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zLAp%+5(f89>|Q8g;J^g=9mH-bpBiwK3Bw?!Plv<1o{t&VOhA zsDqiO>;8AvfT{oh diff --git a/docs/functional/ivy/ivy.functional.ivy.meta.html b/docs/functional/ivy/ivy.functional.ivy.meta.html index 018ba99a..7cdb97c1 100644 --- a/docs/functional/ivy/ivy.functional.ivy.meta.html +++ b/docs/functional/ivy/ivy.functional.ivy.meta.html @@ -1422,7 +1422,7 @@

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 0x7f1e74f912d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

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

    • @@ -1476,7 +1476,7 @@

    Meta#

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

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f1e74f912d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

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

    • @@ -1553,7 +1553,7 @@

    Meta#

    variables (Container) – Variables to be optimized.

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f1e74f912d0>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    • diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html index 8d145a06..c6bd2765 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html @@ -1425,7 +1425,7 @@

    fomaml_stepContainer) – Variables to be optimized during the meta step

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f1e74f912d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

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

    • diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html index b84c1351..c487e098 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html @@ -1425,7 +1425,7 @@

    maml_stepContainer) – Variables to be optimized during the meta step

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f1e74f912d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

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

    • diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html index 4bde191f..8499464e 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html @@ -1422,7 +1422,7 @@

    reptile_stepContainer) – Variables to be optimized.

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

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

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f1e74f912d0>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    • diff --git a/docs/helpers/ivy_tests.test_ivy.helpers.globals.html b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html index 79b13361..e7caa148 100644 --- a/docs/helpers/ivy_tests.test_ivy.helpers.globals.html +++ b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html @@ -1411,7 +1411,7 @@

    Should not be used inside any of the test functions.

    -ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f1e68d5df80>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f301eb25f60>#
    diff --git a/docs/stateful/ivy.stateful.layers.html b/docs/stateful/ivy.stateful.layers.html index c178eab0..16bf658a 100644 --- a/docs/stateful/ivy.stateful.layers.html +++ b/docs/stateful/ivy.stateful.layers.html @@ -1538,8 +1538,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f1e74ca4d90>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f1e74ca4f70>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971990>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a971930>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1576,8 +1576,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f1e74ca4fd0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f1e74ca5030>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a9718d0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a971870>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1615,8 +1615,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f1e74ca5390>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f1e74ca53f0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971810>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a9717b0>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1653,8 +1653,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f1e74ca5450>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f1e74ca54b0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971750>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a9716f0>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1692,8 +1692,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f1e74ca6a10>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f1e74ca5d50>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a9715d0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a971570>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1730,8 +1730,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f1e74ca5cf0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f1e74ca5c90>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971510>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a9714b0>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1794,8 +1794,8 @@

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

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

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f1e74ca5510>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f1e74ca69b0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971690>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a971630>) – Initializer for the bias. Default is Zeros.

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

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

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

    • @@ -1951,7 +1951,7 @@

    • input_channels – Number of input channels for the layer

    • output_channels – Number of output channels for the layer

      • -

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f1e74ca5c30>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971450>) – Initializer for the weights. Default is GlorotUniform.

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

    • return_sequence (default: True) – Whether or not to return the entire output sequence, or just the latest timestep. @@ -2010,8 +2010,8 @@

      • input_channels – Number of input channels for the layer.

      • output_channels – Number of output channels for the layer.

        • -

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f1e74ca4be0>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f1e74ca4c70>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971a50>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a9719f0>) – Initializer for the bias. Default is Zeros.

