, 183, 193, 313, 314, 315, 316, 317, 318, 319, 334, 341, 357, 370, 373, 383, 388, 509, 510, 511, 512, 513, 523, 524, 525, 526, 529, 532, 630, 631, 637, 638, 644, 645, 647, 648, 660, 679, 695, 740, 741, 742, 745, 746, 756, 758, 759, 762, 764, 766, 792, 831, 832, 838, 847, 851], "datatyp": [54, 58, 75, 77, 81, 129, 137, 141, 158, 179, 183, 376, 424, 630, 631, 772, 847, 865], "nativedevic": [54, 56, 58, 67, 77, 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"multiply"]], "fmod": [[250, "fmod"]], "divide": [[241, "divide"]], "erf": [[243, "erf"]], "log1p": [[264, "log1p"]], "nan_to_num": [[275, "nan-to-num"]], "unset_default_device": [[218, "unset-default-device"]], "add": [[224, "add"]], "set_split_factor": [[212, "set-split-factor"]], "unset_default_complex_dtype": [[188, "unset-default-complex-dtype"]], "set_default_float_dtype": [[184, "set-default-float-dtype"]], "percent_used_mem_on_dev": [[208, "percent-used-mem-on-dev"]], "unset_default_int_dtype": [[191, "unset-default-int-dtype"]], "asinh": [[227, "asinh"]], "asin": [[226, "asin"]], "dev_util": [[199, "dev-util"]], "atan2": [[229, "atan2"]], "print_all_ivy_arrays_on_dev": [[209, "print-all-ivy-arrays-on-dev"]], "as_native_dev": [[195, "as-native-dev"]], "to_device": [[215, "to-device"]], "total_mem_on_dev": [[216, "total-mem-on-dev"]], "as_ivy_dev": [[194, "as-ivy-dev"]], "handle_soft_device_variable": [[204, "handle-soft-device-variable"]], "abs": [[221, "abs"]], "valid_dtype": [[193, "valid-dtype"]], "function_supported_devices": [[200, "function-supported-devices"]], "num_ivy_arrays_on_dev": [[207, "num-ivy-arrays-on-dev"]], "unset_default_dtype": [[189, "unset-default-dtype"]], "set_soft_device_mode": [[211, "set-soft-device-mode"]], "unset_default_uint_dtype": [[192, "unset-default-uint-dtype"]], "angle": [[225, "angle"]], "dev": [[198, "dev"]], "set_default_device": [[210, "set-default-device"]], "unset_soft_device_mode": [[219, "unset-soft-device-mode"]], "num_cpu_cores": [[205, "num-cpu-cores"]], "unset_default_float_dtype": [[190, "unset-default-float-dtype"]], "set_default_int_dtype": [[185, "set-default-int-dtype"]], "split_factor": [[213, "split-factor"]], "type_promote_arrays": [[187, "type-promote-arrays"]], "used_mem_on_dev": [[220, "used-mem-on-dev"]], "set_default_uint_dtype": [[186, "set-default-uint-dtype"]], "tpu_is_available": [[217, "tpu-is-available"]], "acos": [[222, "acos"]], "default_device": [[197, "default-device"]], "clear_cached_mem_on_dev": [[196, "clear-cached-mem-on-dev"]], "get_all_ivy_arrays_on_dev": [[202, "get-all-ivy-arrays-on-dev"]], "num_gpus": [[206, "num-gpus"]], "gpu_is_available": [[203, "gpu-is-available"]], "split_func_call": [[214, "split-func-call"]], "acosh": [[223, "acosh"]], "function_unsupported_devices": [[201, "function-unsupported-devices"]], "atan": [[228, "atan"]], "Quickstart": [[33, "Quickstart"]], "Get familiar with Ivy": [[33, "Get-familiar-with-Ivy"]], "Functional API": [[33, "Functional-API"]], "Stateful API": [[33, "Stateful-API"]], "Tracing code": [[33, "Tracing-code"]], "Any function": [[33, "Any-function"], [32, "Any-function"]], "Any library": [[33, "Any-library"], [32, "Any-library"]], "Any model": [[33, "Any-model"], [32, "Any-model"]], "Tutorials And Examples": [[21, "tutorials-and-examples"]], "Learn the basics": [[21, "learn-the-basics"], [22, "learn-the-basics"]], "Guides": [[21, "guides"], [16, "guides"]], "Examples and Demos": [[21, "examples-and-demos"], [3, "examples-and-demos"]], "ODSC Ivy Demo": [[32, "ODSC-Ivy-Demo"]], "Ivy Backend Handler": [[32, "Ivy-Backend-Handler"], [23, "Ivy-Backend-Handler"]], "Data Structures": [[32, "Data-Structures"], [23, "Data-Structures"]], "Ivy Functional API": [[32, "Ivy-Functional-API"], [23, "Ivy-Functional-API"]], "Graph Tracer": [[32, "Graph-Tracer"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[8, "Imports"], [15, "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"]], "2.0: Kornia": [[41, "2.0:-Kornia"]], "3.1: Stable Diffusion": [[43, "3.1:-Stable-Diffusion"]], "0.0: Unify": [[34, "0.0:-Unify"]], "1.0: Lazy vs Eager": [[37, "1.0:-Lazy-vs-Eager"]], "Unify": [[37, "Unify"], [28, "Unify"], [27, "Unify"], [38, "Unify"], [39, "Unify"]], "Compile": [[37, "Compile"], [38, "Compile"], [39, "Compile"]], "Transpile": [[37, "Transpile"], [28, "Transpile"], [27, "Transpile"], [38, "Transpile"], [39, "Transpile"]], "Accelerating PyTorch models with JAX": [[14, "Accelerating-PyTorch-models-with-JAX"]], "Transpile code": [[26, "Transpile-code"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "Write Ivy code": [[23, "Write-Ivy-code"]], "Contents": [[23, "Contents"]], "Installing Ivy": [[23, "Installing-Ivy"]], "Importing Ivy": [[23, "Importing-Ivy"], [0, "Importing-Ivy"]], "0.2: Transpile": [[36, "0.2:-Transpile"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"], [13, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"], [13, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"], [13, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"], [13, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[6, "Comparing-the-results"], [7, "Comparing-the-results"], [13, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[6, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"], [13, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[6, "Conclusion"], [7, "Conclusion"], [13, "Conclusion"]], "How to use decorators": [[28, "How-to-use-decorators"]], "Trace": [[28, "Trace"], [27, "Trace"]], "Transpiling a PyTorch model to build on top": [[17, "Transpiling-a-PyTorch-model-to-build-on-top"]], "0.1: Compile": [[35, "0.1:-Compile"]], "How To Convert Models from PyTorch to PaddlePaddle": [[7, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[7, "About-the-Model"]], "Transpiling the Model": [[7, "Transpiling-the-Model"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Lazy vs Eager": [[27, "Lazy-vs-Eager"]], "3.0: Perceiver": [[42, "3.0:-Perceiver"]], "Training