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

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

    3. -

  3. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fed0e085120>) – The function used for the inner loop optimization. +

  4. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7effe387d120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  5. 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.

    6. @@ -1474,7 +1474,7 @@

    Meta#

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

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

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

    8. -

  8. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fed0e085120>) – The function used for the inner loop optimization. +

  9. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7effe387d120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  10. 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.

    11. @@ -1551,7 +1551,7 @@

    Meta#

    variables (Container) – Variables to be optimized.

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

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

    13. -

  13. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fed0e085120>) – The function used for the inner loop optimization. It takes the learnable +

  14. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7effe387d120>) – 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.

    15. diff --git a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html index 13f957ee9..67e9c246d 100644 --- a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html +++ b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html @@ -1423,7 +1423,7 @@

    fomaml_stepContainer) – Variables to be optimized during the meta step

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

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

    17. -

  17. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fed0e085120>) – The function used for the inner loop optimization. +

  18. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7effe387d120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  19. 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.

    20. diff --git a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html index 625ebe87d..9a7c66e85 100644 --- a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html +++ b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html @@ -1423,7 +1423,7 @@

    maml_stepContainer) – Variables to be optimized during the meta step

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

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

    22. -

  22. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fed0e085120>) – The function used for the inner loop optimization. +

  23. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7effe387d120>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  24. 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.

    25. diff --git a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html index 7e4948b7f..1a8233bc7 100644 --- a/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html +++ b/ivy/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html @@ -1420,7 +1420,7 @@

    reptile_stepContainer) – Variables to be optimized.

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

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

    27. -

  27. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fed0e085120>) – The function used for the inner loop optimization. It takes the learnable +

  28. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7effe387d120>) – 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.

    29. diff --git a/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html b/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html index e90109380..6e4972cf1 100644 --- a/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html +++ b/ivy/docs/helpers/ivy_tests.test_ivy.helpers.globals.html @@ -1409,7 +1409,7 @@

    Should not be used inside any of the test functions.

    -ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7fed01e55f40>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7effd7641f40>#
    diff --git a/ivy/docs/stateful/ivy.stateful.layers.html b/ivy/docs/stateful/ivy.stateful.layers.html index 177eac8bb..02b498626 100644 --- a/ivy/docs/stateful/ivy.stateful.layers.html +++ b/ivy/docs/stateful/ivy.stateful.layers.html @@ -1536,8 +1536,8 @@

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

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

    31. -

  31. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fed0dcade10>) – Initializer for the weights. Default is GlorotUniform.

    32. -

  32. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fed0dcaddb0>) – Initializer for the bias. Default is Zeros.

    33. +

  33. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7effe34a6230>) – Initializer for the weights. Default is GlorotUniform.

    34. +

  34. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7effe34a61d0>) – Initializer for the bias. Default is Zeros.

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

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

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

    38. @@ -1574,8 +1574,8 @@

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

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

    40. -

  40. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fed0dcadd50>) – Initializer for the weights. Default is GlorotUniform.

    41. -

  41. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fed0dcadcf0>) – Initializer for the bias. Default is Zeros.

    42. +

  42. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7effe34a6170>) – Initializer for the weights. Default is GlorotUniform.

    43. +

  43. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7effe34a6110>) – Initializer for the bias. Default is Zeros.

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

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

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

    47. @@ -1613,8 +1613,8 @@

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

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

    49. -

  49. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fed0dcadc90>) – Initializer for the weights. Default is GlorotUniform.

    50. -

  50. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fed0dcadc30>) – Initializer for the bias. Default is Zeros.

    51. +

  51. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7effe34a60b0>) – Initializer for the weights. Default is GlorotUniform.

    52. +

  52. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7effe34a6050>) – Initializer for the bias. Default is Zeros.

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

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

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

    56. @@ -1651,8 +1651,8 @@

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

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

    58. -

  58. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fed0dcadbd0>) – Initializer for the weights. Default is GlorotUniform.

    59. -

  59. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fed0dcadb70>) – Initializer for the bias. Default is Zeros.

    60. +

  60. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7effe34a5ff0>) – Initializer for the weights. Default is GlorotUniform.

    61. +

  61. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7effe34a5f90>) – Initializer for the bias. Default is Zeros.

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

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

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

    65. @@ -1690,8 +1690,8 @@

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

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

    67. -

  67. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fed0dcada50>) – Initializer for the weights. Default is GlorotUniform.

    68. -

  68. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fed0dcad9f0>) – Initializer for the bias. Default is Zeros.

    69. +

  69. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7effe34a5e70>) – Initializer for the weights. Default is GlorotUniform.

    70. +

  70. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7effe34a5e10>) – Initializer for the bias. Default is Zeros.

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

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

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

    74. @@ -1728,8 +1728,8 @@

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

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

    76. -

  76. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fed0dcad990>) – Initializer for the weights. Default is GlorotUniform.

    77. -

  77. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fed0dcad930>) – Initializer for the bias. Default is Zeros.

    78. +

  78. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7effe34a5db0>) – Initializer for the weights. Default is GlorotUniform.

    79. +

  79. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7effe34a5d50>) – Initializer for the bias. Default is Zeros.

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

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

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

    83. @@ -1792,8 +1792,8 @@

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

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

    85. -

  85. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fed0dcadb10>) – Initializer for the weights. Default is GlorotUniform.

    86. -

  86. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fed0dcadab0>) – Initializer for the bias. Default is Zeros.

    87. +

  87. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7effe34a5f30>) – Initializer for the weights. Default is GlorotUniform.

    88. +

  88. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7effe34a5ed0>) – Initializer for the bias. Default is Zeros.

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

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

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

    92. @@ -1949,7 +1949,7 @@

    • input_channels – Number of input channels for the layer

    • output_channels – Number of output channels for the layer

      • -

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fed0dcad8d0>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7effe34a5cf0>) – Initializer for the weights. Default is GlorotUniform.

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

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

      • input_channels – Number of input channels for the layer.

      • output_channels – Number of output channels for the layer.

        • -

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fed0dcaded0>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fed0dcade70>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7effe34a62f0>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7effe34a6290>) – Initializer for the bias. Default is Zeros.

