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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 0x7f20b3979120>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fdde1d61120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

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

    • @@ -1474,7 +1474,7 @@

    Meta#

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

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f20b3979120>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fdde1d61120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

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

    • @@ -1551,7 +1551,7 @@

    Meta#

    variables (Container) – Variables to be optimized.

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f20b3979120>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fdde1d61120>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    • diff --git a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html index ea1e2a950..f69765db6 100644 --- a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html +++ b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html @@ -1423,7 +1423,7 @@

    fomaml_stepContainer) – Variables to be optimized during the meta step

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f20b3979120>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fdde1d61120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

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

    • diff --git a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html index 78bdb734e..726b4744f 100644 --- a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html +++ b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html @@ -1423,7 +1423,7 @@

    maml_stepContainer) – Variables to be optimized during the meta step

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f20b3979120>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fdde1d61120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

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

    • diff --git a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html index 291697340..b2e477e89 100644 --- a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html +++ b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html @@ -1420,7 +1420,7 @@

    reptile_stepContainer) – Variables to be optimized.

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f20b3979120>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fdde1d61120>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    • diff --git a/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html b/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html index 1a65632fa..8dae05cc4 100644 --- a/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html +++ b/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html @@ -1409,7 +1409,7 @@

    Should not be used inside any of the test functions.

    -ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f20a7755f10>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7fddd5b45f00>#
    diff --git a/ivy/docs/stateful/ivy.stateful.layers.html b/ivy/docs/stateful/ivy.stateful.layers.html index 303866cdf..d0ae25b9b 100644 --- a/ivy/docs/stateful/ivy.stateful.layers.html +++ b/ivy/docs/stateful/ivy.stateful.layers.html @@ -1536,8 +1536,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f20b37d0730>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f20b37d06d0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fdde198b6a0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fdde198b640>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1574,8 +1574,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f20b37d0670>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f20b37d0610>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fdde198b5e0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fdde198b580>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1613,8 +1613,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f20b37d05b0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f20b37d0550>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fdde198b520>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fdde198b4c0>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1651,8 +1651,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f20b37d04f0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f20b37d0490>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fdde198b460>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fdde198b400>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1690,8 +1690,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f20b37d0370>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f20b37d0ca0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fdde198b2e0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fdde198b280>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1728,8 +1728,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f20b37d0d00>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f20b37d1150>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fdde198b220>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fdde198b1c0>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1792,8 +1792,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f20b37d0430>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f20b37d03d0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fdde198b3a0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fdde198b340>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1949,7 +1949,7 @@

    • input_channels – Number of input channels for the layer

    • output_channels – Number of output channels for the layer

      • -

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f20b37d1090>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fdde198b160>) – Initializer for the weights. Default is GlorotUniform.

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

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

      • input_channels – Number of input channels for the layer.

      • output_channels – Number of output channels for the layer.

        • -

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f20b37d1240>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f20b37d0790>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fdde198b760>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fdde198b700>) – 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.