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

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

        • diff --git a/searchindex.js b/searchindex.js index 40d73126..f14cdcda 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["demos/Contributor_demos/Credit Card Fraud Detection/Credit_Card_Fraud_Detection", "demos/README", "demos/assets/01_template", "demos/examples_and_demos", "demos/examples_and_demos/alexnet_demo", "demos/examples_and_demos/bert_demo", "demos/examples_and_demos/convnext_to_torch", "demos/examples_and_demos/dinov2_to_paddle", "demos/examples_and_demos/image_segmentation_with_ivy_unet", "demos/examples_and_demos/lstm_tensorflow_to_torch", "demos/examples_and_demos/lstm_torch_to_tensorflow", "demos/examples_and_demos/mmpretrain_to_jax", "demos/examples_and_demos/resnet_demo", "demos/examples_and_demos/resnet_to_tensorflow", "demos/examples_and_demos/torch_to_jax", "demos/examples_and_demos/xgboost_demo", "demos/guides", "demos/guides/01_transpiling_a_torch_model", "demos/guides/02_transpiling_a_haiku_model", 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30, 33, 46, 48, 50, 51], "manual": [2, 6, 7, 13, 14, 15, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 642, 719, 729, 730, 820, 821, 822, 831, 837, 846, 855, 858], "mind": [2, 17, 19, 23, 29, 32, 36, 820, 821, 826, 829, 846, 858, 866], "click": [2, 4, 48, 820, 821, 822, 830, 834, 836, 837, 852], "runtim": [2, 4, 5, 8, 11, 12, 13, 14, 25, 32, 35, 46, 47, 824, 839, 846, 849, 872], "restart": [2, 4, 5, 8, 12, 13, 46, 47, 821, 836], "git": [2, 4, 5, 8, 12, 32, 46, 47, 48, 49, 814, 816, 819, 821, 822, 825, 828, 830, 836, 837, 846, 858], "clone": [2, 4, 8, 12, 32, 46, 48, 49, 814, 816, 822, 836, 858], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 14, 19, 27, 28, 29, 30, 32, 33, 46, 47, 48, 49, 50, 51, 57, 58, 80, 81, 83, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 616, 617, 630, 631, 633, 636, 638, 640, 648, 686, 687, 715, 765, 814, 816, 821, 822, 825, 828, 830, 831, 834, 836, 858, 866], "github": [2, 4, 5, 8, 11, 12, 14, 32, 46, 47, 48, 49, 50, 814, 816, 817, 819, 822, 823, 825, 828, 830, 831, 833, 834, 836, 837, 845, 846, 858, 861, 880], "com": [2, 4, 5, 6, 7, 8, 11, 12, 14, 19, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 825, 828, 830, 831, 836, 858], "unifyai": [2, 4, 8, 12, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 828, 836, 858], "model": [2, 3, 4, 9, 15, 16, 21, 22, 23, 49, 51, 241, 274, 378, 454, 633, 790, 794, 795, 812, 854, 855, 859, 865, 866, 870, 871, 872, 873, 874, 875, 876, 878, 879], "depth": [2, 4, 6, 8, 12, 47, 54, 58, 62, 77, 81, 85, 142, 376, 379, 412, 472, 546, 558, 630, 635, 637, 655, 656, 822, 830, 854, 855, 856, 858], "repositori": [2, 4, 8, 12, 816, 820, 821, 822, 824, 825, 828, 836, 845, 863], "cd": [2, 4, 8, 12, 32, 49, 814, 816, 821, 822, 836, 858], "resnet": [3, 6, 14, 21, 32, 865, 866], "imag": [3, 4, 6, 7, 11, 14, 17, 21, 29, 32, 33, 46, 47, 48, 49, 50, 51, 58, 62, 80, 81, 85, 103, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 284, 285, 287, 288, 292, 376, 395, 396, 412, 413, 414, 416, 546, 633, 635, 637, 650, 651, 652, 653, 654, 657, 658, 659, 793, 814, 821, 836, 849, 851, 852, 854, 856, 858, 865, 866, 872], "classif": [3, 4, 12, 15, 21, 46, 872], "acceler": [3, 21, 831, 843, 870, 874, 875, 876, 877], "convert": [3, 8, 9, 11, 14, 15, 17, 19, 21, 22, 24, 26, 29, 30, 32, 33, 34, 36, 38, 46, 49, 51, 53, 54, 57, 75, 76, 77, 80, 98, 128, 129, 141, 151, 152, 194, 195, 196, 197, 208, 216, 220, 240, 280, 379, 384, 463, 464, 465, 514, 579, 597, 599, 600, 601, 603, 630, 631, 632, 633, 635, 638, 642, 696, 720, 731, 732, 774, 802, 807, 820, 826, 827, 840, 841, 843, 846, 848, 851, 857, 859, 863, 866, 870, 871, 878], "faster": [3, 4, 9, 11, 14, 15, 21, 32, 33, 49, 51, 58, 63, 81, 86, 377, 450, 638, 688, 816, 819, 828, 859, 874, 877], "infer": [3, 6, 7, 9, 11, 13, 14, 15, 21, 25, 35, 37, 38, 47, 49, 51, 54, 58, 59, 62, 65, 77, 81, 82, 85, 88, 127, 129, 132, 136, 137, 141, 144, 150, 159, 160, 161, 162, 163, 313, 314, 376, 379, 383, 412, 497, 511, 557, 591, 592, 630, 631, 635, 637, 640, 660, 707, 802, 803, 824, 827, 831, 832, 846, 851, 856, 866, 870, 871, 874, 876], "mmpretrain": [3, 21], "segment": [3, 21, 58, 81, 331, 332, 333, 370, 828, 833], "unet": [3, 21], "alexnet": [3, 21], "written": [3, 4, 5, 6, 13, 21, 23, 32, 33, 46, 59, 379, 474, 821, 825, 826, 834, 837, 838, 842, 843, 847, 851, 853, 856, 857, 861, 866, 870, 872, 876, 878, 879], "xgboost": [3, 21], "paddlepaddl": [3, 21, 336, 337, 373, 821], "dinov2": [3, 7, 21], "project": [3, 12, 14, 21, 26, 27, 28, 29, 30, 32, 33, 36, 99, 637, 664, 793, 814, 816, 817, 820, 821, 822, 823, 826, 827, 828, 846, 855, 857, 861, 862, 863, 866, 868, 870, 872, 875, 879, 880], "convnext": [3, 6, 11, 13, 21], "finetun": [3, 21, 46], "video": [4, 8, 11, 12, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 815, 820, 821, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 835, 837, 838, 839, 840, 841, 842, 843, 844, 846, 847, 849, 858, 870], "tutori": [4, 6, 7, 8, 11, 12, 13, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 822, 843, 858], "three": [4, 5, 21, 27, 37, 38, 48, 58, 140, 313, 370, 379, 465, 630, 821, 822, 829, 830, 831, 833, 843, 846, 849, 850, 851, 873, 878], "major": [4, 5, 645, 748, 831, 832, 844, 846, 857, 862, 869, 872], "ml": [4, 5, 6, 13, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 48, 51, 815, 819, 843, 850, 851, 852, 854, 855, 856, 860, 862, 863, 866, 868, 869, 870, 871, 872, 875, 877, 879], "framework": [4, 5, 7, 9, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 39, 46, 48, 50, 53, 59, 171, 193, 203, 206, 217, 544, 560, 564, 596, 599, 631, 632, 635, 642, 721, 772, 774, 778, 785, 790, 797, 802, 803, 817, 818, 820, 821, 824, 825, 826, 827, 828, 830, 831, 832, 833, 835, 836, 838, 839, 840, 842, 843, 846, 847, 849, 850, 851, 853, 856, 857, 858, 859, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 873, 876], "sinc": [4, 8, 12, 13, 29, 30, 32, 33, 46, 48, 58, 81, 99, 373, 816, 821, 822, 825, 826, 827, 828, 829, 830, 831, 832, 835, 842, 843, 857, 862, 872, 878], "automat": [4, 8, 9, 12, 13, 30, 32, 33, 38, 820, 821, 822, 824, 827, 828, 830, 831, 837, 839, 842, 846, 849, 850, 852, 855, 856, 858, 859, 863, 872, 875, 879], "sure": [4, 8, 11, 12, 13, 14, 15, 32, 46, 817, 820, 821, 822, 825, 830, 835, 836, 843, 844, 846, 849, 858], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 15, 27, 28, 30, 47, 58, 63, 75, 86, 104, 376, 378, 399, 457, 581, 635, 638, 681, 795, 812, 814, 821, 822, 823, 826, 829, 831, 839, 840, 841, 842, 843, 846, 847, 850, 852, 854, 856, 857, 859, 862, 865, 870, 871, 872, 873, 874, 875, 878, 879], "dm": [4, 5, 8, 11, 14, 32, 33, 44, 46], "haiku": [4, 5, 8, 11, 14, 30, 32, 33, 44, 46, 50, 790, 814, 856, 863, 866, 872], "exit": [4, 8, 12, 13, 32, 33, 832], "download": [4, 6, 7, 12, 13, 17, 19, 32, 33, 47, 48, 51, 816, 821, 828, 846, 865, 866], "imagenet": [4, 6, 13, 19, 47, 49, 814], "class": [4, 6, 7, 8, 12, 13, 15, 17, 19, 23, 32, 33, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 135, 144, 150, 166, 169, 182, 184, 185, 244, 281, 339, 361, 373, 387, 388, 396, 397, 430, 529, 530, 537, 546, 550, 563, 573, 596, 630, 631, 632, 633, 635, 637, 638, 639, 642, 643, 658, 663, 667, 673, 683, 687, 688, 690, 697, 713, 720, 731, 738, 753, 760, 764, 765, 774, 775, 782, 783, 784, 785, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 801, 802, 805, 807, 812, 814, 820, 827, 828, 829, 831, 832, 833, 834, 838, 840, 841, 844, 845, 846, 849, 851, 852, 854, 855, 856, 859, 865, 866, 870, 872, 873, 879], "wget": [4, 6, 8, 12, 46, 47, 50, 821], "raw": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 46, 49, 50, 75, 814, 834, 866, 873], "githubusercont": [4, 6, 8, 12, 46, 50], "hub": [4, 6, 8, 12, 46, 49, 51], "master": [4, 8, 12, 24, 25, 26, 34, 35, 36, 37, 38, 39, 46, 48, 49, 50, 817, 830, 872, 880], "imagenet_class": [4, 12], "categori": [4, 6, 12, 820, 825, 826, 829, 831, 835, 843, 847, 850], "strip": [4, 12, 25, 35, 862], "readlin": [4, 12, 47], "cat": [4, 7, 12, 47, 844, 849, 851, 856, 865, 866], "jpg": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 48, 49, 814, 866], "filenam": [4, 8, 12, 13, 32, 33, 46, 48, 51, 59, 795, 801, 854], "import": [4, 6, 7, 9, 10, 11, 13, 14, 17, 19, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 47, 49, 50, 51, 58, 69, 73, 77, 81, 96, 195, 196, 200, 212, 308, 388, 523, 558, 574, 632, 635, 641, 646, 717, 718, 753, 785, 802, 803, 814, 819, 820, 821, 822, 823, 825, 826, 827, 828, 829, 831, 832, 833, 834, 837, 840, 841, 842, 843, 844, 845, 846, 847, 851, 853, 854, 856, 857, 858, 862, 865, 866, 867, 868, 870, 872, 875, 876, 878], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 14, 47, 48, 51, 54, 58, 67, 75, 77, 81, 90, 103, 106, 107, 108, 127, 128, 129, 131, 132, 133, 136, 137, 138, 139, 141, 142, 143, 144, 146, 147, 148, 149, 150, 194, 195, 196, 197, 198, 199, 200, 201, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 218, 220, 313, 314, 329, 330, 370, 383, 473, 509, 510, 512, 513, 537, 551, 552, 630, 635, 644, 739, 740, 741, 742, 772, 774, 775, 790, 792, 793, 794, 795, 796, 797, 798, 799, 812, 822, 824, 827, 831, 835, 839, 840, 844, 846, 847, 849, 851, 856, 857, 858, 859, 862, 871, 872, 874, 875, 876, 877], "torchvis": [4, 6, 11, 12, 13, 46, 863], "transform": [4, 5, 6, 7, 11, 12, 13, 14, 29, 32, 33, 46, 47, 49, 58, 62, 81, 85, 376, 377, 398, 399, 404, 405, 408, 409, 410, 420, 421, 424, 441, 637, 661, 777, 780, 793, 814, 840, 846, 856, 859, 865, 866, 870, 872, 873, 874], "pil": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 47, 48, 49, 814, 866], "time": [4, 5, 6, 7, 9, 10, 11, 13, 14, 30, 32, 33, 38, 46, 48, 49, 50, 58, 60, 63, 69, 81, 83, 92, 98, 99, 135, 342, 373, 376, 377, 379, 388, 405, 410, 422, 424, 445, 452, 485, 491, 523, 617, 622, 630, 636, 637, 638, 640, 641, 645, 646, 660, 663, 678, 713, 716, 717, 718, 745, 746, 750, 751, 793, 794, 795, 812, 820, 821, 822, 825, 827, 829, 830, 831, 833, 836, 838, 839, 840, 842, 843, 846, 847, 851, 854, 856, 857, 858, 861, 862, 863, 865, 866, 870, 872, 873, 876, 877, 878], "filterwarn": [4, 5, 13], "ignor": [4, 5, 13, 45, 53, 54, 58, 75, 81, 140, 376, 377, 379, 388, 400, 401, 402, 431, 439, 447, 487, 488, 492, 531, 630, 637, 642, 664, 730, 731, 797, 821, 828, 830, 833, 846, 857, 878], "compos": [4, 6, 7, 11, 12, 13, 32, 33, 46, 58, 81, 376, 390, 391, 392, 393, 821, 829, 843, 846, 865, 867, 872, 879], "resiz": [4, 6, 7, 8, 11, 12, 13, 46, 47, 58, 81, 376, 412, 849], "centercrop": [4, 12, 13], "224": [4, 6, 7, 12, 13, 17, 19, 32, 33, 46, 47, 49, 814, 866], "totensor": [4, 6, 7, 11, 12, 13, 46], "485": [4, 12, 13, 46], "456": [4, 12, 13, 46, 846], "406": [4, 12, 13, 46, 58, 81, 398, 541, 635], "229": [4, 12, 13, 46, 280, 633], "225": [4, 12, 13, 46, 48, 235, 633], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 29, 32, 33, 46, 47, 48, 50, 814, 854, 866], "ipython": [4, 8, 12, 27, 28, 29, 30, 32, 33, 51], "displai": [4, 8, 12, 13, 29, 32, 33, 46, 47, 48, 50, 51, 821, 828, 830, 835, 846, 854], "end": [4, 8, 13, 46, 47, 58, 81, 127, 229, 285, 354, 373, 376, 378, 379, 424, 453, 475, 485, 487, 488, 630, 633, 808, 821, 822, 827, 830, 836, 842, 847, 849, 850, 857, 870, 875], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 14, 218, 632, 832], "ivy_model": [4, 5, 8, 12, 49], "ivy_alexnet": 4, "quick": [4, 21, 33, 822, 824, 844, 855], "trace_graph": [4, 5, 8, 12, 25, 26, 27, 28, 32, 33, 35, 36, 37, 38, 39, 40, 49, 795, 814, 851, 856, 864], "moment": [4, 58, 60, 81, 83, 377, 434, 616, 617, 622, 636, 797, 812, 820, 827, 857, 865, 866], "cost": [4, 60, 83, 616, 617, 620, 622, 623, 624, 636, 641, 716, 717, 718, 808, 831, 849, 870], "arg": [4, 6, 8, 9, 10, 11, 12, 13, 17, 19, 27, 28, 30, 32, 33, 37, 38, 39, 50, 53, 75, 97, 107, 123, 204, 214, 602, 629, 630, 632, 635, 772, 774, 789, 790, 793, 794, 795, 799, 802, 807, 812, 814, 826, 831, 832, 835, 841, 842, 843, 849, 851, 855, 865, 866, 867], "asarrai": [4, 5, 8, 11, 12, 47, 54, 58, 59, 70, 77, 81, 82, 93, 128, 386, 515, 516, 546, 557, 561, 562, 592, 593, 594, 630, 635, 637, 646, 647, 651, 751, 755, 835, 840, 843, 844], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 23, 32, 47, 48, 51, 54, 58, 67, 77, 81, 90, 138, 139, 142, 194, 195, 196, 212, 383, 509, 510, 512, 513, 630, 632, 638, 644, 689, 739, 740, 741, 742, 792, 793, 794, 795, 796, 797, 798, 812, 851, 857, 859, 877], "output": [4, 5, 7, 8, 9, 10, 12, 13, 23, 29, 30, 32, 33, 45, 46, 47, 49, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 149, 150, 153, 155, 180, 214, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 318, 319, 323, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 365, 366, 367, 368, 370, 373, 375, 376, 377, 378, 379, 382, 383, 384, 386, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 408, 409, 410, 412, 413, 414, 415, 418, 420, 421, 422, 424, 425, 427, 428, 429, 431, 433, 436, 437, 439, 442, 443, 444, 445, 447, 448, 451, 453, 454, 455, 456, 457, 458, 459, 460, 461, 468, 469, 470, 473, 475, 476, 477, 478, 479, 482, 483, 484, 486, 487, 488, 489, 490, 491, 492, 493, 494, 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44, 802, 844], "deepmind": [18, 863], "perceiverio": [18, 863], "backbon": [18, 46, 814, 851, 