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

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

        • diff --git a/ivy/searchindex.js b/ivy/searchindex.js index ff81eed38..d1d84870a 100644 --- a/ivy/searchindex.js +++ b/ivy/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["demos/Contributor_demos/Credit Card Fraud Detection/Credit_Card_Fraud_Detection", "demos/README", "demos/assets/01_template", "demos/examples_and_demos", "demos/examples_and_demos/alexnet_demo", "demos/examples_and_demos/bert_demo", "demos/examples_and_demos/convnext_to_torch", "demos/examples_and_demos/dinov2_to_paddle", "demos/examples_and_demos/image_segmentation_with_ivy_unet", "demos/examples_and_demos/lstm_tensorflow_to_torch", "demos/examples_and_demos/lstm_torch_to_tensorflow", "demos/examples_and_demos/mmpretrain_to_jax", "demos/examples_and_demos/resnet_demo", "demos/examples_and_demos/torch_to_jax", "demos/examples_and_demos/xgboost_demo", "demos/guides", "demos/guides/01_transpiling_a_torch_model", "demos/guides/02_transpiling_a_haiku_model", "demos/guides/03_transpiling_a_tf_model", 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820, 823, 826, 828, 829, 832, 834, 856, 864], "github": [2, 4, 5, 8, 11, 12, 13, 31, 45, 46, 47, 48, 49, 812, 814, 815, 817, 820, 821, 823, 826, 828, 829, 831, 832, 834, 835, 843, 844, 856, 859, 878], "com": [2, 4, 5, 6, 7, 8, 11, 12, 13, 18, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 823, 826, 828, 829, 834, 856], "unifyai": [2, 4, 8, 12, 31, 45, 46, 47, 48, 49, 812, 814, 819, 820, 826, 834, 856], "model": [2, 3, 4, 9, 14, 15, 20, 21, 22, 48, 50, 240, 273, 377, 453, 632, 789, 793, 794, 810, 812, 852, 853, 857, 863, 864, 868, 869, 870, 871, 872, 873, 874, 876, 877], "depth": [2, 4, 6, 8, 12, 46, 53, 57, 61, 76, 80, 84, 141, 375, 378, 411, 471, 545, 557, 629, 634, 636, 654, 655, 820, 828, 852, 853, 854, 856], "repositori": [2, 4, 8, 12, 814, 818, 819, 820, 822, 823, 826, 834, 843, 861], "cd": [2, 4, 8, 12, 31, 48, 812, 814, 819, 820, 834, 856], "resnet": [3, 6, 13, 20, 31, 863, 864], "imag": [3, 4, 6, 7, 11, 13, 16, 20, 28, 31, 32, 45, 46, 47, 48, 49, 50, 57, 61, 79, 80, 84, 102, 220, 221, 222, 223, 226, 229, 238, 241, 243, 245, 254, 255, 256, 261, 263, 276, 283, 284, 286, 287, 291, 375, 394, 395, 411, 412, 413, 415, 545, 632, 634, 636, 649, 650, 651, 652, 653, 656, 657, 658, 792, 812, 819, 834, 847, 849, 850, 852, 854, 856, 863, 864, 870], "classif": [3, 4, 12, 14, 20, 45, 812, 870], "acceler": [3, 20, 812, 829, 841, 868, 872, 873, 874, 875], "convert": [3, 8, 9, 11, 13, 14, 16, 18, 20, 21, 23, 25, 28, 29, 31, 32, 33, 35, 37, 45, 48, 50, 52, 53, 56, 74, 75, 76, 79, 97, 127, 128, 140, 150, 151, 193, 194, 195, 196, 207, 215, 219, 239, 279, 378, 383, 462, 463, 464, 513, 578, 596, 598, 599, 600, 602, 629, 630, 631, 632, 634, 637, 641, 695, 719, 730, 731, 773, 801, 805, 812, 818, 824, 825, 838, 839, 841, 844, 846, 849, 855, 857, 861, 864, 868, 869, 876], "faster": [3, 4, 9, 11, 13, 14, 20, 31, 32, 48, 50, 57, 62, 80, 85, 376, 449, 637, 687, 814, 817, 826, 857, 872, 875], "infer": [3, 6, 7, 9, 11, 13, 14, 20, 24, 34, 36, 37, 46, 48, 50, 53, 57, 58, 61, 64, 76, 80, 81, 84, 87, 126, 128, 131, 135, 136, 140, 143, 149, 158, 159, 160, 161, 162, 312, 313, 375, 378, 382, 411, 496, 510, 556, 590, 591, 629, 630, 634, 636, 639, 659, 706, 801, 802, 822, 825, 829, 830, 844, 849, 854, 864, 868, 869, 872, 874], "mmpretrain": [3, 20], "segment": [3, 20, 57, 80, 330, 331, 332, 369, 826, 831], "unet": [3, 20], "alexnet": [3, 20], "written": [3, 4, 5, 6, 20, 22, 31, 32, 45, 58, 378, 473, 819, 823, 824, 832, 835, 836, 840, 841, 845, 849, 851, 854, 855, 859, 864, 868, 870, 874, 876, 877], "xgboost": [3, 20], "paddlepaddl": [3, 20, 335, 336, 372, 819], "dinov2": [3, 7, 20], "project": [3, 12, 13, 20, 25, 26, 27, 28, 29, 31, 32, 35, 98, 636, 663, 792, 812, 814, 815, 818, 819, 820, 821, 824, 825, 826, 844, 853, 855, 859, 860, 861, 864, 866, 868, 870, 873, 877, 878], "convnext": [3, 6, 11, 20], "video": [4, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 813, 818, 819, 820, 823, 824, 825, 827, 828, 829, 830, 831, 832, 833, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 847, 856, 868], "tutori": [4, 6, 7, 8, 11, 12, 13, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 32, 812, 820, 841, 856], "three": [4, 5, 20, 26, 36, 37, 47, 57, 139, 312, 369, 378, 464, 629, 819, 820, 827, 828, 829, 831, 841, 844, 847, 848, 849, 871, 876], "major": [4, 5, 644, 747, 829, 830, 842, 844, 855, 860, 867, 870], "ml": [4, 5, 6, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 47, 50, 812, 813, 817, 841, 848, 849, 850, 852, 853, 854, 858, 860, 861, 864, 866, 867, 868, 869, 870, 873, 875, 877], "framework": [4, 5, 7, 9, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 35, 36, 38, 45, 47, 49, 52, 58, 170, 192, 202, 205, 216, 543, 559, 563, 595, 598, 630, 631, 634, 641, 720, 771, 773, 777, 784, 789, 796, 801, 802, 812, 815, 816, 818, 819, 822, 823, 824, 825, 826, 828, 829, 830, 831, 833, 834, 836, 837, 838, 840, 841, 844, 845, 847, 848, 849, 851, 854, 855, 856, 857, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 871, 874], "sinc": [4, 8, 12, 28, 29, 31, 32, 45, 47, 57, 80, 98, 372, 812, 814, 819, 820, 823, 824, 825, 826, 827, 828, 829, 830, 833, 840, 841, 855, 860, 870, 876], "automat": [4, 8, 9, 12, 29, 31, 32, 37, 818, 819, 820, 822, 825, 826, 828, 829, 835, 837, 840, 844, 847, 848, 850, 853, 854, 856, 857, 861, 870, 873, 877], "sure": [4, 8, 11, 12, 13, 14, 31, 45, 815, 818, 819, 820, 823, 828, 833, 834, 841, 842, 844, 847, 856], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 26, 27, 29, 46, 57, 62, 74, 85, 103, 375, 377, 398, 456, 580, 634, 637, 680, 794, 810, 812, 819, 820, 821, 824, 827, 829, 837, 838, 839, 840, 841, 844, 845, 848, 850, 852, 854, 855, 857, 860, 863, 868, 869, 870, 871, 872, 873, 876, 877], "dm": [4, 5, 8, 11, 13, 31, 32, 43, 45], "haiku": [4, 5, 8, 11, 13, 29, 31, 32, 43, 45, 49, 789, 812, 854, 861, 864, 870], "exit": [4, 8, 12, 31, 32, 830], "download": [4, 6, 7, 12, 16, 18, 31, 32, 46, 47, 50, 814, 819, 826, 844, 863, 864], "imagenet": [4, 6, 18, 46, 48, 812], "class": [4, 6, 7, 8, 12, 14, 16, 18, 22, 31, 32, 43, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 134, 143, 149, 165, 168, 181, 183, 184, 243, 280, 338, 360, 372, 386, 387, 395, 396, 429, 528, 529, 536, 545, 549, 562, 572, 595, 629, 630, 631, 632, 634, 636, 637, 638, 641, 642, 657, 662, 666, 672, 682, 686, 687, 689, 696, 712, 719, 730, 737, 752, 759, 763, 764, 773, 774, 781, 782, 783, 784, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 800, 801, 805, 810, 812, 818, 825, 826, 827, 829, 830, 831, 832, 836, 838, 839, 842, 843, 844, 847, 849, 850, 852, 853, 854, 857, 863, 864, 868, 870, 871, 877], "wget": [4, 6, 8, 12, 45, 46, 49, 819], "raw": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 45, 48, 49, 74, 812, 832, 864, 871], "githubusercont": [4, 6, 8, 12, 45, 49], "hub": [4, 6, 8, 12, 45, 48, 50], "master": [4, 8, 12, 23, 24, 25, 33, 34, 35, 36, 37, 38, 45, 47, 48, 49, 815, 828, 870, 878], "imagenet_class": [4, 12], "categori": [4, 6, 12, 818, 823, 824, 827, 829, 833, 841, 845, 848], "strip": [4, 12, 24, 34, 860], "readlin": [4, 12, 46], "cat": [4, 7, 12, 46, 842, 847, 849, 854, 863, 864], "jpg": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 47, 48, 812, 864], "filenam": [4, 8, 12, 31, 32, 45, 47, 50, 58, 794, 800, 852], "import": [4, 6, 7, 9, 10, 11, 13, 16, 18, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 46, 48, 49, 50, 57, 68, 72, 76, 80, 95, 194, 195, 199, 211, 307, 387, 522, 557, 573, 631, 634, 640, 645, 716, 717, 752, 784, 801, 802, 812, 817, 818, 819, 820, 821, 823, 824, 825, 826, 827, 829, 830, 831, 832, 835, 838, 839, 840, 841, 842, 843, 844, 845, 849, 851, 852, 854, 855, 856, 860, 863, 864, 865, 866, 868, 870, 873, 874, 876], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 46, 47, 50, 53, 57, 66, 74, 76, 80, 89, 102, 105, 106, 107, 126, 127, 128, 130, 131, 132, 135, 136, 137, 138, 140, 141, 142, 143, 145, 146, 147, 148, 149, 193, 194, 195, 196, 197, 198, 199, 200, 201, 206, 207, 208, 209, 211, 212, 213, 214, 215, 217, 219, 312, 313, 328, 329, 369, 382, 472, 508, 509, 511, 512, 536, 550, 551, 629, 634, 643, 738, 739, 740, 741, 771, 773, 774, 789, 791, 792, 793, 794, 795, 796, 797, 798, 810, 812, 820, 822, 825, 829, 833, 837, 838, 842, 844, 845, 847, 849, 854, 855, 856, 857, 860, 869, 870, 872, 873, 874, 875], "torchvis": [4, 6, 11, 12, 45, 861], "transform": [4, 5, 6, 7, 11, 12, 13, 28, 31, 32, 45, 46, 48, 57, 61, 80, 84, 375, 376, 397, 398, 403, 404, 407, 408, 409, 419, 420, 423, 440, 636, 660, 776, 779, 792, 812, 838, 844, 854, 857, 863, 864, 868, 870, 871, 872], "pil": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 46, 47, 48, 812, 864], "time": [4, 5, 6, 7, 9, 10, 11, 13, 29, 31, 32, 37, 45, 47, 48, 49, 57, 59, 62, 68, 80, 82, 91, 97, 98, 134, 341, 372, 375, 376, 378, 387, 404, 409, 421, 423, 444, 451, 484, 490, 522, 616, 621, 629, 635, 636, 637, 639, 640, 644, 645, 659, 662, 677, 712, 715, 716, 717, 744, 745, 749, 750, 792, 793, 794, 810, 818, 819, 820, 823, 825, 827, 828, 829, 831, 834, 836, 837, 838, 840, 841, 844, 845, 849, 852, 854, 855, 856, 859, 860, 861, 863, 864, 868, 870, 871, 874, 875, 876], "filterwarn": [4, 5], "ignor": [4, 5, 44, 52, 53, 57, 74, 80, 139, 375, 376, 378, 387, 399, 400, 401, 430, 438, 446, 486, 487, 491, 530, 629, 636, 641, 663, 729, 730, 796, 819, 826, 828, 831, 844, 855, 876], "compos": [4, 6, 7, 11, 12, 31, 32, 45, 57, 80, 375, 389, 390, 391, 392, 819, 827, 841, 844, 863, 865, 870, 877], "resiz": [4, 6, 7, 8, 11, 12, 45, 46, 57, 80, 375, 411, 847], "centercrop": [4, 12], "224": [4, 6, 7, 12, 16, 18, 31, 32, 45, 46, 48, 812, 864], "totensor": [4, 6, 7, 11, 12, 45], "485": [4, 12, 45], "456": [4, 12, 45, 844], "406": [4, 12, 45, 57, 80, 397, 540, 634], "229": [4, 12, 45, 279, 632], "225": [4, 12, 45, 47, 234, 632], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 812, 852, 864], "ipython": [4, 8, 12, 26, 27, 28, 29, 31, 32, 50], "displai": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 50, 819, 826, 828, 833, 844, 852], "end": [4, 8, 45, 46, 57, 80, 126, 228, 284, 353, 372, 375, 377, 378, 423, 452, 474, 484, 486, 487, 629, 632, 806, 812, 819, 820, 825, 828, 834, 840, 845, 847, 848, 855, 868, 873], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 217, 631, 830], "ivy_model": [4, 5, 8, 12, 48], "ivy_alexnet": 4, "quick": [4, 20, 32, 820, 822, 842, 853], "trace_graph": [4, 5, 8, 12, 24, 25, 26, 27, 31, 32, 34, 35, 36, 37, 38, 39, 48, 794, 812, 849, 854, 862], "moment": [4, 57, 59, 80, 82, 376, 433, 615, 616, 621, 635, 796, 810, 818, 825, 855, 863, 864], "cost": [4, 59, 82, 615, 616, 619, 621, 622, 623, 635, 640, 715, 716, 717, 806, 829, 847, 868], "arg": [4, 6, 8, 9, 10, 11, 12, 16, 18, 26, 27, 29, 31, 32, 36, 37, 38, 49, 52, 74, 96, 106, 122, 203, 213, 601, 628, 629, 631, 634, 771, 773, 788, 789, 792, 793, 794, 798, 801, 805, 810, 812, 824, 829, 830, 833, 839, 840, 841, 847, 849, 853, 863, 864, 865], "asarrai": [4, 5, 8, 11, 12, 46, 53, 57, 58, 69, 76, 80, 81, 92, 127, 385, 514, 515, 545, 556, 560, 561, 591, 592, 593, 629, 634, 636, 645, 646, 650, 750, 754, 833, 838, 841, 842], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 22, 31, 46, 47, 50, 53, 57, 66, 76, 80, 89, 137, 138, 141, 193, 194, 195, 211, 382, 508, 509, 511, 512, 629, 631, 637, 643, 688, 738, 739, 740, 741, 791, 792, 793, 794, 795, 796, 797, 810, 849, 855, 857, 875], "output": [4, 5, 7, 8, 9, 10, 12, 22, 28, 29, 31, 32, 44, 45, 46, 48, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 152, 154, 179, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 322, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 364, 365, 366, 367, 369, 372, 374, 375, 376, 377, 378, 381, 382, 383, 385, 387, 388, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 417, 419, 420, 421, 423, 424, 426, 427, 428, 430, 432, 435, 436, 438, 441, 442, 443, 444, 446, 447, 450, 452, 453, 454, 455, 456, 457, 458, 459, 460, 467, 468, 469, 472, 474, 475, 476, 477, 478, 481, 482, 483, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 496, 497, 498, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 515, 520, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 539, 540, 541, 545, 546, 547, 549, 553, 562, 569, 576, 577, 578, 602, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 680, 681, 682, 683, 684, 685, 686, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 709, 710, 711, 712, 714, 731, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 771, 776, 791, 792, 805, 806, 812, 814, 819, 820, 822, 823, 824, 826, 827, 829, 830, 831, 832, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 846, 849, 851, 853, 854, 855, 857, 863, 864, 871], "softmax": [4, 6, 7, 12, 16, 29, 31, 32, 47, 51, 61, 72, 73, 84, 377, 454, 626, 636, 663, 666, 788, 812], "pass": [4, 6, 7, 8, 11, 12, 13, 14, 16, 18, 22, 29, 31, 32, 38, 44, 45, 47, 49, 50, 56, 57, 72, 74, 79, 80, 95, 103, 122, 123, 125, 157, 179, 194, 213, 228, 274, 375, 377, 378, 381, 382, 387, 421, 454, 474, 501, 503, 508, 528, 529, 562, 628, 630, 631, 632, 634, 640, 715, 716, 771, 773, 777, 784, 789, 793, 794, 796, 797, 801, 805, 810, 812, 816, 818, 820, 823, 824, 825, 827, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 844, 847, 855, 863, 864, 865, 868], "argsort": [4, 12, 69, 92, 646, 755, 841], "descend": [4, 12, 69, 92, 637, 646, 687, 688, 753, 756], "top": [4, 12, 15, 20, 29, 31, 32, 45, 46, 57, 64, 80, 319, 369, 377, 378, 452, 494, 545, 634, 700, 812, 819, 820, 829, 834, 841, 843, 844, 847, 852, 853, 870, 874], "logit": [4, 5, 6, 7, 8, 12, 45, 46, 47, 48, 57, 63, 80, 86, 367, 382, 508, 511, 638, 696, 698, 788, 812, 863], "gather": [4, 12, 45, 57, 58, 80, 81, 330, 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634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 662, 663, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 721, 724, 725, 726, 727, 728, 729, 730, 735, 736, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 773, 776, 777, 778, 779, 789, 794, 796, 801, 806, 808, 812, 829, 830, 832, 833, 839, 840, 841, 842, 845, 849, 854, 864], "eagertensor": [16, 22, 43, 801, 842], "deepmind": [17, 861], "perceiverio": [17, 861], "backbon": [17, 45, 812, 849, 852], "TO": [17, 19, 30], "replac": [17, 19, 30, 46, 56, 57, 58, 64, 66, 74, 79, 80, 81, 87, 89, 132, 274, 310, 313, 367, 369, 378, 489, 492, 496, 576, 577, 581, 629, 632, 634, 639, 643, 699, 738, 776, 820, 826, 827, 829, 830, 838, 841, 844, 