        • diff --git a/ivy/searchindex.js b/ivy/searchindex.js index 17d4ac92e..39e702bde 100644 --- a/ivy/searchindex.js +++ b/ivy/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["demos/Contributor_demos/Credit Card Fraud Detection/Credit_Card_Fraud_Detection", "demos/README", "demos/assets/01_template", "demos/examples_and_demos", "demos/examples_and_demos/alexnet_demo", "demos/examples_and_demos/bert_demo", "demos/examples_and_demos/convnext_to_torch", "demos/examples_and_demos/dinov2_to_paddle", "demos/examples_and_demos/image_segmentation_with_ivy_unet", "demos/examples_and_demos/lstm_tensorflow_to_torch", "demos/examples_and_demos/lstm_torch_to_tensorflow", "demos/examples_and_demos/mmpretrain_to_jax", "demos/examples_and_demos/resnet_demo", "demos/examples_and_demos/torch_to_jax", "demos/examples_and_demos/xgboost_demo", "demos/guides", "demos/guides/01_transpiling_a_torch_model", "demos/guides/02_transpiling_a_haiku_model", "demos/guides/03_transpiling_a_tf_model", 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752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 777, 805, 826, 837, 844], "colab": [2, 5, 13, 14, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 45, 47, 49, 50], "manual": [2, 6, 7, 13, 14, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 641, 718, 728, 729, 818, 819, 820, 829, 835, 844, 853, 856], "mind": [2, 16, 18, 22, 28, 31, 35, 818, 819, 824, 827, 844, 856, 864], "click": [2, 4, 47, 818, 819, 820, 828, 832, 834, 835, 850], "runtim": [2, 4, 5, 8, 11, 12, 13, 24, 31, 34, 45, 46, 822, 837, 844, 847, 870], "restart": [2, 4, 5, 8, 12, 45, 46, 819, 834], "git": [2, 4, 5, 8, 12, 31, 45, 46, 47, 48, 812, 814, 817, 819, 820, 823, 826, 828, 834, 835, 844, 856], "clone": [2, 4, 8, 12, 31, 45, 47, 48, 812, 814, 820, 834, 856], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 18, 26, 27, 28, 29, 31, 32, 45, 46, 47, 48, 49, 50, 56, 57, 79, 80, 82, 147, 155, 243, 253, 254, 269, 328, 335, 336, 369, 372, 375, 378, 387, 419, 492, 522, 615, 616, 629, 630, 632, 635, 637, 639, 647, 685, 686, 714, 764, 812, 814, 819, 820, 823, 826, 828, 829, 832, 834, 856, 864], "github": [2, 4, 5, 8, 11, 12, 13, 31, 45, 46, 47, 48, 49, 812, 814, 815, 817, 820, 821, 823, 826, 828, 829, 831, 832, 834, 835, 843, 844, 856, 859, 878], "com": [2, 4, 5, 6, 7, 8, 11, 12, 13, 18, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 823, 826, 828, 829, 834, 856], "unifyai": [2, 4, 8, 12, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 826, 834, 856], "model": [2, 3, 4, 9, 14, 15, 20, 21, 22, 48, 50, 240, 273, 377, 453, 632, 789, 793, 794, 810, 812, 852, 853, 857, 863, 864, 868, 869, 870, 871, 872, 873, 874, 876, 877], "depth": [2, 4, 6, 8, 12, 46, 53, 57, 61, 76, 80, 84, 141, 375, 378, 411, 471, 545, 557, 629, 634, 636, 654, 655, 820, 828, 852, 853, 854, 856], "repositori": [2, 4, 8, 12, 814, 818, 819, 820, 822, 823, 826, 834, 843, 861], "cd": [2, 4, 8, 12, 31, 48, 812, 814, 819, 820, 834, 856], "resnet": [3, 6, 13, 20, 31, 863, 864], "imag": [3, 4, 6, 7, 11, 13, 16, 20, 28, 31, 32, 45, 46, 47, 48, 49, 50, 57, 61, 79, 80, 84, 102, 220, 221, 222, 223, 226, 229, 238, 241, 243, 245, 254, 255, 256, 261, 263, 276, 283, 284, 286, 287, 291, 375, 394, 395, 411, 412, 413, 415, 545, 632, 634, 636, 649, 650, 651, 652, 653, 656, 657, 658, 792, 812, 819, 834, 847, 849, 850, 852, 854, 856, 863, 864, 870], "classif": [3, 4, 12, 14, 20, 45, 812, 870], "acceler": [3, 20, 812, 829, 841, 868, 872, 873, 874, 875], "convert": [3, 8, 9, 11, 13, 14, 16, 18, 20, 21, 23, 25, 28, 29, 31, 32, 33, 35, 37, 45, 48, 50, 52, 53, 56, 74, 75, 76, 79, 97, 127, 128, 140, 150, 151, 193, 194, 195, 196, 207, 215, 219, 239, 279, 378, 383, 462, 463, 464, 513, 578, 596, 598, 599, 600, 602, 629, 630, 631, 632, 634, 637, 641, 695, 719, 730, 731, 773, 801, 805, 812, 818, 824, 825, 838, 839, 841, 844, 846, 849, 855, 857, 861, 864, 868, 869, 876], "faster": [3, 4, 9, 11, 13, 14, 20, 31, 32, 48, 50, 57, 62, 80, 85, 376, 449, 637, 687, 814, 817, 826, 857, 872, 875], "infer": [3, 6, 7, 9, 11, 13, 14, 20, 24, 34, 36, 37, 46, 48, 50, 53, 57, 58, 61, 64, 76, 80, 81, 84, 87, 126, 128, 131, 135, 136, 140, 143, 149, 158, 159, 160, 161, 162, 312, 313, 375, 378, 382, 411, 496, 510, 556, 590, 591, 629, 630, 634, 636, 639, 659, 706, 801, 802, 822, 825, 829, 830, 844, 849, 854, 864, 868, 869, 872, 874], "mmpretrain": [3, 20], "segment": [3, 20, 57, 80, 330, 331, 332, 369, 826, 831], "unet": [3, 20], "alexnet": [3, 20], "written": [3, 4, 5, 6, 20, 22, 31, 32, 45, 58, 378, 473, 819, 823, 824, 832, 835, 836, 840, 841, 845, 849, 851, 854, 855, 859, 864, 868, 870, 874, 876, 877], "xgboost": [3, 20], "paddlepaddl": [3, 20, 335, 336, 372, 819], "dinov2": [3, 7, 20], "project": [3, 12, 13, 20, 25, 26, 27, 28, 29, 31, 32, 35, 98, 636, 663, 792, 812, 814, 815, 818, 819, 820, 821, 824, 825, 826, 844, 853, 855, 859, 860, 861, 864, 866, 868, 870, 873, 877, 878], "convnext": [3, 6, 11, 20], "video": [4, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 813, 818, 819, 820, 823, 824, 825, 827, 828, 829, 830, 831, 832, 833, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 847, 856, 868], "tutori": [4, 6, 7, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 820, 841, 856], "three": [4, 5, 20, 26, 36, 37, 47, 57, 139, 312, 369, 378, 464, 629, 819, 820, 827, 828, 829, 831, 841, 844, 847, 848, 849, 871, 876], "major": [4, 5, 644, 747, 829, 830, 842, 844, 855, 860, 867, 870], "ml": [4, 5, 6, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 47, 50, 812, 813, 817, 841, 848, 849, 850, 852, 853, 854, 858, 860, 861, 864, 866, 867, 868, 869, 870, 873, 875, 877], "framework": [4, 5, 7, 9, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 35, 36, 38, 45, 47, 49, 52, 58, 170, 192, 202, 205, 216, 543, 559, 563, 595, 598, 630, 631, 634, 641, 720, 771, 773, 777, 784, 789, 796, 801, 802, 812, 815, 816, 818, 819, 822, 823, 824, 825, 826, 828, 829, 830, 831, 833, 834, 836, 837, 838, 840, 841, 844, 845, 847, 848, 849, 851, 854, 855, 856, 857, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 871, 874], "sinc": [4, 8, 12, 28, 29, 31, 32, 45, 47, 57, 80, 98, 372, 812, 814, 819, 820, 823, 824, 825, 826, 827, 828, 829, 830, 833, 840, 841, 855, 860, 870, 876], "automat": [4, 8, 9, 12, 29, 31, 32, 37, 818, 819, 820, 822, 825, 826, 828, 829, 835, 837, 840, 844, 847, 848, 850, 853, 854, 856, 857, 861, 870, 873, 877], "sure": [4, 8, 11, 12, 13, 14, 31, 45, 815, 818, 819, 820, 823, 828, 833, 834, 841, 842, 844, 847, 856], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 26, 27, 29, 46, 57, 62, 74, 85, 103, 375, 377, 398, 456, 580, 634, 637, 680, 794, 810, 812, 819, 820, 821, 824, 827, 829, 837, 838, 839, 840, 841, 844, 845, 848, 850, 852, 854, 855, 857, 860, 863, 868, 869, 870, 871, 872, 873, 876, 877], "dm": [4, 5, 8, 11, 13, 31, 32, 43, 45], "haiku": [4, 5, 8, 11, 13, 29, 31, 32, 43, 45, 49, 789, 812, 854, 861, 864, 870], "exit": [4, 8, 12, 31, 32, 830], "download": [4, 6, 7, 12, 16, 18, 31, 32, 46, 47, 50, 814, 819, 826, 844, 863, 864], "imagenet": [4, 6, 18, 46, 48, 812], "class": [4, 6, 7, 8, 12, 14, 16, 18, 22, 31, 32, 43, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 134, 143, 149, 165, 168, 181, 183, 184, 243, 280, 338, 360, 372, 386, 387, 395, 396, 429, 528, 529, 536, 545, 549, 562, 572, 595, 629, 630, 631, 632, 634, 636, 637, 638, 641, 642, 657, 662, 666, 672, 682, 686, 687, 689, 696, 712, 719, 730, 737, 752, 759, 763, 764, 773, 774, 781, 782, 783, 784, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 800, 801, 805, 810, 812, 818, 825, 826, 827, 829, 830, 831, 832, 836, 838, 839, 842, 843, 844, 847, 849, 850, 852, 853, 854, 857, 863, 864, 868, 870, 871, 877], "wget": [4, 6, 8, 12, 45, 46, 49, 819], "raw": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 45, 48, 49, 74, 812, 832, 864, 871], "githubusercont": [4, 6, 8, 12, 45, 49], "hub": [4, 6, 8, 12, 45, 48, 50], "master": [4, 8, 12, 23, 24, 25, 33, 34, 35, 36, 37, 38, 45, 47, 48, 49, 815, 828, 870, 878], "imagenet_class": [4, 12], "categori": [4, 6, 12, 818, 823, 824, 827, 829, 833, 841, 845, 848], "strip": [4, 12, 24, 34, 860], "readlin": [4, 12, 46], "cat": [4, 7, 12, 46, 842, 847, 849, 854, 863, 864], "jpg": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 47, 48, 812, 864], "filenam": [4, 8, 12, 31, 32, 45, 47, 50, 58, 794, 800, 852], "import": [4, 6, 7, 9, 10, 11, 13, 16, 18, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 46, 48, 49, 50, 57, 68, 72, 76, 80, 95, 194, 195, 199, 211, 307, 387, 522, 557, 573, 631, 634, 640, 645, 716, 717, 752, 784, 801, 802, 812, 817, 818, 819, 820, 821, 823, 824, 825, 826, 827, 829, 830, 831, 832, 835, 838, 839, 840, 841, 842, 843, 844, 845, 849, 851, 852, 854, 855, 856, 860, 863, 864, 865, 866, 868, 870, 873, 874, 876], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 46, 47, 50, 53, 57, 66, 74, 76, 80, 89, 102, 105, 106, 107, 126, 127, 128, 130, 131, 132, 135, 136, 137, 138, 140, 141, 142, 143, 145, 146, 147, 148, 149, 193, 194, 195, 196, 197, 198, 199, 200, 201, 206, 207, 208, 209, 211, 212, 213, 214, 215, 217, 219, 312, 313, 328, 329, 369, 382, 472, 508, 509, 511, 512, 536, 550, 551, 629, 634, 643, 738, 739, 740, 741, 771, 773, 774, 789, 791, 792, 793, 794, 795, 796, 797, 798, 810, 812, 820, 822, 825, 829, 833, 837, 838, 842, 844, 845, 847, 849, 854, 855, 856, 857, 860, 869, 870, 872, 873, 874, 875], "torchvis": [4, 6, 11, 12, 45, 861], "transform": [4, 5, 6, 7, 11, 12, 13, 28, 31, 32, 45, 46, 48, 57, 61, 80, 84, 375, 376, 397, 398, 403, 404, 407, 408, 409, 419, 420, 423, 440, 636, 660, 776, 779, 792, 812, 838, 844, 854, 857, 863, 864, 868, 870, 871, 872], "pil": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 46, 47, 48, 812, 864], "time": [4, 5, 6, 7, 9, 10, 11, 13, 29, 31, 32, 37, 45, 47, 48, 49, 57, 59, 62, 68, 80, 82, 91, 97, 98, 134, 341, 372, 375, 376, 378, 387, 404, 409, 421, 423, 444, 451, 484, 490, 522, 616, 621, 629, 635, 636, 637, 639, 640, 644, 645, 659, 662, 677, 712, 715, 716, 717, 744, 745, 749, 750, 792, 793, 794, 810, 818, 819, 820, 823, 825, 827, 828, 829, 831, 834, 836, 837, 838, 840, 841, 844, 845, 849, 852, 854, 855, 856, 859, 860, 861, 863, 864, 868, 870, 871, 874, 875, 876], "filterwarn": [4, 5], "ignor": [4, 5, 44, 52, 53, 57, 74, 80, 139, 375, 376, 378, 387, 399, 400, 401, 430, 438, 446, 486, 487, 491, 530, 629, 636, 641, 663, 729, 730, 796, 819, 826, 828, 831, 844, 855, 876], "compos": [4, 6, 7, 11, 12, 31, 32, 45, 57, 80, 375, 389, 390, 391, 392, 819, 827, 841, 844, 863, 865, 870, 877], "resiz": [4, 6, 7, 8, 11, 12, 45, 46, 57, 80, 375, 411, 847], "centercrop": [4, 12], "224": [4, 6, 7, 12, 16, 18, 31, 32, 45, 46, 48, 812, 864], "totensor": [4, 6, 7, 11, 12, 45], "485": [4, 12, 45], "456": [4, 12, 45, 844], "406": [4, 12, 45, 57, 80, 397, 540, 634], "229": [4, 12, 45, 279, 632], "225": [4, 12, 45, 47, 234, 632], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 812, 852, 864], "ipython": [4, 8, 12, 26, 27, 28, 29, 31, 32, 50], "displai": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 50, 819, 826, 828, 833, 844, 852], "end": [4, 8, 45, 46, 57, 80, 126, 228, 284, 353, 372, 375, 377, 378, 423, 452, 474, 484, 486, 487, 629, 632, 806, 812, 819, 820, 825, 828, 834, 840, 845, 847, 848, 855, 868, 873], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 217, 631, 830], "ivy_model": [4, 5, 8, 12, 48], "ivy_alexnet": 4, "quick": [4, 20, 32, 820, 822, 842, 853], "trace_graph": [4, 5, 8, 12, 24, 25, 26, 27, 31, 32, 34, 35, 36, 37, 38, 39, 48, 794, 812, 849, 854, 862], "moment": [4, 57, 59, 80, 82, 376, 433, 615, 616, 621, 635, 796, 810, 818, 825, 855, 863, 864], "cost": [4, 59, 82, 615, 616, 619, 621, 622, 623, 635, 640, 715, 716, 717, 806, 829, 847, 868], "arg": [4, 6, 8, 9, 10, 11, 12, 16, 18, 26, 27, 29, 31, 32, 36, 37, 38, 49, 52, 74, 96, 106, 122, 203, 213, 601, 628, 629, 631, 634, 771, 773, 788, 789, 792, 793, 794, 798, 801, 805, 810, 812, 824, 829, 830, 833, 839, 840, 841, 847, 849, 853, 863, 864, 865], "asarrai": [4, 5, 8, 11, 12, 46, 53, 57, 58, 69, 76, 80, 81, 92, 127, 385, 514, 515, 545, 556, 560, 561, 591, 592, 593, 629, 634, 636, 645, 646, 650, 750, 754, 833, 838, 841, 842], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 22, 31, 46, 47, 50, 53, 57, 66, 76, 80, 89, 137, 138, 141, 193, 194, 195, 211, 382, 508, 509, 511, 512, 629, 631, 637, 643, 688, 738, 739, 740, 741, 791, 792, 793, 794, 795, 796, 797, 810, 849, 855, 857, 875], "output": [4, 5, 7, 8, 9, 10, 12, 22, 28, 29, 31, 32, 44, 45, 46, 48, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 152, 154, 179, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 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863, 865, 870, 871, 873], "__init__": [4, 8, 16, 18, 31, 32, 43, 44, 45, 47, 74, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 774, 781, 782, 783, 788, 791, 792, 793, 794, 795, 796, 797, 800, 801, 805, 807, 810, 812, 818, 824, 825, 829, 833, 841, 845, 849, 851, 852, 853, 854, 864], "self": [4, 6, 7, 8, 16, 18, 31, 32, 43, 44, 45, 47, 49, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 103, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 128, 129, 131, 133, 134, 136, 137, 138, 139, 140, 141, 143, 145, 146, 147, 149, 152, 153, 154, 155, 163, 165, 168, 171, 172, 173, 175, 177, 180, 197, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 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"mlp": 16, "mixer": 16, "onli": [16, 18, 31, 32, 37, 43, 45, 47, 49, 52, 53, 56, 57, 62, 64, 66, 74, 76, 79, 80, 85, 87, 89, 97, 100, 102, 118, 138, 178, 179, 208, 268, 269, 274, 280, 312, 342, 349, 369, 372, 375, 376, 378, 382, 387, 398, 411, 421, 430, 435, 449, 451, 462, 463, 464, 474, 508, 509, 525, 539, 626, 629, 630, 631, 632, 634, 636, 637, 639, 641, 643, 644, 646, 647, 663, 677, 684, 687, 688, 703, 706, 718, 719, 725, 726, 728, 729, 730, 735, 736, 739, 740, 741, 744, 745, 755, 761, 764, 774, 776, 777, 779, 792, 796, 805, 810, 812, 813, 814, 818, 819, 820, 823, 824, 825, 826, 827, 828, 829, 830, 831, 833, 836, 837, 839, 840, 841, 842, 844, 845, 846, 847, 849, 851, 852, 853, 854, 855, 859, 863, 864, 869, 870, 871, 876, 877], "retriev": [16, 18, 22, 535, 557, 582, 634, 820, 841], "mlp_encod": [16, 31, 32, 812, 864], "create_model": [16, 31, 32, 812, 864], "mixer_b16_224": [16, 31, 32, 812, 864], "nois": [16, 18, 31, 32, 812, 863, 864], "randn": [16, 18, 31, 32, 378, 496, 812, 864], 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135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 163, 164, 165, 166, 167, 168, 170, 171, 172, 173, 174, 175, 176, 177, 178, 180, 192, 193, 194, 196, 197, 198, 199, 200, 201, 202, 204, 205, 206, 207, 209, 212, 213, 214, 215, 216, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 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634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 662, 663, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 721, 724, 725, 726, 727, 728, 729, 730, 735, 736, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 773, 776, 777, 778, 779, 789, 794, 796, 801, 806, 808, 812, 829, 830, 832, 833, 839, 840, 841, 842, 845, 849, 854, 864], "eagertensor": [16, 22, 43, 801, 842], "deepmind": [17, 861], "perceiverio": [17, 861], "backbon": [17, 45, 812, 849, 852], "TO": [17, 19, 30], "replac": [17, 19, 30, 46, 56, 57, 58, 64, 66, 74, 79, 80, 81, 87, 89, 132, 274, 310, 313, 367, 369, 378, 489, 492, 496, 576, 577, 581, 629, 632, 634, 639, 643, 699, 738, 776, 820, 826, 827, 829, 830, 838, 841, 844, 851, 854, 855, 860, 864, 877], "efficientnet": 18, "eff_encod": [18, 