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dZqME z?VHx;8Tp5YI+ZV8EN84hJ%3#oVF~@xQwH}PH6%UNVlu&FdOQ9Mo`Hk zNzl<>7y83Gy@w1Q`iHRmmZN`q%FqEN{?BA0CUe*V@<)hT@{;kRIb(&h`sBspcXP&) zah)|@G%;2P*9q}@iLugEe9SHX-@i@CJ2ZXZ;FR>yCHjp>^IhV@Zfjtng7H0#5|hgK zTkhhP+=B-`+qcjEyPFTB@wxcc!m--%Y{{_}8OO%kCC7#bx|ba5EY!JhtUqYF zjM^3nquTK+MSMVuqW__DMLj)U>_2p>n5X*1W7CkPlKdfFIkLp>6pz)Agb$U7z5XY^ oQX*C(``C`sMkd*P7%y2WmJ**@GFI_VF=tBZz3Y-o#l~m)U(1ejD*ylh diff --git a/ivy/docs/functional/ivy/ivy.functional.ivy.meta.html b/ivy/docs/functional/ivy/ivy.functional.ivy.meta.html index 4413b6772..4855fad08 100644 --- a/ivy/docs/functional/ivy/ivy.functional.ivy.meta.html +++ b/ivy/docs/functional/ivy/ivy.functional.ivy.meta.html @@ -1420,7 +1420,7 @@

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 0x7fd74016a050>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f76d10a6050>) – 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 0x7fd74016a050>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f76d10a6050>) – 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 0x7fd74016a050>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f76d10a6050>) – 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 8a81c581a..438897538 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 0x7fd74016a050>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f76d10a6050>) – 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 383312c27..124146e99 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 0x7fd74016a050>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f76d10a6050>) – 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 965e8c8bc..d50990794 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 0x7fd74016a050>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f76d10a6050>) – 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 f9937b55d..2711a9908 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 0x7fd733f71f10>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f76c4e99f40>#
    diff --git a/ivy/docs/stateful/ivy.stateful.layers.html b/ivy/docs/stateful/ivy.stateful.layers.html index c742b3546..20a3a8789 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 0x7fd73fdb5de0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fd73fdb5d80>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f76d0cee020>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f76d0cedfc0>) – 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 0x7fd73fdb5d20>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fd73fdb5cc0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f76d0cedf60>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f76d0cedf00>) – 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 0x7fd73fdb5c60>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fd73fdb5c00>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f76d0cedea0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f76d0cede40>) – 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 0x7fd73fdb5ba0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fd73fdb5b40>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f76d0cedde0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f76d0cedd80>) – 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 0x7fd73fdb5a20>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fd73fdb59c0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f76d0cedc60>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f76d0cedc00>) – 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 0x7fd73fdb5960>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fd73fdb5900>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f76d0cedba0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f76d0cedb40>) – 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 0x7fd73fdb5ae0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fd73fdb5a80>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f76d0cedd20>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f76d0cedcc0>) – 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 0x7fd73fdb58a0>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f76d0cedae0>) – 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 0x7fd73fdb5ea0>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fd73fdb5e40>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f76d0cee0e0>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f76d0cee080>) – Initializer for the bias. Default is Zeros.