851, 854, 855, 860, 864, 877], "efficientnet": 18, "eff_encod": [18, 812], "efficientnet_v2": [18, 812], "efficientnetv2b0": [18, 812], "storag": [18, 45, 46, 852, 860], "googleapi": [18, 45, 46], "efficientnetv2": 18, "b0_notop": 18, "h5": [18, 74], "24274472": 18, "0u": 18, "torch_eff_encod": [18, 812], "modes_to_trac": 18, "1280": [18, 545, 634, 812], "welcom": [20, 46, 812, 813, 819, 820, 821, 843], "varieti": [20, 823, 828, 829, 830, 844, 846, 866, 868, 872, 873, 876, 877], "organ": [20, 824, 827, 837, 841, 843, 845, 857, 860], "main": [20, 32, 53, 57, 62, 80, 85, 132, 145, 146, 147, 313, 328, 329, 369, 376, 378, 427, 473, 629, 637, 670, 671, 691, 812, 815, 818, 819, 820, 821, 823, 826, 827, 834, 838, 840, 868, 870, 871, 876], "exactli": [20, 24, 34, 43, 44, 48, 290, 632, 818, 827, 828, 829, 830, 831, 833, 844, 847, 859, 861], "rush": [20, 861], "jump": [20, 842], "straight": [20, 812, 828, 841, 844, 851], "quickstart": [20, 812], "introduct": [20, 22, 29, 31, 32, 870], "point": [20, 29, 54, 56, 57, 62, 66, 68, 70, 77, 79, 80, 85, 89, 93, 126, 127, 128, 130, 132, 135, 142, 143, 148, 152, 165, 169, 173, 180, 220, 221, 222, 223, 225, 226, 227, 228, 229, 236, 237, 238, 240, 241, 243, 245, 246, 247, 253, 254, 255, 256, 261, 262, 263, 264, 265, 273, 275, 276, 278, 280, 282, 283, 284, 285, 286, 287, 288, 290, 291, 292, 293, 294, 312, 313, 315, 335, 336, 353, 354, 357, 359, 369, 372, 375, 376, 377, 382, 387, 390, 399, 400, 401, 419, 429, 449, 453, 508, 509, 510, 511, 512, 522, 523, 524, 532, 627, 629, 630, 632, 637, 643, 644, 645, 646, 647, 667, 669, 672, 673, 674, 676, 678, 679, 680, 683, 684, 685, 686, 687, 688, 689, 691, 694, 740, 741, 747, 749, 750, 751, 752, 755, 757, 758, 760, 761, 762, 763, 764, 765, 766, 801, 802, 810, 816, 818, 819, 820, 823, 824, 826, 828, 829, 831, 832, 834, 836, 840, 841, 844, 845, 847, 849, 851, 852, 861, 863, 876], "showcas": [20, 812], "real": [20, 28, 56, 57, 70, 79, 80, 93, 102, 112, 115, 118, 142, 143, 220, 221, 222, 223, 225, 226, 227, 228, 229, 238, 240, 241, 243, 245, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 270, 273, 275, 276, 278, 282, 283, 284, 286, 287, 288, 289, 290, 291, 293, 294, 335, 336, 342, 343, 344, 354, 372, 375, 376, 398, 419, 420, 429, 430, 626, 629, 632, 637, 644, 647, 672, 673, 674, 678, 685, 687, 688, 691, 694, 747, 760, 762, 763, 764, 765, 827, 872], "world": [20, 28, 820, 872], "beginn": [20, 813, 870], "got": [20, 43, 833], "cover": [20, 31, 57, 80, 375, 412, 413, 414, 818, 823, 824, 826, 829, 831, 832, 837, 838, 844, 847, 848], "familiar": [20, 21, 22, 818, 819], "concept": [20, 21, 22], "turn": [20, 21, 24, 34, 61, 84, 97, 98, 399, 400, 401, 636, 659, 792, 819, 826, 827, 830, 831, 841, 844, 861], "unus": [20, 21, 24, 831, 840], "part": [20, 21, 24, 53, 56, 57, 79, 80, 85, 102, 112, 115, 118, 145, 146, 147, 253, 257, 280, 328, 329, 355, 369, 372, 375, 376, 378, 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61, 74, 80, 84, 150, 151, 163, 170, 192, 193, 194, 195, 196, 198, 207, 214, 215, 219, 375, 376, 378, 418, 422, 430, 484, 495, 524, 543, 630, 631, 634, 636, 637, 649, 650, 651, 652, 654, 656, 658, 674, 771, 773, 777, 805, 806, 825, 826, 828, 829, 830, 833, 841, 849, 852], "simplest": [22, 819, 831, 844, 847], "interact": [22, 31, 46, 49, 818, 869, 870, 875], "submodul": [22, 31, 45, 47, 102, 103, 626, 627, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 788, 789, 791, 792, 794, 795, 796, 797, 818, 819, 820, 823, 826, 828, 830, 834, 837, 838, 844, 848, 849, 853, 857], "likewis": [22, 27, 31, 38, 812, 820, 827, 829, 832, 836, 837, 841, 847, 852, 863, 864, 876], "nativearrai": [22, 31, 32, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 68, 70, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 127, 128, 129, 131, 136, 137, 138, 139, 140, 141, 143, 145, 146, 149, 152, 153, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 171, 172, 173, 175, 177, 179, 180, 186, 196, 197, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 313, 314, 317, 318, 322, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 367, 369, 372, 373, 375, 376, 377, 378, 381, 382, 383, 385, 387, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 415, 417, 418, 419, 422, 423, 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701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 719, 720, 721, 725, 726, 727, 730, 735, 736, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 797, 824, 827, 831, 833, 836, 837, 838, 840, 841, 845, 846, 849, 851, 857], "alia": [22, 31, 335, 336, 372, 627, 818, 841, 862, 865], "lastli": [22, 31, 824], "subclass": [22, 31, 32, 838, 841, 847, 864], "dict": [22, 31, 32, 45, 49, 52, 58, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 125, 134, 136, 141, 143, 149, 153, 155, 166, 167, 168, 172, 173, 180, 196, 199, 200, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 302, 303, 304, 305, 306, 307, 309, 310, 311, 313, 325, 334, 335, 336, 337, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 369, 378, 398, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463, 464, 468, 469, 484, 490, 492, 493, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 535, 537, 538, 540, 541, 545, 546, 547, 548, 549, 550, 551, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 572, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 624, 628, 630, 631, 634, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 691, 696, 697, 698, 699, 703, 706, 707, 708, 709, 710, 713, 714, 718, 719, 721, 724, 725, 726, 727, 729, 730, 731, 735, 736, 738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 773, 774, 789, 792, 794, 801, 806, 824, 827, 852, 853, 857, 863, 864, 865], "recurs": [22, 31, 32, 45, 47, 52, 74, 75, 166, 167, 199, 200, 376, 448, 550, 551, 557, 630, 631, 634, 641, 718, 719, 722, 728, 729, 730, 771, 819, 823, 826, 827, 834, 837, 840, 853, 855], "fashion": [22, 778, 844, 864], "native_arrai": [22, 31, 32, 53, 54, 56, 76, 78, 79, 80, 81, 85, 92, 110, 113, 136, 139, 141, 143, 149, 152, 153, 154, 155, 163, 168, 175, 197, 206, 214, 230, 234, 239, 240, 241, 243, 247, 251, 259, 260, 268, 273, 276, 279, 282, 287, 335, 336, 363, 372, 377, 378, 458, 484, 490, 494, 534, 537, 564, 565, 568, 599, 626, 629, 630, 631, 632, 634, 636, 637, 638, 639, 643, 644, 647, 648, 650, 651, 658, 666, 669, 673, 674, 679, 680, 684, 688, 689, 691, 694, 696, 698, 699, 706, 738, 747, 756, 762, 765, 767, 773, 783, 801, 816, 834, 842, 844], "data_class": [22, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 395, 396, 545, 549, 687, 712], "low": [22, 31, 34, 50, 57, 61, 66, 80, 84, 89, 375, 418, 422, 636, 643, 649, 650, 651, 652, 654, 656, 658, 739, 741, 778, 827, 833, 840, 841, 847, 849, 866, 868, 870, 871, 872, 874, 876], "c": [22, 31, 37, 46, 47, 53, 57, 58, 59, 61, 64, 70, 76, 77, 79, 80, 81, 82, 84, 85, 87, 91, 93, 97, 98, 116, 127, 128, 138, 141, 165, 168, 223, 234, 240, 241, 261, 262, 264, 273, 276, 284, 291, 375, 376, 378, 381, 387, 389, 390, 391, 392, 403, 408, 424, 426, 428, 429, 431, 443, 462, 463, 464, 474, 492, 496, 501, 502, 503, 506, 524, 537, 545, 546, 547, 548, 556, 560, 561, 591, 600, 615, 616, 619, 621, 622, 623, 626, 629, 630, 632, 634, 635, 636, 637, 639, 641, 644, 645, 647, 650, 651, 652, 653, 654, 655, 657, 672, 674, 676, 706, 710, 718, 721, 725, 726, 727, 729, 730, 735, 736, 747, 752, 758, 759, 764, 766, 795, 805, 806, 813, 819, 822, 825, 826, 827, 831, 837, 839, 848, 849, 850, 852, 855, 857, 858, 860, 861, 864, 866, 870, 874, 875, 877], "fundament": [22, 31, 828, 841, 847, 849, 859, 870], "signatur": [22, 31, 378, 387, 484, 522, 829, 830, 831, 832, 836, 840, 844, 845, 847, 860, 867, 876], "matmul": [22, 31, 32, 48, 62, 85, 376, 446, 614, 634, 637, 687, 825, 844, 845, 849], "to_n": [22, 31, 32, 43, 52, 75, 849], "jaxlib": [22, 28, 46, 801, 819, 824, 829, 830, 836, 845, 849, 851], "xla_extens": [22, 28, 801, 824, 829, 830, 836, 845, 849, 851], "arrayimpl": [22, 28, 801], "disabl": [22, 31, 57, 80, 378, 492, 794, 810, 826], "array_mod": [22, 31, 578, 602, 634, 846], "set_array_mod": [22, 31, 602, 634, 846], "ultim": [22, 31, 863], "sigmoid": [22, 31, 32, 43, 51, 57, 73, 80, 301, 367, 382, 508, 626, 788, 849, 852, 853], "z": [22, 31, 32, 44, 45, 53, 56, 57, 58, 62, 63, 66, 68, 70, 76, 79, 80, 81, 85, 86, 87, 89, 93, 102, 103, 137, 138, 140, 141, 201, 223, 224, 228, 230, 233, 235, 240, 251, 252, 255, 256, 257, 259, 260, 265, 267, 269, 270, 271, 272, 280, 289, 300, 301, 335, 336, 338, 367, 372, 377, 387, 453, 455, 456, 457, 458, 459, 465, 469, 480, 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223, 240, 273, 283, 381, 382, 506, 508, 632, 637, 643, 668, 686, 738, 812, 823, 829, 831, 838, 849, 854, 864, 868], "div": [23, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 865], "sub": [23, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 57, 62, 64, 74, 75, 79, 80, 81, 85, 87, 103, 272, 376, 378, 387, 430, 470, 479, 499, 528, 529, 557, 634, 637, 639, 640, 671, 691, 708, 715, 716, 717, 818, 820, 822, 827, 833, 841, 842, 844, 851, 852, 853, 865, 866], "with_numpi": 23, "reproduc": [23, 48, 61, 84, 636, 659, 776, 777, 778, 779, 784, 816, 823, 834], "x_": [23, 33, 98, 284, 632, 865], "66391283": 23, "12516928": 23, "38367081": 23, "03102401": 23, "76419425": 23, "52797794": 23, "90346956": 23, "61316347": 23, "27585283": 23, "66309303": 23, "ivy_repo": 23, "sever": [23, 24, 33, 34, 36, 37, 38, 57, 80, 97, 375, 376, 389, 390, 391, 392, 444, 776, 819, 820, 845, 855, 868, 874], "pro": [23, 24, 25, 33, 34, 35, 36, 37, 38], "pick": [24, 34, 791], "trigger": [24, 34, 794, 818, 835], "unif": 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846, 847, 870], "act": [25, 35, 57, 80, 298, 363, 373, 820, 831, 846, 855, 877], "shorthand": [25, 35, 37, 844], "pair": [25, 35, 45, 57, 61, 80, 84, 228, 247, 320, 362, 369, 372, 375, 409, 418, 420, 422, 632, 636, 637, 649, 650, 651, 652, 654, 656, 658, 666, 668, 806], "93968587": 25, "26075466": 25, "22723222": 25, "06276492": 25, "47426987": 25, "72835908": 25, "71737559": 25, "50411096": 25, "65419174": 25, "15576624": 25, "implic": [25, 35, 36, 39, 827], "satisfi": [26, 27, 28, 29, 45, 47, 50, 57, 375, 376, 398, 430, 829, 831], "fw": [26, 27, 28, 29, 61, 84, 387, 522, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 773, 819, 844], "mxnet": [26, 27, 28, 29, 209, 631, 801, 818, 819, 860, 877], "einop": [26, 27, 28, 29, 45, 47, 50, 58, 81, 545, 546, 547, 634, 829, 860], "miniconda": [26, 27, 28, 29], "multienv": [26, 27, 28, 29], "site": [26, 27, 28, 29, 871], "psutil": [26, 27, 28, 29, 45, 47, 50], "termcolor": [26, 27, 28, 29, 45, 47, 50, 74, 103], "colorama": [26, 27, 28, 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818, 819, 820, 821, 822, 828, 831, 840, 841, 842, 844, 850, 855, 856, 861], "practic": [28, 820, 825, 828, 841, 843, 873], "everi": [28, 31, 32, 37, 45, 53, 57, 58, 80, 81, 135, 136, 301, 335, 336, 349, 367, 372, 375, 378, 412, 413, 414, 421, 498, 534, 629, 634, 818, 820, 823, 825, 826, 828, 829, 831, 835, 836, 837, 838, 840, 841, 842, 844, 849, 851, 853, 863, 864, 865, 870], "jax_kornia": [28, 31, 32, 812, 864], "though": [28, 817, 818, 820, 829, 830, 832, 837, 840, 841, 847, 852, 855], "000000000034": [28, 31, 32, 812, 864], "raw_img": [28, 31, 32, 812, 864], "sharp": [28, 31, 32, 812], "prefer": [28, 31, 32, 247, 632, 819, 827, 833, 834, 838, 841, 856, 870], "whole": [29, 57, 80, 378, 381, 491, 504, 505, 507, 820, 826, 835], "full": [29, 57, 62, 80, 84, 85, 97, 98, 100, 165, 252, 260, 323, 324, 325, 326, 327, 369, 376, 377, 378, 449, 450, 456, 457, 485, 488, 579, 588, 603, 611, 629, 630, 632, 634, 636, 637, 651, 653, 654, 655, 657, 680, 684, 686, 687, 777, 784, 812, 819, 820, 826, 829, 832, 833, 836, 837, 841, 844, 847, 849, 855, 860, 861, 868, 870, 876], "complex": [29, 31, 32, 45, 51, 56, 57, 62, 70, 73, 77, 79, 80, 85, 93, 110, 111, 112, 113, 114, 115, 116, 117, 118, 142, 143, 158, 172, 181, 187, 220, 221, 222, 223, 224, 225, 226, 229, 237, 238, 240, 241, 243, 245, 253, 254, 255, 256, 257, 261, 262, 263, 264, 273, 275, 276, 278, 280, 283, 284, 285, 286, 287, 290, 291, 295, 300, 301, 303, 338, 343, 344, 367, 372, 375, 376, 387, 398, 409, 419, 420, 424, 429, 430, 431, 442, 444, 530, 531, 592, 593, 626, 629, 630, 632, 634, 637, 644, 647, 672, 673, 674, 678, 685, 687, 689, 691, 694, 747, 762, 763, 765, 777, 788, 806, 815, 818, 821, 826, 829, 831, 838, 841, 844, 845, 847, 852, 853, 854, 855, 857, 864, 866, 868, 870, 872, 876, 877], "neccessari": 29, "set_random_se": [29, 48], "301436": 29, "_c": 29, "0x7f252c392390": 29, "flatten": [29, 31, 32, 45, 47, 50, 57, 58, 62, 64, 67, 68, 80, 81, 85, 87, 90, 91, 340, 356, 372, 376, 378, 387, 427, 473, 483, 487, 492, 493, 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771, 776, 777, 792, 794, 795, 801, 812, 820, 824, 827, 829, 830, 831, 837, 838, 840, 844, 849, 856, 863, 864], "x0": [31, 32, 50, 81, 537, 634, 831], "normalize_trac": [31, 32], "html": [31, 32, 46, 56, 57, 79, 80, 147, 155, 243, 253, 254, 269, 328, 335, 336, 369, 372, 375, 378, 387, 419, 492, 522, 629, 630, 632, 637, 639, 647, 685, 686, 714, 764, 832, 860], "fname": [31, 32, 48, 50, 794, 852], "anticip": [31, 32], "addition": [31, 32, 827, 840, 841, 876], "normalize_native_comp": [31, 32], "return_backend_compiled_fn": 31, "immedi": [31, 32, 810, 818, 819], "built": [31, 32, 37, 45, 47, 50, 126, 629, 792, 793, 794, 812, 819, 820, 826, 827, 844, 850, 856, 863, 869, 870, 874], "eager_graph": [31, 32, 812, 863, 864], "lazy_graph": [31, 32, 812, 863, 864], "thought": [31, 32, 819, 820, 836, 860, 868], "matter": [31, 32, 37, 831, 859], "haven": [31, 32, 37, 856, 870], "jax_out": [31, 32], "ideal": [31, 32, 828, 829, 841, 847, 852], "worth": [31, 32], "differenti": [31, 32, 295, 365, 366, 367, 374, 870], "chosen": [31, 32, 50, 100, 126, 228, 629, 632, 644, 748, 818, 828, 841], "plai": [31, 32, 377, 456, 812, 815, 819, 821, 824, 830, 834, 841, 844, 854, 870, 873], "role": [31, 32, 812, 815, 820, 821, 830, 841, 850, 871, 873, 877], "dl": [31, 32], "effortlessli": [31, 32], "previous": [31, 32, 603, 634, 801, 818, 819, 825, 837, 839, 844, 849], "default_devic": [31, 32, 206, 209, 210, 211, 217, 218, 631, 830, 833, 834], "as_n": [31, 32, 54, 55, 74, 77, 78, 158, 159, 160, 161, 162, 163, 169, 196, 197, 630, 631, 829], "certainli": [31, 32, 812, 860, 876], "upon": [31, 32, 49, 810, 820, 821, 831, 840, 844, 847, 855, 869, 870], "unnecessari": [31, 32, 841], "extend": [31, 32, 57, 80, 378, 387, 484, 525, 825, 826, 829, 832, 833, 836, 841, 845, 855, 867, 870, 