812], "efficientnet_v2": [18, 812], "efficientnetv2b0": [18, 812], "storag": [18, 45, 46, 852, 860], "googleapi": [18, 45, 46], "efficientnetv2": 18, "b0_notop": 18, "h5": [18, 74], "24274472": 18, "0u": 18, "torch_eff_encod": [18, 812], "modes_to_trac": 18, "1280": [18, 545, 634, 812], "welcom": [20, 46, 812, 813, 819, 820, 821, 843], "varieti": [20, 823, 828, 829, 830, 844, 846, 866, 868, 872, 873, 876, 877], "organ": [20, 824, 827, 837, 841, 843, 845, 857, 860], "main": [20, 32, 53, 57, 62, 80, 85, 132, 145, 146, 147, 313, 328, 329, 369, 376, 378, 427, 473, 629, 637, 670, 671, 691, 812, 815, 818, 819, 820, 821, 823, 826, 827, 834, 838, 840, 868, 870, 871, 876], "exactli": [20, 24, 34, 43, 44, 48, 290, 632, 818, 827, 828, 829, 830, 831, 833, 844, 847, 859, 861], "rush": [20, 861], "jump": [20, 842], "straight": [20, 812, 828, 841, 844, 851], "quickstart": [20, 812], "introduct": [20, 22, 29, 31, 32, 870], "point": [20, 29, 54, 56, 57, 62, 66, 68, 70, 77, 79, 80, 85, 89, 93, 126, 127, 128, 130, 132, 135, 142, 143, 148, 152, 165, 169, 173, 180, 220, 221, 222, 223, 225, 226, 227, 228, 229, 236, 237, 238, 240, 241, 243, 245, 246, 247, 253, 254, 255, 256, 261, 262, 263, 264, 265, 273, 275, 276, 278, 280, 282, 283, 284, 285, 286, 287, 288, 290, 291, 292, 293, 294, 312, 313, 315, 335, 336, 353, 354, 357, 359, 369, 372, 375, 376, 377, 382, 387, 390, 399, 400, 401, 419, 429, 449, 453, 508, 509, 510, 511, 512, 522, 523, 524, 532, 627, 629, 630, 632, 637, 643, 644, 645, 646, 647, 667, 669, 672, 673, 674, 676, 678, 679, 680, 683, 684, 685, 686, 687, 688, 689, 691, 694, 740, 741, 747, 749, 750, 751, 752, 755, 757, 758, 760, 761, 762, 763, 764, 765, 766, 801, 802, 810, 816, 818, 819, 820, 823, 824, 826, 828, 829, 831, 832, 834, 836, 840, 841, 844, 845, 847, 849, 851, 852, 861, 863, 876], "showcas": [20, 812], "real": [20, 28, 56, 57, 70, 79, 80, 93, 102, 112, 115, 118, 142, 143, 220, 221, 222, 223, 225, 226, 227, 228, 229, 238, 240, 241, 243, 245, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 270, 273, 275, 276, 278, 282, 283, 284, 286, 287, 288, 289, 290, 291, 293, 294, 335, 336, 342, 343, 344, 354, 372, 375, 376, 398, 419, 420, 429, 430, 626, 629, 632, 637, 644, 647, 672, 673, 674, 678, 685, 687, 688, 691, 694, 747, 760, 762, 763, 764, 765, 827, 872], "world": [20, 28, 820, 872], "beginn": [20, 813, 870], "got": [20, 43, 833], "cover": [20, 31, 57, 80, 375, 412, 413, 414, 818, 823, 824, 826, 829, 831, 832, 837, 838, 844, 847, 848], "familiar": [20, 21, 22, 818, 819], "concept": [20, 21, 22], "turn": [20, 21, 24, 34, 61, 84, 97, 98, 399, 400, 401, 636, 659, 792, 819, 826, 827, 830, 831, 841, 844, 861], "unus": [20, 21, 24, 831, 840], "part": [20, 21, 24, 53, 56, 57, 79, 80, 85, 102, 112, 115, 118, 145, 146, 147, 253, 257, 280, 328, 329, 355, 369, 372, 375, 376, 378, 387, 419, 430, 484, 532, 626, 629, 632, 637, 673, 674, 773, 812, 818, 819, 820, 821, 823, 826, 829, 835, 837, 840, 841, 844, 845, 847, 849, 850, 854, 855, 863, 864, 865, 868, 870, 875, 876, 877], "lazi": [20, 21, 24, 27, 34, 37, 38, 49], "decor": [20, 21, 26, 28, 29, 37, 49, 539, 634, 776, 778, 784, 816, 823, 824, 827, 829, 830, 834, 837, 840, 841, 842, 847], "kornia": [20, 21, 28, 31, 32, 45, 49, 812, 864], "roundup": 22, "indep": [22, 31], "proof": [22, 31], "delv": [22, 32, 812], "theori": [22, 814, 826], "esenti": [22, 31], "abstract": [22, 31, 32, 791, 796, 812, 827, 829, 840, 841, 844, 847, 853, 859, 868, 870, 872, 873, 877], "quirk": [22, 31], "perk": [22, 31, 812, 824, 827], "under": [22, 31, 32, 57, 377, 456, 457, 805, 812, 818, 819, 822, 823, 830, 831, 832, 835, 841, 842, 844, 847, 848, 849, 852, 854, 855, 863, 864, 870, 873, 877], "hood": [22, 31, 32, 812, 822, 830, 831, 835, 841, 844, 847, 848, 849, 852, 854, 863, 864, 877], "appropi": 22, "string": [22, 31, 32, 47, 57, 58, 61, 74, 80, 84, 150, 151, 163, 170, 192, 193, 194, 195, 196, 198, 207, 214, 215, 219, 375, 376, 378, 418, 422, 430, 484, 495, 524, 543, 630, 631, 634, 636, 637, 649, 650, 651, 652, 654, 656, 658, 674, 771, 773, 777, 805, 806, 825, 826, 828, 829, 830, 833, 841, 849, 852], "simplest": [22, 819, 831, 844, 847], "interact": [22, 31, 46, 49, 818, 869, 870, 875], "submodul": [22, 31, 45, 47, 102, 103, 626, 627, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 788, 789, 791, 792, 794, 795, 796, 797, 818, 819, 820, 823, 826, 828, 830, 834, 837, 838, 844, 848, 849, 853, 857], "likewis": [22, 27, 31, 38, 812, 820, 827, 829, 832, 836, 837, 841, 847, 852, 863, 864, 876], "nativearrai": [22, 31, 32, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 68, 70, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 127, 128, 129, 131, 136, 137, 138, 139, 140, 141, 143, 145, 146, 149, 152, 153, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 171, 172, 173, 175, 177, 179, 180, 186, 196, 197, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 313, 314, 317, 318, 322, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 367, 369, 372, 373, 375, 376, 377, 378, 381, 382, 383, 385, 387, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 415, 417, 418, 419, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 440, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 467, 468, 469, 470, 472, 473, 474, 475, 476, 478, 479, 481, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 522, 523, 524, 525, 526, 534, 537, 538, 540, 541, 545, 546, 547, 549, 552, 553, 554, 555, 556, 558, 560, 561, 562, 565, 568, 569, 571, 576, 577, 578, 581, 590, 591, 592, 593, 594, 595, 597, 599, 600, 602, 613, 615, 616, 617, 619, 621, 622, 623, 624, 626, 628, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 662, 663, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 719, 720, 721, 725, 726, 727, 730, 735, 736, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 797, 824, 827, 831, 833, 836, 837, 838, 840, 841, 845, 846, 849, 851, 857], "alia": [22, 31, 335, 336, 372, 627, 818, 841, 862, 865], "lastli": [22, 31, 824], "subclass": [22, 31, 32, 838, 841, 847, 864], "dict": [22, 31, 32, 45, 49, 52, 58, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 125, 134, 136, 141, 143, 149, 153, 155, 166, 167, 168, 172, 173, 180, 196, 199, 200, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 302, 303, 304, 305, 306, 307, 309, 310, 311, 313, 325, 334, 335, 336, 337, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 369, 378, 398, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463, 464, 468, 469, 484, 490, 492, 493, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 535, 537, 538, 540, 541, 545, 546, 547, 548, 549, 550, 551, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 572, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 624, 628, 630, 631, 634, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 691, 696, 697, 698, 699, 703, 706, 707, 708, 709, 710, 713, 714, 718, 719, 721, 724, 725, 726, 727, 729, 730, 731, 735, 736, 738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 773, 774, 789, 792, 794, 801, 806, 824, 827, 852, 853, 857, 863, 864, 865], "recurs": [22, 31, 32, 45, 47, 52, 74, 75, 166, 167, 199, 200, 376, 448, 550, 551, 557, 630, 631, 634, 641, 718, 719, 722, 728, 729, 730, 771, 819, 823, 826, 827, 834, 837, 840, 853, 855], "fashion": [22, 778, 844, 864], "native_arrai": [22, 31, 32, 53, 54, 56, 76, 78, 79, 80, 81, 85, 92, 110, 113, 136, 139, 141, 143, 149, 152, 153, 154, 155, 163, 168, 175, 197, 206, 214, 230, 234, 239, 240, 241, 243, 247, 251, 259, 260, 268, 273, 276, 279, 282, 287, 335, 336, 363, 372, 377, 378, 458, 484, 490, 494, 534, 537, 564, 565, 568, 599, 626, 629, 630, 631, 632, 634, 636, 637, 638, 639, 643, 644, 647, 648, 650, 651, 658, 666, 669, 673, 674, 679, 680, 684, 688, 689, 691, 694, 696, 698, 699, 706, 738, 747, 756, 762, 765, 767, 773, 783, 801, 816, 834, 842, 844], "data_class": [22, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 395, 396, 545, 549, 687, 712], "low": [22, 31, 34, 50, 57, 61, 66, 80, 84, 89, 375, 418, 422, 636, 643, 649, 650, 651, 652, 654, 656, 658, 739, 741, 778, 827, 833, 840, 841, 847, 849, 866, 868, 870, 871, 872, 874, 876], "c": [22, 31, 37, 46, 47, 53, 57, 58, 59, 61, 64, 70, 76, 77, 79, 80, 81, 82, 84, 85, 87, 91, 93, 97, 98, 116, 127, 128, 138, 141, 165, 168, 223, 234, 240, 241, 261, 262, 264, 273, 276, 284, 291, 375, 376, 378, 381, 387, 389, 390, 391, 392, 403, 408, 424, 426, 428, 429, 431, 443, 462, 463, 464, 474, 492, 496, 501, 502, 503, 506, 524, 537, 545, 546, 547, 548, 556, 560, 561, 591, 600, 615, 616, 619, 621, 622, 623, 626, 629, 630, 632, 634, 635, 636, 637, 639, 641, 644, 645, 647, 650, 651, 652, 653, 654, 655, 657, 672, 674, 676, 706, 710, 718, 721, 725, 726, 727, 729, 730, 735, 736, 747, 752, 758, 759, 764, 766, 795, 805, 806, 813, 819, 822, 825, 826, 827, 831, 837, 839, 848, 849, 850, 852, 855, 857, 858, 860, 861, 864, 866, 870, 874, 875, 877], "fundament": [22, 31, 828, 841, 847, 849, 859, 870], "signatur": [22, 31, 378, 387, 484, 522, 829, 830, 831, 832, 836, 840, 844, 845, 847, 860, 867, 876], "matmul": [22, 31, 32, 48, 62, 85, 376, 446, 614, 634, 637, 687, 825, 844, 845, 849], "to_n": [22, 31, 32, 43, 52, 75, 849], "jaxlib": [22, 28, 46, 801, 819, 824, 829, 830, 836, 845, 849, 851], "xla_extens": [22, 28, 801, 824, 829, 830, 836, 845, 849, 851], "arrayimpl": [22, 28, 801], "disabl": [22, 31, 57, 80, 378, 492, 794, 810, 826], "array_mod": [22, 31, 578, 602, 634, 846], "set_array_mod": [22, 31, 602, 634, 846], "ultim": [22, 31, 863], "sigmoid": [22, 31, 32, 43, 51, 57, 73, 80, 301, 367, 382, 508, 626, 788, 849, 852, 853], "z": [22, 31, 32, 44, 45, 53, 56, 57, 58, 62, 63, 66, 68, 70, 76, 79, 80, 81, 85, 86, 87, 89, 93, 102, 103, 137, 138, 140, 141, 201, 223, 224, 228, 230, 233, 235, 240, 251, 252, 255, 256, 257, 259, 260, 265, 267, 269, 270, 271, 272, 280, 289, 300, 301, 335, 336, 338, 367, 372, 377, 387, 453, 455, 456, 457, 458, 459, 465, 469, 480, 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223, 240, 273, 283, 381, 382, 506, 508, 632, 637, 643, 668, 686, 738, 812, 823, 829, 831, 838, 849, 854, 864, 868], "div": [23, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 865], "sub": [23, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 57, 62, 64, 74, 75, 79, 80, 81, 85, 87, 103, 272, 376, 378, 387, 430, 470, 479, 499, 528, 529, 557, 634, 637, 639, 640, 671, 691, 708, 715, 716, 717, 818, 820, 822, 827, 833, 841, 842, 844, 851, 852, 853, 865, 866], "with_numpi": 23, "reproduc": [23, 48, 61, 84, 636, 659, 776, 777, 778, 779, 784, 816, 823, 834], "x_": [23, 33, 98, 284, 632, 865], "66391283": 23, "12516928": 23, "38367081": 23, "03102401": 23, "76419425": 23, "52797794": 23, "90346956": 23, "61316347": 23, "27585283": 23, "66309303": 23, "ivy_repo": 23, "sever": [23, 24, 33, 34, 36, 37, 38, 57, 80, 97, 375, 376, 389, 390, 391, 392, 444, 776, 819, 820, 845, 855, 868, 874], "pro": [23, 24, 25, 33, 34, 35, 36, 37, 38], "pick": [24, 34, 791], "trigger": [24, 34, 794, 818, 835], "unif": 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846, 847, 870], "act": [25, 35, 57, 80, 298, 363, 373, 820, 831, 846, 855, 877], "shorthand": [25, 35, 37, 844], "pair": [25, 35, 45, 57, 61, 80, 84, 228, 247, 320, 362, 369, 372, 375, 409, 418, 420, 422, 632, 636, 637, 649, 650, 651, 652, 654, 656, 658, 666, 668, 806], "93968587": 25, "26075466": 25, "22723222": 25, "06276492": 25, "47426987": 25, "72835908": 25, "71737559": 25, "50411096": 25, "65419174": 25, "15576624": 25, "implic": [25, 35, 36, 39, 827], "satisfi": [26, 27, 28, 29, 45, 47, 50, 57, 375, 376, 398, 430, 829, 831], "fw": [26, 27, 28, 29, 61, 84, 387, 522, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 773, 819, 844], "mxnet": [26, 27, 28, 29, 209, 631, 801, 818, 819, 860, 877], "einop": [26, 27, 28, 29, 45, 47, 50, 58, 81, 545, 546, 547, 634, 829, 860], "miniconda": [26, 27, 28, 29], "multienv": [26, 27, 28, 29], "site": [26, 27, 28, 29, 871], "psutil": [26, 27, 28, 29, 45, 47, 50], "termcolor": [26, 27, 28, 29, 45, 47, 50, 74, 103], "colorama": [26, 27, 28, 29, 45, 47], "535": [26, 27, 28, 29, 51, 73, 118, 626, 833], "diskcach": [26, 27, 28, 29, 45], "auth": [26, 27, 28, 29], "urllib3": [26, 27, 28, 29, 45], "pyvi": [26, 27, 28, 29, 31, 32], "dill": [26, 27, 28, 29, 45], "astunpars": [26, 27, 28, 29], "cloudpickl": [26, 27, 28, 29], "gast": [26, 27, 28, 29], "wheel": [26, 27, 28, 29, 45, 47, 50, 859], "six": [26, 27, 28, 29, 45, 50, 819, 847], "cachetool": [26, 27, 28, 29], "pyasn1": [26, 27, 28, 29], "rsa": [26, 27, 28, 29], "jinja2": [26, 27, 28, 29], "jsonpickl": [26, 27, 28, 29], "networkx": [26, 27, 28, 29, 50], "charset": [26, 27, 28, 29, 45], "idna": [26, 27, 28, 29, 45], "certifi": [26, 27, 28, 29, 45], "2017": [26, 27, 28, 29, 45, 636, 663], "jedi": [26, 27, 28, 29], "inlin": [26, 27, 28, 29, 826], "prompt": [26, 27, 28, 29, 818, 820], "toolkit": [26, 27, 28, 29, 870, 871, 877], "pygment": [26, 27, 28, 29], "traitlet": [26, 27, 28, 29], "exceptiongroup": [26, 27, 28, 29], "pexpect": [26, 27, 28, 29], "markupsaf": [26, 27, 28, 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56, 62, 79, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 139, 142, 143, 144, 145, 146, 148, 149, 155, 165, 168, 180, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 335, 336, 338, 372, 375, 378, 387, 419, 492, 496, 522, 629, 630, 632, 637, 639, 644, 645, 646, 647, 648, 667, 668, 669, 670, 671, 673, 674, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 693, 694, 700, 702, 703, 704, 706, 707, 709, 710, 714, 744, 745, 747, 748, 749, 750, 751, 752, 753, 756, 760, 761, 762, 763, 764, 765, 766, 767, 768, 817, 819, 820, 832, 834, 835, 844, 867, 870, 877], "sympi": [28, 860], "fsspec": [28, 45], "mpmath": 28, "often": [28, 57, 377, 452, 817, 823, 833, 836, 837, 841, 844, 855, 861, 871, 874, 877], "fortun": [28, 29, 823], "everyth": [28, 46, 805, 812, 818, 819, 820, 821, 822, 828, 831, 840, 841, 842, 844, 850, 855, 856, 861], "practic": [28, 820, 825, 828, 841, 843, 873], "everi": [28, 31, 32, 37, 45, 53, 57, 58, 80, 81, 135, 136, 301, 335, 336, 349, 367, 372, 375, 378, 412, 413, 414, 421, 498, 534, 629, 634, 818, 820, 823, 825, 826, 828, 829, 831, 835, 836, 837, 838, 840, 841, 842, 844, 849, 851, 853, 863, 864, 865, 870], "jax_kornia": [28, 31, 32, 812, 864], "though": [28, 817, 818, 820, 829, 830, 832, 837, 840, 841, 847, 852, 855], "000000000034": [28, 31, 32, 812, 864], "raw_img": [28, 31, 32, 812, 864], "sharp": [28, 31, 32, 812], "prefer": [28, 31, 32, 247, 632, 819, 827, 833, 834, 838, 841, 856, 870], "whole": [29, 57, 80, 378, 381, 491, 504, 505, 507, 820, 826, 835], "full": [29, 57, 62, 80, 84, 85, 97, 98, 100, 165, 252, 260, 323, 324, 325, 326, 327, 369, 376, 377, 378, 449, 450, 456, 457, 485, 488, 579, 588, 603, 611, 629, 630, 632, 634, 636, 637, 651, 653, 654, 655, 657, 680, 684, 686, 687, 777, 784, 812, 819, 820, 826, 829, 832, 833, 836, 837, 841, 844, 847, 849, 855, 860, 861, 868, 870, 876], "complex": [29, 31, 32, 45, 51, 56, 57, 62, 70, 73, 77, 79, 80, 85, 93, 110, 111, 112, 113, 114, 115, 116, 117, 118, 142, 143, 158, 172, 181, 187, 220, 221, 222, 223, 224, 225, 226, 229, 237, 238, 240, 241, 243, 245, 253, 254, 255, 256, 257, 261, 262, 263, 264, 273, 275, 276, 278, 280, 283, 284, 285, 286, 287, 290, 291, 295, 300, 301, 303, 338, 343, 344, 367, 372, 375, 376, 387, 398, 409, 419, 420, 424, 429, 430, 431, 442, 444, 530, 531, 592, 593, 626, 629, 630, 632, 634, 637, 644, 647, 672, 673, 674, 678, 685, 687, 689, 691, 694, 747, 762, 763, 765, 777, 788, 806, 815, 818, 821, 826, 829, 831, 838, 841, 844, 845, 847, 852, 853, 854, 855, 857, 864, 866, 868, 870, 