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

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

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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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"print_all_ivy_arrays_on_dev": [[208, "print-all-ivy-arrays-on-dev"]], "unset_default_device": [[217, "unset-default-device"]], "handle_soft_device_variable": [[203, "handle-soft-device-variable"]], "unset_default_int_dtype": [[190, "unset-default-int-dtype"]], "get_all_ivy_arrays_on_dev": [[201, "get-all-ivy-arrays-on-dev"]], "tpu_is_available": [[216, "tpu-is-available"]], "num_gpus": [[205, "num-gpus"]], "set_default_device": [[209, "set-default-device"]], "unset_default_complex_dtype": [[187, "unset-default-complex-dtype"]], "Deepmind PerceiverIO on GPU": [[46, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[46, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[46, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[46, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[46, "Run-the-demo..."]], "\u2026with torch backend": [[46, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[46, "....with-tensorflow-backend"]], "\u2026with jax backend": [[46, "...with-jax-backend"]], "\u2026with numpy backend": [[46, "...with-numpy-backend"]], "Ivy as a Transpiler Introduction": [[49, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[49, "To-use-the-transpiler:"]], "Transpiler Interface": [[49, "Transpiler-Interface"]], "Telemetry": [[49, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[49, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[49, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[49, "3.-Transpile-Models-\ud83c\udf10"]], "Conversions": [[75, "module-ivy.data_classes.container.conversions"], [52, "module-ivy.data_classes.array.conversions"]], "Image": [[83, "module-ivy.data_classes.container.image"], [60, "module-ivy.data_classes.array.image"]], "Resnet 18": [[50, "Resnet-18"]], "HuggingFace Tensorflow DeiT": [[48, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[48, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "End-to-End Training Pipeline in Ivy": [[47, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[47, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[47, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[47, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[47, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[47, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[47, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[47, "Plotting-the-training-metrics"]], "Save the trained Model": [[47, "Save-the-trained-Model"]], "# Ivy 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"], [8, "Data-Preparation"], [12, "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"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[8, "Imports"], [14, "Imports"], [12, "Imports"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Load the image example \ud83d\uddbc\ufe0f": [[8, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[8, "Visualise-image"], [12, "Visualise-image"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Unify": [[36, "Unify"], [27, "Unify"], [37, "Unify"], [26, "Unify"], [38, "Unify"]], "Compile": [[36, "Compile"], [37, "Compile"], [38, "Compile"]], "Transpile": [[36, "Transpile"], [27, "Transpile"], [37, "Transpile"], [26, "Transpile"], [38, "Transpile"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[45, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[45, "Table-of-Contents"]], "Defining the model": [[45, "Defining-the-model"]], "Model construction": [[45, "Model-construction"]], "Some helper functions": [[45, "Some-helper-functions"]], "Transpiling the model": [[45, "Transpiling-the-model"]], "PyTorch pipeline": [[45, "PyTorch-pipeline"]], "Dataset download": [[45, "Dataset-download"]], "DataLoader": [[45, "DataLoader"]], "Training": [[45, "Training"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "Transpile code": [[25, "Transpile-code"]], "How to use decorators": [[27, "How-to-use-decorators"]], "Trace": [[27, "Trace"], [26, "Trace"]], "0.1: Compile": [[34, "0.1:-Compile"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "Unify code": [[23, "Unify-code"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Learn the basics": [[20, "learn-the-basics"], [21, "learn-the-basics"]], "Guides": [[20, "guides"], [15, "guides"]], "Examples and Demos": [[20, "examples-and-demos"], [3, "examples-and-demos"]], "Trace code": [[24, "Trace-code"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[43, "Installs-\ud83d\udcbe"], [44, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[43, "Imports-\ud83d\udec3"], [44, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[43, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[43, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[43, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[43, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[43, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[43, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[43, "Set-Backend-Framework"]], "Define Model": [[43, "Define-Model"], [44, "Define-Model"]], "Create Model": [[43, "Create-Model"]], "Create Optimizer": [[43, "Create-Optimizer"]], "Input and Target": [[43, "Input-and-Target"]], "Loss Function": [[43, "Loss-Function"]], "Training Loop": [[43, "Training-Loop"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]], "0.0: Unify": [[33, "0.0:-Unify"]], "Quickstart": [[32, "Quickstart"]], "Get familiar with Ivy": [[32, "Get-familiar-with-Ivy"]], "Functional API": [[32, "Functional-API"]], "Stateful API": [[32, "Stateful-API"]], "Tracing code": [[32, "Tracing-code"]], "Any function": [[32, "Any-function"], [31, "Any-function"]], "Any library": [[32, "Any-library"], [31, "Any-library"]], "Any model": [[32, "Any-model"], [31, "Any-model"]], "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."]