876], "infrastructur": [31, 32, 866, 872, 873], "least": [31, 56, 57, 62, 79, 80, 240, 258, 273, 375, 378, 387, 403, 408, 462, 463, 464, 473, 475, 522, 632, 637, 644, 677, 747, 812, 820, 824, 828, 829, 830, 831, 837, 840, 844, 864], "coco": 31, "seamlessli": [32, 844], "therefor": [32, 37, 53, 56, 57, 62, 79, 80, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 149, 155, 171, 175, 179, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 313, 328, 329, 335, 336, 338, 341, 369, 372, 375, 376, 378, 387, 394, 395, 396, 397, 399, 400, 401, 407, 412, 413, 414, 419, 421, 430, 477, 484, 485, 487, 492, 496, 497, 522, 525, 529, 538, 546, 547, 552, 556, 558, 560, 562, 576, 591, 595, 600, 624, 629, 630, 632, 634, 635, 636, 637, 639, 642, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 658, 659, 660, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 693, 694, 695, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 737, 744, 745, 747, 748, 749, 750, 751, 752, 753, 756, 760, 761, 762, 763, 764, 765, 766, 767, 768, 818, 820, 823, 824, 827, 828, 829, 830, 831, 832, 833, 836, 837, 838, 840, 841, 842, 844, 845, 847, 849, 851, 853, 855, 859, 867, 870, 876], "wide": [32, 812, 820, 844, 868, 870], "plenti": 32, "resourc": [32, 813, 818, 819, 828], "visit": [32, 818, 819, 820, 828], "page": [32, 812, 818, 819, 820, 826, 828, 834, 850, 851, 854, 856, 865, 878], "newli": [33, 34, 46, 48, 54, 77, 152, 539, 630, 634, 820, 828, 840, 844], "randon": [33, 34, 36, 37, 38], "mean_": 33, "std_": 33, "detect": [33, 37, 56, 74, 79, 255, 632, 641, 718, 729, 818, 819, 825, 827, 828, 835, 844, 852, 853], "inspect": [33, 37, 535, 634], "__": [33, 34, 35, 36, 37, 38, 74, 831, 852], "script": [34, 812, 819, 820, 823, 828, 831, 849, 855, 870], "comp": 34, "low_level": 34, "chain": [34, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 134, 136, 141, 143, 149, 153, 155, 168, 172, 173, 180, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 299, 303, 304, 305, 306, 307, 309, 310, 311, 313, 334, 335, 336, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 468, 469, 490, 492, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 537, 538, 540, 541, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 619, 624, 640, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 691, 696, 697, 698, 699, 703, 706, 707, 708, 709, 710, 713, 714, 715, 716, 720, 731, 738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 797, 824, 827, 839, 841, 853, 854, 855, 870], "un": [34, 170, 630, 829, 849], "partial_comp": 34, "time_funct": 34, "express": [34, 56, 57, 79, 80, 98, 221, 225, 227, 228, 237, 239, 279, 285, 290, 359, 372, 632, 798, 806, 832, 841, 849, 854, 870, 871], "maxim": [34, 837, 840, 849, 867, 868, 872, 873, 874], "conclud": [35, 845], "collect": [35, 45, 47, 49, 50, 52, 74, 75, 626, 631, 634, 635, 636, 638, 641, 642, 643, 731, 788, 792, 793, 794, 795, 796, 819, 828, 833, 834, 838, 839, 842, 844, 868, 870, 873], "norm_comp": [36, 37], "global": [36, 37, 47, 58, 74, 81, 103, 158, 159, 160, 161, 162, 211, 212, 213, 582, 583, 586, 592, 593, 605, 606, 609, 630, 631, 634, 784, 795, 801, 819, 824, 825, 828, 829, 830, 833, 837, 841, 849, 870], "b": [37, 51, 56, 57, 58, 61, 62, 70, 73, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 98, 101, 102, 103, 110, 111, 112, 113, 114, 115, 116, 117, 127, 128, 129, 134, 135, 136, 138, 141, 143, 149, 152, 153, 154, 155, 163, 173, 175, 180, 197, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 330, 333, 334, 335, 336, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 355, 356, 357, 358, 359, 361, 362, 363, 367, 369, 372, 375, 376, 377, 378, 382, 385, 387, 394, 395, 396, 397, 399, 400, 403, 407, 408, 409, 412, 413, 414, 418, 419, 422, 425, 428, 430, 432, 436, 439, 443, 446, 451, 452, 453, 455, 456, 457, 458, 462, 463, 464, 465, 468, 469, 470, 471, 474, 475, 476, 478, 479, 480, 481, 483, 484, 490, 492, 493, 494, 495, 496, 499, 500, 505, 507, 509, 510, 512, 513, 515, 522, 523, 524, 525, 527, 529, 532, 534, 537, 538, 540, 541, 544, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 569, 576, 577, 591, 592, 593, 595, 599, 600, 613, 615, 616, 617, 619, 621, 622, 623, 624, 626, 629, 630, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 655, 657, 658, 659, 660, 662, 666, 667, 668, 669, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 691, 692, 693, 694, 696, 697, 698, 699, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 721, 724, 725, 726, 727, 729, 730, 735, 736, 737, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 776, 805, 806, 810, 812, 813, 816, 820, 822, 823, 825, 827, 828, 831, 834, 837, 839, 842, 848, 849, 850, 852, 853, 854, 858, 861, 863, 866], "option": [37, 46, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 152, 153, 154, 155, 157, 158, 159, 160, 161, 162, 168, 170, 180, 192, 196, 208, 211, 212, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 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57, 62, 64, 68, 71, 74, 79, 80, 81, 85, 92, 94, 97, 100, 102, 103, 132, 155, 157, 164, 170, 171, 172, 173, 175, 176, 177, 192, 202, 204, 205, 216, 221, 222, 223, 225, 226, 227, 228, 229, 230, 232, 233, 234, 235, 237, 238, 240, 243, 245, 247, 253, 254, 255, 256, 257, 261, 262, 263, 264, 265, 270, 273, 278, 282, 285, 286, 287, 288, 289, 290, 291, 294, 304, 308, 354, 359, 367, 372, 375, 376, 377, 378, 387, 411, 419, 430, 452, 453, 492, 496, 522, 534, 537, 558, 559, 563, 564, 565, 566, 567, 568, 595, 613, 629, 630, 631, 632, 634, 637, 639, 640, 645, 648, 667, 668, 669, 671, 675, 676, 677, 679, 680, 682, 683, 685, 686, 691, 693, 694, 700, 715, 716, 717, 749, 750, 751, 752, 753, 767, 768, 778, 784, 791, 795, 827, 829, 830, 832, 837, 841, 844, 846, 847, 859], "think": [37, 818, 820, 828, 831, 847, 871], "uniqu": [37, 47, 57, 58, 68, 80, 81, 91, 375, 376, 378, 423, 446, 483, 484, 498, 569, 634, 640, 641, 645, 715, 716, 717, 720, 724, 749, 750, 751, 752, 778, 812, 823, 827, 837, 841, 842, 843, 847, 855, 859, 873], "rule": [37, 54, 56, 57, 62, 77, 79, 80, 85, 152, 155, 178, 179, 180, 229, 240, 273, 275, 282, 284, 292, 294, 375, 378, 387, 419, 472, 522, 630, 632, 637, 639, 667, 668, 675, 679, 682, 686, 700, 778, 805, 823, 824, 827, 828, 829, 831, 835, 836, 837, 839, 844, 847, 871], "broadcast": [37, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 97, 102, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 141, 142, 143, 144, 145, 146, 148, 149, 152, 153, 154, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 302, 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369, 372, 615, 616, 619, 620, 621, 622, 623, 629, 635, 640, 641, 717, 719, 730, 794, 796, 797, 829, 830, 851, 853], "word": [52, 126, 378, 477, 629, 643, 741, 789, 792, 827, 840, 841, 857], "args_to_n": [52, 840], "cont_inplac": 52, "decid": [52, 74, 641, 729, 730, 812, 818, 819, 829, 847], "args_to_new_backend": 52, "shallow": [52, 641, 725, 726, 730, 735, 736], "nativevari": 52, "mutabl": [52, 641, 719, 725, 726, 730, 735, 736, 825], "to_ivi": [52, 75, 641, 731, 840], "leaf": [52, 74, 81, 93, 103, 548, 641, 728, 729, 731, 758, 827, 837, 852], "travers": [52, 75, 641, 722, 730, 827, 829, 833, 849], "lowest": [52, 57, 66, 75, 80, 89, 387, 525, 641, 643, 730, 739, 806, 837, 855, 857, 867, 871, 875], "search": [52, 57, 75, 80, 744, 745, 784, 817, 819, 827, 831, 834, 844, 845, 859], "to_new_backend": 52, "_arraywithcr": [53, 102], "boolean": [53, 54, 56, 57, 58, 64, 67, 70, 74, 76, 77, 79, 80, 81, 87, 90, 93, 102, 103, 123, 125, 127, 128, 129, 135, 152, 168, 170, 172, 173, 176, 192, 202, 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"set_default_device": [[209, "set-default-device"]], "unset_default_dtype": [[188, "unset-default-dtype"]], "handle_soft_device_variable": [[203, "handle-soft-device-variable"]], "unset_soft_device_mode": [[218, "unset-soft-device-mode"]], "set_default_uint_dtype": [[185, "set-default-uint-dtype"]], "default_device": [[196, "default-device"]], "type_promote_arrays": [[186, "type-promote-arrays"]], "set_split_factor": [[211, "set-split-factor"]], "atan": [[227, "atan"]], "get_all_ivy_arrays_on_dev": [[201, "get-all-ivy-arrays-on-dev"]], "print_all_ivy_arrays_on_dev": [[208, "print-all-ivy-arrays-on-dev"]], "unset_default_complex_dtype": [[187, "unset-default-complex-dtype"]], "unset_default_float_dtype": [[189, "unset-default-float-dtype"]], "unset_default_device": [[217, "unset-default-device"]], "dev": [[197, "dev"]], "as_native_dev": [[194, "as-native-dev"]], "asin": [[225, "asin"]], "set_default_int_dtype": [[184, "set-default-int-dtype"]], "split_factor": [[212, "split-factor"]], "percent_used_mem_on_dev": [[207, "percent-used-mem-on-dev"]], "acos": [[221, "acos"]], "clear_cached_mem_on_dev": [[195, "clear-cached-mem-on-dev"]], "acosh": [[222, "acosh"]], "add": [[223, "add"]], "angle": [[224, "angle"]], "atan2": [[228, "atan2"]], "dev_util": [[198, "dev-util"]], "valid_dtype": [[192, "valid-dtype"]], "num_gpus": [[205, "num-gpus"]], "used_mem_on_dev": [[219, "used-mem-on-dev"]], "set_soft_device_mode": [[210, "set-soft-device-mode"]], "total_mem_on_dev": [[215, "total-mem-on-dev"]], "asinh": [[226, "asinh"]], "as_ivy_dev": [[193, "as-ivy-dev"]], "gpu_is_available": [[202, "gpu-is-available"]], "function_supported_devices": [[199, "function-supported-devices"]], "End-to-End Training Pipeline in Ivy": [[47, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[47, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[47, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[47, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[47, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[47, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[47, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[47, "Plotting-the-training-metrics"]], "Save the trained Model": [[47, "Save-the-trained-Model"]], "Image": [[60, "module-ivy.data_classes.array.image"], [83, "module-ivy.data_classes.container.image"]], "HuggingFace Tensorflow DeiT": [[48, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[48, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Deepmind PerceiverIO on GPU": [[46, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[46, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[46, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[46, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[46, "Run-the-demo..."]], "\u2026with torch backend": [[46, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[46, "....with-tensorflow-backend"]], "\u2026with jax backend": [[46, "...with-jax-backend"]], "\u2026with numpy backend": [[46, "...with-numpy-backend"]], "Resnet 18": [[50, "Resnet-18"]], "Ivy as a Transpiler Introduction": [[49, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[49, "To-use-the-transpiler:"]], "Transpiler Interface": [[49, "Transpiler-Interface"]], "Telemetry": [[49, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[49, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[49, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[49, "3.-Transpile-Models-\ud83c\udf10"]], "Conversions": [[52, "module-ivy.data_classes.array.conversions"], [75, "module-ivy.data_classes.container.conversions"]], "Unify code": [[23, "Unify-code"]], "Learn the basics": [[21, "learn-the-basics"], [20, "learn-the-basics"]], "ODSC Ivy Demo": [[31, "ODSC-Ivy-Demo"]], "Ivy Backend Handler": [[31, "Ivy-Backend-Handler"], [22, "Ivy-Backend-Handler"]], "Data Structures": [[31, "Data-Structures"], [22, "Data-Structures"]], "Ivy Functional API": [[31, "Ivy-Functional-API"], [22, "Ivy-Functional-API"]], "Graph Tracer": [[31, "Graph-Tracer"]], "Any function": [[31, "Any-function"], [32, "Any-function"]], "Any library": [[31, "Any-library"], [32, "Any-library"]], "Any model": [[31, "Any-model"], [32, "Any-model"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "0.0: Unify": [[33, "0.0:-Unify"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[6, "Comparing-the-results"], [7, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[6, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[6, "Conclusion"], [7, "Conclusion"]], "How To Convert Models from PyTorch to PaddlePaddle": [[7, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[7, "About-the-Model"]], "Transpiling the Model": [[7, "Transpiling-the-Model"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "Unify": [[37, "Unify"], [36, "Unify"], [26, "Unify"], [27, "Unify"], [38, "Unify"]], "Compile": [[37, "Compile"], [36, "Compile"], [38, "Compile"]], "Transpile": [[37, "Transpile"], [36, "Transpile"], [26, "Transpile"], [27, "Transpile"], [38, "Transpile"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Guides": [[20, "guides"], [15, "guides"]], "Examples and Demos": [[20, "examples-and-demos"], [3, "examples-and-demos"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Installation": [[4, "Installation"], [12, "Installation"]], "Data Preparation": [[4, "Data-Preparation"], [8, "Data-Preparation"], [5, "Data-Preparation"], [12, "Data-Preparation"]], "Ivy AlexNet inference in Torch": [[4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[4, "TensorFlow-inference"]], "JAX inference": [[4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[43, "Installs-\ud83d\udcbe"], [44, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[43, "Imports-\ud83d\udec3"], [44, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[43, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[43, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[43, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[43, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[43, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[43, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[43, "Set-Backend-Framework"]], "Define Model": [[43, "Define-Model"], [44, "Define-Model"]], "Create Model": [[43, "Create-Model"]], "Create Optimizer": [[43, "Create-Optimizer"]], "Input and Target": [[43, "Input-and-Target"]], "Loss Function": [[43, "Loss-Function"]], "Training Loop": [[43, "Training-Loop"]], "Transpile any library": [[28, "Transpile-any-library"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Importing Ivy": [[0, "Importing-Ivy"], [22, "Importing-Ivy"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[8, "Imports"], [14, "Imports"], [12, "Imports"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Load the image example \ud83d\uddbc\ufe0f": [[8, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[8, "Visualise-image"], [12, "Visualise-image"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "0.1: Compile": [[34, "0.1:-Compile"]], "1.3: Dynamic vs Static": [[39, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[39, "Dynamic"]], "Static": [[39, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[39, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Quickstart": [[32, "Quickstart"]], "Get familiar with Ivy": [[32, "Get-familiar-with-Ivy"]], "Functional API": [[32, "Functional-API"]], "Stateful API": [[32, "Stateful-API"]], "Tracing code": [[32, "Tracing-code"]], "Accelerating XGBoost with JAX": [[14, "Accelerating-XGBoost-with-JAX"]], "Tests": [[14, "Tests"]], "Loading the Data": [[14, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[14, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[14, "JAX-backend"]], "Tensorflow backend": [[14, "Tensorflow-backend"]], "PyTorch backend": [[14, "PyTorch-backend"]], "More exhaustive example": [[14, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[14, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[14, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[14, "Comparison-of-Metrics"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Write Ivy code": [[22, "Write-Ivy-code"]], "Contents": [[22, "Contents"]], "Installing Ivy": [[22, "Installing-Ivy"]], "Compilation of a Basic Function": [[44, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[44, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[44, "Function-compilation-\ud83d\udee0"]], "Set backend": [[44, "Set-backend"]], "Sample input": [[44, "Sample-input"]], "Define function to compile": [[44, "Define-function-to-compile"]], "Compile the function": [[44, "Compile-the-function"]], "Check results": [[44, "Check-results"], [44, "id1"]], "Compiling simple neural network \ud83e\udde0": [[44, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[44, "Create-model"]], "Define input": [[44, "Define-input"]], "Compile network": [[44, "Compile-network"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[45, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[45, "Table-of-Contents"]], "Defining the model": [[45, "Defining-the-model"]], "Model construction": [[45, "Model-construction"]], "Some helper functions": [[45, "Some-helper-functions"]], "Transpiling the model": [[45, "Transpiling-the-model"]], "PyTorch pipeline": [[45, "PyTorch-pipeline"]], "Dataset download": [[45, "Dataset-download"]], "DataLoader": [[45, "DataLoader"]], "Training": [[45, "Training"]], "Transpile code": [[25, "Transpile-code"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]], "Trace code": [[24, "Trace-code"]], "# 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"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Trace": [[26, "Trace"], [27, "Trace"]], "How to use decorators": [[27, "How-to-use-decorators"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]]}, "indexentries": {"_arraywithactivations (class in ivy.data_classes.array.activations)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations"]], "_abc_impl (ivy.data_classes.array.activations._arraywithactivations attribute)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations._abc_impl"]], "gelu() (ivy.data_classes.array.activations._arraywithactivations method)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations.gelu"]], "hardswish() (ivy.data_classes.array.activations._arraywithactivations method)": [[51, "ivy.data_classes.array.activations._ArrayWithActivations.hardswish"]], "ivy.data_classes.array.activations": [[51, "module-ivy.data_classes.array.activations"]], "leaky_relu() 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8, 43, 56, 57, 79, 80, 81, 84, 89, 119, 137, 226, 228, 240, 242, 253, 315, 348, 349, 369, 372, 418, 532, 547, 560, 592, 626, 629, 632, 634, 637, 641, 643, 650, 676, 682, 726, 741], "050000": 0, "max": [0, 43, 45, 54, 57, 58, 62, 70, 77, 80, 81, 85, 93, 165, 168, 271, 335, 372, 375, 376, 377, 378, 394, 395, 396, 412, 413, 414, 415, 417, 419, 430, 452, 489, 491, 492, 540, 541, 546, 562, 576, 577, 630, 632, 634, 637, 647, 678, 680, 683, 776, 792, 796, 828, 841, 867], "25691": 0, "160000": 0, "reveal": 0, "outlier": [0, 844], "receiv": [0, 6, 45, 49, 97, 536, 572, 634, 640, 715, 716, 717, 792, 810, 815, 819, 820, 829, 830, 844, 847], "anomali": 0, "financi": 0, "behavior": [0, 4, 8, 57, 68, 240, 247, 273, 282, 388, 533, 580, 604, 632, 634, 645, 749, 750, 751, 752, 818, 826, 827, 828, 829, 840, 841, 842, 844, 847, 849, 855, 867], "associ": [0, 12, 57, 62, 80, 85, 223, 273, 378, 387, 461, 525, 632, 637, 680, 683, 695, 773, 820, 829, 837, 838, 841, 842, 844, 855], "122": [0, 13, 54, 168, 238, 632], "211321": 0, "256": [0, 4, 8, 12, 56, 81, 283, 284, 593, 636, 651, 653, 776], "683288": 0, "250000": 0, "105": [0, 62, 84, 636, 637, 659, 660, 675, 682], "890000": 0, "2125": 0, "870000": 0, "deepen": 0, "averag": [0, 6, 7, 45, 47, 57, 59, 63, 80, 82, 86, 375, 377, 381, 387, 389, 390, 394, 395, 396, 454, 455, 456, 457, 458, 459, 506, 522, 615, 616, 621, 635, 636, 638, 640, 663, 696, 715, 716, 791, 792], "across": [0, 1, 12, 13, 14, 26, 27, 28, 29, 43, 57, 67, 74, 80, 81, 90, 102, 211, 212, 240, 247, 273, 291, 377, 381, 452, 503, 506, 537, 558, 594, 631, 632, 634, 636, 641, 644, 659, 663, 724, 744, 745, 792, 818, 823, 829, 831, 833, 836, 837, 839, 844, 847, 868, 870, 875], "all": [0, 1, 2, 4, 5, 6, 7, 8, 12, 13, 16, 17, 18, 19, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 44, 45, 47, 48, 50, 52, 53, 57, 58, 61, 62, 64, 66, 71, 72, 74, 75, 76, 79, 80, 81, 84, 85, 87, 89, 94, 95, 97, 98, 126, 134, 141, 145, 146, 147, 201, 208, 240, 244, 272, 273, 328, 329, 341, 360, 369, 372, 375, 376, 377, 378, 387, 409, 418, 420, 421, 422, 430, 435, 445, 446, 448, 451, 452, 473, 484, 492, 498, 528, 534, 537, 554, 574, 575, 592, 599, 600, 614, 617, 629, 631, 632, 634, 635, 636, 637, 639, 640, 641, 643, 644, 648, 659, 662, 663, 668, 680, 685, 686, 689, 694, 703, 707, 709, 715, 716, 717, 718, 719, 720, 729, 730, 731, 732, 738, 741, 746, 771, 773, 776, 777, 778, 779, 791, 792, 798, 801, 806, 808, 810, 812, 813, 816, 818, 819, 820, 821, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 847, 848, 849, 851, 852, 853, 854, 855, 856, 857, 859, 860, 861, 863, 864, 866, 867, 868, 869, 870, 871, 873, 876, 877, 878], "group": [0, 6, 57, 80, 378, 381, 498, 502, 636, 641, 649, 656, 657, 720, 810, 821, 823, 827, 829, 837, 841, 842, 866, 869, 875], "calcul": [0, 4, 14, 45, 56, 57, 58, 63, 70, 74, 79, 80, 81, 85, 86, 93, 103, 220, 221, 222, 223, 224, 225, 226, 227, 228, 237, 238, 240, 243, 244, 245, 261, 262, 263, 264, 265, 266, 271, 272, 273, 278, 285, 286, 287, 289, 290, 291, 297, 307, 335, 336, 349, 359, 372, 375, 376, 377, 378, 381, 387, 394, 395, 396, 430, 452, 457, 484, 501, 503, 529, 569, 632, 634, 637, 638, 647, 674, 682, 685, 696, 697, 698, 760, 761, 762, 763, 764, 765, 766, 776, 778, 791, 792, 795, 818, 832, 849, 860, 863], "pictur": [0, 47, 812, 818, 849, 859], "vital": [0, 854, 859], "select": [0, 22, 31, 36, 49, 57, 70, 80, 93, 376, 378, 387, 430, 443, 492, 493, 496, 523, 524, 647, 757, 758, 818, 819, 820, 828, 834, 840, 844, 849, 851, 854, 855, 870, 873, 874], "guid": [0, 16, 29, 812, 813, 818, 819, 820, 826, 835, 841, 843, 876], "recogn": [0, 47, 815, 821], "both": [0, 6, 9, 11, 12, 13, 14, 16, 18, 26, 28, 31, 32, 36, 37, 44, 46, 53, 56, 57, 58, 61, 62, 76, 79, 80, 81, 84, 85, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 155, 171, 175, 178, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 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863, 864, 870, 873, 875, 876, 877], "groupbi": 0, "94838": 0, "202258": 0, "008258": 0, "006271": 0, "012171": 0, "007860": 0, "005453": 0, "002419": 0, "009637": 0, "000987": 0, "004467": 0, "000644": 0, "001235": [0, 47], "000024": 0, "000070": 0, "000182": 0, "000072": 0, "000089": 0, "000295": 0, "000131": 0, "80746": 0, "806911": 0, "771948": 0, "623778": 0, "033281": 0, "542029": 0, "151225": 0, "397737": 0, "568731": 0, "570636": 0, "581123": 0, "372319": 0, "713588": 0, "014049": 0, "040308": 0, "105130": 0, "041449": 0, "051648": 0, "170575": 0, "075667": 0, "In": [0, 3, 4, 5, 6, 16, 18, 20, 22, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 43, 45, 50, 55, 57, 58, 64, 78, 80, 81, 87, 97, 98, 207, 214, 215, 219, 223, 240, 241, 247, 255, 256, 273, 276, 282, 284, 375, 378, 381, 399, 400, 401, 421, 462, 463, 464, 470, 472, 474, 475, 476, 477, 479, 483, 489, 490, 499, 501, 503, 535, 555, 562, 580, 631, 632, 634, 637, 639, 643, 685, 702, 703, 704, 706, 708, 709, 711, 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823, 825, 826, 827, 828, 829, 831, 834, 835, 836, 838, 839, 840, 841, 842, 844, 845, 849, 850, 851, 852, 853, 854, 855, 863, 864, 865, 870, 871, 878], "take": [0, 4, 6, 12, 22, 29, 31, 32, 37, 43, 45, 48, 57, 62, 64, 70, 80, 87, 97, 122, 123, 125, 141, 280, 287, 302, 367, 375, 376, 378, 395, 403, 408, 413, 423, 432, 446, 467, 474, 493, 523, 524, 628, 629, 632, 636, 637, 639, 640, 663, 677, 681, 706, 717, 757, 776, 784, 791, 792, 805, 810, 812, 813, 818, 819, 820, 823, 824, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 837, 840, 841, 842, 844, 847, 849, 851, 853, 854, 855, 856, 861, 863, 864, 867, 868, 876], "random": [0, 6, 9, 11, 13, 16, 18, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 38, 45, 47, 48, 57, 61, 74, 80, 84, 323, 324, 325, 326, 327, 369, 376, 377, 434, 445, 451, 457, 508, 509, 510, 511, 512, 636, 659, 738, 739, 740, 741, 742, 743, 776, 778, 791, 805, 806, 812, 818, 830, 842, 844, 845, 854, 864, 865, 870], "match": [0, 1, 54, 57, 74, 77, 80, 152, 247, 282, 339, 341, 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863, 864, 865, 866, 867, 868, 869, 871, 874], "sinc": [4, 8, 12, 28, 29, 31, 32, 45, 47, 57, 80, 98, 372, 812, 814, 819, 820, 823, 824, 825, 826, 827, 828, 829, 830, 833, 840, 841, 855, 860, 870, 876], "automat": [4, 8, 9, 12, 29, 31, 32, 37, 818, 819, 820, 822, 825, 826, 828, 829, 835, 837, 840, 844, 847, 848, 850, 853, 854, 856, 857, 861, 870, 873, 877], "sure": [4, 8, 11, 12, 13, 14, 31, 45, 815, 818, 819, 820, 823, 828, 833, 834, 841, 842, 844, 847, 856], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 26, 27, 29, 46, 57, 62, 74, 85, 103, 375, 377, 398, 456, 580, 634, 637, 680, 794, 810, 812, 819, 820, 821, 824, 827, 829, 837, 838, 839, 840, 841, 844, 845, 848, 850, 852, 854, 855, 857, 860, 863, 868, 869, 870, 871, 872, 873, 876, 877], "dm": [4, 5, 8, 11, 13, 31, 32, 43, 45], "haiku": [4, 5, 8, 11, 13, 29, 31, 32, 43, 45, 49, 789, 812, 854, 861, 864, 870], "exit": [4, 8, 12, 31, 32, 830], "download": [4, 6, 7, 12, 16, 18, 31, 32, 46, 47, 50, 814, 819, 826, 844, 863, 864], "imagenet": [4, 6, 18, 46, 48, 812], "class": [4, 6, 7, 8, 12, 14, 16, 18, 22, 31, 32, 43, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 134, 143, 149, 165, 168, 181, 183, 184, 243, 280, 338, 360, 372, 386, 387, 395, 396, 429, 528, 529, 536, 545, 549, 562, 572, 595, 629, 630, 631, 632, 634, 636, 637, 638, 641, 642, 657, 662, 666, 672, 682, 686, 687, 689, 696, 712, 719, 730, 737, 752, 759, 763, 764, 773, 774, 781, 782, 783, 784, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 800, 801, 805, 810, 812, 818, 825, 826, 827, 829, 830, 831, 832, 836, 838, 839, 842, 843, 844, 847, 849, 850, 852, 853, 854, 857, 863, 864, 868, 870, 871, 877], "wget": [4, 6, 8, 12, 45, 46, 49, 819], "raw": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 45, 48, 49, 74, 812, 832, 864, 871], "githubusercont": [4, 6, 8, 12, 45, 49], "hub": [4, 6, 8, 12, 45, 48, 50], "master": [4, 8, 12, 23, 24, 25, 33, 34, 35, 36, 37, 38, 45, 47, 48, 49, 815, 828, 870, 878], "imagenet_class": [4, 12], "categori": [4, 6, 12, 818, 823, 824, 827, 829, 833, 841, 845, 848], "strip": [4, 12, 24, 34, 860], "readlin": [4, 12, 46], "cat": [4, 7, 12, 46, 842, 847, 849, 854, 863, 864], "jpg": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 47, 48, 812, 864], "filenam": [4, 8, 12, 31, 32, 45, 47, 50, 58, 794, 800, 852], "import": [4, 6, 7, 9, 10, 11, 13, 16, 18, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 45, 46, 48, 49, 50, 57, 68, 72, 76, 80, 95, 194, 195, 199, 211, 307, 387, 522, 557, 573, 631, 634, 640, 645, 716, 717, 752, 784, 801, 802, 812, 817, 818, 819, 820, 821, 823, 824, 825, 826, 827, 829, 830, 831, 832, 835, 838, 839, 840, 841, 842, 843, 844, 845, 849, 851, 852, 854, 855, 856, 860, 863, 864, 865, 866, 868, 870, 873, 874, 876], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 46, 47, 50, 53, 57, 66, 74, 76, 80, 89, 102, 105, 106, 107, 126, 127, 128, 130, 131, 132, 135, 136, 137, 138, 140, 141, 142, 143, 145, 146, 147, 148, 149, 193, 194, 195, 196, 197, 198, 199, 200, 201, 206, 207, 208, 209, 211, 212, 213, 214, 215, 217, 219, 312, 313, 328, 329, 369, 382, 472, 508, 509, 511, 512, 536, 550, 551, 629, 634, 643, 738, 739, 740, 741, 771, 773, 774, 789, 791, 792, 793, 794, 795, 796, 797, 798, 810, 812, 820, 822, 825, 829, 833, 837, 838, 842, 844, 845, 847, 849, 854, 855, 856, 857, 860, 869, 870, 872, 873, 874, 875], "torchvis": [4, 6, 11, 12, 45, 861], "transform": [4, 5, 6, 7, 11, 12, 13, 28, 31, 32, 45, 46, 48, 57, 61, 80, 84, 375, 376, 397, 398, 403, 404, 407, 408, 409, 419, 420, 423, 440, 636, 660, 776, 779, 792, 812, 838, 844, 854, 857, 863, 864, 868, 870, 871, 872], "pil": [4, 6, 7, 8, 11, 12, 13, 28, 31, 32, 46, 47, 48, 812, 864], "time": [4, 5, 6, 7, 9, 10, 11, 13, 29, 31, 32, 37, 45, 47, 48, 49, 57, 59, 62, 68, 80, 82, 91, 97, 98, 134, 341, 372, 375, 376, 378, 387, 404, 409, 421, 423, 444, 451, 484, 490, 522, 616, 621, 629, 635, 636, 637, 639, 640, 644, 645, 659, 662, 677, 712, 715, 716, 717, 744, 745, 749, 750, 792, 793, 794, 810, 818, 819, 820, 823, 825, 827, 828, 829, 831, 834, 836, 837, 838, 840, 841, 844, 845, 849, 852, 854, 855, 856, 859, 860, 861, 863, 864, 868, 870, 871, 874, 875, 876], "filterwarn": [4, 5], "ignor": [4, 5, 44, 52, 53, 57, 74, 80, 139, 375, 376, 378, 387, 399, 400, 401, 430, 438, 446, 486, 487, 491, 530, 629, 636, 641, 663, 729, 730, 796, 819, 826, 828, 831, 844, 855, 876], "compos": [4, 6, 7, 11, 12, 31, 32, 45, 57, 80, 375, 389, 390, 391, 392, 819, 827, 841, 844, 863, 865, 870, 877], "resiz": [4, 6, 7, 8, 11, 12, 45, 46, 57, 80, 375, 411, 847], "centercrop": [4, 12], "224": [4, 6, 7, 12, 16, 18, 31, 32, 45, 46, 48, 812, 864], "totensor": [4, 6, 7, 11, 12, 45], "485": [4, 12, 45], "456": [4, 12, 45, 844], "406": [4, 12, 45, 57, 80, 397, 540, 634], "229": [4, 12, 45, 279, 632], "225": [4, 12, 45, 47, 234, 632], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 812, 852, 864], "ipython": [4, 