872, 876, 877], "neccessari": 29, "set_random_se": [29, 48], "301436": 29, "_c": 29, "0x7f252c392390": 29, "flatten": [29, 31, 32, 45, 47, 50, 57, 58, 62, 64, 67, 68, 80, 81, 85, 87, 90, 91, 340, 356, 372, 376, 378, 387, 427, 473, 483, 487, 492, 493, 496, 498, 520, 527, 528, 529, 530, 531, 532, 545, 549, 634, 637, 639, 644, 645, 675, 682, 694, 700, 705, 707, 744, 745, 749, 750, 751, 752, 771, 773, 812, 840, 847], "keyword": [29, 31, 32, 47, 49, 52, 53, 57, 74, 80, 103, 139, 274, 375, 378, 387, 423, 484, 522, 536, 539, 572, 601, 629, 632, 634, 637, 641, 647, 688, 724, 765, 771, 773, 777, 793, 794, 805, 818, 824, 827, 829, 830, 838, 840, 841, 842, 844, 845, 847, 852, 863, 864, 865], "input_arrai": [29, 31, 32, 840], "torch_model": [29, 31, 32, 49], "159": [29, 73, 110, 626, 636, 660], "thank": [29, 852, 860], "fledg": [29, 819, 849, 850], "output_arrai": [29, 31, 32, 57, 454], "0893": 29, "1504": 29, "1372": 29, "0991": 29, "0867": 29, "0851": 29, "0911": 29, "0804": 29, "0926": 29, "0881": 29, "softmaxbackward0": 29, "furthermor": 29, "relat": [29, 247, 632, 812, 814, 817, 818, 819, 820, 826, 833, 841, 844, 845, 846, 847, 864, 873], "continu": [29, 31, 32, 47, 125, 287, 295, 367, 628, 632, 812, 817, 818, 819, 822, 823, 834, 840, 843, 844, 855, 860, 861, 870], "regress": [30, 870, 877], "checkout": [31, 46, 820, 823, 844], "f705efe7cb5d18df17ce6c1e20f04d0eb4933f48": 31, "theoret": 31, "aspect": [31, 32, 813, 839, 852, 870], "easiest": [31, 812, 814, 819, 856], "defer": [31, 32, 818, 824, 829, 830, 837, 840, 841, 844, 876], "similarli": [31, 44, 139, 147, 223, 328, 335, 336, 369, 372, 629, 632, 825, 829, 841, 847, 851, 876], "essenc": [31, 871, 876], "becom": [31, 57, 80, 97, 346, 372, 378, 464, 639, 699, 801, 820, 821, 827, 829, 831, 833, 840, 855, 859, 861, 863], "slide": [31, 57, 61, 80, 84, 375, 394, 395, 396, 412, 413, 414, 415, 418, 422, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 792], "regressor": [31, 32, 812], "input_dim": [31, 32, 46, 812], "output_dim": [31, 32, 46, 812], "linear0": [31, 32, 43, 812, 852, 853], "linear1": [31, 32, 43, 812, 852, 853], "instanti": [31, 32, 784, 832], "adam": [31, 32, 43, 47, 59, 82, 536, 615, 616, 621, 634, 635, 796, 812, 852, 853, 854, 870], "n_training_exampl": [31, 32, 812], "2000": [31, 32, 80, 314, 369, 812], "random_norm": [31, 32, 61, 62, 66, 84, 85, 89, 545, 634, 636, 637, 643, 651, 653, 654, 655, 657, 658, 662, 687, 812], "linspac": [31, 32, 53, 76, 126, 629, 812, 836, 847, 849, 877], "pred": [31, 32, 46, 47, 57, 63, 80, 86, 377, 453, 456, 638, 696, 697, 698, 812, 827, 837, 840], "gradient": [31, 32, 45, 47, 57, 80, 97, 213, 364, 372, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 631, 640, 715, 716, 717, 773, 784, 796, 812, 822, 845, 852, 853, 855, 870], "grad": [31, 32, 43, 47, 615, 635, 796, 812, 839, 852, 853, 854], "execute_with_gradi": [31, 32, 43, 47, 635, 812, 852, 853, 854, 855], "lambda": [31, 32, 48, 50, 80, 123, 125, 297, 307, 544, 557, 617, 618, 620, 625, 628, 634, 635, 637, 641, 673, 725, 726, 730, 812, 818, 837, 838, 839, 842, 847, 849, 852], "2d": [31, 32, 47, 57, 80, 97, 313, 369, 375, 376, 378, 387, 390, 391, 399, 400, 442, 449, 463, 473, 522, 792, 810, 812, 841, 847], "5f": [31, 32, 812], "nonetheless": [31, 32], "extract": [31, 32, 39, 46, 57, 80, 98, 378, 467, 493, 841, 843, 845, 866, 870, 871, 876], "gc": [31, 32, 557, 634], "decompos": [31, 32, 57, 80, 97, 100, 323, 324, 325, 326, 327, 348, 355, 369, 372, 376, 440, 445, 448, 451, 841, 854], "said": [31, 32, 778, 845, 861, 863], "otherwis": [31, 32, 49, 52, 53, 54, 56, 57, 58, 61, 62, 67, 68, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 126, 128, 129, 134, 136, 137, 138, 141, 143, 149, 152, 153, 155, 156, 158, 159, 160, 161, 162, 171, 175, 179, 180, 196, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 300, 303, 304, 305, 306, 307, 309, 310, 311, 313, 323, 324, 325, 326, 327, 334, 335, 336, 337, 338, 340, 341, 342, 350, 351, 357, 359, 361, 362, 363, 367, 369, 372, 375, 376, 378, 381, 394, 395, 396, 399, 400, 401, 419, 432, 447, 449, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463, 464, 468, 469, 470, 472, 474, 475, 476, 483, 490, 492, 493, 494, 496, 499, 501, 503, 504, 505, 507, 509, 521, 522, 523, 524, 525, 534, 537, 538, 540, 541, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 569, 576, 577, 591, 592, 593, 595, 597, 599, 600, 601, 613, 617, 619, 624, 628, 629, 630, 631, 632, 634, 635, 636, 637, 640, 641, 644, 645, 646, 647, 648, 650, 651, 652, 653, 659, 660, 661, 663, 666, 667, 668, 669, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 687, 691, 693, 694, 696, 697, 698, 699, 702, 703, 704, 706, 707, 708, 709, 710, 711, 713, 714, 715, 716, 731, 738, 739, 740, 741, 743, 744, 745, 746, 748, 749, 750, 751, 752, 753, 755, 757, 758, 760, 761, 762, 763, 764, 765, 766, 767, 768, 771, 776, 777, 792, 794, 795, 801, 812, 820, 824, 827, 829, 830, 831, 837, 838, 840, 844, 849, 856, 863, 864], "x0": [31, 32, 50, 81, 537, 634, 831], "normalize_trac": [31, 32], "html": [31, 32, 46, 56, 57, 79, 80, 147, 155, 243, 253, 254, 269, 328, 335, 336, 369, 372, 375, 378, 387, 419, 492, 522, 629, 630, 632, 637, 639, 647, 685, 686, 714, 764, 832, 860], "fname": [31, 32, 48, 50, 794, 852], "anticip": [31, 32], "addition": [31, 32, 827, 840, 841, 876], "normalize_native_comp": [31, 32], "return_backend_compiled_fn": 31, "immedi": [31, 32, 810, 818, 819], "built": [31, 32, 37, 45, 47, 50, 126, 629, 792, 793, 794, 812, 819, 820, 826, 827, 844, 850, 856, 863, 869, 870, 874], "eager_graph": [31, 32, 812, 863, 864], "lazy_graph": [31, 32, 812, 863, 864], "thought": [31, 32, 819, 820, 836, 860, 868], "matter": [31, 32, 37, 831, 859], "haven": [31, 32, 37, 856, 870], "jax_out": [31, 32], "ideal": [31, 32, 828, 829, 841, 847, 852], "worth": [31, 32], "differenti": [31, 32, 295, 365, 366, 367, 374, 870], "chosen": [31, 32, 50, 100, 126, 228, 629, 632, 644, 748, 818, 828, 841], "plai": [31, 32, 377, 456, 812, 815, 819, 821, 824, 830, 834, 841, 844, 854, 870, 873], "role": [31, 32, 812, 815, 820, 821, 830, 841, 850, 871, 873, 877], "dl": [31, 32], "effortlessli": [31, 32], "previous": [31, 32, 603, 634, 801, 818, 819, 825, 837, 839, 844, 849], "default_devic": [31, 32, 206, 209, 210, 211, 217, 218, 631, 830, 833, 834], "as_n": [31, 32, 54, 55, 74, 77, 78, 158, 159, 160, 161, 162, 163, 169, 196, 197, 630, 631, 829], "certainli": [31, 32, 812, 860, 876], "upon": [31, 32, 49, 810, 820, 821, 831, 840, 844, 847, 855, 869, 870], "unnecessari": [31, 32, 841], "extend": [31, 32, 57, 80, 378, 387, 484, 525, 825, 826, 829, 832, 833, 836, 841, 845, 855, 867, 870, 876], "infrastructur": [31, 32, 866, 872, 873], "least": [31, 56, 57, 62, 79, 80, 240, 258, 273, 375, 378, 387, 403, 408, 462, 463, 464, 473, 475, 522, 632, 637, 644, 677, 747, 812, 820, 824, 828, 829, 830, 831, 837, 840, 844, 864], "coco": 31, "seamlessli": [32, 844], "therefor": [32, 37, 53, 56, 57, 62, 79, 80, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 149, 155, 171, 175, 179, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 313, 328, 329, 335, 336, 338, 341, 369, 372, 375, 376, 378, 387, 394, 395, 396, 397, 399, 400, 401, 407, 412, 413, 414, 419, 421, 430, 477, 484, 485, 487, 492, 496, 497, 522, 525, 529, 538, 546, 547, 552, 556, 558, 560, 562, 576, 591, 595, 600, 624, 629, 630, 632, 634, 635, 636, 637, 639, 642, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 658, 659, 660, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 693, 694, 695, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 737, 744, 745, 747, 748, 749, 750, 751, 752, 753, 756, 760, 761, 762, 763, 764, 765, 766, 767, 768, 818, 820, 823, 824, 827, 828, 829, 830, 831, 832, 833, 836, 837, 838, 840, 841, 842, 844, 845, 847, 849, 851, 853, 855, 859, 867, 870, 876], "wide": [32, 812, 820, 844, 868, 870], "plenti": 32, "resourc": [32, 813, 818, 819, 828], "visit": [32, 818, 819, 820, 828], "page": [32, 812, 818, 819, 820, 826, 828, 834, 850, 851, 854, 856, 865, 878], "newli": [33, 34, 46, 48, 54, 77, 152, 539, 630, 634, 820, 828, 840, 844], "randon": [33, 34, 36, 37, 38], "mean_": 33, "std_": 33, "detect": [33, 37, 56, 74, 79, 255, 632, 641, 718, 729, 818, 819, 825, 827, 828, 835, 844, 852, 853], "inspect": [33, 37, 535, 634], "__": [33, 34, 35, 36, 37, 38, 74, 831, 852], "script": [34, 812, 819, 820, 823, 828, 831, 849, 855, 870], "comp": 34, "low_level": 34, "chain": [34, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 134, 136, 141, 143, 149, 153, 155, 168, 172, 173, 180, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 299, 303, 304, 305, 306, 307, 309, 310, 311, 313, 334, 335, 336, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 468, 469, 490, 492, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 537, 538, 540, 541, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 619, 624, 640, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 691, 696, 697, 698, 699, 703, 706, 707, 708, 709, 710, 713, 714, 715, 716, 720, 731, 738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 797, 824, 827, 839, 841, 853, 854, 855, 870], "un": [34, 170, 630, 829, 849], "partial_comp": 34, "time_funct": 34, "express": [34, 56, 57, 79, 80, 98, 221, 225, 227, 228, 237, 239, 279, 285, 290, 359, 372, 632, 798, 806, 832, 841, 849, 854, 870, 871], "maxim": [34, 837, 840, 849, 867, 868, 872, 873, 874], "conclud": [35, 845], "collect": [35, 45, 47, 49, 50, 52, 74, 75, 626, 631, 634, 635, 636, 638, 641, 642, 643, 731, 788, 792, 793, 794, 795, 796, 819, 828, 833, 834, 838, 839, 842, 844, 868, 870, 873], "norm_comp": [36, 37], "global": [36, 37, 47, 58, 74, 81, 103, 158, 159, 160, 161, 162, 211, 212, 213, 582, 583, 586, 592, 593, 605, 606, 609, 630, 631, 634, 784, 795, 801, 819, 824, 825, 828, 829, 830, 833, 837, 841, 849, 870], "b": [37, 51, 56, 57, 58, 61, 62, 70, 73, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 98, 101, 102, 103, 110, 111, 112, 113, 114, 115, 116, 117, 127, 128, 129, 134, 135, 136, 138, 141, 143, 149, 152, 153, 154, 155, 163, 173, 175, 180, 197, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 330, 333, 334, 335, 336, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 355, 356, 357, 358, 359, 361, 362, 363, 367, 369, 372, 375, 376, 377, 378, 382, 385, 387, 394, 395, 396, 397, 399, 400, 403, 407, 408, 409, 412, 413, 414, 418, 419, 422, 425, 428, 430, 432, 436, 439, 443, 446, 451, 452, 453, 455, 456, 457, 458, 462, 463, 464, 465, 468, 469, 470, 471, 474, 475, 476, 478, 479, 480, 481, 483, 484, 490, 492, 493, 494, 495, 496, 499, 500, 505, 507, 509, 510, 512, 513, 515, 522, 523, 524, 525, 527, 529, 532, 534, 537, 538, 540, 541, 544, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 569, 576, 577, 591, 592, 593, 595, 599, 600, 613, 615, 616, 617, 619, 621, 622, 623, 624, 626, 629, 630, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 655, 657, 658, 659, 660, 662, 666, 667, 668, 669, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 696, 697, 698, 699, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 721, 724, 725, 726, 727, 729, 730, 735, 736, 737, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 776, 805, 806, 810, 812, 813, 816, 820, 822, 823, 825, 827, 828, 831, 834, 837, 839, 842, 848, 849, 850, 852, 853, 854, 858, 861, 863, 866], "option": [37, 46, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 152, 153, 154, 155, 157, 158, 159, 160, 161, 162, 168, 170, 180, 192, 196, 208, 211, 212, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 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696, 698, 699, 737, 759, 761, 764, 777, 779, 818, 822, 829, 830, 831, 840, 847, 849, 857, 870, 874, 876], "broadcast_to": [54, 77, 630, 829], "can_cast": [54, 77, 630, 829, 837, 841], "accord": [54, 57, 58, 64, 70, 77, 87, 93, 155, 165, 223, 234, 240, 247, 273, 284, 319, 369, 375, 378, 420, 484, 552, 555, 576, 577, 630, 632, 634, 637, 639, 647, 693, 701, 714, 764, 766, 771, 778, 798, 805, 818, 819, 823, 829, 835, 837, 841, 844], "finfo": [54, 77, 630, 844], "resolut": [54, 77, 165, 630, 820], "4028235e": [54, 165, 630], "iinfo": [54, 77, 630], "integ": [54, 56, 57, 61, 62, 64, 66, 70, 71, 74, 79, 80, 81, 84, 85, 87, 89, 93, 94, 102, 103, 126, 135, 168, 169, 175, 179, 180, 184, 220, 230, 231, 232, 233, 234, 235, 236, 246, 247, 258, 270, 275, 278, 282, 283, 293, 294, 330, 331, 332, 335, 336, 340, 345, 346, 369, 372, 375, 378, 382, 385, 387, 403, 408, 418, 421, 422, 423, 470, 479, 484, 492, 496, 499, 508, 509, 510, 511, 512, 514, 515, 520, 522, 523, 524, 529, 532, 555, 571, 581, 614, 629, 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628, 631, 632, 634, 636, 640, 642, 663, 717, 737, 792, 806, 818, 819, 820, 825, 829, 831, 832, 835, 837, 839, 840, 841, 844, 845, 847, 851, 852, 854, 863, 870, 871, 872, 876], "__dlpack__": [55, 78, 133, 214, 629, 631], "caveat": [55, 78, 214, 377, 456, 631], "portabl": [55, 78, 214, 631, 812, 868], "_arraywithelementwis": [56, 102], "ab": [56, 62, 72, 79, 95, 102, 103, 278, 334, 351, 372, 378, 491, 632, 637, 641, 678, 688, 694, 726, 729, 773, 805, 806, 816, 824, 829, 834, 838, 841, 844, 867], "absolut": [56, 57, 62, 72, 74, 79, 80, 85, 102, 220, 284, 334, 351, 354, 360, 372, 376, 377, 430, 447, 453, 455, 632, 637, 678, 679, 680, 685, 771, 773, 776, 778, 779, 813, 819], "aco": [56, 79, 632], "invers": [56, 57, 62, 79, 80, 85, 221, 222, 225, 226, 227, 228, 229, 344, 372, 375, 385, 398, 407, 409, 419, 514, 632, 637, 676, 679, 683, 798, 829], "cosin": [56, 79, 221, 222, 237, 238, 312, 315, 369, 375, 397, 407, 632, 792], "acosh": [56, 79, 166, 167, 630, 632, 816, 834], "area": [56, 57, 79, 80, 84, 222, 226, 229, 375, 411, 418, 422, 632, 815, 840, 847, 860, 866], "hyperbol": [56, 79, 222, 226, 229, 238, 286, 290, 291, 304, 308, 367, 632], "sector": [56, 79, 222, 226, 229, 632, 860], "multipli": [56, 57, 61, 70, 79, 80, 84, 97, 223, 289, 352, 375, 376, 411, 442, 443, 523, 524, 632, 636, 647, 659, 757, 763, 820, 824, 825, 827, 831], "angl": [56, 79, 228, 238, 286, 291, 350, 372, 632], "deg": [56, 79, 224, 632], "radian": [56, 57, 79, 80, 221, 224, 225, 227, 228, 237, 239, 279, 285, 290, 359, 372, 632, 832], "degre": [56, 57, 70, 79, 80, 93, 224, 239, 279, 322, 369, 378, 490, 632, 647, 764, 766, 869], "1j": [56, 79, 80, 224, 225, 237, 238, 243, 245, 257, 280, 285, 286, 290, 338, 592, 632, 634], "2j": [56, 57, 79, 80, 224, 253, 338, 375, 403, 408, 593, 632, 634], "3j": [56, 57, 79, 80, 224, 257, 280, 338, 372, 632], "35619449": [56, 224, 632], "78539816": [56, 224, 632], "135": [56, 224, 540, 632, 634], "asin": [56, 79, 632], "sine": [56, 79, 225, 226, 285, 286, 632], "927": 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"set-default-device"]], "asin": [[225, "asin"]], "type_promote_arrays": [[186, "type-promote-arrays"]], "total_mem_on_dev": [[215, "total-mem-on-dev"]], "function_supported_devices": [[199, "function-supported-devices"]], "percent_used_mem_on_dev": [[207, "percent-used-mem-on-dev"]], "unset_soft_device_mode": [[218, "unset-soft-device-mode"]], "angle": [[224, "angle"]], "default_device": [[196, "default-device"]], "as_ivy_dev": [[193, "as-ivy-dev"]], "clear_cached_mem_on_dev": [[195, "clear-cached-mem-on-dev"]], "atan": [[227, "atan"]], "dev": [[197, "dev"]], "unset_default_complex_dtype": [[187, "unset-default-complex-dtype"]], "print_all_ivy_arrays_on_dev": [[208, "print-all-ivy-arrays-on-dev"]], "unset_default_int_dtype": [[190, "unset-default-int-dtype"]], "set_default_uint_dtype": [[185, "set-default-uint-dtype"]], "num_gpus": [[205, "num-gpus"]], "asinh": [[226, "asinh"]], "atanh": [[229, "atanh"]], "unset_default_dtype": [[188, "unset-default-dtype"]], "gpu_is_available": [[202, "gpu-is-available"]], "set_default_int_dtype": [[184, "set-default-int-dtype"]], "to_device": [[214, "to-device"]], "add": [[223, "add"]], "num_ivy_arrays_on_dev": [[206, "num-ivy-arrays-on-dev"]], "num_cpu_cores": [[204, "num-cpu-cores"]], "acosh": [[222, "acosh"]], "as_native_dev": [[194, "as-native-dev"]], "get_all_ivy_arrays_on_dev": [[201, "get-all-ivy-arrays-on-dev"]], "function_unsupported_devices": [[200, "function-unsupported-devices"]], "split_factor": [[212, "split-factor"]], "handle_soft_device_variable": [[203, "handle-soft-device-variable"]], "atan2": [[228, "atan2"]], "Deepmind PerceiverIO on GPU": [[46, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[46, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[46, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[46, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[46, "Run-the-demo..."]], "\u2026with torch backend": [[46, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[46, "....with-tensorflow-backend"]], "\u2026with jax backend": [[46, "...with-jax-backend"]], "\u2026with numpy backend": [[46, "...with-numpy-backend"]], "Resnet 18": [[50, "Resnet-18"]], "Conversions": [[75, "module-ivy.data_classes.container.conversions"], [52, "module-ivy.data_classes.array.conversions"]], "HuggingFace Tensorflow DeiT": [[48, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[48, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Image": [[83, "module-ivy.data_classes.container.image"], [60, "module-ivy.data_classes.array.image"]], "Ivy as a Transpiler Introduction": [[49, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[49, "To-use-the-transpiler:"]], "Transpiler Interface": [[49, "Transpiler-Interface"]], "Telemetry": [[49, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[49, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[49, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[49, "3.