854], "TO": [18, 20, 31], "efficientnet": 19, "eff_encod": [19, 814], "efficientnet_v2": [19, 814], "efficientnetv2b0": [19, 814], "storag": [19, 46, 47, 854, 862], "googleapi": [19, 46, 47], "efficientnetv2": 19, "b0_notop": 19, "h5": [19, 75], "24274472": 19, "0u": 19, "torch_eff_encod": [19, 814], "modes_to_trac": 19, "1280": [19, 546, 635, 814], "welcom": [21, 47, 814, 815, 821, 822, 823, 845], "varieti": [21, 825, 830, 831, 832, 846, 848, 868, 870, 874, 875, 878, 879], "organ": [21, 826, 829, 839, 843, 845, 847, 859, 862], "main": [21, 33, 54, 58, 63, 81, 86, 133, 146, 147, 148, 314, 329, 330, 370, 377, 379, 428, 474, 630, 638, 671, 672, 692, 814, 817, 820, 821, 822, 823, 825, 828, 829, 836, 840, 842, 870, 872, 873, 878], "exactli": [21, 25, 35, 44, 45, 49, 291, 633, 820, 829, 830, 831, 832, 833, 835, 846, 849, 861, 863], "rush": [21, 863], "jump": [21, 844], "straight": [21, 814, 830, 843, 846, 853], "quickstart": [21, 814], "introduct": [21, 23, 30, 32, 33, 872], "point": [21, 30, 55, 57, 58, 63, 67, 69, 71, 78, 80, 81, 86, 90, 94, 127, 128, 129, 131, 133, 136, 143, 144, 149, 153, 166, 170, 174, 181, 221, 222, 223, 224, 226, 227, 228, 229, 230, 237, 238, 239, 241, 242, 244, 246, 247, 248, 254, 255, 256, 257, 262, 263, 264, 265, 266, 274, 276, 277, 279, 281, 283, 284, 285, 286, 287, 288, 289, 291, 292, 293, 294, 295, 313, 314, 316, 336, 337, 354, 355, 358, 360, 370, 373, 376, 377, 378, 383, 388, 391, 400, 401, 402, 420, 430, 450, 454, 509, 510, 511, 512, 513, 523, 524, 525, 533, 628, 630, 631, 633, 638, 644, 645, 646, 647, 648, 668, 670, 673, 674, 675, 677, 679, 680, 681, 684, 685, 686, 687, 688, 689, 690, 692, 695, 741, 742, 748, 750, 751, 752, 753, 756, 758, 759, 761, 762, 763, 764, 765, 766, 767, 802, 803, 812, 818, 820, 821, 822, 825, 826, 828, 830, 831, 833, 834, 836, 838, 842, 843, 846, 847, 849, 851, 853, 854, 863, 865, 878], "showcas": [21, 814], "real": 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81, 85, 151, 152, 164, 171, 193, 194, 195, 196, 197, 199, 208, 215, 216, 220, 376, 377, 379, 419, 423, 431, 485, 496, 525, 544, 631, 632, 635, 637, 638, 650, 651, 652, 653, 655, 657, 659, 675, 772, 774, 778, 807, 808, 827, 828, 830, 831, 832, 835, 843, 851, 854], "simplest": [23, 821, 833, 846, 849], "interact": [23, 32, 47, 50, 820, 871, 872, 877], "submodul": [23, 32, 46, 48, 103, 104, 627, 628, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 789, 790, 792, 793, 795, 796, 797, 798, 820, 821, 822, 825, 828, 830, 832, 836, 839, 840, 846, 850, 851, 855, 859], "likewis": [23, 28, 32, 39, 822, 829, 831, 834, 838, 839, 843, 849, 854, 865, 866, 878], "nativearrai": [23, 32, 33, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 69, 71, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 103, 107, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123, 124, 126, 128, 129, 130, 132, 137, 138, 139, 140, 141, 142, 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46, 48, 53, 75, 76, 167, 168, 200, 201, 377, 449, 551, 552, 558, 631, 632, 635, 642, 719, 720, 723, 729, 730, 731, 772, 821, 825, 828, 829, 836, 839, 842, 855, 857], "fashion": [23, 779, 846, 866], "native_arrai": [23, 32, 33, 54, 55, 57, 77, 79, 80, 81, 82, 86, 93, 111, 114, 137, 140, 142, 144, 150, 153, 154, 155, 156, 164, 169, 176, 198, 207, 215, 231, 235, 240, 241, 242, 244, 248, 252, 260, 261, 269, 274, 277, 280, 283, 288, 336, 337, 364, 373, 378, 379, 459, 485, 491, 495, 535, 538, 565, 566, 569, 600, 627, 630, 631, 632, 633, 635, 637, 638, 639, 640, 644, 645, 648, 649, 651, 652, 659, 667, 670, 674, 675, 680, 681, 685, 689, 690, 692, 695, 697, 699, 700, 707, 739, 748, 757, 763, 766, 768, 774, 784, 802, 818, 836, 844, 846], "data_class": [23, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 396, 397, 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"x0": [32, 33, 51, 82, 538, 635, 833], "normalize_trac": [32, 33], "html": [32, 33, 47, 57, 58, 80, 81, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 630, 631, 633, 638, 640, 648, 686, 687, 715, 765, 834, 862], "fname": [32, 33, 49, 51, 795, 854], "anticip": [32, 33], "addition": [32, 33, 829, 842, 843, 878], "normalize_native_comp": [32, 33], "return_backend_compiled_fn": 32, "immedi": [32, 33, 812, 814, 820, 821], "built": [32, 33, 38, 46, 48, 51, 127, 630, 793, 794, 795, 821, 822, 828, 829, 846, 852, 858, 865, 871, 872, 876], "eager_graph": [32, 33, 814, 865, 866], "lazy_graph": [32, 33, 814, 865, 866], "thought": [32, 33, 821, 822, 838, 862, 870], "matter": [32, 33, 38, 833, 861], "haven": [32, 33, 38, 858, 872], "jax_out": [32, 33], "ideal": [32, 33, 830, 831, 843, 849, 854], "worth": [32, 33], "differenti": [32, 33, 296, 366, 367, 368, 375, 872], "chosen": [32, 33, 51, 101, 127, 229, 630, 633, 645, 749, 820, 830, 843], "plai": [32, 33, 378, 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336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 376, 379, 388, 395, 396, 397, 398, 400, 401, 402, 404, 408, 409, 410, 413, 414, 415, 419, 420, 423, 424, 425, 426, 427, 428, 430, 431, 432, 433, 434, 435, 437, 441, 442, 443, 444, 445, 446, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 469, 470, 471, 472, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 508, 510, 511, 512, 513, 514, 515, 516, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 538, 539, 541, 542, 545, 546, 547, 548, 549, 550, 553, 554, 557, 559, 561, 562, 563, 565, 566, 567, 569, 570, 572, 577, 578, 592, 593, 594, 595, 596, 598, 600, 601, 614, 616, 617, 620, 622, 623, 624, 625, 651, 652, 653, 654, 655, 656, 659, 660, 661, 663, 667, 668, 669, 671, 672, 673, 674, 675, 676, 677, 678, 679, 681, 684, 685, 686, 688, 692, 693, 695, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 767, 768, 769, 826, 833, 834, 849], "docstr": [52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 154, 155, 156, 166, 169, 173, 174, 181, 198, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 300, 301, 302, 304, 305, 306, 307, 308, 310, 311, 312, 313, 314, 315, 316, 318, 319, 320, 323, 330, 332, 333, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 373, 376, 379, 388, 395, 396, 397, 398, 400, 401, 402, 404, 408, 409, 410, 413, 414, 415, 419, 420, 423, 424, 425, 426, 427, 428, 430, 431, 432, 433, 434, 435, 437, 441, 442, 443, 444, 445, 446, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 469, 470, 471, 472, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 508, 510, 511, 512, 513, 514, 515, 516, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 538, 539, 541, 542, 545, 546, 547, 548, 549, 550, 553, 554, 557, 559, 561, 562, 563, 565, 566, 567, 569, 570, 572, 577, 578, 592, 593, 594, 595, 596, 598, 600, 601, 614, 615, 616, 617, 620, 622, 623, 624, 625, 630, 631, 633, 635, 638, 640, 645, 646, 647, 648, 649, 651, 652, 653, 654, 655, 656, 659, 660, 661, 663, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 694, 695, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 819, 820, 824, 828, 837, 838, 839, 840, 843, 845, 847], "liter": [52, 57, 58, 63, 74, 80, 81, 86, 111, 112, 113, 114, 115, 116, 117, 118, 119, 292, 296, 301, 302, 304, 368, 376, 377, 379, 382, 398, 408, 412, 420, 435, 441, 446, 449, 452, 485, 507, 627, 633, 638, 647, 679, 695, 756, 789, 849], "magnitud": [52, 57, 58, 74, 80, 81, 111, 112, 113, 114, 115, 116, 117, 118, 119, 221, 224, 241, 248, 274, 292, 296, 301, 302, 304, 368, 627, 633, 638, 688, 689, 789, 831], "handle_complex_input": [52, 57, 58, 74, 80, 81, 111, 112, 113, 114, 115, 116, 117, 118, 119, 292, 296, 301, 302, 304, 368, 627, 633, 789, 840], "element": [52, 54, 57, 58, 59, 62, 63, 65, 67, 68, 69, 71, 74, 75, 77, 78, 80, 81, 82, 85, 86, 88, 90, 91, 92, 94, 99, 103, 104, 107, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 130, 136, 137, 146, 147, 148, 164, 166, 169, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 302, 304, 306, 307, 308, 310, 311, 312, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 343, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 368, 370, 373, 376, 377, 378, 379, 388, 389, 400, 401, 402, 405, 410, 413, 414, 415, 419, 421, 422, 423, 429, 430, 431, 453, 463, 464, 465, 475, 476, 477, 479, 482, 492, 493, 495, 497, 499, 521, 522, 524, 525, 526, 527, 528, 529, 531, 532, 534, 538, 541, 542, 553, 554, 570, 572, 592, 593, 594, 596, 600, 601, 627, 630, 633, 635, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 660, 669, 671, 673, 674, 678, 683, 685, 686, 688, 692, 700, 703, 704, 705, 706, 707, 708, 709, 710, 719, 722, 728, 739, 747, 748, 749, 750, 751, 752, 753, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 772, 774, 777, 779, 793, 808, 834, 844, 846, 849, 851, 876], "138": [52, 111, 627], "165": [52, 111, 627, 637, 661], "hardswish": [52, 58, 74, 81, 299, 368, 627, 789], "leaky_relu": [52, 74, 81, 296, 627, 778], "alpha": [52, 57, 58, 74, 80, 81, 108, 113, 224, 290, 296, 297, 305, 309, 315, 368, 370, 377, 382, 383, 431, 507, 510, 511, 512, 627, 633, 789, 838, 843, 844], "slope": [52, 58, 74, 81, 113, 296, 297, 303, 305, 309, 368, 627, 789], "leaki": [52, 74, 113, 627, 789], "log_softmax": [52, 74, 627, 789], "0719": [52, 74, 114], "mish": [52, 74, 627, 789], "30340147": [52, 115, 627], "86509842": [52, 74, 115, 627], "269": [52, 117], "881": [52, 57, 80, 117, 227, 240, 280, 633], "422": [52, 118, 627], "155": [52, 85, 118, 627, 637, 661], "softplu": [52, 74, 627, 789, 849], "beta": [52, 58, 66, 74, 81, 89, 119, 305, 309, 315, 318, 319, 368, 370, 377, 378, 382, 383, 431, 459, 507, 511, 512, 627, 643, 738, 789, 814, 849], "threshold": [52, 57, 58, 74, 80, 81, 119, 272, 273, 312, 338, 368, 373, 378, 379, 454, 459, 492, 627, 633, 789, 849], "union": [52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123, 124, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 368, 370, 373, 374, 376, 377, 378, 379, 382, 383, 384, 386, 388, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 408, 409, 410, 412, 413, 414, 415, 416, 418, 419, 420, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 441, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 468, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 484, 485, 486, 487, 488, 489, 491, 492, 493, 494, 495, 497, 498, 499, 500, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 535, 538, 539, 541, 542, 546, 547, 548, 549, 550, 553, 554, 555, 556, 557, 559, 561, 562, 563, 565, 566, 569, 570, 572, 573, 577, 578, 582, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 614, 615, 616, 617, 618, 619, 620, 622, 623, 624, 625, 627, 629, 630, 631, 632, 633, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 663, 664, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 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, 720, 721, 722, 726, 727, 728, 730, 731, 736, 737, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 774, 777, 792, 797, 798, 826, 829, 831, 832, 833, 835, 838, 839, 842, 847, 849, 851, 856, 865, 866, 867], "3461": [52, 74, 119, 627], "6491": [52, 74, 119, 627], "_array_to_new_backend": 53, "_to_ivi": 53, "_to_n": 53, "to_ignor": [53, 73, 96, 642, 730, 731], "_to_new_backend": 53, "args_to_ivi": 53, "include_deriv": [53, 76, 642, 720, 731, 774], "nest": [53, 75, 76, 104, 107, 244, 568, 598, 615, 618, 633, 635, 636, 641, 716, 717, 719, 720, 721, 722, 723, 724, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 797, 826, 828, 829, 839, 841, 847, 854, 855, 857, 859, 872], "unchang": [53, 57, 376, 379, 421, 475, 637, 660], "deriv": [53, 54, 58, 60, 76, 77, 81, 83, 132, 137, 144, 150, 314, 318, 343, 370, 373, 616, 617, 620, 621, 622, 623, 624, 630, 636, 641, 642, 718, 720, 731, 795, 797, 798, 831, 832, 853, 855], "word": [53, 127, 379, 478, 630, 644, 742, 790, 793, 829, 842, 843, 859], "args_to_n": [53, 842], "cont_inplac": 53, "decid": [53, 75, 642, 730, 731, 820, 821, 831, 849], "args_to_new_backend": 53, "shallow": [53, 642, 726, 727, 731, 736, 737], "nativevari": 53, "mutabl": [53, 642, 720, 726, 727, 731, 736, 737, 827], "to_ivi": [53, 76, 642, 732, 842], "leaf": [53, 75, 82, 94, 104, 549, 642, 729, 730, 732, 759, 829, 839, 854], "travers": [53, 76, 642, 723, 731, 829, 831, 835, 851], "lowest": [53, 58, 67, 76, 81, 90, 388, 526, 642, 644, 731, 740, 808, 839, 857, 859, 869, 873, 877], "search": [53, 58, 76, 81, 745, 746, 785, 819, 821, 829, 833, 836, 846, 847, 861], "to_new_backend": 53, "_arraywithcr": [54, 103], "boolean": [54, 55, 57, 58, 59, 65, 68, 71, 75, 77, 78, 80, 81, 82, 88, 91, 94, 103, 104, 124, 126, 128, 129, 130, 136, 153, 169, 171, 173, 174, 177, 193, 203, 211, 217, 231, 232, 233, 234, 235, 236, 268, 269, 270, 271, 336, 337, 352, 373, 377, 379, 435, 446, 452, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 493, 500, 535, 538, 549, 556, 559, 560, 564, 565, 566, 567, 568, 569, 570, 579, 582, 585, 586, 588, 589, 614, 629, 630, 631, 632, 633, 635, 637, 640, 641, 642, 645, 648, 664, 703, 704, 705, 707, 709, 710, 712, 714, 716, 717, 729, 747, 748, 749, 761, 763, 777, 778, 779, 780, 785, 796, 829, 831, 839, 843, 846, 849], "never": [54, 58, 65, 77, 81, 88, 129, 379, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 500, 556, 635, 640, 703, 704, 705, 707, 709, 710, 712, 714, 822, 831, 842, 843, 846], "valueerror": [54, 58, 65, 77, 81, 88, 92, 129, 376, 378, 410, 421, 458, 463, 464, 471, 473, 475, 476, 477, 484, 500, 640, 703, 