8, 12, 26, 27, 28, 29, 31, 32, 50], "displai": [4, 8, 12, 28, 31, 32, 45, 46, 47, 49, 50, 819, 826, 828, 833, 844, 852], "end": [4, 8, 45, 46, 57, 80, 126, 228, 284, 353, 372, 375, 377, 378, 423, 452, 474, 484, 486, 487, 629, 632, 806, 812, 819, 820, 825, 828, 834, 840, 845, 847, 848, 855, 868, 873], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 217, 631, 830], "ivy_model": [4, 5, 8, 12, 48], "ivy_alexnet": 4, "quick": [4, 20, 32, 820, 822, 842, 853], "trace_graph": [4, 5, 8, 12, 24, 25, 26, 27, 31, 32, 34, 35, 36, 37, 38, 39, 48, 794, 812, 849, 854, 862], "moment": [4, 57, 59, 80, 82, 376, 433, 615, 616, 621, 635, 796, 810, 818, 825, 855, 863, 864], "cost": [4, 59, 82, 615, 616, 619, 621, 622, 623, 635, 640, 715, 716, 717, 806, 829, 847, 868], "arg": [4, 6, 8, 9, 10, 11, 12, 16, 18, 26, 27, 29, 31, 32, 36, 37, 38, 49, 52, 74, 96, 106, 122, 203, 213, 601, 628, 629, 631, 634, 771, 773, 788, 789, 792, 793, 794, 798, 801, 805, 810, 812, 824, 829, 830, 833, 839, 840, 841, 847, 849, 853, 863, 864, 865], "asarrai": [4, 5, 8, 11, 12, 46, 53, 57, 58, 69, 76, 80, 81, 92, 127, 385, 514, 515, 545, 556, 560, 561, 591, 592, 593, 629, 634, 636, 645, 646, 650, 750, 754, 833, 838, 841, 842], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 22, 31, 46, 47, 50, 53, 57, 66, 76, 80, 89, 137, 138, 141, 193, 194, 195, 211, 382, 508, 509, 511, 512, 629, 631, 637, 643, 688, 738, 739, 740, 741, 791, 792, 793, 794, 795, 796, 797, 810, 849, 855, 857, 875], "output": [4, 5, 7, 8, 9, 10, 12, 22, 28, 29, 31, 32, 44, 45, 46, 48, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 102, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 152, 154, 179, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 322, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 364, 365, 366, 367, 369, 372, 374, 375, 376, 377, 378, 381, 382, 383, 385, 387, 388, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 417, 419, 420, 421, 423, 424, 426, 427, 428, 430, 432, 435, 436, 438, 441, 442, 443, 444, 446, 447, 450, 452, 453, 454, 455, 456, 457, 458, 459, 460, 467, 468, 469, 472, 474, 475, 476, 477, 478, 481, 482, 483, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 496, 497, 498, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 515, 520, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 539, 540, 541, 545, 546, 547, 549, 553, 562, 569, 576, 577, 578, 602, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 641, 642, 643, 644, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 680, 681, 682, 683, 684, 685, 686, 688, 689, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 709, 710, 711, 712, 714, 731, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 771, 776, 791, 792, 805, 806, 812, 814, 819, 820, 822, 823, 824, 826, 827, 829, 830, 831, 832, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 846, 849, 851, 853, 854, 855, 857, 863, 864, 871], "softmax": [4, 6, 7, 12, 16, 29, 31, 32, 47, 51, 61, 72, 73, 84, 377, 454, 626, 636, 663, 666, 788, 812], "pass": [4, 6, 7, 8, 11, 12, 13, 14, 16, 18, 22, 29, 31, 32, 38, 44, 45, 47, 49, 50, 56, 57, 72, 74, 79, 80, 95, 103, 122, 123, 125, 157, 179, 194, 213, 228, 274, 375, 377, 378, 381, 382, 387, 421, 454, 474, 501, 503, 508, 528, 529, 562, 628, 630, 631, 632, 634, 640, 715, 716, 771, 773, 777, 784, 789, 793, 794, 796, 797, 801, 805, 810, 812, 816, 818, 820, 823, 824, 825, 827, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 844, 847, 855, 863, 864, 865, 868], "argsort": [4, 12, 69, 92, 646, 755, 841], "descend": [4, 12, 69, 92, 637, 646, 687, 688, 753, 756], "top": [4, 12, 15, 20, 29, 31, 32, 45, 46, 57, 64, 80, 319, 369, 377, 378, 452, 494, 545, 634, 700, 812, 819, 820, 829, 834, 841, 843, 844, 847, 852, 853, 870, 874], "logit": [4, 5, 6, 7, 8, 12, 45, 46, 47, 48, 57, 63, 80, 86, 367, 382, 508, 511, 638, 696, 698, 788, 812, 863], "gather": [4, 12, 45, 57, 58, 80, 81, 330, 331, 332, 369, 553, 555, 634, 877], "to_list": [4, 12, 58, 81, 634], "arrai": [4, 5, 6, 7, 9, 10, 12, 13, 14, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 43, 44, 45, 46, 47, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 97, 98, 100, 103, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 152, 153, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 169, 171, 172, 173, 175, 177, 178, 179, 180, 186, 196, 197, 201, 206, 208, 210, 213, 214, 218, 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, 321, 322, 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, 367, 369, 372, 373, 375, 376, 377, 378, 381, 382, 383, 385, 387, 388, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 411, 412, 413, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 467, 468, 469, 470, 471, 472, 473, 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, 507, 508, 509, 510, 511, 512, 513, 514, 515, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 537, 538, 540, 541, 544, 545, 546, 547, 548, 549, 552, 553, 554, 555, 556, 558, 559, 560, 561, 562, 564, 565, 566, 567, 568, 569, 571, 572, 574, 575, 576, 577, 578, 580, 581, 587, 588, 590, 591, 592, 593, 594, 595, 597, 598, 599, 600, 601, 602, 610, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 724, 725, 726, 727, 730, 731, 735, 736, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 771, 773, 778, 784, 791, 792, 793, 794, 797, 801, 805, 806, 808, 812, 816, 818, 819, 820, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 837, 838, 839, 840, 841, 842, 844, 845, 846, 847, 848, 849, 850, 852, 853, 854, 855, 857, 864, 865, 868, 869, 870, 872, 876, 877], "282": [4, 12], "281": [4, 12, 45, 47], "285": [4, 12, 80], "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], "dropout": [4, 61, 84, 375, 399, 400, 401, 636, 661, 663, 666, 792, 852], "torch_output": [4, 8, 9, 12], "dim": [4, 12, 47, 57, 74, 76, 80, 141, 313, 369, 375, 378, 393, 403, 404, 405, 408, 416, 474, 496, 629, 636, 649, 656, 657, 662, 778, 792, 812, 829, 841, 842, 847], "torch_class": [4, 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852, 861, 863, 876], "showcas": [20, 812], "real": [20, 28, 56, 57, 70, 79, 80, 93, 102, 112, 115, 118, 142, 143, 220, 221, 222, 223, 225, 226, 227, 228, 229, 238, 240, 241, 243, 245, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 270, 273, 275, 276, 278, 282, 283, 284, 286, 287, 288, 289, 290, 291, 293, 294, 335, 336, 342, 343, 344, 354, 372, 375, 376, 398, 419, 420, 429, 430, 626, 629, 632, 637, 644, 647, 672, 673, 674, 678, 685, 687, 688, 691, 694, 747, 760, 762, 763, 764, 765, 827, 872], "world": [20, 28, 820, 872], "beginn": [20, 813, 870], "got": [20, 43, 833], "cover": [20, 31, 57, 80, 375, 412, 413, 414, 818, 823, 824, 826, 829, 831, 832, 837, 838, 844, 847, 848], "familiar": [20, 21, 22, 818, 819], "concept": [20, 21, 22], "turn": [20, 21, 24, 34, 61, 84, 97, 98, 399, 400, 401, 636, 659, 792, 819, 826, 827, 830, 831, 841, 844, 861], "unus": [20, 21, 24, 831, 840], "part": [20, 21, 24, 53, 56, 57, 79, 80, 85, 102, 112, 115, 118, 145, 146, 147, 253, 257, 280, 328, 329, 355, 369, 372, 375, 376, 378, 387, 419, 430, 484, 532, 626, 629, 632, 637, 673, 674, 773, 812, 818, 819, 820, 821, 823, 826, 829, 835, 837, 840, 841, 844, 845, 847, 849, 850, 854, 855, 863, 864, 865, 868, 870, 875, 876, 877], "lazi": [20, 21, 24, 27, 34, 37, 38, 49], "decor": [20, 21, 26, 28, 29, 37, 49, 539, 634, 776, 778, 784, 816, 823, 824, 827, 829, 830, 834, 837, 840, 841, 842, 847], "kornia": [20, 21, 28, 31, 32, 45, 49, 812, 864], "roundup": 22, "indep": [22, 31], "proof": [22, 31], "delv": [22, 32, 812], "theori": [22, 814, 826], "esenti": [22, 31], "abstract": [22, 31, 32, 791, 796, 812, 827, 829, 840, 841, 844, 847, 853, 859, 868, 870, 872, 873, 877], "quirk": [22, 31], "perk": [22, 31, 812, 824, 827], "under": [22, 31, 32, 57, 377, 456, 457, 805, 812, 818, 819, 822, 823, 830, 831, 832, 835, 841, 842, 844, 847, 848, 849, 852, 854, 855, 863, 864, 870, 873, 877], "hood": [22, 31, 32, 812, 822, 830, 831, 835, 841, 844, 847, 848, 849, 852, 854, 863, 864, 877], "appropi": 22, "string": [22, 31, 32, 47, 57, 58, 61, 74, 80, 84, 150, 151, 163, 170, 192, 193, 194, 195, 196, 198, 207, 214, 215, 219, 375, 376, 378, 418, 422, 430, 484, 495, 524, 543, 630, 631, 634, 636, 637, 649, 650, 651, 652, 654, 656, 658, 674, 771, 773, 777, 805, 806, 825, 826, 828, 829, 830, 833, 841, 849, 852], "simplest": [22, 819, 831, 844, 847], "interact": [22, 31, 46, 49, 818, 869, 870, 875], "submodul": [22, 31, 45, 47, 102, 103, 626, 627, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 788, 789, 791, 792, 794, 795, 796, 797, 818, 819, 820, 823, 826, 828, 830, 834, 837, 838, 844, 848, 849, 853, 857], "likewis": [22, 27, 31, 38, 812, 820, 827, 829, 832, 836, 837, 841, 847, 852, 863, 864, 876], "nativearrai": [22, 31, 32, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 68, 70, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 102, 106, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 127, 128, 129, 131, 136, 137, 138, 139, 140, 141, 143, 145, 146, 149, 152, 153, 154, 155, 158, 159, 160, 161, 162, 163, 165, 168, 171, 172, 173, 175, 177, 179, 180, 186, 196, 197, 213, 214, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 313, 314, 317, 318, 322, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 367, 369, 372, 373, 375, 376, 377, 378, 381, 382, 383, 385, 387, 389, 390, 391, 392, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 407, 408, 409, 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691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 718, 719, 720, 721, 725, 726, 727, 730, 735, 736, 737, 738, 739, 740, 741, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 797, 824, 827, 831, 833, 836, 837, 838, 840, 841, 845, 846, 849, 851, 857], "alia": [22, 31, 335, 336, 372, 627, 818, 841, 862, 865], "lastli": [22, 31, 824], "subclass": [22, 31, 32, 838, 841, 847, 864], "dict": [22, 31, 32, 45, 49, 52, 58, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 125, 134, 136, 141, 143, 149, 153, 155, 166, 167, 168, 172, 173, 180, 196, 199, 200, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 302, 303, 304, 305, 306, 307, 309, 310, 311, 313, 325, 334, 335, 336, 337, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 369, 378, 398, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463, 464, 468, 469, 484, 490, 492, 493, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 535, 537, 538, 540, 541, 545, 546, 547, 548, 549, 550, 551, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 572, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 624, 628, 630, 631, 634, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 691, 696, 697, 698, 699, 703, 706, 707, 708, 709, 710, 713, 714, 718, 719, 721, 724, 725, 726, 727, 729, 730, 731, 735, 736, 738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 773, 774, 789, 792, 794, 801, 806, 824, 827, 852, 853, 857, 863, 864, 865], "recurs": [22, 31, 32, 45, 47, 52, 74, 75, 166, 167, 199, 200, 376, 448, 550, 551, 557, 630, 631, 634, 641, 718, 719, 722, 728, 729, 730, 771, 819, 823, 826, 827, 834, 837, 840, 853, 855], "fashion": [22, 778, 844, 864], "native_arrai": [22, 31, 32, 53, 54, 56, 76, 78, 79, 80, 81, 85, 92, 110, 113, 136, 139, 141, 143, 149, 152, 153, 154, 155, 163, 168, 175, 197, 206, 214, 230, 234, 239, 240, 241, 243, 247, 251, 259, 260, 268, 273, 276, 279, 282, 287, 335, 336, 363, 372, 377, 378, 458, 484, 490, 494, 534, 537, 564, 565, 568, 599, 626, 629, 630, 631, 632, 634, 636, 637, 638, 639, 643, 644, 647, 648, 650, 651, 658, 666, 669, 673, 674, 679, 680, 684, 688, 689, 691, 694, 696, 698, 699, 706, 738, 747, 756, 762, 765, 767, 773, 783, 801, 816, 834, 842, 844], "data_class": [22, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 395, 396, 545, 549, 687, 712], "low": [22, 31, 34, 50, 57, 61, 66, 80, 84, 89, 375, 418, 422, 636, 643, 649, 650, 651, 652, 654, 656, 658, 739, 741, 778, 827, 833, 840, 841, 847, 849, 866, 868, 870, 871, 872, 874, 876], "c": [22, 31, 37, 46, 47, 53, 57, 58, 59, 61, 64, 70, 76, 77, 79, 80, 81, 82, 84, 85, 87, 91, 93, 97, 98, 116, 127, 128, 138, 141, 165, 168, 223, 234, 240, 241, 261, 262, 264, 273, 276, 284, 291, 375, 376, 378, 381, 387, 389, 390, 391, 392, 403, 408, 424, 426, 428, 429, 431, 443, 462, 463, 464, 474, 492, 496, 501, 502, 503, 506, 524, 537, 545, 546, 547, 548, 556, 560, 561, 591, 600, 615, 616, 619, 621, 622, 623, 626, 629, 630, 632, 634, 635, 636, 637, 639, 641, 644, 645, 647, 650, 651, 652, 653, 654, 655, 657, 672, 674, 676, 706, 710, 718, 721, 725, 726, 727, 729, 730, 735, 736, 747, 752, 758, 759, 764, 766, 795, 805, 806, 813, 819, 822, 825, 826, 827, 831, 837, 839, 848, 849, 850, 852, 855, 857, 858, 860, 861, 864, 866, 870, 874, 875, 877], "fundament": [22, 31, 828, 841, 847, 849, 859, 870], "signatur": [22, 31, 378, 387, 484, 522, 829, 830, 831, 832, 836, 840, 844, 845, 847, 860, 867, 876], "matmul": [22, 31, 32, 48, 62, 85, 376, 446, 614, 634, 637, 687, 825, 844, 845, 849], "to_n": [22, 31, 32, 43, 52, 75, 849], "jaxlib": [22, 28, 46, 801, 819, 824, 829, 830, 836, 845, 849, 851], "xla_extens": [22, 28, 801, 824, 829, 830, 836, 845, 849, 851], "arrayimpl": [22, 28, 801], "disabl": [22, 31, 57, 80, 378, 492, 794, 810, 826], "array_mod": [22, 31, 578, 602, 634, 846], "set_array_mod": [22, 31, 602, 634, 846], "ultim": [22, 31, 863], "sigmoid": [22, 31, 32, 43, 51, 57, 73, 80, 301, 367, 382, 508, 626, 788, 849, 852, 853], "z": [22, 31, 32, 44, 45, 53, 56, 57, 58, 62, 63, 66, 68, 70, 76, 79, 80, 81, 85, 86, 87, 89, 93, 102, 103, 137, 138, 140, 141, 201, 223, 224, 228, 230, 233, 235, 240, 251, 252, 255, 256, 257, 259, 260, 265, 267, 269, 270, 271, 272, 280, 289, 300, 301, 335, 336, 338, 367, 372, 377, 387, 453, 455, 456, 457, 458, 459, 465, 469, 480, 521, 522, 525, 532, 537, 549, 552, 553, 560, 561, 577, 590, 592, 593, 601, 614, 629, 631, 632, 634, 637, 638, 639, 641, 643, 644, 645, 647, 668, 677, 682, 683, 687, 694, 696, 697, 698, 699, 721, 725, 727, 735, 739, 740, 741, 744, 749, 759, 760, 762, 763, 764, 791, 812, 825, 827, 830, 831, 849, 851, 863], "divid": [22, 27, 31, 32, 48, 56, 57, 58, 64, 74, 79, 80, 87, 102, 103, 247, 381, 454, 501, 502, 503, 506, 592, 632, 634, 639, 708, 824, 827, 831, 835, 844], "exp": [22, 31, 32, 56, 57, 79, 80, 116, 118, 245, 265, 278, 301, 367, 375, 377, 403, 408, 457, 626, 632, 637, 685, 839, 841], "entir": [22, 31, 32, 34, 47, 57, 70, 71, 74, 80, 81, 93, 94, 213, 243, 245, 285, 286, 335, 336, 372, 375, 378, 387, 399, 400, 401, 484, 525, 558, 631, 632, 647, 648, 760, 761, 762, 763, 764, 765, 766, 767, 768, 792, 806, 818, 819, 820, 