-Transpile-Models-\ud83c\udf10"]], "End-to-End Training Pipeline in Ivy": [[47, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[47, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[47, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[47, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[47, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[47, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[47, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[47, "Plotting-the-training-metrics"]], "Save the trained Model": [[47, "Save-the-trained-Model"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Unify": [[36, "Unify"], [26, "Unify"], [27, "Unify"], [38, "Unify"], [37, "Unify"]], "Compile": [[36, "Compile"], [38, "Compile"], [37, "Compile"]], "Transpile": [[36, "Transpile"], [26, "Transpile"], [27, "Transpile"], [38, "Transpile"], [37, "Transpile"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "Transpile code": [[25, "Transpile-code"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Data Preparation": [[5, "Data-Preparation"], [12, "Data-Preparation"], [8, "Data-Preparation"], [4, "Data-Preparation"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "Learn the basics": [[21, "learn-the-basics"], [20, "learn-the-basics"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "1.3: Dynamic vs Static": [[39, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[39, "Dynamic"]], "Static": [[39, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[39, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Trace": [[26, "Trace"], [27, "Trace"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "Unify code": [[23, "Unify-code"]], "How to use decorators": [[27, "How-to-use-decorators"]], "Transpile any library": [[28, "Transpile-any-library"]], "Examples and Demos": [[3, "examples-and-demos"], [20, "examples-and-demos"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Importing Ivy": [[0, "Importing-Ivy"], [22, "Importing-Ivy"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[6, "Comparing-the-results"], [7, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[6, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[6, "Conclusion"], [7, "Conclusion"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "Compilation of a Basic Function": [[44, "Compilation-of-a-Basic-Function"]], "Installs \ud83d\udcbe": [[44, "Installs-\ud83d\udcbe"], [43, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[44, "Imports-\ud83d\udec3"], [43, "Imports-\ud83d\udec3"]], "Import Ivy compiler": [[44, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[44, "Function-compilation-\ud83d\udee0"]], "Set backend": [[44, "Set-backend"]], "Sample input": [[44, "Sample-input"]], "Define function to compile": [[44, "Define-function-to-compile"]], "Compile the function": [[44, "Compile-the-function"]], "Check results": [[44, "Check-results"], [44, "id1"]], "Compiling simple neural network \ud83e\udde0": [[44, "Compiling-simple-neural-network-\ud83e\udde0"]], "Define Model": [[44, "Define-Model"], [43, "Define-Model"]], "Create model": [[44, "Create-model"]], "Define input": [[44, "Define-input"]], "Compile network": [[44, "Compile-network"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "0.1: Compile": [[34, "0.1:-Compile"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [4, "Installation"]], "Imports": [[12, "Imports"], [8, "Imports"], [14, "Imports"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Load the image example \ud83d\uddbc\ufe0f": [[12, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [8, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[12, "Visualise-image"], [8, "Visualise-image"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[43, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[43, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[43, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[43, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[43, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[43, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[43, "Set-Backend-Framework"]], "Create Model": [[43, "Create-Model"]], "Create Optimizer": [[43, "Create-Optimizer"]], "Input and Target": [[43, "Input-and-Target"]], "Loss Function": [[43, "Loss-Function"]], "Training Loop": [[43, "Training-Loop"]], "Write a model using Ivy": [[30, "Write-a-model-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"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Guides": [[20, "guides"], [15, "guides"]], "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)"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "Trace code": [[24, "Trace-code"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "0.0: Unify": [[33, "0.0:-Unify"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[45, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[45, "Table-of-Contents"]], "Defining the model": [[45, "Defining-the-model"]], "Model construction": [[45, "Model-construction"]], "Some helper functions": [[45, "Some-helper-functions"]], "Transpiling the model": [[45, "Transpiling-the-model"]], "PyTorch pipeline": [[45, "PyTorch-pipeline"]], "Dataset download": [[45, "Dataset-download"]], "DataLoader": [[45, "DataLoader"]], "Training": [[45, "Training"]], "Accelerating XGBoost with JAX": [[14, "Accelerating-XGBoost-with-JAX"]], "Tests": [[14, "Tests"]], "Loading the Data": [[14, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[14, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[14, "JAX-backend"]], "Tensorflow backend": [[14, "Tensorflow-backend"]], "PyTorch backend": [[14, "PyTorch-backend"]], "More exhaustive example": [[14, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[14, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[14, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[14, "Comparison-of-Metrics"]], "ODSC Ivy Demo": [[31, "ODSC-Ivy-Demo"]], "Ivy Backend Handler": [[31, "Ivy-Backend-Handler"], [22, "Ivy-Backend-Handler"]], "Data Structures": [[31, "Data-Structures"], [22, "Data-Structures"]], "Ivy Functional API": [[31, "Ivy-Functional-API"], [22, "Ivy-Functional-API"]], "Graph Tracer": [[31, "Graph-Tracer"]], "Any function": [[31, "Any-function"], [32, "Any-function"]], "Any library": [[31, "Any-library"], [32, "Any-library"]], "Any model": [[31, "Any-model"], [32, "Any-model"]], "Write Ivy code": [[22, "Write-Ivy-code"]], "Contents": [[22, "Contents"]], "Installing Ivy": [[22, "Installing-Ivy"]], "Quickstart": [[32, "Quickstart"]], "Get familiar with Ivy": [[32, "Get-familiar-with-Ivy"]], "Functional API": [[32, "Functional-API"]], "Stateful API": [[32, "Stateful-API"]], "Tracing code": [[32, "Tracing-code"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]]}, "indexentries": {"_arraywithactivations (class in ivy.data_classes.array.activations)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations"]], "_abc_impl 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551, 634, 637, 668, 677, 820, 824, 827, 828, 829, 831, 833, 837, 844, 854, 870], "them": [0, 3, 4, 11, 13, 16, 18, 20, 31, 32, 37, 376, 446, 539, 575, 634, 776, 792, 812, 814, 818, 820, 821, 823, 824, 825, 826, 827, 828, 829, 833, 835, 838, 840, 841, 842, 844, 846, 849, 851, 852, 853, 855, 857, 858, 859, 860, 861, 862, 863, 864, 865, 867, 868, 870, 872, 876], "achiev": [0, 11, 13, 14, 31, 812, 813, 815, 821, 828, 829, 837, 838, 844, 847, 852, 854, 857], "concaten": [0, 43, 57, 58, 64, 80, 85, 378, 469, 545, 549, 634, 636, 639, 663, 682, 700, 776, 842, 847, 849, 852], "along": [0, 46, 51, 53, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 73, 74, 76, 79, 80, 81, 85, 86, 87, 89, 90, 92, 93, 94, 97, 98, 100, 113, 117, 122, 137, 138, 213, 287, 290, 292, 330, 331, 332, 335, 336, 340, 341, 356, 363, 369, 372, 373, 375, 376, 377, 378, 381, 387, 397, 403, 404, 407, 408, 409, 419, 420, 445, 456, 469, 470, 471, 473, 475, 476, 484, 489, 492, 494, 496, 504, 505, 506, 507, 523, 524, 525, 527, 528, 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738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 777, 805, 826, 837, 844], "colab": [2, 5, 13, 14, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 45, 47, 49, 50], "manual": [2, 6, 7, 13, 14, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 641, 718, 728, 729, 818, 819, 820, 829, 835, 844, 853, 856], "mind": [2, 16, 18, 22, 28, 31, 35, 818, 819, 824, 827, 844, 856, 864], "click": [2, 4, 47, 818, 819, 820, 828, 832, 834, 835, 850], "runtim": [2, 4, 5, 8, 11, 12, 13, 24, 31, 34, 45, 46, 822, 837, 844, 847, 870], "restart": [2, 4, 5, 8, 12, 45, 46, 819, 834], "git": [2, 4, 5, 8, 12, 31, 45, 46, 47, 48, 812, 814, 817, 819, 820, 823, 826, 828, 834, 835, 844, 856], "clone": [2, 4, 8, 12, 31, 45, 47, 48, 812, 814, 820, 834, 856], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 18, 26, 27, 28, 29, 31, 32, 45, 46, 47, 48, 49, 50, 56, 57, 79, 80, 82, 147, 155, 243, 253, 254, 269, 328, 335, 336, 369, 372, 375, 378, 387, 419, 492, 522, 615, 616, 629, 630, 632, 635, 637, 639, 647, 685, 686, 714, 764, 812, 814, 819, 820, 823, 826, 828, 829, 832, 834, 856, 864], "github": [2, 4, 5, 8, 11, 12, 13, 31, 45, 46, 47, 48, 49, 812, 814, 815, 817, 820, 821, 823, 826, 828, 829, 831, 832, 834, 835, 843, 844, 856, 859, 878], "com": [2, 4, 5, 6, 7, 8, 11, 12, 13, 18, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 823, 826, 828, 829, 834, 856], "unifyai": [2, 4, 8, 12, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 826, 834, 856], "model": [2, 3, 4, 9, 14, 15, 20, 21, 22, 48, 50, 240, 273, 377, 453, 632, 789, 793, 794, 810, 812, 852, 853, 857, 863, 864, 868, 869, 870, 871, 872, 873, 874, 876, 877], "depth": [2, 4, 6, 8, 12, 46, 53, 57, 61, 76, 80, 84, 141, 375, 378, 411, 471, 545, 557, 629, 634, 636, 654, 655, 820, 828, 852, 853, 854, 856], "repositori": [2, 4, 8, 12, 814, 818, 819, 820, 822, 823, 826, 834, 843, 861], "cd": [2, 4, 8, 12, 31, 48, 812, 814, 819, 820, 834, 856], "resnet": [3, 6, 13, 20, 31, 863, 864], "imag": [3, 4, 6, 7, 11, 13, 16, 20, 28, 31, 32, 45, 46, 47, 48, 49, 50, 57, 61, 79, 80, 84, 102, 220, 221, 222, 223, 226, 229, 238, 241, 243, 245, 254, 255, 256, 261, 263, 276, 283, 284, 286, 287, 291, 375, 394, 395, 411, 412, 413, 415, 545, 632, 634, 636, 649, 650, 651, 652, 653, 656, 657, 658, 792, 812, 819, 834, 847, 849, 850, 852, 854, 856, 863, 864, 870], "classif": [3, 4, 12, 14, 20, 45, 812, 870], "acceler": [3, 20, 812, 829, 841, 868, 872, 873, 874, 875], "convert": [3, 8, 9, 11, 13, 14, 16, 18, 20, 21, 23, 25, 28, 29, 31, 32, 33, 35, 37, 45, 48, 50, 52, 53, 56, 74, 75, 76, 79, 97, 127, 128, 140, 150, 151, 193, 194, 195, 196, 207, 215, 219, 239, 279, 378, 383, 462, 463, 464, 513, 578, 596, 598, 599, 600, 602, 629, 630, 631, 632, 634, 637, 641, 695, 719, 730, 731, 773, 801, 805, 812, 818, 824, 825, 838, 839, 841, 844, 846, 849, 855, 857, 861, 864, 868, 869, 876], "faster": [3, 4, 9, 11, 13, 14, 20, 31, 32, 48, 50, 57, 62, 80, 85, 376, 449, 637, 687, 814, 817, 826, 857, 872, 875], "infer": [3, 6, 7, 9, 11, 13, 14, 20, 24, 34, 36, 37, 46, 48, 50, 53, 57, 58, 61, 64, 76, 80, 81, 84, 87, 126, 128, 131, 135, 136, 140, 143, 149, 158, 159, 160, 161, 162, 312, 313, 375, 378, 382, 411, 496, 510, 556, 590, 591, 629, 630, 634, 636, 639, 659, 706, 801, 802, 822, 825, 829, 830, 844, 849, 854, 864, 868, 869, 872, 874], "mmpretrain": [3, 20], "segment": [3, 20, 57, 80, 330, 331, 332, 369, 826, 831], "unet": [3, 20], "alexnet": [3, 20], "written": [3, 4, 5, 6, 20, 22, 31, 32, 45, 58, 378, 473, 819, 823, 824, 832, 835, 836, 840, 841, 845, 849, 851, 854, 855, 859, 864, 868, 870, 874, 876, 877], "xgboost": [3, 20], "paddlepaddl": [3, 20, 335, 336, 372, 819], "dinov2": [3, 7, 20], "project": [3, 12, 13, 20, 25, 26, 27, 28, 29, 31, 32, 35, 98, 636, 663, 792, 812, 814, 815, 818, 819, 820, 821, 824, 825, 826, 844, 853, 855, 859, 860, 861, 864, 866, 868, 870, 873, 877, 878], "convnext": [3, 6, 11, 20], "video": [4, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 813, 818, 819, 820, 823, 824, 825, 827, 828, 829, 830, 831, 832, 833, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 847, 856, 868], "tutori": [4, 6, 7, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 820, 841, 856], "three": [4, 5, 20, 26, 36, 37, 47, 57, 139, 312, 369, 378, 464, 629, 819, 820, 827, 828, 829, 831, 841, 844, 847, 848, 849, 871, 876], "major": [4, 5, 644, 747, 829, 830, 842, 844, 855, 860, 867, 870], "ml": [4, 5, 6, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 47, 50, 812, 813, 817, 841, 848, 849, 850, 852, 853, 854, 858, 860, 861, 864, 866, 867, 868, 869, 870, 873, 875, 877], "framework": [4, 5, 7, 9, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 35, 36, 38, 45, 47, 49, 52, 58, 170, 192, 202, 205, 216, 543, 559, 563, 595, 598, 630, 631, 634, 641, 720, 771, 773, 777, 784, 789, 796, 801, 802, 812, 815, 816, 818, 819, 822, 823, 824, 825, 826, 828, 829, 830, 831, 833, 834, 836, 837, 838, 840, 841, 844, 845, 847, 848, 849, 851, 854, 855, 856, 857, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 871, 874], "sinc": [4, 8, 12, 28, 29, 31, 32, 45, 47, 57, 80, 98, 372, 812, 814, 819, 820, 823, 824, 825, 826, 827, 828, 829, 830, 833, 840, 841, 855, 860, 870, 876], "automat": [4, 8, 9, 12, 29, 31, 32, 37, 818, 819, 820, 822, 825, 826, 828, 829, 835, 837, 840, 844, 847, 848, 850, 853, 854, 856, 857, 861, 870, 873, 877], "sure": [4, 8, 11, 12, 13, 14, 31, 45, 815, 818, 819, 820, 823, 828, 833, 834, 841, 842, 844, 847, 856], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 26, 27, 29, 46, 57, 62, 