PyTorch ResNet in your TensorFlow Projects": [[13, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Transpiling a PyTorch model to TensorFlow": [[13, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "Installation": [[13, "Installation"], [4, "Installation"], [12, "Installation"]], "Load the Data": [[13, "Load-the-Data"]], "Visualize a few images": [[13, "Visualize-a-few-images"]], "Load the pre-trained model": [[13, "Load-the-pre-trained-model"]], "Basic Operations with Ivy": [[44, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[44, "Installs-\ud83d\udcbe"], [45, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[44, "Imports-\ud83d\udec3"], [45, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[44, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[44, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[44, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[44, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[44, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[44, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[44, "Set-Backend-Framework"]], "Define Model": [[44, "Define-Model"], [45, "Define-Model"]], "Create Model": [[44, "Create-Model"]], "Create Optimizer": [[44, "Create-Optimizer"]], "Input and Target": [[44, "Input-and-Target"]], "Loss Function": [[44, "Loss-Function"]], "Training Loop": [[44, "Training-Loop"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "1.1: Framework Selection": [[38, "1.1:-Framework-Selection"]], "Trace code": [[25, "Trace-code"]], "Transpile any library": [[29, "Transpile-any-library"]], "Accelerating XGBoost with JAX": [[15, "Accelerating-XGBoost-with-JAX"]], "Tests": [[15, "Tests"]], "Loading the Data": [[15, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[15, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[15, "JAX-backend"]], "Tensorflow backend": [[15, "Tensorflow-backend"]], "PyTorch backend": [[15, "PyTorch-backend"]], "More exhaustive example": [[15, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[15, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[15, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[15, "Comparison-of-Metrics"]], "Transpile any model": [[30, "Transpile-any-model"]], "Round up": [[30, "Round-up"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Ivy AlexNet inference in Torch": [[4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[4, "TensorFlow-inference"]], "JAX inference": [[4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "1.2: As a Decorator": [[39, "1.2:-As-a-Decorator"]], "1.3: Dynamic vs Static": [[40, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[40, "Dynamic"]], "Static": [[40, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[40, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Transpiling a haiku model to build on top": [[18, "Transpiling-a-haiku-model-to-build-on-top"]], "Transpiling a Tensorflow model to build on top": [[19, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Compilation of a Basic Function": [[45, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[45, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[45, "Function-compilation-\ud83d\udee0"]], "Set backend": [[45, "Set-backend"]], "Sample input": [[45, "Sample-input"]], "Define function to compile": [[45, "Define-function-to-compile"]], "Compile the function": [[45, "Compile-the-function"]], "Check results": [[45, "Check-results"], [45, "id1"]], "Compiling simple neural network \ud83e\udde0": [[45, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[45, "Create-model"]], "Define input": [[45, "Define-input"]], "Compile network": [[45, "Compile-network"]], "# 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"]], "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"]], "Developing a convolutional network using Ivy": [[20, "Developing-a-convolutional-network-using-Ivy"]], "Write a model using Ivy": [[31, "Write-a-model-using-Ivy"]], "Unify code": [[24, "Unify-code"]], "End-to-End Training Pipeline in Ivy": [[48, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[48, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[48, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[48, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[48, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[48, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[48, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[48, "Plotting-the-training-metrics"]], "Save the trained Model": [[48, "Save-the-trained-Model"]], "Image": [[61, "module-ivy.data_classes.array.image"], [84, "module-ivy.data_classes.container.image"]], "Ivy as a Transpiler Introduction": [[50, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[50, "To-use-the-transpiler:"]], "Transpiler Interface": [[50, "Transpiler-Interface"]], "Telemetry": [[50, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[50, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[50, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[50, "3.-Transpile-Models-\ud83c\udf10"]], "Conversions": [[53, "module-ivy.data_classes.array.conversions"], [76, "module-ivy.data_classes.container.conversions"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[46, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[46, "Table-of-Contents"]], "Defining the model": [[46, "Defining-the-model"]], "Model construction": [[46, "Model-construction"]], "Some helper functions": [[46, "Some-helper-functions"]], "Transpiling the model": [[46, "Transpiling-the-model"]], "PyTorch pipeline": [[46, "PyTorch-pipeline"]], "Dataset download": [[46, "Dataset-download"]], "DataLoader": [[46, "DataLoader"]], "Training": [[46, "Training"]], "Deepmind PerceiverIO on GPU": [[47, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[47, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[47, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and 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