823, 824, 827, 829, 831, 833, 840, 841, 842, 844, 847, 849, 852, 853, 854, 855, 860, 861, 864, 870, 876, 877], "congratul": [22, 28], "independ": [22, 32, 57, 66, 80, 89, 223, 240, 273, 283, 381, 382, 506, 508, 632, 637, 643, 668, 686, 738, 812, 823, 829, 831, 838, 849, 854, 864, 868], "div": [23, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 865], "sub": [23, 24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 57, 62, 64, 74, 75, 79, 80, 81, 85, 87, 103, 272, 376, 378, 387, 430, 470, 479, 499, 528, 529, 557, 634, 637, 639, 640, 671, 691, 708, 715, 716, 717, 818, 820, 822, 827, 833, 841, 842, 844, 851, 852, 853, 865, 866], "with_numpi": 23, "reproduc": [23, 48, 61, 84, 636, 659, 776, 777, 778, 779, 784, 816, 823, 834], "x_": [23, 33, 98, 284, 632, 865], "66391283": 23, "12516928": 23, "38367081": 23, "03102401": 23, "76419425": 23, "52797794": 23, "90346956": 23, "61316347": 23, "27585283": 23, "66309303": 23, "ivy_repo": 23, "sever": [23, 24, 33, 34, 36, 37, 38, 57, 80, 97, 375, 376, 389, 390, 391, 392, 444, 776, 819, 820, 845, 855, 868, 874], "pro": [23, 24, 25, 33, 34, 35, 36, 37, 38], "pick": [24, 34, 791], "trigger": [24, 34, 794, 818, 835], "unif": [24, 26, 27, 34, 36, 813, 851, 860, 866, 876], "55563945": 24, "65538704": 24, "14150524": 24, "46951997": 24, "30220294": 24, "14739668": 24, "57017946": 24, "91962677": 24, "51029003": 24, "59644395": 24, "constitu": [24, 34, 74, 854], "5556394": 24, "655387": 24, "1415051": 24, "4695197": 24, "3022028": 24, "1473966": 24, "5701794": 24, "91962665": 24, "51028997": 24, "5964439": 24, "985": 24, "000": [24, 79, 274, 776, 816, 828, 834], "On": [24, 31, 32, 819, 829, 830, 835, 841, 844, 847, 850, 854], "hand": [24, 56, 376, 446, 776, 812, 823, 829, 830, 835, 837, 844, 855], "learnt": [25, 35], "ivy_norm": 25, "jax_norm": [25, 31, 32], "wider": [25, 35, 585, 608, 634, 829, 846, 876], "avoid": [25, 35, 37, 57, 64, 80, 240, 245, 247, 263, 273, 377, 378, 381, 454, 462, 463, 464, 470, 472, 474, 475, 476, 479, 483, 490, 499, 501, 502, 503, 539, 555, 557, 580, 585, 608, 632, 634, 639, 702, 703, 704, 706, 708, 709, 711, 713, 778, 779, 819, 820, 825, 826, 827, 828, 829, 833, 838, 841, 844, 845, 846, 847, 870], "act": [25, 35, 57, 80, 298, 363, 373, 820, 831, 846, 855, 877], "shorthand": [25, 35, 37, 844], "pair": [25, 35, 45, 57, 61, 80, 84, 228, 247, 320, 362, 369, 372, 375, 409, 418, 420, 422, 632, 636, 637, 649, 650, 651, 652, 654, 656, 658, 666, 668, 806], "93968587": 25, "26075466": 25, "22723222": 25, "06276492": 25, "47426987": 25, "72835908": 25, "71737559": 25, "50411096": 25, "65419174": 25, "15576624": 25, "implic": [25, 35, 36, 39, 827], "satisfi": [26, 27, 28, 29, 45, 47, 50, 57, 375, 376, 398, 430, 829, 831], "fw": [26, 27, 28, 29, 61, 84, 387, 522, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 773, 819, 844], "mxnet": [26, 27, 28, 29, 209, 631, 801, 818, 819, 860, 877], "einop": [26, 27, 28, 29, 45, 47, 50, 58, 81, 545, 546, 547, 634, 829, 860], "miniconda": [26, 27, 28, 29], "multienv": [26, 27, 28, 29], "site": [26, 27, 28, 29, 871], "psutil": [26, 27, 28, 29, 45, 47, 50], "termcolor": [26, 27, 28, 29, 45, 47, 50, 74, 103], "colorama": [26, 27, 28, 29, 45, 47], "535": [26, 27, 28, 29, 51, 73, 118, 626, 833], "diskcach": [26, 27, 28, 29, 45], "auth": [26, 27, 28, 29], "urllib3": [26, 27, 28, 29, 45], "pyvi": [26, 27, 28, 29, 31, 32], "dill": [26, 27, 28, 29, 45], "astunpars": [26, 27, 28, 29], "cloudpickl": [26, 27, 28, 29], "gast": [26, 27, 28, 29], "wheel": [26, 27, 28, 29, 45, 47, 50, 859], "six": [26, 27, 28, 29, 45, 50, 819, 847], "cachetool": [26, 27, 28, 29], "pyasn1": [26, 27, 28, 29], "rsa": [26, 27, 28, 29], "jinja2": [26, 27, 28, 29], "jsonpickl": [26, 27, 28, 29], "networkx": [26, 27, 28, 29, 50], "charset": [26, 27, 28, 29, 45], "idna": [26, 27, 28, 29, 45], "certifi": [26, 27, 28, 29, 45], "2017": [26, 27, 28, 29, 45, 636, 663], "jedi": [26, 27, 28, 29], "inlin": [26, 27, 28, 29, 826], "prompt": [26, 27, 28, 29, 818, 820], "toolkit": [26, 27, 28, 29, 870, 871, 877], "pygment": [26, 27, 28, 29], "traitlet": [26, 27, 28, 29], "exceptiongroup": [26, 27, 28, 29], 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80, 81, 85, 96, 97, 375, 376, 397, 398, 402, 403, 404, 407, 408, 409, 419, 420, 426, 430, 504, 505, 507, 540, 541, 562, 634, 637, 678, 694, 737, 792, 796, 845], "slow": [26, 36, 814, 819, 826], "34431235": [26, 27], "51129461": [26, 27], "06686894": [26, 27], "36452447": [26, 27], "98795534": [26, 27], "15493582": [26, 27], "91630631": [26, 27], "41939619": [26, 27], "78909753": [26, 27], "19475674": [26, 27], "norm_trac": 26, "norm_tran": [26, 36], "know": [26, 27, 36, 37, 38, 68, 645, 749, 750, 751, 752, 812, 814, 818, 820, 830, 838, 842, 844, 847, 861, 865, 871], "07": [27, 45, 47, 59, 63, 79, 82, 86, 89, 228, 261, 264, 265, 284, 375, 407, 605, 615, 616, 618, 619, 620, 621, 632, 634, 635, 638, 697, 698, 740, 793, 796, 853], "981554": 27, "happen": [27, 31, 32, 292, 632, 812, 819, 820, 821, 830, 840, 844, 852, 861, 863, 864], "wherea": [27, 38, 80, 375, 421, 820, 824, 827, 829, 830, 831, 836, 837, 844, 854, 867], "subtract": [27, 31, 32, 56, 79, 102, 103, 134, 378, 484, 629, 632, 824, 827, 831], "filelock": [28, 45], "extens": [28, 45, 56, 62, 79, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 139, 142, 143, 144, 145, 146, 148, 149, 155, 165, 168, 180, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 335, 336, 338, 372, 375, 378, 387, 419, 492, 496, 522, 629, 630, 632, 637, 639, 644, 645, 646, 647, 648, 667, 668, 669, 670, 671, 673, 674, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 693, 694, 700, 702, 703, 704, 706, 707, 709, 710, 714, 744, 745, 747, 748, 749, 750, 751, 752, 753, 756, 760, 761, 762, 763, 764, 765, 766, 767, 768, 817, 819, 820, 832, 834, 835, 844, 867, 870, 877], "sympi": [28, 860], "fsspec": [28, 45], "mpmath": 28, "often": [28, 57, 377, 452, 817, 823, 833, 836, 837, 841, 844, 855, 861, 871, 874, 877], "fortun": [28, 29, 823], "everyth": [28, 46, 805, 812, 818, 819, 820, 821, 822, 828, 831, 840, 841, 842, 844, 850, 855, 856, 861], "practic": [28, 820, 825, 828, 841, 843, 873], "everi": [28, 31, 32, 37, 45, 53, 57, 58, 80, 81, 135, 136, 301, 335, 336, 349, 367, 372, 375, 378, 412, 413, 414, 421, 498, 534, 629, 634, 818, 820, 823, 825, 826, 828, 829, 831, 835, 836, 837, 838, 840, 841, 842, 844, 849, 851, 853, 863, 864, 865, 870], "jax_kornia": [28, 31, 32, 812, 864], "though": [28, 817, 818, 820, 829, 830, 832, 837, 840, 841, 847, 852, 855], "000000000034": [28, 31, 32, 812, 864], "raw_img": [28, 31, 32, 812, 864], "sharp": [28, 31, 32, 812], "prefer": [28, 31, 32, 247, 632, 819, 827, 833, 834, 838, 841, 856, 870], "whole": [29, 57, 80, 378, 381, 491, 504, 505, 507, 820, 826, 835], "full": [29, 57, 62, 80, 84, 85, 97, 98, 100, 165, 252, 260, 323, 324, 325, 326, 327, 369, 376, 377, 378, 449, 450, 456, 457, 485, 488, 579, 588, 603, 611, 629, 630, 632, 634, 636, 637, 651, 653, 654, 655, 657, 680, 684, 686, 687, 777, 784, 812, 819, 820, 826, 829, 832, 833, 836, 837, 841, 844, 847, 849, 855, 860, 861, 868, 870, 876], "complex": [29, 31, 32, 45, 51, 56, 57, 62, 70, 73, 77, 79, 80, 85, 93, 110, 111, 112, 113, 114, 115, 116, 117, 118, 142, 143, 158, 172, 181, 187, 220, 221, 222, 223, 224, 225, 226, 229, 237, 238, 240, 241, 243, 245, 253, 254, 255, 256, 257, 261, 262, 263, 264, 273, 275, 276, 278, 280, 283, 284, 285, 286, 287, 290, 291, 295, 300, 301, 303, 338, 343, 344, 367, 372, 375, 376, 387, 398, 409, 419, 420, 424, 429, 430, 431, 442, 444, 530, 531, 592, 593, 626, 629, 630, 632, 634, 637, 644, 647, 672, 673, 674, 678, 685, 687, 689, 691, 694, 747, 762, 763, 765, 777, 788, 806, 815, 818, 821, 826, 829, 831, 838, 841, 844, 845, 847, 852, 853, 854, 855, 857, 864, 866, 868, 870, 872, 876, 877], "neccessari": 29, "set_random_se": [29, 48], "301436": 29, "_c": 29, "0x7f252c392390": 29, "flatten": [29, 31, 32, 45, 47, 50, 57, 58, 62, 64, 67, 68, 80, 81, 85, 87, 90, 91, 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295, 367, 628, 632, 812, 817, 818, 819, 822, 823, 834, 840, 843, 844, 855, 860, 861, 870], "regress": [30, 870, 877], "checkout": [31, 46, 820, 823, 844], "f705efe7cb5d18df17ce6c1e20f04d0eb4933f48": 31, "theoret": 31, "aspect": [31, 32, 813, 839, 852, 870], "easiest": [31, 812, 814, 819, 856], "defer": [31, 32, 818, 824, 829, 830, 837, 840, 841, 844, 876], "similarli": [31, 44, 139, 147, 223, 328, 335, 336, 369, 372, 629, 632, 825, 829, 841, 847, 851, 876], "essenc": [31, 871, 876], "becom": [31, 57, 80, 97, 346, 372, 378, 464, 639, 699, 801, 820, 821, 827, 829, 831, 833, 840, 855, 859, 861, 863], "slide": [31, 57, 61, 80, 84, 375, 394, 395, 396, 412, 413, 414, 415, 418, 422, 636, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 792], "regressor": [31, 32, 812], "input_dim": [31, 32, 46, 812], "output_dim": [31, 32, 46, 812], "linear0": [31, 32, 43, 812, 852, 853], "linear1": [31, 32, 43, 812, 852, 853], "instanti": [31, 32, 784, 832], "adam": [31, 32, 43, 47, 59, 82, 536, 615, 616, 621, 634, 635, 796, 812, 852, 853, 854, 870], "n_training_exampl": [31, 32, 812], "2000": [31, 32, 80, 314, 369, 812], "random_norm": [31, 32, 61, 62, 66, 84, 85, 89, 545, 634, 636, 637, 643, 651, 653, 654, 655, 657, 658, 662, 687, 812], "linspac": [31, 32, 53, 76, 126, 629, 812, 836, 847, 849, 877], "pred": [31, 32, 46, 47, 57, 63, 80, 86, 377, 453, 456, 638, 696, 697, 698, 812, 827, 837, 840], "gradient": [31, 32, 45, 47, 57, 80, 97, 213, 364, 372, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 631, 640, 715, 716, 717, 773, 784, 796, 812, 822, 845, 852, 853, 855, 870], "grad": [31, 32, 43, 47, 615, 635, 796, 812, 839, 852, 853, 854], "execute_with_gradi": [31, 32, 43, 47, 635, 812, 852, 853, 854, 855], "lambda": [31, 32, 48, 50, 80, 123, 125, 297, 307, 544, 557, 617, 618, 620, 625, 628, 634, 635, 637, 641, 673, 725, 726, 730, 812, 818, 837, 838, 839, 842, 847, 849, 852], "2d": [31, 32, 47, 57, 80, 97, 313, 369, 375, 376, 378, 387, 390, 391, 399, 400, 442, 449, 463, 473, 522, 792, 810, 812, 841, 847], "5f": [31, 32, 812], "nonetheless": [31, 32], "extract": [31, 32, 39, 46, 57, 80, 98, 378, 467, 493, 841, 843, 845, 866, 870, 871, 876], "gc": [31, 32, 557, 634], "decompos": [31, 32, 57, 80, 97, 100, 323, 324, 325, 326, 327, 348, 355, 369, 372, 376, 440, 445, 448, 451, 841, 854], "said": [31, 32, 778, 845, 861, 863], "otherwis": [31, 32, 49, 52, 53, 54, 56, 57, 58, 61, 62, 67, 68, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 123, 126, 128, 129, 134, 136, 137, 138, 141, 143, 149, 152, 153, 155, 156, 158, 159, 160, 161, 162, 171, 175, 179, 180, 196, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 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[31, 32], "differenti": [31, 32, 295, 365, 366, 367, 374, 870], "chosen": [31, 32, 50, 100, 126, 228, 629, 632, 644, 748, 818, 828, 841], "plai": [31, 32, 377, 456, 812, 815, 819, 821, 824, 830, 834, 841, 844, 854, 870, 873], "role": [31, 32, 812, 815, 820, 821, 830, 841, 850, 871, 873, 877], "dl": [31, 32], "effortlessli": [31, 32], "previous": [31, 32, 603, 634, 801, 818, 819, 825, 837, 839, 844, 849], "default_devic": [31, 32, 206, 209, 210, 211, 217, 218, 631, 830, 833, 834], "as_n": [31, 32, 54, 55, 74, 77, 78, 158, 159, 160, 161, 162, 163, 169, 196, 197, 630, 631, 829], "certainli": [31, 32, 812, 860, 876], "upon": [31, 32, 49, 810, 820, 821, 831, 840, 844, 847, 855, 869, 870], "unnecessari": [31, 32, 841], "extend": [31, 32, 57, 80, 378, 387, 484, 525, 825, 826, 829, 832, 833, 836, 841, 845, 855, 867, 870, 876], "infrastructur": [31, 32, 866, 872, 873], "least": [31, 56, 57, 62, 79, 80, 240, 258, 273, 375, 378, 387, 403, 408, 462, 463, 464, 473, 475, 522, 632, 637, 644, 677, 747, 812, 820, 824, 828, 829, 830, 831, 837, 840, 844, 864], "coco": 31, "seamlessli": [32, 844], "therefor": [32, 37, 53, 56, 57, 62, 79, 80, 126, 127, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 149, 155, 171, 175, 179, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 240, 241, 243, 245, 246, 247, 251, 252, 253, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 269, 270, 273, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 313, 328, 329, 335, 336, 338, 341, 369, 372, 375, 376, 378, 387, 394, 395, 396, 397, 399, 400, 401, 407, 412, 413, 414, 419, 421, 430, 477, 484, 485, 487, 492, 496, 497, 522, 525, 529, 538, 546, 547, 552, 556, 558, 560, 562, 576, 591, 595, 600, 624, 629, 630, 632, 634, 635, 636, 637, 639, 642, 644, 645, 646, 647, 648, 650, 651, 652, 653, 654, 658, 659, 660, 663, 666, 667, 668, 669, 670, 671, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 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80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 97, 110, 111, 112, 113, 114, 115, 116, 117, 118, 134, 136, 141, 143, 149, 153, 155, 168, 172, 173, 180, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 294, 295, 296, 297, 299, 303, 304, 305, 306, 307, 309, 310, 311, 313, 334, 335, 336, 338, 340, 342, 350, 351, 357, 359, 361, 362, 363, 399, 400, 401, 419, 452, 453, 454, 455, 456, 457, 458, 459, 468, 469, 490, 492, 494, 496, 501, 503, 504, 505, 507, 509, 522, 523, 524, 525, 534, 537, 538, 540, 541, 545, 546, 547, 548, 549, 552, 553, 556, 558, 560, 561, 562, 564, 565, 568, 576, 577, 591, 592, 593, 595, 597, 599, 600, 613, 619, 624, 640, 641, 650, 651, 652, 653, 659, 660, 666, 667, 668, 673, 674, 675, 676, 677, 678, 680, 682, 684, 685, 691, 696, 697, 698, 699, 703, 706, 707, 708, 709, 710, 713, 714, 715, 716, 720, 731, 738, 739, 740, 741, 743, 746, 749, 750, 751, 752, 753, 757, 758, 761, 763, 764, 766, 767, 768, 797, 824, 827, 839, 841, 853, 854, 855, 870], "un": [34, 170, 630, 829, 849], "partial_comp": 34, "time_funct": 34, "express": [34, 56, 57, 79, 80, 98, 221, 225, 227, 228, 237, 239, 279, 285, 290, 359, 372, 632, 798, 806, 832, 841, 849, 854, 870, 871], "maxim": [34, 837, 840, 849, 867, 868, 872, 873, 874], "conclud": [35, 845], "collect": [35, 45, 47, 49, 50, 52, 74, 75, 626, 631, 634, 635, 636, 638, 641, 642, 643, 731, 788, 792, 793, 794, 795, 796, 819, 828, 833, 834, 838, 839, 842, 844, 868, 870, 873], "norm_comp": [36, 37], "global": [36, 37, 47, 58, 74, 81, 103, 158, 159, 160, 161, 162, 211, 212, 213, 582, 583, 586, 592, 593, 605, 606, 609, 630, 631, 634, 784, 795, 801, 819, 824, 825, 828, 829, 830, 833, 837, 841, 849, 870], "b": [37, 51, 56, 57, 58, 61, 62, 70, 73, 76, 77, 78, 79, 80, 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"normalize_via_oper": 37, "determin": [37, 56, 57, 62, 64, 