74, 85, 103, 375, 377, 398, 456, 580, 634, 637, 680, 794, 810, 812, 819, 820, 821, 824, 827, 829, 837, 838, 839, 840, 841, 844, 845, 848, 850, 852, 854, 855, 857, 860, 863, 868, 869, 870, 871, 872, 873, 876, 877], "dm": [4, 5, 8, 11, 13, 31, 32, 43, 45], "haiku": [4, 5, 8, 11, 13, 29, 31, 32, 43, 45, 49, 789, 812, 854, 861, 864, 870], "exit": [4, 8, 12, 31, 32, 830], "download": [4, 6, 7, 12, 16, 18, 31, 32, 46, 47, 50, 814, 819, 826, 844, 863, 864], "imagenet": [4, 6, 18, 46, 48, 812], "class": [4, 6, 7, 8, 12, 14, 16, 18, 22, 31, 32, 43, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 134, 143, 149, 165, 168, 181, 183, 184, 243, 280, 338, 360, 372, 386, 387, 395, 396, 429, 528, 529, 536, 545, 549, 562, 572, 595, 629, 630, 631, 632, 634, 636, 637, 638, 641, 642, 657, 662, 666, 672, 682, 686, 687, 689, 696, 712, 719, 730, 737, 752, 759, 763, 764, 773, 774, 781, 782, 783, 784, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 800, 801, 805, 810, 812, 818, 825, 826, 827, 829, 830, 831, 832, 836, 838, 839, 842, 843, 844, 847, 849, 850, 852, 853, 854, 857, 863, 864, 868, 870, 871, 877], "wget": [4, 6, 8, 12, 45, 46, 49, 819], "raw": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 45, 48, 49, 74, 812, 832, 864, 871], "githubusercont": [4, 6, 8, 12, 45, 49], "hub": [4, 6, 8, 12, 45, 48, 50], "master": [4, 8, 12, 23, 24, 25, 33, 34, 35, 36, 37, 38, 45, 47, 48, 49, 815, 828, 870, 878], "imagenet_class": [4, 12], "categori": [4, 6, 12, 818, 823, 824, 827, 829, 833, 841, 845, 848], "strip": [4, 12, 24, 34, 860], "readlin": [4, 12, 46], "cat": [4, 7, 12, 46, 842, 847, 849, 854, 863, 864], "jpg": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 47, 48, 812, 864], "filenam": [4, 8, 12, 31, 32, 45, 47, 50, 58, 794, 800, 852], "import": [4, 6, 7, 9, 10, 11, 13, 16, 18, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 46, 48, 49, 50, 57, 68, 72, 76, 80, 95, 194, 195, 199, 211, 307, 387, 522, 557, 573, 631, 634, 640, 645, 716, 717, 752, 784, 801, 802, 812, 817, 818, 819, 820, 821, 823, 824, 825, 826, 827, 829, 830, 831, 832, 835, 838, 839, 840, 841, 842, 843, 844, 845, 849, 851, 852, 854, 855, 856, 860, 863, 864, 865, 866, 868, 870, 873, 874, 876], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 46, 47, 50, 53, 57, 66, 74, 76, 80, 89, 102, 105, 106, 107, 126, 127, 128, 130, 131, 132, 135, 136, 137, 138, 140, 141, 142, 143, 145, 146, 147, 148, 149, 193, 194, 195, 196, 197, 198, 199, 200, 201, 206, 207, 208, 209, 211, 212, 213, 214, 215, 217, 219, 312, 313, 328, 329, 369, 382, 472, 508, 509, 511, 512, 536, 550, 551, 629, 634, 643, 738, 739, 740, 741, 771, 773, 774, 789, 791, 792, 793, 794, 795, 796, 797, 798, 810, 812, 820, 822, 825, 829, 833, 837, 838, 842, 844, 845, 847, 849, 854, 855, 856, 857, 860, 869, 870, 872, 873, 874, 875], "torchvis": [4, 6, 11, 12, 45, 861], "transform": [4, 5, 6, 7, 11, 12, 13, 28, 31, 32, 45, 46, 48, 57, 61, 80, 84, 375, 376, 397, 398, 403, 404, 407, 408, 409, 419, 420, 423, 440, 636, 660, 776, 779, 792, 812, 838, 844, 854, 857, 863, 864, 868, 870, 871, 872], "pil": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 46, 47, 48, 812, 864], "time": [4, 5, 6, 7, 9, 10, 11, 13, 29, 31, 32, 37, 45, 47, 48, 49, 57, 59, 62, 68, 80, 82, 91, 97, 98, 134, 341, 372, 375, 376, 378, 387, 404, 409, 421, 423, 444, 451, 484, 490, 522, 616, 621, 629, 635, 636, 637, 639, 640, 644, 645, 659, 662, 677, 712, 715, 716, 717, 744, 745, 749, 750, 792, 793, 794, 810, 818, 819, 820, 823, 825, 827, 828, 829, 831, 834, 836, 837, 838, 840, 841, 844, 845, 849, 852, 854, 855, 856, 859, 860, 861, 863, 864, 868, 870, 871, 874, 875, 876], "filterwarn": [4, 5], "ignor": [4, 5, 44, 52, 53, 57, 74, 80, 139, 375, 376, 378, 387, 399, 400, 401, 430, 438, 446, 486, 487, 491, 530, 629, 636, 641, 663, 729, 730, 796, 819, 826, 828, 831, 844, 855, 876], "compos": [4, 6, 7, 11, 12, 31, 32, 45, 57, 80, 375, 389, 390, 391, 392, 819, 827, 841, 844, 863, 865, 870, 877], "resiz": [4, 6, 7, 8, 11, 12, 45, 46, 57, 80, 375, 411, 847], "centercrop": [4, 12], "224": [4, 6, 7, 12, 16, 18, 31, 32, 45, 46, 48, 812, 864], "totensor": [4, 6, 7, 11, 12, 45], "485": [4, 12, 45], "456": [4, 12, 45, 844], "406": [4, 12, 45, 57, 80, 397, 540, 634], "229": [4, 12, 45, 279, 632], "225": [4, 12, 45, 47, 234, 632], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 812, 852, 864], "ipython": [4, 8, 12, 26, 27, 28, 29, 31, 32, 50], "displai": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 50, 819, 826, 828, 833, 844, 852], "end": [4, 8, 45, 46, 57, 80, 126, 228, 284, 353, 372, 375, 377, 378, 423, 452, 474, 484, 486, 487, 629, 632, 806, 812, 819, 820, 825, 828, 834, 840, 845, 847, 848, 855, 868, 873], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 217, 631, 830], "ivy_model": [4, 5, 8, 12, 48], "ivy_alexnet": 4, "quick": [4, 20, 32, 820, 822, 842, 853], "trace_graph": [4, 5, 8, 12, 24, 25, 26, 27, 31, 32, 34, 35, 36, 37, 38, 39, 48, 794, 812, 849, 854, 862], "moment": [4, 57, 59, 80, 82, 376, 433, 615, 616, 621, 635, 796, 810, 818, 825, 855, 863, 864], "cost": [4, 59, 82, 615, 616, 619, 621, 622, 623, 635, 640, 715, 716, 717, 806, 829, 847, 868], "arg": [4, 6, 8, 9, 10, 11, 12, 16, 18, 26, 27, 29, 31, 32, 36, 37, 38, 49, 52, 74, 96, 106, 122, 203, 213, 601, 628, 629, 631, 634, 771, 773, 788, 789, 792, 793, 794, 798, 801, 805, 810, 812, 824, 829, 830, 833, 839, 840, 841, 847, 849, 853, 863, 864, 865], "asarrai": [4, 5, 8, 11, 12, 46, 53, 57, 58, 69, 76, 80, 81, 92, 127, 385, 514, 515, 545, 556, 560, 561, 591, 592, 593, 629, 634, 636, 645, 646, 650, 750, 754, 833, 838, 841, 842], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 22, 31, 46, 47, 50, 53, 57, 66, 76, 80, 89, 137, 138, 141, 193, 194, 195, 211, 382, 508, 509, 511, 512, 629, 631, 637, 643, 688, 738, 739, 740, 741, 791, 792, 793, 794, 795, 796, 797, 810, 849, 855, 857, 875], "output": [4, 5, 7, 8, 9, 10, 12, 22, 28, 29, 31, 32, 44, 45, 46, 48, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 152, 154, 179, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 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79, 80, 81, 84, 85, 89, 93, 103, 164, 234, 244, 278, 287, 288, 346, 372, 375, 397, 407, 545, 546, 593, 621, 630, 632, 634, 635, 636, 637, 641, 647, 651, 653, 655, 657, 658, 679, 682, 692, 726, 730, 740, 759, 763, 819, 829, 852, 853, 867, 875], "data_format": [4, 47, 57, 61, 80, 84, 375, 381, 390, 394, 395, 396, 399, 400, 401, 412, 413, 414, 415, 417, 501, 502, 503, 506, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 776, 792, 795, 812], "nchw": [4, 47, 57, 61, 80, 84, 375, 381, 390, 395, 400, 413, 417, 506, 636, 649, 652, 653, 656, 657, 658, 792, 812], "relu": [4, 8, 12, 29, 31, 32, 43, 50, 51, 57, 72, 73, 80, 112, 302, 303, 311, 367, 626, 788, 812, 842, 852, 853], "maxpool2d": [4, 8, 12, 45, 792, 812], "192": [4, 47, 776, 805], "384": [4, 82, 615, 635, 641, 718], "avgpool": [4, 12], "adaptiveavgpool2d": [4, 12, 792], "classifi": [4, 7, 14, 16, 18, 31, 32, 45, 47, 48, 812, 818, 863, 864], "prob": [4, 6, 7, 47, 57, 61, 80, 84, 89, 375, 382, 399, 400, 401, 508, 636, 643, 659, 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273, 632, 805, 819, 823, 829, 841, 844, 847], "mlp": 16, "mixer": 16, "onli": [16, 18, 31, 32, 37, 43, 45, 47, 49, 52, 53, 56, 57, 62, 64, 66, 74, 76, 79, 80, 85, 87, 89, 97, 100, 102, 118, 138, 178, 179, 208, 268, 269, 274, 280, 312, 342, 349, 369, 372, 375, 376, 378, 382, 387, 398, 411, 421, 430, 435, 449, 451, 462, 463, 464, 474, 508, 509, 525, 539, 626, 629, 630, 631, 632, 634, 636, 637, 639, 641, 643, 644, 646, 647, 663, 677, 684, 687, 688, 703, 706, 718, 719, 725, 726, 728, 729, 730, 735, 736, 739, 740, 741, 744, 745, 755, 761, 764, 774, 776, 777, 779, 792, 796, 805, 810, 812, 813, 814, 818, 819, 820, 823, 824, 825, 826, 827, 828, 829, 830, 831, 833, 836, 837, 839, 840, 841, 842, 844, 845, 846, 847, 849, 851, 852, 853, 854, 855, 859, 863, 864, 869, 870, 871, 876, 877], "retriev": [16, 18, 22, 535, 557, 582, 634, 820, 841], "mlp_encod": [16, 31, 32, 812, 864], "create_model": [16, 31, 32, 812, 864], "mixer_b16_224": [16, 31, 32, 812, 864], "nois": [16, 18, 31, 32, 812, 863, 864], "randn": [16, 18, 31, 32, 378, 496, 812, 864], "tf_mlp_encod": [16, 31, 32], "output_torch": [16, 18], "output_tf": [16, 18], "output_dens": [16, 31, 32, 812], "dens": [16, 29, 31, 32, 316, 369, 792, 812], "unit": [16, 31, 32, 57, 73, 80, 97, 98, 110, 112, 113, 114, 115, 116, 117, 118, 295, 296, 299, 303, 305, 306, 309, 310, 311, 367, 504, 505, 626, 812, 819, 823, 829, 841, 842, 844, 855, 871, 874], "mention": [16, 18, 31, 32, 37, 818, 819, 820, 824, 831, 836, 837, 840, 841, 844, 847, 860, 865, 870], "fulli": [16, 18, 20, 21, 24, 29, 31, 32, 45, 57, 80, 387, 529, 792, 812, 824, 829, 836, 839, 847, 849, 850, 851, 852, 853, 854, 855, 861, 865, 868, 869, 870, 876, 877], "ground": [16, 18, 377, 453, 771, 773, 784, 816, 834, 841, 844, 859], "ret": [16, 18, 31, 32, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 163, 164, 165, 166, 167, 168, 170, 171, 172, 173, 174, 175, 176, 177, 178, 180, 192, 193, 194, 196, 197, 198, 199, 200, 201, 202, 204, 205, 206, 207, 209, 212, 213, 214, 215, 216, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 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623, 624, 625, 626, 628, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 662, 663, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 721, 724, 725, 726, 727, 728, 729, 730, 735, 736, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 773, 776, 777, 778, 779, 789, 794, 796, 801, 806, 808, 812, 829, 830, 832, 833, 839, 840, 841, 842, 845, 849, 854, 864], "eagertensor": [16, 22, 43, 801, 842], "deepmind": [17, 861], "perceiverio": [17, 861], "backbon": [17, 45, 812, 849, 852], "TO": [17, 19, 30], "replac": [17, 19, 30, 46, 56, 57, 58, 64, 66, 74, 79, 80, 81, 87, 89, 132, 274, 310, 313, 367, 369, 378, 489, 492, 496, 576, 577, 581, 629, 632, 634, 639, 643, 699, 738, 776, 820, 826, 827, 829, 830, 838, 841, 844, 851, 854, 855, 860, 864, 877], "efficientnet": 18, "eff_encod": [18, 812], "efficientnet_v2": [18, 812], "efficientnetv2b0": [18, 812], "storag": [18, 45, 46, 852, 860], "googleapi": [18, 45, 46], "efficientnetv2": 18, "b0_notop": 18, "h5": [18, 74], "24274472": 18, "0u": 18, "torch_eff_encod": [18, 812], "modes_to_trac": 18, "1280": [18, 545, 634, 812], "welcom": [20, 46, 812, 813, 819, 820, 821, 843], "varieti": [20, 823, 828, 829, 830, 844, 846, 866, 868, 872, 873, 876, 877], "organ": [20, 824, 827, 837, 841, 843, 845, 857, 860], "main": [20, 32, 53, 57, 62, 80, 85, 132, 145, 146, 147, 313, 328, 329, 369, 376, 378, 427, 473, 629, 637, 670, 671, 691, 812, 815, 818, 819, 820, 821, 823, 826, 827, 834, 838, 840, 868, 870, 871, 876], "exactli": [20, 24, 34, 43, 44, 48, 290, 632, 818, 827, 828, 829, 830, 831, 833, 844, 847, 859, 861], "rush": [20, 861], "jump": [20, 842], "straight": [20, 812, 828, 841, 844, 851], "quickstart": [20, 812], "introduct": [20, 22, 29, 31, 32, 870], "point": [20, 29, 54, 56, 57, 62, 66, 68, 70, 77, 79, 80, 85, 89, 93, 126, 127, 128, 130, 132, 135, 142, 143, 148, 152, 165, 169, 173, 180, 220, 221, 222, 223, 225, 226, 227, 228, 229, 236, 237, 238, 240, 241, 243, 245, 246, 247, 253, 254, 255, 256, 261, 262, 263, 264, 265, 273, 275, 276, 278, 280, 282, 283, 284, 285, 286, 287, 288, 290, 291, 292, 293, 294, 312, 313, 315, 335, 336, 353, 354, 357, 359, 369, 372, 375, 376, 377, 382, 387, 390, 399, 400, 401, 419, 429, 449, 453, 508, 509, 510, 511, 512, 522, 523, 524, 532, 627, 629, 630, 632, 637, 643, 644, 645, 646, 647, 667, 669, 672, 673, 674, 676, 678, 679, 680, 683, 684, 685, 686, 687, 688, 689, 691, 694, 740, 741, 747, 749, 750, 751, 752, 755, 757, 758, 760, 761, 762, 763, 764, 765, 766, 801, 802, 810, 816, 818, 819, 820, 823, 824, 826, 828, 829, 831, 832, 834, 836, 840, 841, 844, 845, 847, 849, 851, 852, 861, 863, 876], "showcas": [20, 812], "real": [20, 28, 56, 57, 70, 79, 80, 93, 102, 112, 115, 118, 142, 143, 220, 221, 222, 223, 225, 226, 227, 228, 229, 238, 240, 241, 243, 245, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 270, 273, 275, 276, 278, 282, 283, 284, 286, 287, 288, 289, 290, 291, 293, 294, 335, 336, 342, 343, 344, 354, 372, 375, 376, 398, 419, 420, 429, 430, 626, 629, 632, 637, 644, 647, 672, 673, 674, 678, 685, 687, 688, 691, 694, 747, 760, 762, 763, 764, 765, 827, 872], "world": [20, 28, 820, 872], "beginn": [20, 813, 870], "got": [20, 43, 833], "cover": [20, 31, 57, 80, 375, 412, 413, 414, 818, 823, 824, 826, 829, 831, 832, 837, 838, 844, 847, 848], "familiar": [20, 21, 22, 818, 819], "concept": [20, 21, 22], "turn": [20, 21, 24, 34, 61, 84, 97, 98, 399, 400, 401, 636, 659, 792, 819, 826, 827, 830, 831, 841, 844, 861], "unus": [20, 21, 24, 831, 840], "part": [20, 21, 24, 53, 56, 57, 79, 80, 85, 102, 112, 115, 118, 145, 146, 147, 253, 257, 280, 328, 329, 355, 369, 372, 375, 376, 378, 387, 419, 430, 484, 532, 626, 629, 632, 637, 673, 674, 773, 812, 818, 819, 820, 821, 823, 826, 829, 835, 837, 840, 841, 844, 845, 847, 849, 850, 854, 855, 863, 864, 865, 868, 870, 875, 876, 877], "lazi": [20, 21, 24, 27, 34, 37, 38, 49], "decor": [20, 21, 26, 28, 29, 37, 49, 539, 634, 776, 778, 784, 816, 823, 824, 827, 829, 830, 834, 837, 840, 841, 842, 847], "kornia": [20, 21, 28, 31, 32, 45, 49, 812, 864], "roundup": 22, "indep": [22, 31], "proof": [22, 31], "delv": [22, 32, 812], "theori": [22, 814, 826], "esenti": [22, 31], "abstract": [22, 31, 32, 791, 796, 812, 827, 829, 840, 841, 844, 847, 853, 859, 868, 870, 872, 873, 877], "quirk": [22, 31], "perk": [22, 31, 812, 824, 827], "under": [22, 31, 32, 57, 377, 456, 457, 805, 812, 818, 819, 822, 823, 830, 831, 832, 835, 841, 842, 844, 847, 848, 849, 852, 854, 855, 863, 864, 870, 873, 877], "hood": [22, 31, 32, 812, 822, 830, 831, 835, 841, 844, 847, 848, 849, 852, 854, 863, 864, 877], "appropi": 22, "string": [22, 31, 32, 47, 57, 58, 61, 74, 80, 84, 150, 151, 163, 170, 192, 193, 194, 195, 196, 198, 207, 214, 215, 219, 375, 376, 378, 418, 422, 430, 484, 495, 524, 543, 630, 631, 634, 636, 637, 649, 650, 651, 652, 654, 656, 658, 674, 771, 773, 777, 805, 806, 825, 826, 828, 829, 830, 833, 841, 849, 852], "simplest": [22, 819, 831, 844, 847], "interact": [22, 31, 46, 49, 818, 869, 870, 875], "submodul": [22, 31, 45, 47, 102, 103, 626, 627, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 788, 789, 791, 792, 794, 795, 796, 797, 818, 819, 820, 823, 826, 828, 830, 834, 837, 838, 844, 848, 849, 853, 857], "likewis": [22, 27, 31, 38, 812, 820, 827, 829, 832, 836, 837, 841, 847, 852, 863, 864, 876], "nativearrai": [22, 31, 32, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 68, 70, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 127, 128, 129, 131, 136, 137, 138, 139, 140, 141, 143, 145, 146, 149, 152, 153, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 171, 172, 173, 175, 177, 179, 180, 186, 196, 197, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 313, 314, 317, 318, 322, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 367, 369, 372, 373, 375, 376, 377, 378, 381, 382, 383, 385, 387, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 415, 417, 418, 419, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 440, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 467, 468, 469, 470, 472, 473, 474, 475, 476, 478, 479, 481, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 522, 523, 524, 525, 526, 534, 537, 538, 540, 541, 545, 546, 547, 549, 552, 553, 554, 555, 556, 558, 560, 561, 562, 565, 568, 569, 571, 576, 577, 578, 581, 590, 591, 592, 593, 594, 