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[6, "Comparing-the-results"], [7, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[6, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[6, "Conclusion"], [7, "Conclusion"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "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"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Importing Ivy": [[0, "Importing-Ivy"], [22, "Importing-Ivy"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Accelerating XGBoost with JAX": [[14, "Accelerating-XGBoost-with-JAX"]], "Tests": [[14, "Tests"]], "Loading the Data": [[14, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[14, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[14, "JAX-backend"]], "Tensorflow backend": [[14, "Tensorflow-backend"]], "PyTorch backend": [[14, "PyTorch-backend"]], "More exhaustive example": [[14, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[14, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[14, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[14, "Comparison-of-Metrics"]], "Transpile any library": [[28, "Transpile-any-library"]], "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"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "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"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [4, "Installation"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "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)"]], "Write Ivy code": [[22, "Write-Ivy-code"]], "Contents": [[22, "Contents"]], "Installing Ivy": [[22, "Installing-Ivy"]]}, "indexentries": {"_arraywithactivations (class in ivy.data_classes.array.activations)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations"]], "_abc_impl (ivy.data_classes.array.activations._arraywithactivations attribute)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations._abc_impl"]], "gelu() (ivy.data_classes.array.activations._arraywithactivations method)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations.gelu"]], "hardswish() (ivy.data_classes.array.activations._arraywithactivations method)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations.hardswish"]], "ivy.data_classes.array.activations": [[51, 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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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"dev-util"]], "atanh": [[229, "atanh"]], "num_gpus": [[205, "num-gpus"]], "num_cpu_cores": [[204, "num-cpu-cores"]], "abs": [[220, "abs"]], "percent_used_mem_on_dev": [[207, "percent-used-mem-on-dev"]], "total_mem_on_dev": [[215, "total-mem-on-dev"]], "add": [[223, "add"]], "get_all_ivy_arrays_on_dev": [[201, "get-all-ivy-arrays-on-dev"]], "angle": [[224, "angle"]], "set_split_factor": [[211, "set-split-factor"]], "gpu_is_available": [[202, "gpu-is-available"]], "clear_cached_mem_on_dev": [[195, "clear-cached-mem-on-dev"]], "split_func_call": [[213, "split-func-call"]], "acosh": [[222, "acosh"]], "acos": [[221, "acos"]], "Image": [[83, "module-ivy.data_classes.container.image"], [60, "module-ivy.data_classes.array.image"]], "End-to-End Training Pipeline in Ivy": [[47, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[47, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[47, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[47, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[47, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[47, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[47, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[47, "Plotting-the-training-metrics"]], "Save the trained Model": [[47, "Save-the-trained-Model"]], "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": [[52, "module-ivy.data_classes.array.conversions"], [75, "module-ivy.data_classes.container.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"]], "Resnet 18": [[50, "Resnet-18"]], "HuggingFace Tensorflow DeiT": [[48, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[48, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "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"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Installation": [[4, "Installation"], [12, "Installation"]], "Data Preparation": [[4, "Data-Preparation"], [8, "Data-Preparation"], [5, "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)"]], "Trace code": [[24, "Trace-code"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "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"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[8, "Imports"], [14, "Imports"], [12, "Imports"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Load the image example \ud83d\uddbc\ufe0f": [[8, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[8, "Visualise-image"], [12, "Visualise-image"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Unify": [[26, "Unify"], [37, "Unify"], [27, "Unify"], [38, "Unify"], [36, "Unify"]], "Trace": [[26, "Trace"], [27, "Trace"]], "Transpile": [[26, "Transpile"], [37, "Transpile"], [27, "Transpile"], [38, "Transpile"], [36, "Transpile"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "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"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Unify code": [[23, "Unify-code"]], "How To Convert Models from PyTorch to PaddlePaddle": [[7, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[7, "About-the-Model"]], "Transpiling the Model": [[7, "Transpiling-the-Model"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Learn the basics": [[21, "learn-the-basics"], [20, "learn-the-basics"]], "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"]], "Quickstart": [[32, "Quickstart"]], "Get familiar with Ivy": [[32, "Get-familiar-with-Ivy"]], "Functional API": [[32, "Functional-API"]], "Stateful API": [[32, "Stateful-API"]], "Tracing code": [[32, "Tracing-code"]], "Any function": [[32, "Any-function"], [31, "Any-function"]], "Any library": [[32, "Any-library"], [31, "Any-library"]], "Any model": [[32, "Any-model"], [31, "Any-model"]], "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:"]], "ODSC Ivy Demo": [[31, "ODSC-Ivy-Demo"]], "Graph Tracer": [[31, "Graph-Tracer"]], "Transpile code": [[25, "Transpile-code"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "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"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "Compile": [[37, "Compile"], [38, "Compile"], [36, "Compile"]], "0.1: Compile": [[34, "0.1:-Compile"]], "Guides": [[15, "guides"], [20, "guides"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "Examples and Demos": [[3, "examples-and-demos"], [20, "examples-and-demos"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "How to use decorators": [[27, "How-to-use-decorators"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "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"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "0.0: Unify": [[33, "0.0:-Unify"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "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"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]]}, "indexentries": {"_arraywithactivations (class in ivy.data_classes.array.activations)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations"]], "_abc_impl (ivy.data_classes.array.activations._arraywithactivations attribute)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations._abc_impl"]], "gelu() (ivy.data_classes.array.activations._arraywithactivations method)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations.gelu"]], "hardswish() (ivy.data_classes.array.activations._arraywithactivations method)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations.hardswish"]], "ivy.data_classes.array.activations": [[51, 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