704, 705, 707, 709, 710, 712, 714, 753, 779, 809, 835], "buffer": [54, 77, 81, 88, 129, 135, 463, 464, 471, 473, 475, 476, 477, 484, 500, 630, 703, 704, 705, 707, 709, 710, 712, 714, 794, 795, 842, 857], "nativedtyp": [54, 55, 58, 62, 63, 67, 68, 71, 77, 81, 86, 90, 91, 94, 127, 128, 129, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 149, 150, 152, 153, 158, 159, 160, 161, 162, 163, 164, 165, 170, 171, 175, 177, 179, 183, 193, 313, 314, 315, 316, 317, 318, 319, 334, 341, 357, 370, 373, 383, 388, 509, 510, 511, 512, 513, 523, 524, 525, 526, 529, 532, 630, 631, 637, 638, 644, 645, 647, 648, 660, 679, 695, 740, 741, 742, 745, 746, 756, 758, 759, 762, 764, 766, 792, 831, 832, 838, 847, 851], "datatyp": [54, 58, 75, 77, 81, 129, 137, 141, 158, 179, 183, 376, 424, 630, 631, 772, 847, 865], "nativedevic": [54, 56, 58, 67, 77, 79, 81, 90, 127, 128, 129, 131, 132, 133, 136, 137, 138, 139, 141, 142, 143, 144, 148, 149, 150, 195, 196, 197, 198, 199, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 220, 313, 314, 329, 370, 383, 509, 510, 512, 513, 630, 632, 644, 739, 740, 741, 742, 792, 797, 798, 831, 832, 835, 838, 847], "39999998": [54, 128, 129, 630, 646, 751], "5999999": [54, 58, 81, 85, 128, 129, 298, 368, 377, 426, 630, 637, 660, 667], "0999999": [54, 71, 128, 129, 298, 308, 311, 354, 368, 373, 630, 762], "10000038": [54, 128, 129, 630], "90786433e": [54, 128, 129, 630], "310": [54, 128, 129, 630], "copy_arrai": [54, 77, 630], "to_ivy_arrai": [54, 77, 130, 630], "empty_lik": [54, 58, 77, 81, 265, 377, 429, 630, 633], "uniniti": [54, 131, 132, 630, 837], "from_dlpack": [54, 77, 630], "full_lik": [54, 77, 630, 847], "fill_valu": [54, 58, 68, 77, 81, 91, 136, 137, 253, 261, 379, 383, 493, 513, 630, 633, 645, 748, 831, 844, 847], "scalar": [54, 57, 58, 59, 63, 74, 77, 80, 81, 82, 86, 98, 113, 137, 142, 224, 245, 290, 296, 339, 340, 342, 347, 350, 352, 354, 359, 373, 376, 377, 378, 379, 424, 431, 453, 463, 464, 465, 474, 479, 601, 614, 630, 633, 635, 638, 695, 831, 841, 843, 857, 872], "fill": [54, 57, 58, 67, 68, 75, 77, 80, 81, 90, 91, 131, 136, 137, 139, 142, 143, 144, 149, 150, 275, 314, 370, 377, 379, 383, 435, 441, 446, 452, 474, 493, 494, 510, 512, 513, 630, 633, 644, 645, 740, 748, 792, 820, 844], "000123": [54, 137, 630], "stop": [54, 58, 60, 77, 81, 83, 127, 138, 139, 214, 377, 446, 452, 579, 617, 620, 622, 623, 624, 625, 630, 632, 635, 636, 641, 642, 716, 717, 718, 730, 797, 812, 838, 841, 849, 851, 857, 872], "num": [54, 77, 138, 139, 630, 777, 822, 838, 851], "endpoint": [54, 77, 138, 139, 630, 792, 838], "logspac": [54, 77, 630, 851], "sequenc": [54, 58, 62, 63, 65, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 104, 111, 112, 113, 114, 115, 116, 117, 118, 119, 133, 135, 137, 139, 142, 144, 150, 154, 156, 169, 173, 174, 181, 215, 221, 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573, 577, 578, 592, 593, 594, 596, 598, 600, 601, 614, 615, 618, 619, 620, 625, 630, 633, 635, 636, 637, 638, 640, 642, 648, 649, 650, 651, 652, 653, 654, 655, 657, 659, 660, 661, 662, 664, 667, 668, 669, 674, 675, 676, 677, 678, 679, 681, 683, 685, 686, 692, 695, 697, 698, 699, 700, 701, 703, 704, 706, 707, 708, 709, 710, 711, 714, 715, 719, 726, 736, 739, 740, 741, 742, 744, 747, 750, 751, 752, 753, 754, 758, 759, 761, 762, 763, 764, 765, 766, 767, 768, 769, 793, 796, 798, 822, 830, 831, 832, 833, 835, 846, 847, 849, 851, 856, 875], "on_valu": [54, 77, 139, 142, 630], "off_valu": [54, 77, 139, 142, 630], "evenli": [54, 57, 58, 62, 65, 75, 77, 80, 81, 85, 88, 127, 138, 139, 293, 376, 419, 423, 630, 633, 637, 640, 650, 651, 652, 653, 655, 657, 659, 709], "hint": [54, 57, 58, 63, 80, 81, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 149, 150, 156, 172, 176, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 235, 236, 237, 238, 239, 241, 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846, 859], "464": [54, 57, 90, 139, 228, 229, 633], "15888336": [54, 139], "2154": [54, 139], "43469003": [54, 139], "meshgrid": [54, 77, 630], "spars": [54, 58, 64, 77, 81, 87, 140, 317, 370, 377, 435, 446, 452, 630, 639, 699], "xy": [54, 77, 140, 630], "coordin": [54, 57, 68, 80, 81, 91, 140, 148, 229, 291, 321, 322, 329, 350, 370, 384, 514, 630, 633, 645, 748], "conserv": [54, 140, 630], "cartesian": [54, 140, 630], "matrix": [54, 58, 59, 62, 63, 81, 82, 85, 86, 98, 99, 101, 103, 140, 146, 147, 148, 329, 330, 370, 377, 379, 388, 427, 430, 431, 434, 435, 436, 438, 441, 442, 443, 444, 445, 446, 447, 448, 451, 452, 483, 523, 535, 541, 630, 635, 637, 638, 661, 668, 670, 672, 673, 674, 675, 677, 678, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 690, 692, 693, 696, 777, 779, 792, 793, 808, 812, 820, 831, 843, 870, 872], "ij": [54, 71, 140, 630, 648, 760, 808], "rank": [54, 58, 63, 65, 72, 81, 86, 88, 95, 98, 99, 100, 101, 102, 107, 140, 324, 325, 326, 327, 328, 370, 377, 379, 388, 435, 436, 446, 449, 452, 485, 493, 497, 533, 630, 638, 640, 645, 649, 669, 671, 679, 681, 685, 687, 692, 694, 695, 702, 703, 711, 714, 715, 748, 768, 769, 815, 880], "ni": [54, 140, 630], "xi": [54, 140, 630], "scatter": [54, 59, 77, 82, 142, 577, 578, 630, 635, 828, 842, 849, 879], "unless": [54, 58, 63, 77, 81, 142, 274, 335, 352, 357, 373, 630, 633, 638, 681, 827, 832, 842, 857, 866, 867], "ones_lik": [54, 77, 630, 827, 856, 869], "tril": [54, 77, 630], "whose": [54, 57, 58, 59, 63, 65, 69, 71, 77, 80, 81, 82, 86, 88, 92, 94, 99, 101, 103, 137, 146, 147, 223, 227, 230, 238, 239, 240, 279, 280, 286, 287, 291, 292, 293, 330, 344, 345, 349, 353, 354, 356, 360, 370, 377, 379, 430, 451, 484, 493, 499, 540, 596, 630, 633, 635, 638, 640, 646, 648, 668, 670, 672, 673, 674, 675, 676, 677, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 692, 695, 704, 708, 750, 751, 752, 759, 760, 779, 817, 834, 846], "innermost": [54, 58, 63, 86, 146, 147, 330, 370, 377, 430, 630, 638, 668, 670, 672, 673, 674, 675, 677, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 692], "mxn": [54, 58, 63, 86, 146, 147, 330, 370, 630, 638, 672, 679, 681, 682, 684, 685, 689, 692], "matric": [54, 58, 63, 81, 86, 98, 99, 103, 140, 146, 147, 330, 370, 377, 379, 430, 435, 436, 438, 444, 445, 450, 474, 630, 637, 638, 661, 668, 670, 672, 673, 674, 675, 676, 677, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 692, 693, 779, 818, 836, 872], "diagon": [54, 58, 63, 81, 86, 99, 133, 146, 147, 148, 314, 329, 330, 370, 377, 379, 428, 431, 441, 447, 474, 630, 638, 671, 692], "triangular": [54, 58, 63, 86, 146, 147, 148, 329, 330, 370, 377, 447, 630, 638, 668, 674, 675, 681, 685], "triu": [54, 77, 630], "upper": [54, 58, 63, 67, 81, 86, 90, 133, 147, 148, 314, 330, 370, 377, 388, 447, 526, 630, 638, 644, 668, 674, 675, 685, 742, 831, 842, 846], "zeros_lik": [54, 58, 77, 153, 270, 379, 493, 616, 617, 620, 622, 623, 624, 630, 631, 633, 636, 638, 640, 685, 700, 843, 849], "data_typ": [55, 58, 78, 81, 183, 631, 828, 831, 846, 847], "_arraywithdatatyp": [55, 103], "irrespect": [55, 63, 78, 86, 153, 631, 638, 688, 829, 842, 853, 879], "promot": [55, 57, 58, 63, 78, 80, 81, 86, 93, 103, 104, 153, 156, 179, 180, 181, 187, 222, 223, 224, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 238, 239, 241, 244, 246, 248, 262, 263, 264, 265, 266, 271, 274, 279, 283, 286, 287, 288, 289, 290, 291, 292, 295, 347, 355, 360, 373, 376, 388, 420, 523, 586, 609, 631, 633, 635, 638, 640, 648, 668, 669, 676, 677, 678, 679, 680, 681, 683, 684, 686, 687, 694, 695, 701, 711, 754, 762, 765, 777, 778, 823, 825, 834, 835, 839, 848], "nan": [55, 57, 58, 59, 69, 71, 78, 80, 81, 82, 153, 221, 222, 223, 224, 226, 227, 228, 229, 230, 237, 238, 239, 240, 241, 242, 244, 246, 247, 248, 249, 250, 255, 256, 257, 262, 263, 264, 265, 266, 269, 274, 275, 277, 279, 280, 283, 284, 285, 286, 287, 288, 291, 292, 294, 301, 335, 336, 337, 348, 352, 357, 360, 368, 373, 379, 388, 493, 521, 522, 529, 530, 531, 532, 559, 614, 628, 631, 633, 635, 646, 648, 649, 750, 751, 752, 753, 761, 762, 763, 765, 766, 767, 768, 769, 777, 780, 825, 831, 834, 841, 847, 848], "infin": [55, 57, 59, 63, 78, 80, 86, 153, 221, 222, 223, 224, 227, 228, 229, 230, 237, 238, 239, 241, 242, 244, 246, 247, 248, 255, 256, 262, 263, 264, 265, 266, 269, 274, 275, 277, 279, 283, 284, 286, 287, 288, 291, 292, 294, 336, 337, 360, 373, 559, 628, 631, 633, 635, 638, 648, 649, 686, 695, 761, 763, 768, 769, 825, 834], "desir": [55, 56, 58, 68, 71, 75, 78, 79, 81, 91, 94, 98, 153, 155, 156, 215, 320, 361, 370, 373, 379, 388, 483, 529, 532, 533, 631, 632, 638, 645, 648, 690, 747, 762, 792, 793, 822, 827, 830, 831, 832, 843, 851, 861, 865, 872], "broadcast_arrai": [55, 78, 631], "mix": [55, 57, 78, 80, 81, 82, 87, 90, 103, 104, 154, 167, 168, 181, 200, 201, 231, 234, 235, 236, 241, 242, 248, 252, 260, 261, 271, 274, 277, 283, 378, 388, 459, 530, 549, 551, 552, 553, 554, 563, 598, 601, 631, 632, 633, 635, 637, 638, 639, 640, 643, 648, 651, 653, 656, 658, 659, 661, 667, 668, 690, 697, 699, 700, 738, 760, 762, 765, 778, 780, 820, 824, 831, 832, 833, 842, 849, 851, 859, 872, 876, 878], "broadcast_to": [55, 78, 631, 831], "can_cast": [55, 78, 631, 831, 839, 843], "accord": [55, 58, 59, 65, 71, 78, 88, 94, 156, 166, 224, 235, 241, 248, 274, 285, 320, 370, 376, 379, 421, 485, 553, 556, 577, 578, 631, 633, 635, 638, 640, 648, 694, 702, 715, 765, 767, 772, 779, 799, 807, 820, 821, 825, 831, 837, 839, 843, 846], "finfo": [55, 78, 631, 846], "resolut": [55, 78, 166, 631, 822], "4028235e": [55, 166, 631], "iinfo": [55, 78, 631], "integ": [55, 57, 58, 62, 63, 65, 67, 71, 72, 75, 80, 81, 82, 85, 86, 88, 90, 94, 95, 103, 104, 127, 136, 169, 170, 176, 180, 181, 185, 221, 231, 232, 233, 234, 235, 236, 237, 247, 248, 259, 271, 276, 279, 283, 284, 294, 295, 331, 332, 333, 336, 337, 341, 346, 347, 370, 373, 376, 379, 383, 386, 388, 404, 409, 419, 422, 423, 424, 471, 480, 485, 493, 497, 500, 509, 510, 511, 512, 513, 515, 516, 521, 523, 524, 525, 530, 533, 556, 572, 582, 615, 630, 631, 633, 635, 637, 638, 640, 644, 647, 648, 649, 650, 651, 652, 653, 655, 657, 659, 669, 671, 680, 694, 695, 709, 739, 740, 741, 742, 743, 744, 756, 758, 759, 761, 762, 763, 764, 765, 766, 767, 768, 769, 777, 778, 779, 780, 785, 793, 808, 822, 829, 831, 841, 844, 846, 851, 853], "119": [55, 169], "1220": [55, 169], "int16": [55, 58, 67, 71, 78, 90, 156, 160, 162, 167, 169, 176, 191, 388, 524, 525, 631, 648, 740, 758, 759, 764, 766, 777, 778, 831, 843, 846, 851], "32768": [55, 78, 169, 594, 635], "32767": [55, 78, 169], "is_bool_dtyp": [55, 78, 631], "is_float_dtyp": [55, 78, 631, 847], "is_int_dtyp": [55, 78, 631, 844, 847], "is_uint_dtyp": [55, 78, 631, 844, 847], "result_typ": [55, 78, 631, 831], "arrays_and_dtyp": [55, 78, 181, 631], "_arraywithdevic": [56, 103], "move": [56, 58, 79, 81, 148, 211, 215, 219, 329, 370, 379, 484, 630, 632, 795, 822, 832, 847], "addit": [56, 58, 59, 66, 79, 81, 82, 89, 124, 126, 215, 224, 284, 378, 382, 388, 453, 507, 522, 527, 546, 547, 548, 615, 629, 632, 633, 635, 637, 641, 643, 664, 718, 738, 793, 808, 820, 821, 822, 827, 831, 833, 834, 837, 839, 841, 842, 843, 846, 847, 849, 853, 854, 856, 865, 872, 873, 874, 878], "__dlpack__": [56, 79, 134, 215, 630, 632], "caveat": [56, 79, 215, 378, 457, 632], "portabl": [56, 79, 215, 632, 814, 870], "_arraywithelementwis": [57, 103], "ab": [57, 63, 73, 80, 96, 103, 104, 279, 335, 352, 373, 379, 492, 633, 638, 642, 679, 689, 695, 727, 730, 774, 807, 808, 818, 826, 831, 836, 840, 843, 846, 869], "absolut": [57, 58, 63, 73, 75, 80, 81, 86, 103, 221, 285, 335, 352, 355, 361, 373, 377, 378, 431, 448, 454, 456, 633, 638, 679, 680, 681, 686, 772, 774, 777, 779, 780, 815, 821], "aco": [57, 80, 633], "invers": [57, 58, 63, 80, 81, 86, 222, 223, 226, 227, 228, 229, 230, 345, 373, 376, 386, 399, 408, 410, 420, 515, 633, 638, 677, 680, 684, 799, 831], "cosin": [57, 80, 222, 223, 238, 239, 313, 316, 370, 376, 398, 408, 633, 793], "acosh": [57, 80, 167, 168, 631, 633, 818, 836], 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"Define-Model"]], "Create model": [[45, "Create-model"]], "Define input": [[45, "Define-input"]], "Compile network": [[45, "Compile-network"]], "Write a model using Ivy": [[31, "Write-a-model-using-Ivy"]], "Unify code": [[24, "Unify-code"]], "Developing a convolutional network using Ivy": [[20, "Developing-a-convolutional-network-using-Ivy"]], "How to use decorators": [[28, "How-to-use-decorators"]], "Unify": [[28, "Unify"], [27, "Unify"], [37, "Unify"], [39, "Unify"], [38, "Unify"]], "Trace": [[28, "Trace"], [27, "Trace"]], "Transpile": [[28, "Transpile"], [27, "Transpile"], [37, "Transpile"], [39, "Transpile"], [38, "Transpile"]], "0.2: Transpile": [[36, "0.2:-Transpile"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [13, "Installation"], [4, "Installation"]], "Imports": [[12, "Imports"], [15, "Imports"], [8, "Imports"]], "Data Preparation": [[12, "Data-Preparation"], [4, "Data-Preparation"], [8, "Data-Preparation"], [5, "Data-Preparation"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Load the image example \ud83d\uddbc\ufe0f": [[12, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [8, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[12, "Visualise-image"], [8, "Visualise-image"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "0.1: Compile": [[35, "0.1:-Compile"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "Accelerating XGBoost with JAX": [[15, "Accelerating-XGBoost-with-JAX"]], "Tests": [[15, "Tests"]], "Loading the Data": [[15, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[15, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[15, "JAX-backend"]], "Tensorflow backend": [[15, "Tensorflow-backend"]], "PyTorch backend": [[15, "PyTorch-backend"]], "More exhaustive example": [[15, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[15, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[15, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[15, "Comparison-of-Metrics"]], "Training PyTorch ResNet in your TensorFlow Projects": [[13, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Framework Incompatibility": [[13, "Framework-Incompatibility"], [6, "Framework-Incompatibility"]], "Transpiling a PyTorch model to TensorFlow": [[13, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "About the transpiled model": [[13, "About-the-transpiled-model"], [6, "About-the-transpiled-model"]], "Setting-up the source model": [[13, "Setting-up-the-source-model"], [6, "Setting-up-the-source-model"]], "Load the Data": [[13, "Load-the-Data"]], "Visualize a few images": [[13, "Visualize-a-few-images"]], "Load the pre-trained model": [[13, "Load-the-pre-trained-model"]], "Converting the model from TensorFlow to PyTorch": [[13, "Converting-the-model-from-TensorFlow-to-PyTorch"], [6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "2.0: Kornia": [[41, "2.0:-Kornia"]], "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)"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Transpiling a PyTorch model to build on top": [[17, "Transpiling-a-PyTorch-model-to-build-on-top"]], "ODSC Ivy Demo": [[32, "ODSC-Ivy-Demo"]], "Ivy Backend Handler": [[32, "Ivy-Backend-Handler"], [23, "Ivy-Backend-Handler"]], "Data Structures": [[32, "Data-Structures"], [23, "Data-Structures"]], "Ivy Functional API": [[32, "Ivy-Functional-API"], [23, "Ivy-Functional-API"]], "Graph Tracer": [[32, "Graph-Tracer"]], "Transpiling a haiku model to build on top": [[18, "Transpiling-a-haiku-model-to-build-on-top"]], "Lazy vs Eager": [[27, "Lazy-vs-Eager"]], "Transpiling a Tensorflow model to build on top": [[19, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "1.0: Lazy vs Eager": [[37, "1.0:-Lazy-vs-Eager"]], "Compile": [[37, "Compile"], [39, "Compile"], [38, "Compile"]], "Trace code": [[25, "Trace-code"]], "3.0: Perceiver": [[42, "3.0:-Perceiver"]], "1.2: As a Decorator": [[39, "1.2:-As-a-Decorator"]], "Transpile code": [[26, "Transpile-code"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "Write Ivy code": [[23, "Write-Ivy-code"]], "Contents": [[23, "Contents"]], "Installing Ivy": [[23, "Installing-Ivy"]], "Importing Ivy": [[23, "Importing-Ivy"], [0, "Importing-Ivy"]], "Learn the basics": [[22, "learn-the-basics"], [21, "learn-the-basics"]], "Examples and Demos": [[3, "examples-and-demos"], [21, "examples-and-demos"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "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:"]], "3.1: Stable Diffusion": [[43, "3.1:-Stable-Diffusion"]], "Transpile any model": [[30, "Transpile-any-model"]], "Round up": [[30, "Round-up"]], "Tutorials And Examples": [[21, "tutorials-and-examples"]], "Guides": [[21, "guides"], [16, "guides"]], "Basic Operations with Ivy": [[44, "Basic-Operations-with-Ivy"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[44, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[44, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[44, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[44, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[44, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[44, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[44, "Set-Backend-Framework"]], "Create Model": [[44, "Create-Model"]], "Create Optimizer": [[44, "Create-Optimizer"]], "Input and Target": [[44, "Input-and-Target"]], "Loss Function": [[44, "Loss-Function"]], "Training Loop": [[44, "Training-Loop"]], "1.1: Framework Selection": [[38, "1.1:-Framework-Selection"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "Accelerating PyTorch models with JAX": [[14, "Accelerating-PyTorch-models-with-JAX"]], "0.0: Unify": [[34, "0.0:-Unify"]], "Resnet 18": [[51, "Resnet-18"]], "Image": [[84, "module-ivy.data_classes.container.image"], [61, "module-ivy.data_classes.array.image"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[46, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[46, "Table-of-Contents"]], "Defining the model": [[46, "Defining-the-model"]], "Model construction": [[46, "Model-construction"]], "Some helper functions": [[46, "Some-helper-functions"]], "Transpiling the model": [[46, "Transpiling-the-model"]], "PyTorch pipeline": [[46, "PyTorch-pipeline"]], "Dataset download": [[46, "Dataset-download"]], "DataLoader": [[46, "DataLoader"]], "Training": [[46, "Training"]], "Conversions": [[53, "module-ivy.data_classes.array.conversions"], [76, "module-ivy.data_classes.container.conversions"]], "HuggingFace Tensorflow DeiT": [[49, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[49, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Deepmind PerceiverIO on GPU": [[47, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[47, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[47, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[47, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[47, "Run-the-demo..."]], "\u2026with torch backend": [[47, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[47, "....with-tensorflow-backend"]], "\u2026with jax backend": [[47, "...with-jax-backend"]], "\u2026with numpy backend": [[47, "...with-numpy-backend"]], "End-to-End Training Pipeline in Ivy": [[48, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[48, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[48, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[48, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[48, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[48, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[48, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[48, "Plotting-the-training-metrics"]], "Save the trained Model": [[48, "Save-the-trained-Model"]], "Ivy as a Transpiler Introduction": [[50, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[50, "To-use-the-transpiler:"]], "Transpiler Interface": [[50, "Transpiler-Interface"]], "Telemetry": [[50, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[50, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[50, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. 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865, 868, 872], "document": [1, 6, 7, 13, 23, 32, 65, 248, 336, 337, 373, 615, 633, 635, 711, 815, 816, 819, 822, 828, 830, 831, 833, 842, 843, 844, 846, 854, 856], "sphinx": [1, 816, 828], "websit": [1, 50, 814, 821, 825, 862], "alreadi": [2, 6, 13, 14, 24, 27, 28, 29, 30, 32, 33, 38, 46, 48, 51, 58, 63, 75, 81, 86, 237, 247, 274, 284, 294, 379, 388, 464, 465, 485, 521, 530, 633, 638, 676, 683, 807, 808, 820, 821, 822, 827, 829, 831, 832, 838, 842, 843, 849, 857, 858, 872, 874, 879], "instal": [2, 7, 8, 9, 10, 11, 14, 15, 17, 19, 24, 25, 26, 27, 28, 29, 30, 32, 33, 46, 48, 49, 50, 51, 816, 821, 822, 827, 828, 836, 837], "skip": [2, 5, 13, 48, 58, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 111, 112, 113, 114, 115, 116, 117, 118, 119, 135, 137, 142, 144, 150, 154, 156, 181, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 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30, 33, 46, 48, 50, 51], "manual": [2, 6, 7, 13, 14, 15, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 642, 719, 729, 730, 820, 821, 822, 831, 837, 846, 855, 858], "mind": [2, 17, 19, 23, 29, 32, 36, 820, 821, 826, 829, 846, 858, 866], "click": [2, 4, 48, 820, 821, 822, 830, 834, 836, 837, 852], "runtim": [2, 4, 5, 8, 11, 12, 13, 14, 25, 32, 35, 46, 47, 824, 839, 846, 849, 872], "restart": [2, 4, 5, 8, 12, 13, 46, 47, 821, 836], "git": [2, 4, 5, 8, 12, 32, 46, 47, 48, 49, 814, 816, 819, 821, 822, 825, 828, 830, 836, 837, 846, 858], "clone": [2, 4, 8, 12, 32, 46, 48, 49, 814, 816, 822, 836, 858], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 14, 19, 27, 28, 29, 30, 32, 33, 46, 47, 48, 49, 50, 51, 57, 58, 80, 81, 83, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 616, 617, 630, 631, 633, 636, 638, 640, 648, 686, 687, 715, 765, 814, 816, 821, 822, 825, 828, 830, 831, 834, 836, 858, 866], "github": [2, 4, 5, 8, 11, 12, 14, 32, 46, 47, 48, 49, 50, 814, 816, 817, 819, 822, 823, 825, 828, 830, 831, 833, 834, 836, 837, 845, 846, 858, 861, 880], "com": [2, 4, 5, 6, 7, 8, 11, 12, 14, 19, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 825, 828, 830, 831, 836, 858], "unifyai": [2, 4, 8, 12, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 828, 836, 858], "model": [2, 3, 4, 9, 15, 16, 21, 22, 23, 49, 51, 241, 274, 378, 454, 633, 790, 794, 795, 812, 854, 855, 859, 865, 866, 870, 871, 872, 873, 874, 875, 876, 878, 879], "depth": [2, 4, 6, 8, 12, 47, 54, 58, 62, 77, 81, 85, 142, 376, 379, 412, 472, 546, 558, 630, 635, 637, 655, 656, 822, 830, 854, 855, 856, 858], "repositori": [2, 4, 8, 12, 816, 820, 821, 822, 824, 825, 828, 836, 845, 863], "cd": [2, 4, 8, 12, 32, 49, 814, 816, 821, 822, 836, 858], "resnet": [3, 6, 14, 21, 32, 865, 866], "imag": [3, 4, 6, 7, 11, 14, 17, 21, 29, 32, 33, 46, 47, 48, 49, 50, 51, 58, 62, 80, 81, 85, 103, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 284, 285, 287, 288, 292, 376, 395, 396, 412, 413, 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635, 637, 640, 660, 707, 802, 803, 824, 827, 831, 832, 846, 851, 856, 866, 870, 871, 874, 876], "mmpretrain": [3, 21], "segment": [3, 21, 58, 81, 331, 332, 333, 370, 828, 833], "unet": [3, 21], "alexnet": [3, 21], "written": [3, 4, 5, 6, 13, 21, 23, 32, 33, 46, 59, 379, 474, 821, 825, 826, 834, 837, 838, 842, 843, 847, 851, 853, 856, 857, 861, 866, 870, 872, 876, 878, 879], "xgboost": [3, 21], "paddlepaddl": [3, 21, 336, 337, 373, 821], "dinov2": [3, 7, 21], "project": [3, 12, 14, 21, 26, 27, 28, 29, 30, 32, 33, 36, 99, 637, 664, 793, 814, 816, 817, 820, 821, 822, 823, 826, 827, 828, 846, 855, 857, 861, 862, 863, 866, 868, 870, 872, 875, 879, 880], "convnext": [3, 6, 11, 13, 21], "finetun": [3, 21, 46], "video": [4, 8, 11, 12, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 815, 820, 821, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 835, 837, 838, 839, 840, 841, 842, 843, 844, 846, 847, 849, 858, 870], "tutori": [4, 6, 7, 8, 11, 12, 13, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 822, 843, 858], "three": [4, 5, 21, 27, 37, 38, 48, 58, 140, 313, 370, 379, 465, 630, 821, 822, 829, 830, 831, 833, 843, 846, 849, 850, 851, 873, 878], "major": [4, 5, 645, 748, 831, 832, 844, 846, 857, 862, 869, 872], "ml": [4, 5, 6, 13, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 48, 51, 815, 819, 843, 850, 851, 852, 854, 855, 856, 860, 862, 863, 866, 868, 869, 870, 871, 872, 875, 877, 879], "framework": [4, 5, 7, 9, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 39, 46, 48, 50, 53, 59, 171, 193, 203, 206, 217, 544, 560, 564, 596, 599, 631, 632, 635, 642, 721, 772, 774, 778, 785, 790, 797, 802, 803, 817, 818, 820, 821, 824, 825, 826, 827, 828, 830, 831, 832, 833, 835, 836, 838, 839, 840, 842, 843, 846, 847, 849, 850, 851, 853, 856, 857, 858, 859, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 873, 876], "sinc": [4, 8, 12, 13, 29, 30, 32, 33, 46, 48, 58, 81, 99, 373, 816, 821, 822, 825, 826, 827, 828, 829, 830, 831, 832, 835, 842, 843, 857, 862, 872, 878], "automat": [4, 8, 9, 12, 13, 30, 32, 33, 38, 820, 821, 822, 824, 827, 828, 830, 831, 837, 839, 842, 846, 849, 850, 852, 855, 856, 858, 859, 863, 872, 875, 879], "sure": [4, 8, 11, 12, 13, 14, 15, 32, 46, 817, 820, 821, 822, 825, 830, 835, 836, 843, 844, 846, 849, 858], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 15, 27, 28, 30, 47, 58, 63, 75, 86, 104, 376, 378, 399, 457, 581, 635, 638, 681, 795, 812, 814, 821, 822, 823, 826, 829, 831, 839, 840, 841, 842, 843, 846, 847, 850, 852, 854, 856, 857, 859, 862, 865, 870, 871, 872, 873, 874, 875, 878, 879], "dm": [4, 5, 8, 11, 14, 32, 33, 44, 46], "haiku": [4, 5, 8, 11, 14, 30, 32, 33, 44, 46, 50, 790, 814, 856, 863, 866, 872], "exit": [4, 8, 12, 13, 32, 33, 832], "download": [4, 6, 7, 12, 13, 17, 19, 32, 33, 47, 48, 51, 816, 821, 828, 846, 865, 866], "imagenet": [4, 6, 13, 19, 47, 49, 814], "class": [4, 6, 7, 8, 12, 13, 15, 17, 19, 23, 32, 33, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 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795, 812, 820, 821, 822, 825, 827, 829, 830, 831, 833, 836, 838, 839, 840, 842, 843, 846, 847, 851, 854, 856, 857, 858, 861, 862, 863, 865, 866, 870, 872, 873, 876, 877, 878], "filterwarn": [4, 5, 13], "ignor": [4, 5, 13, 45, 53, 54, 58, 75, 81, 140, 376, 377, 379, 388, 400, 401, 402, 431, 439, 447, 487, 488, 492, 531, 630, 637, 642, 664, 730, 731, 797, 821, 828, 830, 833, 846, 857, 878], "compos": [4, 6, 7, 11, 12, 13, 32, 33, 46, 58, 81, 376, 390, 391, 392, 393, 821, 829, 843, 846, 865, 867, 872, 879], "resiz": [4, 6, 7, 8, 11, 12, 13, 46, 47, 58, 81, 376, 412, 