595, 597, 599, 600, 602, 613, 615, 616, 617, 619, 621, 622, 623, 624, 626, 628, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 662, 663, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 719, 720, 721, 725, 726, 727, 730, 735, 736, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 797, 824, 827, 831, 833, 836, 837, 838, 840, 841, 845, 846, 849, 851, 857], "alia": [22, 31, 335, 336, 372, 627, 818, 841, 862, 865], "lastli": [22, 31, 824], "subclass": [22, 31, 32, 838, 841, 847, 864], "dict": [22, 31, 32, 45, 49, 52, 58, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 125, 134, 136, 141, 143, 149, 153, 155, 166, 167, 168, 172, 173, 180, 196, 199, 200, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 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824, 827, 852, 853, 857, 863, 864, 865], "recurs": [22, 31, 32, 45, 47, 52, 74, 75, 166, 167, 199, 200, 376, 448, 550, 551, 557, 630, 631, 634, 641, 718, 719, 722, 728, 729, 730, 771, 819, 823, 826, 827, 834, 837, 840, 853, 855], "fashion": [22, 778, 844, 864], "native_arrai": [22, 31, 32, 53, 54, 56, 76, 78, 79, 80, 81, 85, 92, 110, 113, 136, 139, 141, 143, 149, 152, 153, 154, 155, 163, 168, 175, 197, 206, 214, 230, 234, 239, 240, 241, 243, 247, 251, 259, 260, 268, 273, 276, 279, 282, 287, 335, 336, 363, 372, 377, 378, 458, 484, 490, 494, 534, 537, 564, 565, 568, 599, 626, 629, 630, 631, 632, 634, 636, 637, 638, 639, 643, 644, 647, 648, 650, 651, 658, 666, 669, 673, 674, 679, 680, 684, 688, 689, 691, 694, 696, 698, 699, 706, 738, 747, 756, 762, 765, 767, 773, 783, 801, 816, 834, 842, 844], "data_class": [22, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 395, 396, 545, 549, 687, 712], "low": [22, 31, 34, 50, 57, 61, 66, 80, 84, 89, 375, 418, 422, 636, 643, 649, 650, 651, 652, 654, 656, 658, 739, 741, 778, 827, 833, 840, 841, 847, 849, 866, 868, 870, 871, 872, 874, 876], "c": [22, 31, 37, 46, 47, 53, 57, 58, 59, 61, 64, 70, 76, 77, 79, 80, 81, 82, 84, 85, 87, 91, 93, 97, 98, 116, 127, 128, 138, 141, 165, 168, 223, 234, 240, 241, 261, 262, 264, 273, 276, 284, 291, 375, 376, 378, 381, 387, 389, 390, 391, 392, 403, 408, 424, 426, 428, 429, 431, 443, 462, 463, 464, 474, 492, 496, 501, 502, 503, 506, 524, 537, 545, 546, 547, 548, 556, 560, 561, 591, 600, 615, 616, 619, 621, 622, 623, 626, 629, 630, 632, 634, 635, 636, 637, 639, 641, 644, 645, 647, 650, 651, 652, 653, 654, 655, 657, 672, 674, 676, 706, 710, 718, 721, 725, 726, 727, 729, 730, 735, 736, 747, 752, 758, 759, 764, 766, 795, 805, 806, 813, 819, 822, 825, 826, 827, 831, 837, 839, 848, 849, 850, 852, 855, 857, 858, 860, 861, 864, 866, 870, 874, 875, 877], "fundament": [22, 31, 828, 841, 847, 849, 859, 870], "signatur": [22, 31, 378, 387, 484, 522, 829, 830, 831, 832, 836, 840, 844, 845, 847, 860, 867, 876], "matmul": [22, 31, 32, 48, 62, 85, 376, 446, 614, 634, 637, 687, 825, 844, 845, 849], "to_n": [22, 31, 32, 43, 52, 75, 849], "jaxlib": [22, 28, 46, 801, 819, 824, 829, 830, 836, 845, 849, 851], "xla_extens": [22, 28, 801, 824, 829, 830, 836, 845, 849, 851], "arrayimpl": [22, 28, 801], "disabl": [22, 31, 57, 80, 378, 492, 794, 810, 826], "array_mod": [22, 31, 578, 602, 634, 846], "set_array_mod": [22, 31, 602, 634, 846], "ultim": [22, 31, 863], "sigmoid": [22, 31, 32, 43, 51, 57, 73, 80, 301, 367, 382, 508, 626, 788, 849, 852, 853], "z": [22, 31, 32, 44, 45, 53, 56, 57, 58, 62, 63, 66, 68, 70, 76, 79, 80, 81, 85, 86, 87, 89, 93, 102, 103, 137, 138, 140, 141, 201, 223, 224, 228, 230, 233, 235, 240, 251, 252, 255, 256, 257, 259, 260, 265, 267, 269, 270, 271, 272, 280, 289, 300, 301, 335, 336, 338, 367, 372, 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819, 820, 825, 826, 827, 828, 829, 833, 838, 841, 844, 845, 846, 847, 870], "act": [25, 35, 57, 80, 298, 363, 373, 820, 831, 846, 855, 877], "shorthand": [25, 35, 37, 844], "pair": [25, 35, 45, 57, 61, 80, 84, 228, 247, 320, 362, 369, 372, 375, 409, 418, 420, 422, 632, 636, 637, 649, 650, 651, 652, 654, 656, 658, 666, 668, 806], "93968587": 25, "26075466": 25, "22723222": 25, "06276492": 25, "47426987": 25, "72835908": 25, "71737559": 25, "50411096": 25, "65419174": 25, "15576624": 25, "implic": [25, 35, 36, 39, 827], "satisfi": [26, 27, 28, 29, 45, 47, 50, 57, 375, 376, 398, 430, 829, 831], "fw": [26, 27, 28, 29, 61, 84, 387, 522, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 773, 819, 844], "mxnet": [26, 27, 28, 29, 209, 631, 801, 818, 819, 860, 877], "einop": [26, 27, 28, 29, 45, 47, 50, 58, 81, 545, 546, 547, 634, 829, 860], "miniconda": [26, 27, 28, 29], "multienv": [26, 27, 28, 29], "site": [26, 27, 28, 29, 871], "psutil": [26, 27, 28, 29, 45, 47, 50], "termcolor": [26, 27, 28, 29, 45, 47, 50, 74, 103], "colorama": [26, 27, 28, 29, 45, 47], "535": [26, 27, 28, 29, 51, 73, 118, 626, 833], "diskcach": [26, 27, 28, 29, 45], "auth": [26, 27, 28, 29], "urllib3": [26, 27, 28, 29, 45], "pyvi": [26, 27, 28, 29, 31, 32], "dill": [26, 27, 28, 29, 45], "astunpars": [26, 27, 28, 29], "cloudpickl": [26, 27, 28, 29], "gast": [26, 27, 28, 29], "wheel": [26, 27, 28, 29, 45, 47, 50, 859], "six": [26, 27, 28, 29, 45, 50, 819, 847], "cachetool": [26, 27, 28, 29], "pyasn1": [26, 27, 28, 29], "rsa": [26, 27, 28, 29], "jinja2": [26, 27, 28, 29], "jsonpickl": [26, 27, 28, 29], "networkx": [26, 27, 28, 29, 50], "charset": [26, 27, 28, 29, 45], "idna": [26, 27, 28, 29, 45], "certifi": [26, 27, 28, 29, 45], "2017": [26, 27, 28, 29, 45, 636, 663], "jedi": [26, 27, 28, 29], "inlin": [26, 27, 28, 29, 826], "prompt": [26, 27, 28, 29, 818, 820], "toolkit": [26, 27, 28, 29, 870, 871, 877], "pygment": [26, 27, 28, 29], "traitlet": [26, 27, 28, 29], "exceptiongroup": [26, 27, 28, 29], 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80, 81, 85, 96, 97, 375, 376, 397, 398, 402, 403, 404, 407, 408, 409, 419, 420, 426, 430, 504, 505, 507, 540, 541, 562, 634, 637, 678, 694, 737, 792, 796, 845], "slow": [26, 36, 814, 819, 826], "34431235": [26, 27], "51129461": [26, 27], "06686894": [26, 27], "36452447": [26, 27], "98795534": [26, 27], "15493582": [26, 27], "91630631": [26, 27], "41939619": [26, 27], "78909753": [26, 27], "19475674": [26, 27], "norm_trac": 26, "norm_tran": [26, 36], "know": [26, 27, 36, 37, 38, 68, 645, 749, 750, 751, 752, 812, 814, 818, 820, 830, 838, 842, 844, 847, 861, 865, 871], "07": [27, 45, 47, 59, 63, 79, 82, 86, 89, 228, 261, 264, 265, 284, 375, 407, 605, 615, 616, 618, 619, 620, 621, 632, 634, 635, 638, 697, 698, 740, 793, 796, 853], "981554": 27, "happen": [27, 31, 32, 292, 632, 812, 819, 820, 821, 830, 840, 844, 852, 861, 863, 864], "wherea": [27, 38, 80, 375, 421, 820, 824, 827, 829, 830, 831, 836, 837, 844, 854, 867], "subtract": [27, 31, 32, 56, 79, 102, 103, 134, 378, 484, 629, 632, 824, 827, 831], "filelock": [28, 45], "extens": [28, 45, 56, 62, 79, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 139, 142, 143, 144, 145, 146, 148, 149, 155, 165, 168, 180, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 335, 336, 338, 372, 375, 378, 387, 419, 492, 496, 522, 629, 630, 632, 637, 639, 644, 645, 646, 647, 648, 667, 668, 669, 670, 671, 673, 674, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 693, 694, 700, 702, 703, 704, 706, 707, 709, 710, 714, 744, 745, 747, 748, 749, 750, 751, 752, 753, 756, 760, 761, 762, 763, 764, 765, 766, 767, 768, 817, 819, 820, 832, 834, 835, 844, 867, 870, 877], "sympi": [28, 860], "fsspec": [28, 45], "mpmath": 28, "often": [28, 57, 377, 452, 817, 823, 833, 836, 837, 841, 844, 855, 861, 871, 874, 877], "fortun": [28, 29, 823], "everyth": [28, 46, 805, 812, 818, 819, 820, 821, 822, 828, 831, 840, 841, 842, 844, 850, 855, 856, 861], "practic": [28, 820, 825, 828, 841, 843, 873], "everi": [28, 31, 32, 37, 45, 53, 57, 58, 80, 81, 135, 136, 301, 335, 336, 349, 367, 372, 375, 378, 412, 413, 414, 421, 498, 534, 629, 634, 818, 820, 823, 825, 826, 828, 829, 831, 835, 836, 837, 838, 840, 841, 842, 844, 849, 851, 853, 863, 864, 865, 870], "jax_kornia": [28, 31, 32, 812, 864], "though": [28, 817, 818, 820, 829, 830, 832, 837, 840, 841, 847, 852, 855], "000000000034": [28, 31, 32, 812, 864], "raw_img": [28, 31, 32, 812, 864], "sharp": [28, 31, 32, 812], "prefer": [28, 31, 32, 247, 632, 819, 827, 833, 834, 838, 841, 856, 870], "whole": [29, 57, 80, 378, 381, 491, 504, 505, 507, 820, 826, 835], "full": [29, 57, 62, 80, 84, 85, 97, 98, 100, 165, 252, 260, 323, 324, 325, 326, 327, 369, 376, 377, 378, 449, 450, 456, 457, 485, 488, 579, 588, 603, 611, 629, 630, 632, 634, 636, 637, 651, 653, 654, 655, 657, 680, 684, 686, 687, 777, 784, 812, 819, 820, 826, 829, 832, 833, 836, 837, 841, 844, 847, 849, 855, 860, 861, 868, 870, 876], "complex": [29, 31, 32, 45, 51, 56, 57, 62, 70, 73, 77, 79, 80, 85, 93, 110, 111, 112, 113, 114, 115, 116, 117, 118, 142, 143, 158, 172, 181, 187, 220, 221, 222, 223, 224, 225, 226, 229, 237, 238, 240, 241, 243, 245, 253, 254, 255, 256, 257, 261, 262, 263, 264, 273, 275, 276, 278, 280, 283, 284, 285, 286, 287, 290, 291, 295, 300, 301, 303, 338, 343, 344, 367, 372, 375, 376, 387, 398, 409, 419, 420, 424, 429, 430, 431, 442, 444, 530, 531, 592, 593, 626, 629, 630, 632, 634, 637, 644, 647, 672, 673, 674, 678, 685, 687, 689, 691, 694, 747, 762, 763, 765, 777, 788, 806, 815, 818, 821, 826, 829, 831, 838, 841, 844, 845, 847, 852, 853, 854, 855, 857, 864, 866, 868, 870, 872, 876, 877], "neccessari": 29, "set_random_se": [29, 48], "301436": 29, "_c": 29, "0x7f252c392390": 29, "flatten": [29, 31, 32, 45, 47, 50, 57, 58, 62, 64, 67, 68, 80, 81, 85, 87, 90, 91, 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295, 367, 628, 632, 812, 817, 818, 819, 822, 823, 834, 840, 843, 844, 855, 860, 861, 870], "regress": [30, 870, 877], "checkout": [31, 46, 820, 823, 844], "f705efe7cb5d18df17ce6c1e20f04d0eb4933f48": 31, "theoret": 31, "aspect": [31, 32, 813, 839, 852, 870], "easiest": [31, 812, 814, 819, 856], "defer": [31, 32, 818, 824, 829, 830, 837, 840, 841, 844, 876], "similarli": [31, 44, 139, 147, 223, 328, 335, 336, 369, 372, 629, 632, 825, 829, 841, 847, 851, 876], "essenc": [31, 871, 876], "becom": [31, 57, 80, 97, 346, 372, 378, 464, 639, 699, 801, 820, 821, 827, 829, 831, 833, 840, 855, 859, 861, 863], "slide": [31, 57, 61, 80, 84, 375, 394, 395, 396, 412, 413, 414, 415, 418, 422, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 792], "regressor": [31, 32, 812], "input_dim": [31, 32, 46, 812], "output_dim": [31, 32, 46, 812], "linear0": [31, 32, 43, 812, 852, 853], "linear1": [31, 32, 43, 812, 852, 853], "instanti": [31, 32, 784, 832], "adam": [31, 32, 43, 47, 59, 82, 536, 615, 616, 621, 634, 635, 796, 812, 852, 853, 854, 870], "n_training_exampl": [31, 32, 812], "2000": [31, 32, 80, 314, 369, 812], "random_norm": [31, 32, 61, 62, 66, 84, 85, 89, 545, 634, 636, 637, 643, 651, 653, 654, 655, 657, 658, 662, 687, 812], "linspac": [31, 32, 53, 76, 126, 629, 812, 836, 847, 849, 877], "pred": [31, 32, 46, 47, 57, 63, 80, 86, 377, 453, 456, 638, 696, 697, 698, 812, 827, 837, 840], "gradient": [31, 32, 45, 47, 57, 80, 97, 213, 364, 372, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 631, 640, 715, 716, 717, 773, 784, 796, 812, 822, 845, 852, 853, 855, 870], "grad": [31, 32, 43, 47, 615, 635, 796, 812, 839, 852, 853, 854], "execute_with_gradi": [31, 32, 43, 47, 635, 812, 852, 853, 854, 855], "lambda": [31, 32, 48, 50, 80, 123, 125, 297, 307, 544, 557, 617, 618, 620, 625, 628, 634, 635, 637, 641, 673, 725, 726, 730, 812, 818, 837, 838, 839, 842, 847, 849, 852], "2d": [31, 32, 47, 57, 80, 97, 313, 369, 375, 376, 378, 387, 390, 391, 399, 400, 442, 449, 463, 473, 522, 792, 810, 812, 841, 847], "5f": [31, 32, 812], "nonetheless": [31, 32], "extract": [31, 32, 39, 46, 57, 80, 98, 378, 467, 493, 841, 843, 845, 866, 870, 871, 876], "gc": [31, 32, 557, 634], "decompos": [31, 32, 57, 80, 97, 100, 323, 324, 325, 326, 327, 348, 355, 369, 372, 376, 440, 445, 448, 451, 841, 854], "said": [31, 32, 778, 845, 861, 863], "otherwis": [31, 32, 49, 52, 53, 54, 56, 57, 58, 61, 62, 67, 68, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 126, 128, 129, 134, 136, 137, 138, 141, 143, 149, 152, 153, 155, 156, 158, 159, 160, 161, 162, 171, 175, 179, 180, 196, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 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758, 760, 761, 762, 763, 764, 765, 766, 767, 768, 771, 776, 777, 792, 794, 795, 801, 812, 820, 824, 827, 829, 830, 831, 837, 838, 840, 844, 849, 856, 863, 864], "x0": [31, 32, 50, 81, 537, 634, 831], "normalize_trac": [31, 32], "html": [31, 32, 46, 56, 57, 79, 80, 147, 155, 243, 253, 254, 269, 328, 335, 336, 369, 372, 375, 378, 387, 419, 492, 522, 629, 630, 632, 637, 639, 647, 685, 686, 714, 764, 832, 860], "fname": [31, 32, 48, 50, 794, 852], "anticip": [31, 32], "addition": [31, 32, 827, 840, 841, 876], "normalize_native_comp": [31, 32], "return_backend_compiled_fn": 31, "immedi": [31, 32, 810, 818, 819], "built": [31, 32, 37, 45, 47, 50, 126, 629, 792, 793, 794, 812, 819, 820, 826, 827, 844, 850, 856, 863, 869, 870, 874], "eager_graph": [31, 32, 812, 863, 864], "lazy_graph": [31, 32, 812, 863, 864], "thought": [31, 32, 819, 820, 836, 860, 868], "matter": [31, 32, 37, 831, 859], "haven": [31, 32, 37, 856, 870], "jax_out": [31, 32], "ideal": [31, 32, 828, 829, 841, 847, 852], "worth": [31, 32], "differenti": [31, 32, 295, 365, 366, 367, 374, 870], "chosen": [31, 32, 50, 100, 126, 228, 629, 632, 644, 748, 818, 828, 841], "plai": [31, 32, 377, 456, 812, 815, 819, 821, 824, 830, 834, 841, 844, 854, 870, 873], "role": [31, 32, 812, 815, 820, 821, 830, 841, 850, 871, 873, 877], "dl": [31, 32], "effortlessli": [31, 32], "previous": [31, 32, 603, 634, 801, 818, 819, 825, 837, 839, 844, 849], "default_devic": [31, 32, 206, 209, 210, 211, 217, 218, 631, 830, 833, 834], "as_n": [31, 32, 54, 55, 74, 77, 78, 158, 159, 160, 161, 162, 163, 169, 196, 197, 630, 631, 829], "certainli": [31, 32, 812, 860, 876], "upon": [31, 32, 49, 810, 820, 821, 831, 840, 844, 847, 855, 869, 870], "unnecessari": [31, 32, 841], "extend": [31, 32, 57, 80, 378, 387, 484, 525, 825, 826, 829, 832, 833, 836, 841, 845, 855, 867, 870, 876], "infrastructur": [31, 32, 866, 872, 873], "least": [31, 56, 57, 62, 79, 80, 240, 258, 273, 375, 378, 387, 403, 408, 462, 463, 464, 473, 475, 522, 632, 637, 644, 677, 747, 812, 820, 824, 828, 829, 830, 831, 837, 840, 844, 864], "coco": 31, "seamlessli": [32, 844], "therefor": [32, 37, 53, 56, 57, 62, 79, 80, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 149, 155, 171, 175, 179, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 313, 328, 329, 335, 336, 338, 341, 369, 372, 375, 376, 378, 387, 394, 395, 396, 397, 399, 400, 401, 407, 412, 413, 414, 419, 421, 430, 477, 484, 485, 487, 492, 496, 497, 522, 525, 529, 538, 546, 547, 552, 556, 558, 560, 562, 576, 591, 595, 600, 624, 629, 630, 632, 634, 635, 636, 637, 639, 642, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 658, 659, 660, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 693, 694, 695, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 737, 744, 745, 747, 748, 749, 750, 751, 752, 753, 756, 760, 761, 762, 763, 764, 765, 766, 767, 768, 818, 820, 823, 824, 827, 828, 829, 830, 831, 832, 833, 836, 837, 838, 840, 841, 842, 844, 845, 847, 849, 851, 853, 855, 859, 867, 870, 876], "wide": [32, 812, 820, 844, 868, 870], "plenti": 32, "resourc": [32, 813, 818, 819, 828], "visit": [32, 818, 819, 820, 828], "page": [32, 812, 818, 819, 820, 826, 828, 834, 850, 851, 854, 856, 865, 878], "newli": [33, 34, 46, 48, 54, 77, 152, 539, 630, 634, 820, 828, 840, 844], "randon": [33, 34, 36, 37, 