68, 71, 74, 79, 80, 81, 85, 92, 94, 97, 100, 102, 103, 132, 155, 157, 164, 170, 171, 172, 173, 175, 176, 177, 192, 202, 204, 205, 216, 221, 222, 223, 225, 226, 227, 228, 229, 230, 232, 233, 234, 235, 237, 238, 240, 243, 245, 247, 253, 254, 255, 256, 257, 261, 262, 263, 264, 265, 270, 273, 278, 282, 285, 286, 287, 288, 289, 290, 291, 294, 304, 308, 354, 359, 367, 372, 375, 376, 377, 378, 387, 411, 419, 430, 452, 453, 492, 496, 522, 534, 537, 558, 559, 563, 564, 565, 566, 567, 568, 595, 613, 629, 630, 631, 632, 634, 637, 639, 640, 645, 648, 667, 668, 669, 671, 675, 676, 677, 679, 680, 682, 683, 685, 686, 691, 693, 694, 700, 715, 716, 717, 749, 750, 751, 752, 753, 767, 768, 778, 784, 791, 795, 827, 829, 830, 832, 837, 841, 844, 846, 847, 859], "think": [37, 818, 820, 828, 831, 847, 871], "uniqu": [37, 47, 57, 58, 68, 80, 81, 91, 375, 376, 378, 423, 446, 483, 484, 498, 569, 634, 640, 641, 645, 715, 716, 717, 720, 724, 749, 750, 751, 752, 778, 812, 823, 827, 837, 841, 842, 843, 847, 855, 859, 873], "rule": [37, 54, 56, 57, 62, 77, 79, 80, 85, 152, 155, 178, 179, 180, 229, 240, 273, 275, 282, 284, 292, 294, 375, 378, 387, 419, 472, 522, 630, 632, 637, 639, 667, 668, 675, 679, 682, 686, 700, 778, 805, 823, 824, 827, 828, 829, 831, 835, 836, 837, 839, 844, 847, 871], "broadcast": [37, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 73, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93, 94, 97, 102, 103, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 141, 142, 143, 144, 145, 146, 148, 149, 152, 153, 154, 214, 220, 221, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 247, 251, 252, 254, 255, 256, 257, 259, 260, 261, 262, 263, 264, 265, 267, 268, 269, 270, 271, 272, 273, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 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739, 740, 741, 743, 744, 745, 746, 748, 752, 753, 757, 758, 760, 761, 762, 763, 764, 765, 766, 767, 768, 776, 778, 805, 827, 829, 831, 832, 833, 844, 845, 849], "elementwis": [37, 57, 65, 80, 88, 300, 302, 362, 367, 637, 642, 692, 737, 837, 845, 849], "taken": [37, 57, 62, 80, 85, 341, 372, 375, 420, 637, 671, 691, 818, 828, 841, 845, 854, 871], "account": [37, 47, 49, 57, 64, 80, 87, 287, 378, 474, 632, 639, 706, 791, 805, 819, 828, 832, 841, 845, 863], "fact": [37, 97, 820, 823, 828, 841, 844, 849, 852], "consum": [37, 773, 827, 828, 836, 842, 844], "thrown": [37, 562, 634, 819, 824, 830, 833, 835, 855], "doesn": [37, 562, 580, 634, 771, 792, 818, 819, 825, 827, 828, 829, 830, 831, 834, 835, 837, 839, 844, 847, 849, 855, 863, 868], "consider": [37, 818, 831, 836, 847, 859, 867, 868], "standalon": [38, 818, 824, 844, 857, 866, 871, 876, 877], "static": [38, 57, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 106, 107, 129, 319, 375, 396, 409, 414, 423, 445, 451, 490, 502, 595, 629, 636, 663, 682, 789, 794, 841, 846, 855, 869, 870, 871], "flow": [39, 827, 863, 870, 871], "statement": [39, 44, 828, 840, 844, 847, 855, 863, 864], "opposit": 39, "exclud": [39, 70, 80, 93, 126, 147, 328, 369, 523, 524, 629, 643, 741, 757, 776, 779, 801, 831, 849, 863], "todo": [40, 41, 42, 47, 50, 80, 524, 818, 829, 841], "aim": [43, 816, 820, 823, 834, 838, 841, 844, 848, 868, 870, 873], "interfac": [43, 76, 134, 629, 851, 854, 855, 857, 860, 866, 867, 868, 869, 870, 874, 877], "set_framework": [43, 50], "underneath": [43, 828, 868], "sai": [43, 818, 819, 834, 838, 851, 861, 878], "clip": [43, 56, 57, 64, 79, 80, 81, 87, 271, 272, 378, 467, 492, 493, 540, 541, 632, 634, 639, 827, 837, 839, 840, 852, 854, 867], "a_min": 43, "a_max": 43, "tensforflow": 43, "clip_by_valu": [43, 854, 867], "clip_value_min": 43, "clip_value_max": 43, "clamp": [43, 57, 80, 300, 367, 854], "49": [43, 47, 57, 66, 80, 84, 85, 287, 375, 376, 387, 397, 407, 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"valid_dtype": [[192, "valid-dtype"]], "asin": [[225, "asin"]], "as_ivy_dev": [[193, "as-ivy-dev"]], "unset_default_device": [[217, "unset-default-device"]], "unset_default_uint_dtype": [[191, "unset-default-uint-dtype"]], "percent_used_mem_on_dev": [[207, "percent-used-mem-on-dev"]], "acosh": [[222, "acosh"]], "split_func_call": [[213, "split-func-call"]], "atanh": [[229, "atanh"]], "tpu_is_available": [[216, "tpu-is-available"]], "as_native_dev": [[194, "as-native-dev"]], "unset_default_dtype": [[188, "unset-default-dtype"]], "type_promote_arrays": [[186, "type-promote-arrays"]], "handle_soft_device_variable": [[203, "handle-soft-device-variable"]], "num_gpus": [[205, "num-gpus"]], "set_split_factor": [[211, "set-split-factor"]], "total_mem_on_dev": [[215, "total-mem-on-dev"]], "num_ivy_arrays_on_dev": [[206, "num-ivy-arrays-on-dev"]], "set_default_int_dtype": [[184, "set-default-int-dtype"]], "unset_default_float_dtype": [[189, "unset-default-float-dtype"]], "dev_util": [[198, "dev-util"]], "asinh": [[226, "asinh"]], "unset_default_int_dtype": [[190, "unset-default-int-dtype"]], "function_unsupported_devices": [[200, "function-unsupported-devices"]], "function_supported_devices": [[199, "function-supported-devices"]], "to_device": [[214, "to-device"]], "abs": [[220, "abs"]], "angle": [[224, "angle"]], "set_default_uint_dtype": [[185, "set-default-uint-dtype"]], "unset_default_complex_dtype": [[187, "unset-default-complex-dtype"]], "get_all_ivy_arrays_on_dev": [[201, "get-all-ivy-arrays-on-dev"]], "gpu_is_available": [[202, "gpu-is-available"]], "split_factor": [[212, "split-factor"]], "acos": [[221, "acos"]], "set_default_device": [[209, "set-default-device"]], "Ivy as a Transpiler Introduction": [[49, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[49, "To-use-the-transpiler:"]], "Transpiler Interface": [[49, "Transpiler-Interface"]], "Telemetry": [[49, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[49, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[49, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[49, "3.-Transpile-Models-\ud83c\udf10"]], "End-to-End Training Pipeline in Ivy": [[47, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[47, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[47, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[47, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[47, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[47, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[47, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[47, "Plotting-the-training-metrics"]], "Save the trained Model": [[47, "Save-the-trained-Model"]], "Conversions": [[75, "module-ivy.data_classes.container.conversions"], [52, "module-ivy.data_classes.array.conversions"]], "Image": [[83, "module-ivy.data_classes.container.image"], [60, "module-ivy.data_classes.array.image"]], "Deepmind PerceiverIO on GPU": [[46, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[46, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[46, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[46, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[46, "Run-the-demo..."]], "\u2026with torch backend": [[46, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[46, "....with-tensorflow-backend"]], "\u2026with jax backend": [[46, "...with-jax-backend"]], "\u2026with numpy backend": [[46, "...with-numpy-backend"]], "Resnet 18": [[50, "Resnet-18"]], "HuggingFace Tensorflow DeiT": [[48, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[48, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "0.0: Unify": [[33, "0.0:-Unify"]], "Trace code": [[24, "Trace-code"]], "ODSC Ivy Demo": [[31, "ODSC-Ivy-Demo"]], "Ivy Backend Handler": [[31, "Ivy-Backend-Handler"], [22, "Ivy-Backend-Handler"]], "Data Structures": [[31, "Data-Structures"], [22, "Data-Structures"]], "Ivy Functional API": [[31, "Ivy-Functional-API"], [22, "Ivy-Functional-API"]], "Graph Tracer": [[31, "Graph-Tracer"]], "Any function": [[31, "Any-function"], [32, "Any-function"]], "Any library": [[31, "Any-library"], [32, "Any-library"]], "Any model": [[31, "Any-model"], [32, "Any-model"]], "Examples and Demos": [[3, "examples-and-demos"], [20, "examples-and-demos"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Unify code": [[23, "Unify-code"]], "Transpile code": [[25, "Transpile-code"]], "How to use decorators": [[27, "How-to-use-decorators"]], "Unify": [[27, "Unify"], [36, "Unify"], [37, "Unify"], [26, "Unify"], [38, "Unify"]], "Trace": [[27, "Trace"], [26, "Trace"]], "Transpile": [[27, "Transpile"], [36, "Transpile"], [37, "Transpile"], [26, "Transpile"], [38, "Transpile"]], "Quickstart": [[32, "Quickstart"]], "Get familiar with Ivy": [[32, "Get-familiar-with-Ivy"]], "Functional API": [[32, "Functional-API"]], "Stateful API": [[32, "Stateful-API"]], "Tracing code": [[32, "Tracing-code"]], "Learn the basics": [[21, "learn-the-basics"], [20, "learn-the-basics"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "0.2: Transpile": [[35, "0.2:-Transpile"]], "Compilation of a Basic Function": [[44, "Compilation-of-a-Basic-Function"]], "Installs \ud83d\udcbe": [[44, "Installs-\ud83d\udcbe"], [43, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[44, "Imports-\ud83d\udec3"], [43, "Imports-\ud83d\udec3"]], "Import Ivy compiler": [[44, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[44, "Function-compilation-\ud83d\udee0"]], "Set backend": [[44, "Set-backend"]], "Sample input": [[44, "Sample-input"]], "Define function to compile": [[44, "Define-function-to-compile"]], "Compile the function": [[44, "Compile-the-function"]], "Check results": [[44, "Check-results"], [44, "id1"]], "Compiling simple neural network \ud83e\udde0": [[44, "Compiling-simple-neural-network-\ud83e\udde0"]], "Define Model": [[44, "Define-Model"], [43, "Define-Model"]], "Create model": [[44, "Create-model"]], "Define input": [[44, "Define-input"]], "Compile network": [[44, "Compile-network"]], "Transpiling a haiku model to build on top": [[17, "Transpiling-a-haiku-model-to-build-on-top"]], "2.0: Kornia": [[40, "2.0:-Kornia"]], "Accelerating PyTorch models with JAX": [[13, "Accelerating-PyTorch-models-with-JAX"]], "Transpiling a PyTorch model to build on top": [[16, "Transpiling-a-PyTorch-model-to-build-on-top"]], "3.0: Perceiver": [[41, "3.0:-Perceiver"]], "0.1: Compile": [[34, "0.1:-Compile"]], "1.0: Lazy vs Eager": [[36, "1.0:-Lazy-vs-Eager"]], "Compile": [[36, "Compile"], [37, "Compile"], [38, "Compile"]], "Developing a convolutional network using Ivy": [[19, "Developing-a-convolutional-network-using-Ivy"]], "1.1: Framework Selection": [[37, "1.1:-Framework-Selection"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Data Preparation": [[5, "Data-Preparation"], [8, "Data-Preparation"], [12, "Data-Preparation"], [4, "Data-Preparation"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Importing Ivy": [[0, "Importing-Ivy"], [22, "Importing-Ivy"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Tutorials And Examples": [[20, "tutorials-and-examples"]], "Guides": [[20, "guides"], [15, "guides"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Imports": [[8, "Imports"], [12, "Imports"], [14, "Imports"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Load the image example \ud83d\uddbc\ufe0f": [[8, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[8, "Visualise-image"], [12, "Visualise-image"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [4, "Installation"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "Transpiling a Tensorflow model to build on top": [[18, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "1.3: Dynamic vs Static": [[39, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[39, "Dynamic"]], "Static": [[39, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[39, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[45, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[45, "Table-of-Contents"]], "Defining the model": [[45, "Defining-the-model"]], "Model construction": [[45, "Model-construction"]], "Some helper functions": [[45, "Some-helper-functions"]], "Transpiling the model": [[45, "Transpiling-the-model"]], "PyTorch pipeline": [[45, "PyTorch-pipeline"]], "Dataset download": [[45, "Dataset-download"]], "DataLoader": [[45, "DataLoader"]], "Training": [[45, "Training"]], "Basic Operations with Ivy": [[43, "Basic-Operations-with-Ivy"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[43, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[43, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[43, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[43, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[43, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[43, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[43, "Set-Backend-Framework"]], "Create Model": [[43, "Create-Model"]], "Create Optimizer": [[43, "Create-Optimizer"]], "Input and Target": [[43, "Input-and-Target"]], "Loss Function": [[43, "Loss-Function"]], "Training Loop": [[43, "Training-Loop"]], "Transpile any model": [[29, "Transpile-any-model"]], "Round up": [[29, "Round-up"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[6, "Comparing-the-results"], [7, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[6, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[6, "Conclusion"], [7, "Conclusion"]], "Transpile any library": [[28, "Transpile-any-library"]], "3.1: Stable Diffusion": [[42, "3.1:-Stable-Diffusion"]], "Write Ivy code": [[22, "Write-Ivy-code"]], "Contents": [[22, "Contents"]], "Installing Ivy": [[22, "Installing-Ivy"]], "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)"]], "Lazy vs Eager": [[26, "Lazy-vs-Eager"]], "Accelerating XGBoost with JAX": [[14, "Accelerating-XGBoost-with-JAX"]], "Tests": [[14, "Tests"]], "Loading the Data": [[14, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[14, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[14, "JAX-backend"]], "Tensorflow backend": [[14, "Tensorflow-backend"]], "PyTorch backend": [[14, "PyTorch-backend"]], "More exhaustive example": [[14, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[14, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[14, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[14, "Comparison-of-Metrics"]], "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"]], "1.2: As a Decorator": [[38, "1.2:-As-a-Decorator"]], "Write a model using Ivy": [[30, "Write-a-model-using-Ivy"]]}, "indexentries": {"_arraywithactivations (class in 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    Meta#

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

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