849], "centercrop": [4, 12, 13], "224": [4, 6, 7, 12, 13, 17, 19, 32, 33, 46, 47, 49, 814, 866], "totensor": [4, 6, 7, 11, 12, 13, 46], "485": [4, 12, 13, 46], "456": [4, 12, 13, 46, 846], "406": [4, 12, 13, 46, 58, 81, 398, 541, 635], "229": [4, 12, 13, 46, 280, 633], "225": [4, 12, 13, 46, 48, 235, 633], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 29, 32, 33, 46, 47, 48, 50, 814, 854, 866], "ipython": [4, 8, 12, 27, 28, 29, 30, 32, 33, 51], "displai": [4, 8, 12, 13, 29, 32, 33, 46, 47, 48, 50, 51, 821, 828, 830, 835, 846, 854], "end": [4, 8, 13, 46, 47, 58, 81, 127, 229, 285, 354, 373, 376, 378, 379, 424, 453, 475, 485, 487, 488, 630, 633, 808, 821, 822, 827, 830, 836, 842, 847, 849, 850, 857, 870, 875], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 14, 218, 632, 832], "ivy_model": [4, 5, 8, 12, 49], "ivy_alexnet": 4, "quick": [4, 21, 33, 822, 824, 844, 855], "trace_graph": [4, 5, 8, 12, 25, 26, 27, 28, 32, 33, 35, 36, 37, 38, 39, 40, 49, 795, 814, 851, 856, 864], "moment": [4, 58, 60, 81, 83, 377, 434, 616, 617, 622, 636, 797, 812, 820, 827, 857, 865, 866], "cost": [4, 60, 83, 616, 617, 620, 622, 623, 624, 636, 641, 716, 717, 718, 808, 831, 849, 870], "arg": [4, 6, 8, 9, 10, 11, 12, 13, 17, 19, 27, 28, 30, 32, 33, 37, 38, 39, 50, 53, 75, 97, 107, 123, 204, 214, 602, 629, 630, 632, 635, 772, 774, 789, 790, 793, 794, 795, 799, 802, 807, 812, 814, 826, 831, 832, 835, 841, 842, 843, 849, 851, 855, 865, 866, 867], "asarrai": [4, 5, 8, 11, 12, 47, 54, 58, 59, 70, 77, 81, 82, 93, 128, 386, 515, 516, 546, 557, 561, 562, 592, 593, 594, 630, 635, 637, 646, 647, 651, 751, 755, 835, 840, 843, 844], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 23, 32, 47, 48, 51, 54, 58, 67, 77, 81, 90, 138, 139, 142, 194, 195, 196, 212, 383, 509, 510, 512, 513, 630, 632, 638, 644, 689, 739, 740, 741, 742, 792, 793, 794, 795, 796, 797, 798, 812, 851, 857, 859, 877], "output": [4, 5, 7, 8, 9, 10, 12, 13, 23, 29, 30, 32, 33, 45, 46, 47, 49, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 149, 150, 153, 155, 180, 214, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 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731, 732, 736, 737, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 772, 774, 779, 785, 792, 793, 794, 795, 798, 802, 807, 808, 810, 814, 818, 820, 821, 822, 824, 827, 828, 829, 831, 832, 833, 834, 835, 836, 839, 840, 841, 842, 843, 844, 846, 847, 848, 849, 850, 851, 852, 854, 855, 856, 857, 859, 866, 867, 870, 871, 872, 874, 878, 879], "282": [4, 12], "281": [4, 12, 46, 48], "285": [4, 12, 81], "64773697": 4, "29496649": 4, "04526037": 4, "tiger": [4, 12], "tabbi": [4, 7, 12], "egyptian": [4, 12], "torch_alexnet": 4, "alexnet_weight": 4, "imagenet1k_v1": [4, 12, 13], "dropout": [4, 62, 85, 376, 400, 401, 402, 637, 662, 664, 667, 793, 854], "torch_output": [4, 8, 9, 12], "dim": [4, 12, 48, 58, 75, 77, 81, 142, 314, 370, 376, 379, 394, 404, 405, 406, 409, 417, 475, 497, 630, 637, 650, 657, 658, 663, 779, 793, 831, 843, 844, 849], "torch_class": [4, 12], "torch_logit": [4, 12], "tensor": [4, 5, 6, 9, 11, 12, 13, 14, 17, 19, 23, 24, 27, 28, 30, 32, 33, 34, 38, 44, 46, 54, 57, 58, 59, 62, 63, 64, 65, 67, 71, 75, 77, 80, 81, 82, 85, 86, 87, 88, 90, 94, 97, 130, 138, 139, 142, 148, 164, 180, 272, 273, 303, 320, 324, 325, 326, 327, 328, 329, 338, 361, 368, 370, 373, 376, 377, 378, 379, 388, 389, 395, 396, 399, 403, 412, 413, 414, 415, 422, 424, 426, 433, 434, 435, 436, 439, 441, 443, 445, 446, 449, 451, 452, 453, 455, 458, 459, 475, 478, 483, 486, 487, 488, 489, 492, 497, 498, 529, 534, 577, 578, 630, 631, 633, 635, 637, 638, 639, 640, 644, 648, 660, 663, 664, 679, 690, 697, 707, 709, 739, 762, 793, 802, 808, 812, 814, 826, 827, 831, 832, 836, 838, 839, 842, 843, 844, 846, 847, 849, 851, 853, 854, 856, 857, 859, 861, 865, 866, 867, 869, 870, 873, 875, 876, 879], "6477": 4, "2950": 4, "0453": 4, "grad_fn": [4, 12, 30, 44, 619, 626, 636, 854], "takebackward0": [4, 12], "great": [4, 7, 8, 822, 846, 851, 853, 862, 863, 878], "simpl": [4, 7, 17, 21, 22, 24, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 44, 46, 48, 51, 58, 81, 388, 523, 779, 793, 808, 814, 820, 821, 822, 826, 828, 829, 831, 832, 833, 834, 839, 842, 843, 846, 847, 849, 853, 855, 856, 857, 859, 861, 865, 866, 871, 872, 873, 874], "let": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 19, 23, 24, 25, 27, 28, 29, 30, 32, 33, 34, 35, 37, 38, 39, 44, 46, 47, 49, 51, 59, 71, 82, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 285, 287, 288, 292, 553, 554, 633, 635, 638, 648, 692, 762, 764, 765, 766, 767, 814, 820, 823, 826, 828, 829, 830, 831, 832, 833, 834, 835, 836, 843, 844, 846, 847, 848, 849, 851, 853, 854, 855, 856, 863, 865, 866, 879], "ll": [4, 6, 7, 8, 9, 11, 13, 14, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 47, 814, 815, 817, 818, 820, 821, 822, 823, 828, 833, 836, 837, 841, 842, 854, 858, 863, 865, 866], "try": [4, 6, 7, 13, 24, 34, 44, 47, 51, 75, 602, 635, 792, 802, 814, 820, 821, 822, 825, 826, 829, 830, 831, 835, 837, 842, 844, 851, 853, 857, 860, 862, 863, 867], "tf": [4, 6, 8, 9, 10, 13, 14, 17, 19, 24, 27, 28, 30, 32, 33, 34, 35, 37, 39, 44, 49, 50, 790, 814, 826, 831, 832, 838, 842, 843, 846, 847, 849, 851, 856, 857, 859, 865, 866, 867, 872], "onc": [4, 6, 8, 32, 33, 44, 46, 63, 67, 86, 90, 214, 377, 430, 632, 638, 644, 673, 674, 675, 688, 739, 814, 820, 821, 822, 829, 830, 831, 832, 833, 836, 837, 842, 843, 846, 849, 851, 854, 857, 858, 863, 865], "set": [4, 7, 9, 17, 19, 25, 32, 33, 35, 38, 46, 47, 48, 49, 50, 53, 58, 59, 62, 63, 68, 70, 71, 75, 81, 82, 85, 86, 91, 93, 94, 116, 119, 126, 146, 148, 182, 183, 184, 185, 186, 197, 210, 211, 212, 213, 214, 229, 329, 341, 357, 359, 364, 370, 373, 374, 376, 377, 378, 379, 388, 399, 420, 424, 428, 432, 435, 453, 458, 459, 475, 485, 488, 495, 523, 528, 529, 530, 531, 532, 533, 535, 539, 546, 558, 563, 579, 580, 581, 583, 584, 585, 586, 587, 588, 589, 590, 596, 604, 627, 629, 630, 631, 632, 633, 635, 637, 638, 642, 644, 645, 647, 648, 660, 667, 669, 679, 681, 684, 687, 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284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 299, 300, 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 326, 335, 336, 337, 338, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 370, 379, 399, 400, 401, 402, 420, 453, 454, 455, 456, 457, 458, 459, 460, 463, 464, 465, 469, 470, 485, 491, 493, 494, 495, 497, 502, 504, 505, 506, 508, 510, 523, 524, 525, 526, 535, 536, 538, 539, 541, 542, 546, 547, 548, 549, 550, 551, 552, 553, 554, 557, 559, 561, 562, 563, 565, 566, 569, 573, 577, 578, 592, 593, 594, 596, 598, 600, 601, 614, 625, 629, 631, 632, 635, 642, 651, 652, 653, 654, 660, 661, 667, 668, 669, 674, 675, 676, 677, 678, 679, 681, 683, 685, 686, 692, 697, 698, 699, 700, 704, 707, 708, 709, 710, 711, 714, 715, 719, 720, 722, 725, 726, 727, 728, 730, 731, 732, 736, 737, 739, 740, 741, 742, 744, 747, 750, 751, 752, 753, 754, 758, 759, 762, 764, 765, 767, 768, 769, 774, 775, 790, 793, 795, 802, 808, 826, 829, 854, 855, 859, 865, 866, 867], "recurs": [23, 32, 33, 46, 48, 53, 75, 76, 167, 168, 200, 201, 377, 449, 551, 552, 558, 631, 632, 635, 642, 719, 720, 723, 729, 730, 731, 772, 821, 825, 828, 829, 836, 839, 842, 855, 857], "fashion": [23, 779, 846, 866], "native_arrai": [23, 32, 33, 54, 55, 57, 77, 79, 80, 81, 82, 86, 93, 111, 114, 137, 140, 142, 144, 150, 153, 154, 155, 156, 164, 169, 176, 198, 207, 215, 231, 235, 240, 241, 242, 244, 248, 252, 260, 261, 269, 274, 277, 280, 283, 288, 336, 337, 364, 373, 378, 379, 459, 485, 491, 495, 535, 538, 565, 566, 569, 600, 627, 630, 631, 632, 633, 635, 637, 638, 639, 640, 644, 645, 648, 649, 651, 652, 659, 667, 670, 674, 675, 680, 681, 685, 689, 690, 692, 695, 697, 699, 700, 707, 739, 748, 757, 763, 766, 768, 774, 784, 802, 818, 836, 844, 846], "data_class": [23, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 396, 397, 546, 550, 688, 713], "low": [23, 32, 35, 51, 58, 62, 67, 81, 85, 90, 376, 419, 423, 637, 644, 650, 651, 652, 653, 655, 657, 659, 740, 742, 779, 829, 835, 842, 843, 849, 851, 868, 870, 872, 873, 874, 876, 878], "c": [23, 32, 38, 47, 48, 54, 58, 59, 60, 62, 65, 71, 77, 78, 80, 81, 82, 83, 85, 86, 88, 92, 94, 98, 99, 117, 128, 129, 139, 142, 166, 169, 224, 235, 241, 242, 262, 263, 265, 274, 277, 285, 292, 376, 377, 379, 382, 388, 390, 391, 392, 393, 404, 409, 425, 427, 429, 430, 432, 444, 463, 464, 465, 475, 493, 497, 502, 503, 504, 507, 525, 538, 546, 547, 548, 549, 557, 561, 562, 592, 601, 616, 617, 620, 622, 623, 624, 627, 630, 631, 633, 635, 636, 637, 638, 640, 642, 645, 646, 648, 651, 652, 653, 654, 655, 656, 658, 673, 675, 677, 707, 711, 719, 722, 726, 727, 728, 730, 731, 736, 737, 748, 753, 759, 760, 765, 767, 796, 807, 808, 815, 821, 824, 827, 828, 829, 833, 839, 841, 850, 851, 852, 854, 857, 859, 860, 862, 863, 866, 868, 872, 876, 877, 879], "fundament": [23, 32, 830, 843, 849, 851, 861, 872], "signatur": [23, 32, 379, 388, 485, 523, 831, 832, 833, 834, 838, 842, 846, 847, 849, 862, 869, 878], "matmul": [23, 32, 33, 49, 63, 86, 377, 447, 615, 635, 638, 688, 827, 846, 847, 851], "to_n": [23, 32, 33, 44, 53, 76, 851], "jaxlib": [23, 29, 47, 802, 821, 826, 831, 832, 838, 847, 851, 853], "xla_extens": [23, 29, 802, 826, 831, 832, 838, 847, 851, 853], "arrayimpl": [23, 29, 802], "disabl": [23, 32, 58, 81, 379, 493, 795, 812, 828], "array_mod": [23, 32, 579, 603, 635, 848], "set_array_mod": [23, 32, 603, 635, 848], "ultim": [23, 32, 865], "sigmoid": [23, 32, 33, 44, 52, 58, 74, 81, 302, 368, 383, 509, 627, 789, 851, 854, 855], "z": [23, 32, 33, 45, 46, 54, 57, 58, 59, 63, 64, 67, 69, 71, 77, 80, 81, 82, 86, 87, 88, 90, 94, 103, 104, 138, 139, 141, 142, 202, 224, 225, 229, 231, 234, 236, 241, 252, 253, 256, 257, 258, 260, 261, 266, 268, 270, 271, 272, 273, 281, 290, 301, 302, 336, 337, 339, 368, 373, 378, 388, 454, 456, 457, 458, 459, 460, 466, 470, 481, 522, 523, 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241, 274, 284, 382, 383, 507, 509, 633, 638, 644, 669, 687, 739, 814, 825, 831, 833, 840, 851, 856, 866, 870], "div": [24, 25, 26, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 867], "sub": [24, 25, 26, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 58, 63, 65, 75, 76, 80, 81, 82, 86, 88, 104, 273, 377, 379, 388, 431, 471, 480, 500, 529, 530, 558, 635, 638, 640, 641, 672, 692, 709, 716, 717, 718, 820, 822, 824, 829, 835, 843, 844, 846, 853, 854, 855, 867, 868], "with_numpi": 24, "reproduc": [24, 49, 62, 85, 637, 660, 777, 778, 779, 780, 785, 818, 825, 836], "x_": [24, 34, 99, 285, 633, 867], "66391283": 24, "12516928": 24, "38367081": 24, "03102401": 24, "76419425": 24, "52797794": 24, "90346956": 24, "61316347": 24, "27585283": 24, "66309303": 24, "ivy_repo": 24, "sever": [24, 25, 34, 35, 37, 38, 39, 58, 81, 98, 376, 377, 390, 391, 392, 393, 445, 777, 821, 822, 847, 857, 870, 876], "pro": [24, 25, 26, 34, 35, 36, 37, 38, 39], "pick": [25, 35, 792], "trigger": [25, 35, 795, 820, 837], "unif": [25, 27, 28, 35, 37, 815, 853, 862, 868, 878], "55563945": 25, "65538704": 25, "14150524": 25, "46951997": 25, "30220294": 25, "14739668": 25, "57017946": 25, "91962677": 25, "51029003": 25, "59644395": 25, "constitu": [25, 35, 75, 856], "5556394": 25, "655387": 25, "1415051": 25, "4695197": 25, "3022028": 25, "1473966": 25, "5701794": 25, "91962665": 25, "51028997": 25, "5964439": 25, "985": 25, "000": [25, 80, 275, 777, 818, 830, 836], "On": [25, 32, 33, 821, 831, 832, 837, 843, 846, 849, 852, 856], "hand": [25, 57, 377, 447, 777, 825, 831, 832, 837, 839, 846, 857], "learnt": [26, 36], "ivy_norm": 26, "jax_norm": [26, 32, 33], "wider": [26, 36, 586, 609, 635, 831, 848, 878], "avoid": [26, 36, 38, 58, 65, 81, 241, 246, 248, 264, 274, 378, 379, 382, 455, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 500, 502, 503, 504, 540, 556, 558, 581, 586, 609, 633, 635, 640, 703, 704, 705, 707, 709, 710, 712, 714, 779, 780, 821, 822, 827, 828, 829, 830, 831, 835, 840, 843, 846, 847, 848, 849, 872], "act": [26, 36, 58, 81, 299, 364, 374, 822, 833, 848, 857, 879], "shorthand": [26, 36, 38, 846], "pair": [26, 36, 46, 58, 62, 81, 85, 229, 248, 321, 363, 370, 373, 376, 410, 419, 421, 423, 633, 637, 638, 650, 651, 652, 653, 655, 657, 659, 667, 669, 808], "93968587": 26, "26075466": 26, "22723222": 26, "06276492": 26, "47426987": 26, "72835908": 26, "71737559": 26, "50411096": 26, "65419174": 26, "15576624": 26, "implic": [26, 36, 37, 40, 829], "fw": [27, 28, 29, 30, 62, 85, 388, 523, 637, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 774, 821, 846], "mxnet": [27, 28, 29, 30, 210, 632, 802, 820, 821, 862, 879], "einop": [27, 28, 29, 30, 46, 48, 51, 59, 82, 546, 547, 548, 635, 831, 862], "miniconda": [27, 28, 29, 30], "multienv": [27, 28, 29, 30], "site": [27, 28, 29, 30, 873], "psutil": [27, 28, 29, 30, 46, 48, 51], "termcolor": [27, 28, 29, 30, 46, 48, 51, 75, 104], "colorama": [27, 28, 29, 30, 46, 48], "535": [27, 28, 29, 30, 52, 74, 119, 627, 835], "diskcach": [27, 28, 29, 30, 46], "auth": [27, 28, 29, 30], "urllib3": [27, 28, 29, 30, 46], "pyvi": [27, 28, 29, 30, 32, 33], "dill": [27, 28, 29, 30, 46], "astunpars": [27, 28, 29, 30], "cloudpickl": [27, 28, 29, 30], "gast": [27, 28, 29, 30], "wheel": [27, 28, 29, 30, 46, 48, 51, 