38], "mean_": 33, "std_": 33, "detect": [33, 37, 56, 74, 79, 255, 632, 641, 718, 729, 818, 819, 825, 827, 828, 835, 844, 852, 853], "inspect": [33, 37, 535, 634], "__": [33, 34, 35, 36, 37, 38, 74, 831, 852], "script": [34, 812, 819, 820, 823, 828, 831, 849, 855, 870], "comp": 34, "low_level": 34, "chain": [34, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 134, 136, 141, 143, 149, 153, 155, 168, 172, 173, 180, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 299, 303, 304, 305, 306, 307, 309, 310, 311, 313, 334, 335, 336, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 468, 469, 490, 492, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 537, 538, 540, 541, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 619, 624, 640, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 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81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 98, 101, 102, 103, 110, 111, 112, 113, 114, 115, 116, 117, 127, 128, 129, 134, 135, 136, 138, 141, 143, 149, 152, 153, 154, 155, 163, 173, 175, 180, 197, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 330, 333, 334, 335, 336, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 355, 356, 357, 358, 359, 361, 362, 363, 367, 369, 372, 375, 376, 377, 378, 382, 385, 387, 394, 395, 396, 397, 399, 400, 403, 407, 408, 409, 412, 413, 414, 418, 419, 422, 425, 428, 430, 432, 436, 439, 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639, 684, 699, 841, 847], "data_typ": [54, 57, 77, 80, 182, 630, 826, 829, 844, 845], "_arraywithdatatyp": [54, 102], "irrespect": [54, 62, 77, 85, 152, 630, 637, 687, 827, 840, 851, 877], "promot": [54, 56, 57, 62, 77, 79, 80, 85, 92, 102, 103, 152, 155, 178, 179, 180, 186, 221, 222, 223, 225, 226, 227, 228, 229, 230, 232, 233, 234, 235, 237, 238, 240, 243, 245, 247, 261, 262, 263, 264, 265, 270, 273, 278, 282, 285, 286, 287, 288, 289, 290, 291, 294, 346, 354, 359, 372, 375, 387, 419, 522, 585, 608, 630, 632, 634, 637, 639, 647, 667, 668, 675, 676, 677, 678, 679, 680, 682, 683, 685, 686, 693, 694, 700, 710, 753, 761, 764, 776, 777, 821, 823, 832, 833, 837, 846], "nan": [54, 56, 57, 58, 68, 70, 77, 79, 80, 81, 152, 220, 221, 222, 223, 225, 226, 227, 228, 229, 236, 237, 238, 239, 240, 241, 243, 245, 246, 247, 248, 249, 254, 255, 256, 261, 262, 263, 264, 265, 268, 273, 274, 276, 278, 279, 282, 283, 284, 285, 286, 287, 290, 291, 293, 300, 334, 335, 336, 347, 351, 356, 359, 367, 372, 378, 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81, 88, 123, 125, 214, 223, 283, 377, 381, 387, 452, 506, 521, 526, 545, 546, 547, 614, 628, 631, 632, 634, 636, 640, 642, 663, 717, 737, 792, 806, 818, 819, 820, 825, 829, 831, 832, 835, 837, 839, 840, 841, 844, 845, 847, 851, 852, 854, 863, 870, 871, 872, 876], "__dlpack__": [55, 78, 133, 214, 629, 631], "caveat": [55, 78, 214, 377, 456, 631], "portabl": [55, 78, 214, 631, 812, 868], "_arraywithelementwis": [56, 102], "ab": [56, 62, 72, 79, 95, 102, 103, 278, 334, 351, 372, 378, 491, 632, 637, 641, 678, 688, 694, 726, 729, 773, 805, 806, 816, 824, 829, 834, 838, 841, 844, 867], "absolut": [56, 57, 62, 72, 74, 79, 80, 85, 102, 220, 284, 334, 351, 354, 360, 372, 376, 377, 430, 447, 453, 455, 632, 637, 678, 679, 680, 685, 771, 773, 776, 778, 779, 813, 819], "aco": [56, 79, 632], "invers": [56, 57, 62, 79, 80, 85, 221, 222, 225, 226, 227, 228, 229, 344, 372, 375, 385, 398, 407, 409, 419, 514, 632, 637, 676, 679, 683, 798, 829], "cosin": [56, 79, 221, 222, 237, 238, 312, 315, 369, 375, 397, 407, 632, 792], "acosh": [56, 79, 166, 167, 630, 632, 816, 834], "area": [56, 57, 79, 80, 84, 222, 226, 229, 375, 411, 418, 422, 632, 815, 840, 847, 860, 866], "hyperbol": [56, 79, 222, 226, 229, 238, 286, 290, 291, 304, 308, 367, 632], "sector": [56, 79, 222, 226, 229, 632, 860], "multipli": [56, 57, 61, 70, 79, 80, 84, 97, 223, 289, 352, 375, 376, 411, 442, 443, 523, 524, 632, 636, 647, 659, 757, 763, 820, 824, 825, 827, 831], "angl": [56, 79, 228, 238, 286, 291, 350, 372, 632], "deg": [56, 79, 224, 632], "radian": [56, 57, 79, 80, 221, 224, 225, 227, 228, 237, 239, 279, 285, 290, 359, 372, 632, 832], "degre": [56, 57, 70, 79, 80, 93, 224, 239, 279, 322, 369, 378, 490, 632, 647, 764, 766, 869], "1j": [56, 79, 80, 224, 225, 237, 238, 243, 245, 257, 280, 285, 286, 290, 338, 592, 632, 634], "2j": [56, 57, 79, 80, 224, 253, 338, 375, 403, 408, 593, 632, 634], "3j": [56, 57, 79, 80, 224, 257, 280, 338, 372, 632], "35619449": [56, 224, 632], "78539816": [56, 224, 632], "135": [56, 224, 540, 632, 634], "asin": [56, 79, 632], "sine": [56, 79, 225, 226, 285, 286, 632], "927": [56, 79, 225], "asinh": [56, 79, 225, 632], "atan": [56, 79, 632], "tangent": [56, 79, 227, 228, 229, 290, 291, 304, 308, 365, 367, 374, 632, 832], "785": [56, 79, 227, 228, 632], "atan2": [56, 79, 632], "quotient": [56, 79, 228, 240, 247, 632], "588": [56, 228, 632], "inf": [56, 57, 58, 62, 79, 80, 81, 85, 228, 245, 254, 255, 256, 257, 261, 262, 264, 274, 300, 344, 354, 367, 372, 376, 387, 426, 525, 558, 613, 627, 632, 634, 636, 637, 664, 678, 694, 776, 779, 816, 829, 834, 839], "719": [56, 228, 632], "atanh": [56, 79, 632], "549": [56, 79, 84, 229, 632, 636, 660], "bitwise_and": [56, 79, 632], "bitwise_invert": [56, 79, 632], "bitiwse_invert": [56, 231], "bitwise_left_shift": [56, 79, 632], "bitwise_or": [56, 79, 632], "bitwise_right_shift": [56, 79, 102, 632], "bitwise_xor": [56, 79, 102, 632], "ceil": [56, 57, 79, 80, 97, 100, 126, 375, 394, 395, 396, 412, 413, 414, 417, 629, 632, 792, 840], "416": [56, 237, 632], "540": [56, 237], "990": [56, 237], "cosh": [56, 79, 237, 632], "deg2rad": [56, 79, 632], "180": [56, 79, 239, 279, 632], "270": [56, 79, 239, 279, 632], "360": [56, 79, 239, 279, 632, 828], "dividend": [56, 79, 240, 247, 282, 294, 632], "divisor": [56, 57, 59, 70, 79, 80, 82, 93, 240, 247, 250, 251, 282, 294, 375, 378, 394, 395, 396, 470, 479, 499, 615, 616, 621, 632, 635, 647, 764, 766, 792, 796], "375": [56, 241, 276], "erf": [56, 79, 343, 372, 632], "exponenti": [56, 57, 79, 80, 242, 243, 245, 265, 278, 295, 305, 367, 376, 441, 632], "gauss": [56, 79, 242, 632], "328": [56, 242, 290, 632], "677": [56, 242], "842": [56, 242, 290, 632], "71828198": [56, 79, 243], "38905573": [56, 79, 243], "08553696": [56, 79, 243, 632], "exp2": [56, 79, 632], "expm1": [56, 79, 632, 829], "244": [56, 245, 812], "918": [56, 245], "147": [56, 245, 632], "floor": [56, 57, 79, 80, 97, 100, 234, 247, 375, 394, 395, 396, 398, 412, 413, 414, 417, 632, 792, 840], "floor_divid": 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"set_default_uint_dtype": [[185, "set-default-uint-dtype"]], "split_factor": [[212, "split-factor"]], "type_promote_arrays": [[186, "type-promote-arrays"]], "valid_dtype": [[192, "valid-dtype"]], "asinh": [[226, "asinh"]], "num_gpus": [[205, "num-gpus"]], "print_all_ivy_arrays_on_dev": [[208, "print-all-ivy-arrays-on-dev"]], "split_func_call": [[213, "split-func-call"]], "asin": [[225, "asin"]], "function_unsupported_devices": [[200, "function-unsupported-devices"]], "unset_default_device": [[217, "unset-default-device"]], "default_device": [[196, "default-device"]], "num_cpu_cores": [[204, "num-cpu-cores"]], "set_default_device": [[209, "set-default-device"]], "atan": [[227, "atan"]], "set_default_int_dtype": [[184, "set-default-int-dtype"]], "num_ivy_arrays_on_dev": [[206, "num-ivy-arrays-on-dev"]], "total_mem_on_dev": [[215, "total-mem-on-dev"]], "function_supported_devices": [[199, "function-supported-devices"]], "set_split_factor": [[211, "set-split-factor"]], "dev_util": [[198, "dev-util"]], "unset_default_complex_dtype": [[187, "unset-default-complex-dtype"]], "atan2": [[228, "atan2"]], "gpu_is_available": [[202, "gpu-is-available"]], "angle": [[224, "angle"]], "percent_used_mem_on_dev": [[207, "percent-used-mem-on-dev"]], "as_ivy_dev": [[193, "as-ivy-dev"]], "set_soft_device_mode": [[210, "set-soft-device-mode"]], "unset_default_float_dtype": [[189, "unset-default-float-dtype"]], "abs": [[220, "abs"]], "unset_default_dtype": [[188, "unset-default-dtype"]], "acos": [[221, "acos"]], "get_all_ivy_arrays_on_dev": [[201, "get-all-ivy-arrays-on-dev"]], "clear_cached_mem_on_dev": [[195, "clear-cached-mem-on-dev"]], "End-to-End Training Pipeline in Ivy": [[47, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[47, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[47, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[47, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[47, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[47, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[47, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[47, "Plotting-the-training-metrics"]], "Save the trained Model": [[47, "Save-the-trained-Model"]], "HuggingFace Tensorflow DeiT": [[48, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[48, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Deepmind PerceiverIO on GPU": [[46, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[46, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[46, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[46, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[46, "Run-the-demo..."]], "\u2026with torch backend": [[46, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[46, "....with-tensorflow-backend"]], "\u2026with jax backend": [[46, "...with-jax-backend"]], "\u2026with numpy backend": [[46, "...with-numpy-backend"]], "Conversions": [[75, "module-ivy.data_classes.container.conversions"], [52, "module-ivy.data_classes.array.conversions"]], "Ivy as a Transpiler Introduction": [[49, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[49, "To-use-the-transpiler:"]], "Transpiler Interface": [[49, "Transpiler-Interface"]], "Telemetry": [[49, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[49, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[49, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[49, "3.-Transpile-Models-\ud83c\udf10"]], "Image": [[83, "module-ivy.data_classes.container.image"], [60, "module-ivy.data_classes.array.image"]], "Resnet 18": [[50, "Resnet-18"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Unify": [[36, "Unify"], [37, "Unify"], [38, "Unify"], [26, "Unify"], [27, "Unify"]], "Compile": [[36, "Compile"], [37, "Compile"], [38, "Compile"]], "Transpile": [[36, "Transpile"], [37, "Transpile"], [38, "Transpile"], [26, "Transpile"], [27, "Transpile"]], "Examples and Demos": [[3, "examples-and-demos"], [20, "examples-and-demos"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[6, "Comparing-the-results"], [7, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[6, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[6, "Conclusion"], [7, "Conclusion"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Trace code": [[24, "Trace-code"]], "Write Ivy code": [[22, "Write-Ivy-code"]], "Contents": [[22, "Contents"]], "Installing Ivy": [[22, "Installing-Ivy"]], "Importing Ivy": [[22, "Importing-Ivy"], [0, "Importing-Ivy"]], "Ivy Backend Handler": [[22, "Ivy-Backend-Handler"], [31, "Ivy-Backend-Handler"]], "Data Structures": [[22, "Data-Structures"], [31, "Data-Structures"]], "Ivy Functional API": [[22, "Ivy-Functional-API"], [31, "Ivy-Functional-API"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Learn the basics": [[20, "learn-the-basics"], [21, "learn-the-basics"]], "Guides": [[20, "guides"], [15, "guides"]], "ODSC Ivy Demo": [[31, "ODSC-Ivy-Demo"]], "Graph Tracer": [[31, "Graph-Tracer"]], "Any function": [[31, "Any-function"], [32, "Any-function"]], "Any library": [[31, "Any-library"], [32, "Any-library"]], "Any model": [[31, "Any-model"], [32, "Any-model"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "1.3: Dynamic vs Static": [[39, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[39, "Dynamic"]], "Static": [[39, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[39, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Transpile any library": [[28, "Transpile-any-library"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[43, "Installs-\ud83d\udcbe"], [44, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[43, "Imports-\ud83d\udec3"], [44, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[43, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[43, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[43, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[43, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[43, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[43, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[43, "Set-Backend-Framework"]], "Define Model": [[43, "Define-Model"], [44, "Define-Model"]], "Create Model": [[43, "Create-Model"]], "Create Optimizer": [[43, "Create-Optimizer"]], "Input and Target": [[43, "Input-and-Target"]], "Loss Function": [[43, "Loss-Function"]], "Training Loop": [[43, "Training-Loop"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "Transpile code": [[25, "Transpile-code"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "0.0: Unify": [[33, "0.0:-Unify"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "Compilation of a Basic Function": [[44, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[44, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[44, "Function-compilation-\ud83d\udee0"]], "Set backend": [[44, "Set-backend"]], "Sample input": [[44, "Sample-input"]], "Define function to compile": [[44, "Define-function-to-compile"]], "Compile the function": [[44, "Compile-the-function"]], "Check results": [[44, "Check-results"], [44, "id1"]], "Compiling simple neural network \ud83e\udde0": [[44, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[44, "Create-model"]], "Define input": [[44, "Define-input"]], "Compile network": [[44, "Compile-network"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Accelerating XGBoost with JAX": [[14, "Accelerating-XGBoost-with-JAX"]], "Imports": [[14, "Imports"], [8, "Imports"], [12, "Imports"]], "Tests": [[14, "Tests"]], "Loading the Data": [[14, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[14, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[14, "JAX-backend"]], "Tensorflow backend": [[14, "Tensorflow-backend"]], "PyTorch backend": [[14, "PyTorch-backend"]], "More exhaustive example": [[14, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[14, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[14, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[14, "Comparison-of-Metrics"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Installation": [[4, "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)"]], "Quickstart": [[32, "Quickstart"]], "Get familiar with Ivy": [[32, "Get-familiar-with-Ivy"]], "Functional API": [[32, "Functional-API"]], "Stateful API": [[32, "Stateful-API"]], "Tracing code": [[32, "Tracing-code"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[45, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[45, "Table-of-Contents"]], "Defining the model": [[45, "Defining-the-model"]], "Model construction": [[45, "Model-construction"]], "Some helper functions": [[45, "Some-helper-functions"]], "Transpiling the model": [[45, "Transpiling-the-model"]], "PyTorch pipeline": [[45, "PyTorch-pipeline"]], "Dataset download": [[45, "Dataset-download"]], "DataLoader": [[45, "DataLoader"]], "Training": [[45, "Training"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Trace": [[26, "Trace"], [27, "Trace"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "Image Segmentation with Ivy UNet": 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