861], "six": [27, 28, 29, 30, 46, 51, 821, 849], "cachetool": [27, 28, 29, 30], "pyasn1": [27, 28, 29, 30], "rsa": [27, 28, 29, 30], "jsonpickl": [27, 28, 29, 30], "charset": [27, 28, 29, 30, 46], "idna": [27, 28, 29, 30, 46], "certifi": [27, 28, 29, 30, 46], "2017": [27, 28, 29, 30, 46, 637, 664], "jedi": [27, 28, 29, 30], "inlin": [27, 28, 29, 30, 828], "prompt": [27, 28, 29, 30, 820, 822], "toolkit": [27, 28, 29, 30, 872, 873, 879], "pygment": [27, 28, 29, 30], "traitlet": [27, 28, 29, 30], "exceptiongroup": [27, 28, 29, 30], "pexpect": [27, 28, 29, 30], "parso": [27, 28, 29, 30], "ptyprocess": [27, 28, 29, 30], "wcwidth": [27, 28, 29, 30], "asttoken": [27, 28, 29, 30], "pure": [27, 28, 29, 30, 38, 48, 834, 838, 843, 849, 853, 856, 857, 872, 878, 879], "lazili": [27, 28, 29, 32, 33, 37, 39, 50, 814, 865, 866, 867], "actual": [27, 37, 818, 822, 824, 830, 836, 839, 840, 842, 843, 844, 846, 849, 850, 855, 857, 873, 878], "occur": [27, 32, 33, 37, 50, 55, 57, 69, 78, 80, 92, 156, 275, 291, 631, 633, 645, 646, 745, 746, 750, 751, 752, 753, 825, 830, 832, 835, 848], "altern": [27, 37, 47, 58, 81, 86, 98, 99, 335, 343, 344, 345, 349, 351, 352, 353, 354, 356, 357, 358, 362, 363, 373, 820, 821, 828, 842, 854, 875], "assum": [27, 28, 37, 38, 39, 54, 57, 58, 59, 62, 63, 64, 80, 81, 82, 85, 86, 87, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 149, 150, 156, 172, 176, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 235, 236, 237, 238, 239, 241, 242, 244, 246, 247, 248, 252, 253, 254, 255, 256, 257, 258, 261, 263, 264, 265, 266, 268, 269, 270, 271, 274, 276, 277, 278, 279, 281, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 314, 330, 336, 337, 339, 342, 360, 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[27, 28], "36452447": [27, 28], "98795534": [27, 28], "15493582": [27, 28], "91630631": [27, 28], "41939619": [27, 28], "78909753": [27, 28], "19475674": [27, 28], "norm_trac": 27, "norm_tran": [27, 37], "know": [27, 28, 37, 38, 39, 69, 646, 750, 751, 752, 753, 814, 816, 820, 822, 832, 840, 844, 846, 849, 863, 867, 873], "07": [28, 46, 48, 60, 64, 80, 83, 87, 90, 229, 262, 265, 266, 285, 376, 408, 606, 616, 617, 619, 620, 621, 622, 633, 635, 636, 639, 698, 699, 741, 794, 797, 855], "981554": 28, "happen": [28, 32, 33, 293, 633, 814, 821, 822, 823, 832, 842, 846, 854, 863, 865, 866], "wherea": [28, 39, 81, 376, 422, 822, 826, 829, 831, 832, 833, 838, 839, 846, 856, 869], "subtract": [28, 32, 33, 57, 80, 103, 104, 135, 379, 485, 630, 633, 826, 829, 833], "often": [29, 58, 378, 453, 819, 825, 835, 838, 839, 843, 846, 857, 863, 873, 876, 879], "fortun": [29, 30, 825], "everyth": [29, 47, 807, 814, 820, 821, 822, 823, 824, 830, 833, 842, 843, 844, 846, 852, 857, 858, 863], "practic": [29, 822, 827, 830, 843, 845, 875], "jax_kornia": [29, 32, 33, 814, 866], "000000000034": [29, 32, 33, 814, 866], "raw_img": [29, 32, 33, 814, 866], "sharp": [29, 32, 33, 814], "prefer": [29, 32, 33, 248, 633, 821, 829, 835, 836, 840, 843, 858, 872], "whole": [30, 58, 81, 379, 382, 492, 505, 506, 508, 822, 828, 837], "full": [30, 58, 63, 81, 85, 86, 98, 99, 101, 166, 253, 261, 324, 325, 326, 327, 328, 370, 377, 378, 379, 450, 451, 457, 458, 486, 489, 580, 589, 604, 612, 630, 631, 633, 635, 637, 638, 652, 654, 655, 656, 658, 681, 685, 687, 688, 778, 785, 814, 821, 822, 828, 831, 834, 835, 838, 839, 843, 846, 849, 851, 857, 862, 863, 870, 872, 878], "complex": [30, 32, 33, 46, 52, 57, 58, 63, 71, 74, 78, 80, 81, 86, 94, 111, 112, 113, 114, 115, 116, 117, 118, 119, 143, 144, 159, 173, 182, 188, 221, 222, 223, 224, 225, 226, 227, 230, 238, 239, 241, 242, 244, 246, 254, 255, 256, 257, 258, 262, 263, 264, 265, 274, 276, 277, 279, 281, 284, 285, 286, 287, 288, 291, 292, 296, 301, 302, 304, 339, 344, 345, 368, 373, 376, 377, 388, 399, 410, 420, 421, 425, 430, 431, 432, 443, 445, 531, 532, 593, 594, 627, 630, 631, 633, 635, 638, 645, 648, 673, 674, 675, 679, 686, 688, 690, 692, 695, 748, 763, 764, 766, 778, 789, 808, 817, 820, 823, 828, 831, 833, 840, 843, 846, 847, 849, 854, 855, 856, 857, 859, 866, 868, 870, 872, 874, 878, 879], "neccessari": 30, "set_random_se": [30, 49], "301436": 30, "_c": 30, "0x7f252c392390": 30, "flatten": [30, 32, 33, 46, 48, 51, 58, 59, 63, 65, 68, 69, 81, 82, 86, 88, 91, 92, 341, 357, 373, 377, 379, 388, 428, 474, 484, 488, 493, 494, 497, 499, 521, 528, 529, 530, 531, 532, 533, 546, 550, 635, 638, 640, 645, 646, 676, 683, 695, 701, 706, 708, 745, 746, 750, 751, 752, 753, 772, 774, 814, 842, 849], "keyword": [30, 32, 33, 48, 50, 53, 54, 58, 75, 81, 104, 140, 275, 376, 379, 388, 424, 485, 523, 537, 540, 573, 602, 630, 633, 635, 638, 642, 648, 689, 725, 766, 772, 774, 778, 794, 795, 807, 820, 826, 829, 831, 832, 840, 842, 843, 844, 846, 847, 849, 854, 865, 866, 867], "input_arrai": [30, 32, 33, 842], "torch_model": [30, 32, 33, 50], "159": [30, 74, 111, 627, 637, 661], "thank": [30, 854, 862], "fledg": [30, 821, 851, 852], "output_arrai": [30, 32, 33, 58, 455], "0893": 30, "1504": 30, "1372": 30, "0991": 30, "0867": 30, "0851": 30, "0911": 30, "0804": 30, "0926": 30, "0881": 30, "softmaxbackward0": 30, "furthermor": 30, "relat": [30, 248, 633, 814, 816, 819, 820, 821, 822, 828, 835, 843, 846, 847, 848, 849, 866, 875], "regress": [31, 872, 879], "checkout": [32, 47, 822, 825, 846], "f705efe7cb5d18df17ce6c1e20f04d0eb4933f48": 32, "theoret": 32, "aspect": [32, 33, 815, 841, 854, 872], "easiest": [32, 814, 816, 821, 858], "defer": [32, 33, 820, 826, 831, 832, 839, 842, 843, 846, 878], "similarli": [32, 45, 140, 148, 224, 329, 336, 337, 370, 373, 630, 633, 827, 831, 843, 849, 853, 878], "essenc": [32, 873, 878], "becom": [32, 58, 81, 98, 347, 373, 379, 465, 640, 700, 802, 822, 823, 829, 831, 833, 835, 842, 857, 861, 863, 865], "slide": [32, 58, 62, 81, 85, 376, 395, 396, 397, 413, 414, 415, 416, 419, 423, 637, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 793], "regressor": [32, 33], "input_dim": [32, 33, 47], "output_dim": [32, 33, 47], "linear0": [32, 33, 44, 854, 855], "linear1": [32, 33, 44, 854, 855], "adam": [32, 33, 44, 48, 60, 83, 537, 616, 617, 622, 635, 636, 797, 854, 855, 856, 872], "n_training_exampl": [32, 33], "2000": [32, 33, 81, 315, 370], "random_norm": [32, 33, 62, 63, 67, 85, 86, 90, 546, 635, 637, 638, 644, 652, 654, 655, 656, 658, 659, 663, 688], "linspac": [32, 33, 54, 77, 127, 630, 838, 849, 851, 879], "execute_with_gradi": [32, 33, 44, 48, 636, 854, 855, 856, 857], "lambda": [32, 33, 49, 51, 81, 124, 126, 298, 308, 545, 558, 618, 619, 621, 626, 629, 635, 636, 638, 642, 674, 726, 727, 731, 820, 839, 840, 841, 844, 849, 851, 854], "2d": [32, 33, 48, 58, 81, 98, 314, 370, 376, 377, 379, 388, 391, 392, 400, 401, 443, 450, 464, 474, 523, 793, 812, 843, 849], "5f": [32, 33], "nonetheless": [32, 33], "gc": [32, 33, 558, 635], "decompos": [32, 33, 58, 81, 98, 101, 324, 325, 326, 327, 328, 349, 356, 370, 373, 377, 441, 446, 449, 452, 843, 856], "said": [32, 33, 779, 847, 863, 865], "otherwis": [32, 33, 50, 53, 54, 55, 57, 58, 59, 62, 63, 68, 69, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 98, 111, 112, 113, 114, 115, 116, 117, 118, 119, 124, 127, 129, 130, 135, 137, 138, 139, 142, 144, 150, 153, 154, 156, 157, 159, 160, 161, 162, 163, 172, 176, 180, 181, 197, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 299, 300, 301, 304, 305, 306, 307, 308, 310, 311, 312, 314, 324, 325, 326, 327, 328, 335, 336, 337, 338, 339, 341, 342, 343, 351, 352, 358, 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"x0": [32, 33, 51, 82, 538, 635, 833], "normalize_trac": [32, 33], "html": [32, 33, 47, 57, 58, 80, 81, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 630, 631, 633, 638, 640, 648, 686, 687, 715, 765, 834, 862], "fname": [32, 33, 49, 51, 795, 854], "anticip": [32, 33], "addition": [32, 33, 829, 842, 843, 878], "normalize_native_comp": [32, 33], "return_backend_compiled_fn": 32, "immedi": [32, 33, 812, 814, 820, 821], "built": [32, 33, 38, 46, 48, 51, 127, 630, 793, 794, 795, 821, 822, 828, 829, 846, 852, 858, 865, 871, 872, 876], "eager_graph": [32, 33, 814, 865, 866], "lazy_graph": [32, 33, 814, 865, 866], "thought": [32, 33, 821, 822, 838, 862, 870], "matter": [32, 33, 38, 833, 861], "haven": [32, 33, 38, 858, 872], "jax_out": [32, 33], "ideal": [32, 33, 830, 831, 843, 849, 854], "worth": [32, 33], "differenti": [32, 33, 296, 366, 367, 368, 375, 872], "chosen": [32, 33, 51, 101, 127, 229, 630, 633, 645, 749, 820, 830, 843], "plai": [32, 33, 378, 457, 814, 817, 821, 823, 826, 832, 836, 843, 846, 856, 872, 875], "role": [32, 33, 814, 817, 822, 823, 832, 843, 852, 873, 875, 879], "dl": [32, 33], "effortlessli": [32, 33], "previous": [32, 33, 604, 635, 802, 820, 821, 827, 839, 841, 846, 851], "default_devic": [32, 33, 207, 210, 211, 212, 218, 219, 632, 832, 835, 836], "as_n": [32, 33, 55, 56, 75, 78, 79, 159, 160, 161, 162, 163, 164, 170, 197, 198, 631, 632, 831], "certainli": [32, 33, 862, 878], "unnecessari": [32, 33, 843], "extend": [32, 33, 58, 81, 379, 388, 485, 526, 827, 828, 831, 834, 835, 838, 843, 847, 857, 869, 872, 878], "infrastructur": [32, 33, 868, 874, 875], "least": [32, 57, 58, 63, 80, 81, 241, 259, 274, 376, 379, 388, 404, 409, 463, 464, 465, 474, 476, 523, 633, 638, 645, 678, 748, 822, 826, 830, 831, 832, 833, 839, 842, 846, 866], "coco": 32, "seamlessli": [33, 846], "therefor": [33, 38, 54, 57, 58, 63, 80, 81, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 148, 149, 150, 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832, 833, 834, 835, 838, 839, 840, 842, 843, 844, 846, 847, 849, 851, 853, 855, 857, 861, 869, 872, 878], "wide": [33, 814, 822, 846, 870, 872], "plenti": 33, "resourc": [33, 815, 820, 821, 830], "visit": [33, 820, 821, 822, 830], "page": [33, 814, 820, 821, 822, 828, 830, 836, 852, 853, 856, 858, 867, 880], "newli": [34, 35, 47, 49, 55, 78, 153, 540, 631, 635, 822, 830, 842, 846], "randon": [34, 35, 37, 38, 39], "mean_": 34, "std_": 34, "detect": [34, 38, 57, 75, 80, 256, 633, 642, 719, 730, 820, 821, 827, 829, 830, 837, 846, 854, 855], "inspect": [34, 38, 536, 635], "__": [34, 35, 36, 37, 38, 39, 75, 833, 854], "script": [35, 814, 821, 822, 825, 830, 833, 851, 857, 872], "comp": 35, "low_level": 35, "chain": [35, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 98, 111, 112, 113, 114, 115, 116, 117, 118, 119, 135, 137, 142, 144, 150, 154, 156, 169, 173, 174, 181, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 300, 304, 305, 306, 307, 308, 310, 311, 312, 314, 335, 336, 337, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 400, 401, 402, 420, 453, 454, 455, 456, 457, 458, 459, 460, 469, 470, 491, 493, 495, 497, 502, 504, 505, 506, 508, 510, 523, 524, 525, 526, 535, 538, 539, 541, 542, 546, 547, 548, 549, 550, 553, 554, 557, 559, 561, 562, 563, 565, 566, 569, 577, 578, 592, 593, 594, 596, 598, 600, 601, 614, 620, 625, 641, 642, 651, 652, 653, 654, 660, 661, 667, 668, 669, 674, 675, 676, 677, 678, 679, 681, 683, 685, 686, 692, 697, 698, 699, 700, 704, 707, 708, 709, 710, 711, 714, 715, 716, 717, 721, 732, 739, 740, 741, 742, 744, 747, 750, 751, 752, 753, 754, 758, 759, 762, 764, 765, 767, 768, 769, 798, 826, 829, 841, 843, 855, 856, 857, 872], "un": [35, 171, 631, 831, 851], "partial_comp": 35, "time_funct": 35, "express": [35, 57, 58, 80, 81, 99, 222, 226, 228, 229, 238, 240, 280, 286, 291, 360, 373, 633, 799, 808, 834, 843, 851, 856, 872, 873], "maxim": [35, 839, 842, 851, 869, 870, 874, 875, 876], "conclud": [36, 847], "norm_comp": [37, 38], "global": [37, 38, 48, 59, 75, 82, 104, 159, 160, 161, 162, 163, 212, 213, 214, 583, 584, 587, 593, 594, 606, 607, 610, 631, 632, 635, 785, 796, 802, 821, 826, 827, 830, 831, 832, 835, 839, 843, 851, 872], "b": [38, 52, 57, 58, 59, 62, 63, 71, 74, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 98, 99, 102, 103, 104, 111, 112, 113, 114, 115, 116, 117, 118, 128, 129, 130, 135, 136, 137, 139, 142, 144, 150, 153, 154, 155, 156, 164, 174, 176, 181, 198, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 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233, 234, 235, 236, 238, 239, 241, 244, 246, 248, 254, 255, 256, 257, 258, 262, 263, 264, 265, 266, 271, 274, 279, 283, 286, 287, 288, 289, 290, 291, 292, 295, 305, 309, 355, 360, 368, 373, 376, 377, 378, 379, 388, 412, 420, 431, 453, 454, 493, 497, 523, 535, 538, 559, 560, 564, 565, 566, 567, 568, 569, 596, 614, 630, 631, 632, 633, 635, 638, 640, 641, 646, 649, 668, 669, 670, 672, 676, 677, 678, 680, 681, 683, 684, 686, 687, 692, 694, 695, 701, 716, 717, 718, 750, 751, 752, 753, 754, 768, 769, 779, 785, 792, 796, 829, 831, 832, 834, 839, 843, 846, 848, 849, 861], "think": [38, 820, 822, 830, 833, 849, 873], "uniqu": [38, 48, 58, 59, 69, 81, 82, 92, 376, 377, 379, 424, 447, 484, 485, 499, 570, 635, 641, 642, 646, 716, 717, 718, 721, 725, 750, 751, 752, 753, 779, 814, 825, 829, 839, 843, 844, 845, 849, 857, 861, 875], "rule": [38, 55, 57, 58, 63, 78, 80, 81, 86, 153, 156, 179, 180, 181, 230, 241, 274, 276, 283, 285, 293, 295, 376, 379, 388, 420, 473, 523, 631, 633, 638, 640, 668, 669, 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822, 831, 842, 843, 846], "valueerror": [54, 58, 65, 77, 81, 88, 92, 129, 376, 378, 410, 421, 458, 463, 464, 471, 473, 475, 476, 477, 484, 500, 640, 703, 704, 705, 707, 709, 710, 712, 714, 753, 779, 809, 835], "buffer": [54, 77, 81, 88, 129, 135, 463, 464, 471, 473, 475, 476, 477, 484, 500, 630, 703, 704, 705, 707, 709, 710, 712, 714, 794, 795, 842, 857], "nativedtyp": [54, 55, 58, 62, 63, 67, 68, 71, 77, 81, 86, 90, 91, 94, 127, 128, 129, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 149, 150, 152, 153, 158, 159, 160, 161, 162, 163, 164, 165, 170, 171, 175, 177, 179, 183, 193, 313, 314, 315, 316, 317, 318, 319, 334, 341, 357, 370, 373, 383, 388, 509, 510, 511, 512, 513, 523, 524, 525, 526, 529, 532, 630, 631, 637, 638, 644, 645, 647, 648, 660, 679, 695, 740, 741, 742, 745, 746, 756, 758, 759, 762, 764, 766, 792, 831, 832, 838, 847, 851], "datatyp": [54, 58, 75, 77, 81, 129, 137, 141, 158, 179, 183, 376, 424, 630, 631, 772, 847, 865], "nativedevic": [54, 56, 58, 67, 77, 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"fmod"]], "log1p": [[264, "log1p"]], "expm1": [[246, "expm1"]], "isnan": [[257, "isnan"]], "greater": [[252, "greater"]], "deg2rad": [[240, "deg2rad"]], "floor_divide": [[248, "floor-divide"]], "bitwise_and": [[231, "bitwise-and"]], "cosh": [[239, "cosh"]], "logical_not": [[269, "logical-not"]], "bitwise_or": [[234, "bitwise-or"]], "print_all_ivy_arrays_on_dev": [[209, "print-all-ivy-arrays-on-dev"]], "to_device": [[215, "to-device"]], "set_split_factor": [[212, "set-split-factor"]], "valid_dtype": [[193, "valid-dtype"]], "num_gpus": [[206, "num-gpus"]], "asin": [[226, "asin"]], "unset_default_int_dtype": [[191, "unset-default-int-dtype"]], "set_soft_device_mode": [[211, "set-soft-device-mode"]], "atan": [[228, "atan"]], "set_default_uint_dtype": [[186, "set-default-uint-dtype"]], "unset_default_float_dtype": [[190, "unset-default-float-dtype"]], "default_device": [[197, "default-device"]], "as_native_dev": [[195, "as-native-dev"]], "tpu_is_available": [[217, "tpu-is-available"]], "acosh": [[223, "acosh"]], "acos": [[222, "acos"]], "atan2": [[229, "atan2"]], "asinh": [[227, "asinh"]], "function_unsupported_devices": [[201, "function-unsupported-devices"]], "get_all_ivy_arrays_on_dev": [[202, "get-all-ivy-arrays-on-dev"]], "used_mem_on_dev": [[220, "used-mem-on-dev"]], "add": [[224, "add"]], "clear_cached_mem_on_dev": [[196, "clear-cached-mem-on-dev"]], "as_ivy_dev": [[194, "as-ivy-dev"]], "unset_default_complex_dtype": [[188, "unset-default-complex-dtype"]], "function_supported_devices": [[200, "function-supported-devices"]], "set_default_device": [[210, "set-default-device"]], "num_cpu_cores": [[205, "num-cpu-cores"]], "set_default_int_dtype": [[185, "set-default-int-dtype"]], "handle_soft_device_variable": [[204, "handle-soft-device-variable"]], "percent_used_mem_on_dev": [[208, "percent-used-mem-on-dev"]], "unset_default_device": [[218, "unset-default-device"]], "dev": [[198, "dev"]], "dev_util": [[199, "dev-util"]], "total_mem_on_dev": [[216, "total-mem-on-dev"]], "unset_soft_device_mode": [[219, "unset-soft-device-mode"]], "angle": [[225, "angle"]], "unset_default_dtype": [[189, "unset-default-dtype"]], "unset_default_uint_dtype": [[192, "unset-default-uint-dtype"]], "split_factor": [[213, "split-factor"]], "type_promote_arrays": [[187, "type-promote-arrays"]], "set_default_float_dtype": [[184, "set-default-float-dtype"]], "abs": [[221, "abs"]], "gpu_is_available": [[203, "gpu-is-available"]], "num_ivy_arrays_on_dev": [[207, "num-ivy-arrays-on-dev"]], "split_func_call": [[214, "split-func-call"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [13, "Installation"], [4, "Installation"]], "Imports": [[12, "Imports"], [8, "Imports"], [15, "Imports"]], "Data Preparation": [[12, "Data-Preparation"], [8, "Data-Preparation"], [4, "Data-Preparation"], [5, "Data-Preparation"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Load the image example \ud83d\uddbc\ufe0f": [[12, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [8, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[12, "Visualise-image"], [8, "Visualise-image"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "0.2: Transpile": [[36, "0.2:-Transpile"]], "Write Ivy code": [[23, "Write-Ivy-code"]], "Contents": [[23, "Contents"]], "Installing Ivy": [[23, "Installing-Ivy"]], "Importing Ivy": [[23, "Importing-Ivy"], [0, "Importing-Ivy"]], "Ivy Backend Handler": [[23, "Ivy-Backend-Handler"], [32, "Ivy-Backend-Handler"]], "Data Structures": [[23, "Data-Structures"], [32, "Data-Structures"]], "Ivy Functional API": [[23, "Ivy-Functional-API"], [32, "Ivy-Functional-API"]], "Tutorials And Examples": [[21, "tutorials-and-examples"]], "Learn the basics": [[21, "learn-the-basics"], [22, "learn-the-basics"]], "Guides": [[21, "guides"], [16, "guides"]], "Examples and Demos": [[21, "examples-and-demos"], [3, "examples-and-demos"]], "Unify code": [[24, "Unify-code"]], "ODSC Ivy Demo": [[32, "ODSC-Ivy-Demo"]], "Graph Tracer": [[32, "Graph-Tracer"]], "Any function": [[32, "Any-function"], [33, "Any-function"]], "Any library": [[32, "Any-library"], [33, "Any-library"]], "Any model": [[32, "Any-model"], [33, "Any-model"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "3.1: Stable Diffusion": [[43, "3.1:-Stable-Diffusion"]], "Transpile any library": [[29, "Transpile-any-library"]], "Accelerating PyTorch models with JAX": [[14, "Accelerating-PyTorch-models-with-JAX"]], "1.0: Lazy vs Eager": [[37, "1.0:-Lazy-vs-Eager"]], "Unify": [[37, "Unify"], [28, "Unify"], [38, "Unify"], [27, "Unify"], [39, "Unify"]], "Compile": [[37, "Compile"], [38, "Compile"], [39, "Compile"]], "Transpile": [[37, "Transpile"], [28, "Transpile"], [38, "Transpile"], [27, "Transpile"], [39, "Transpile"]], "Training PyTorch ResNet in your TensorFlow Projects": [[13, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Framework Incompatibility": [[13, "Framework-Incompatibility"], [6, "Framework-Incompatibility"]], "Transpiling a PyTorch model to TensorFlow": [[13, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "About the transpiled model": [[13, "About-the-transpiled-model"], [6, "About-the-transpiled-model"]], "Setting-up the source model": [[13, "Setting-up-the-source-model"], [6, "Setting-up-the-source-model"]], "Load the Data": [[13, "Load-the-Data"]], "Visualize a few images": [[13, "Visualize-a-few-images"]], "Load the pre-trained model": [[13, "Load-the-pre-trained-model"]], "Converting the model from TensorFlow to PyTorch": [[13, "Converting-the-model-from-TensorFlow-to-PyTorch"], [6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[13, "Comparing-the-results"], [7, "Comparing-the-results"], [6, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[13, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"], [6, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[13, "Conclusion"], [7, "Conclusion"], [6, "Conclusion"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "How to use decorators": [[28, "How-to-use-decorators"]], "Trace": [[28, "Trace"], [27, "Trace"]], "0.0: Unify": [[34, "0.0:-Unify"]], "Transpile any model": [[30, "Transpile-any-model"]], "Round up": [[30, "Round-up"]], "Basic Operations with Ivy": [[44, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[44, "Installs-\ud83d\udcbe"], [45, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[44, "Imports-\ud83d\udec3"], [45, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[44, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[44, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[44, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[44, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[44, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[44, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[44, "Set-Backend-Framework"]], "Define Model": [[44, "Define-Model"], [45, "Define-Model"]], "Create Model": [[44, "Create-Model"]], "Create Optimizer": [[44, "Create-Optimizer"]], "Input and Target": [[44, "Input-and-Target"]], "Loss Function": [[44, "Loss-Function"]], "Training Loop": [[44, "Training-Loop"]], "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"]], "1.1: Framework Selection": [[38, "1.1:-Framework-Selection"]], "Compilation of a Basic Function": [[45, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[45, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[45, "Function-compilation-\ud83d\udee0"]], "Set backend": [[45, "Set-backend"]], "Sample input": [[45, "Sample-input"]], "Define function to compile": [[45, "Define-function-to-compile"]], "Compile the function": [[45, "Compile-the-function"]], "Check results": [[45, "Check-results"], [45, "id1"]], "Compiling simple neural network \ud83e\udde0": [[45, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[45, "Create-model"]], "Define input": [[45, "Define-input"]], "Compile network": [[45, "Compile-network"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "1.3: Dynamic vs Static": [[40, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[40, "Dynamic"]], "Static": [[40, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[40, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Lazy vs Eager": [[27, "Lazy-vs-Eager"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "1.2: As a Decorator": [[39, "1.2:-As-a-Decorator"]], "Transpiling a haiku model to build on top": [[18, "Transpiling-a-haiku-model-to-build-on-top"]], "Quickstart": [[33, "Quickstart"]], "Get familiar with Ivy": [[33, "Get-familiar-with-Ivy"]], "Functional API": [[33, "Functional-API"]], "Stateful API": [[33, "Stateful-API"]], "Tracing code": [[33, "Tracing-code"]], "Transpile code": [[26, "Transpile-code"]], "Transpiling a Tensorflow model to build on top": [[19, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "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:"]], "0.1: Compile": [[35, "0.1:-Compile"]], "Trace code": [[25, "Trace-code"]], "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 a model using Ivy": [[31, "Write-a-model-using-Ivy"]], "Accelerating XGBoost with JAX": [[15, "Accelerating-XGBoost-with-JAX"]], "Tests": [[15, "Tests"]], "Loading the Data": [[15, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[15, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[15, "JAX-backend"]], "Tensorflow backend": [[15, "Tensorflow-backend"]], "PyTorch backend": [[15, "PyTorch-backend"]], "More exhaustive example": [[15, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[15, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[15, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[15, "Comparison-of-Metrics"]], "Transpiling a PyTorch model to build on top": [[17, "Transpiling-a-PyTorch-model-to-build-on-top"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "Developing a convolutional network using Ivy": [[20, "Developing-a-convolutional-network-using-Ivy"]], "2.0: Kornia": [[41, "2.0:-Kornia"]], "3.0: Perceiver": [[42, "3.0:-Perceiver"]], "End-to-End Training Pipeline in Ivy": [[48, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[48, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[48, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[48, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[48, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[48, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[48, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[48, "Plotting-the-training-metrics"]], "Save the trained Model": [[48, "Save-the-trained-Model"]], "Image": [[84, "module-ivy.data_classes.container.image"], [61, "module-ivy.data_classes.array.image"]], "Conversions": [[76, "module-ivy.data_classes.container.conversions"], [53, "module-ivy.data_classes.array.conversions"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[46, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[46, "Table-of-Contents"]], "Defining the model": [[46, "Defining-the-model"]], "Model construction": [[46, "Model-construction"]], "Some helper functions": [[46, "Some-helper-functions"]], "Transpiling the model": [[46, "Transpiling-the-model"]], "PyTorch pipeline": [[46, "PyTorch-pipeline"]], "Dataset download": [[46, "Dataset-download"]], "DataLoader": [[46, "DataLoader"]], "Training": [[46, "Training"]], "Ivy as a Transpiler Introduction": [[50, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[50, "To-use-the-transpiler:"]], "Transpiler Interface": [[50, "Transpiler-Interface"]], "Telemetry": [[50, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[50, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[50, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[50, "3.-Transpile-Models-\ud83c\udf10"]], "HuggingFace Tensorflow DeiT": [[49, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[49, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Deepmind PerceiverIO on GPU": [[47, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[47, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[47, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[47, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[47, "Run-the-demo..."]], "\u2026with torch backend": [[47, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[47, "....with-tensorflow-backend"]], "\u2026with jax backend": [[47, "...with-jax-backend"]], "\u2026with numpy backend": [[47, "...with-numpy-backend"]], "Resnet 18": [[51, "Resnet-18"]]}, "indexentries": 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