From 010419e7b546bd6b4015b2571c70605c2097b3d8 Mon Sep 17 00:00:00 2001
From: pytorchbot <soumith+bot@pytorch.org>
Date: Tue, 25 Jun 2024 23:07:17 +0000
Subject: [PATCH] Automated tutorials push

---
 .../introyt1_tutorial.ipynb                   |    4 +-
 .../template_tutorial.ipynb                   |    4 +-
 .../tensors_deeper_tutorial.ipynb             |    4 +-
 .../trainingyt.ipynb                          |    4 +-
 .../captumyt.ipynb                            |    4 +-
 .../tensorboardyt_tutorial.ipynb              |    4 +-
 .../autogradyt_tutorial.ipynb                 |    4 +-
 .../modelsyt_tutorial.ipynb                   |    4 +-
 _images/sphx_glr_coding_ddpg_001.png          |  Bin 24097 -> 27572 bytes
 .../sphx_glr_dqn_with_rnn_tutorial_001.png    |  Bin 29022 -> 30068 bytes
 .../sphx_glr_neural_style_tutorial_004.png    |  Bin 276016 -> 276047 bytes
 _images/sphx_glr_reinforcement_ppo_001.png    |  Bin 76203 -> 75723 bytes
 .../sphx_glr_reinforcement_q_learning_001.png |  Bin 35125 -> 44935 bytes
 ...x_glr_spatial_transformer_tutorial_001.png |  Bin 114702 -> 115646 bytes
 _images/sphx_glr_torchvision_tutorial_002.png |  Bin 878947 -> 882884 bytes
 _sources/advanced/coding_ddpg.rst.txt         |   42 +-
 .../dynamic_quantization_tutorial.rst.txt     |    6 +-
 .../advanced/neural_style_tutorial.rst.txt    |   82 +-
 .../numpy_extensions_tutorial.rst.txt         |    2 +-
 _sources/advanced/pendulum.rst.txt            | 2454 ++++++++---------
 .../advanced/semi_structured_sparse.rst.txt   | 1036 ++++---
 .../Intro_to_TorchScript_tutorial.rst.txt     |    4 +-
 .../basics/autogradqs_tutorial.rst.txt        |    4 +-
 .../basics/buildmodel_tutorial.rst.txt        |    2 +-
 .../beginner/basics/data_tutorial.rst.txt     |   59 +-
 .../basics/optimization_tutorial.rst.txt      |   95 +-
 .../basics/quickstart_tutorial.rst.txt        |   53 +-
 .../basics/saveloadrun_tutorial.rst.txt       |   65 +-
 .../basics/transforms_tutorial.rst.txt        |   47 +-
 .../beginner/blitz/autograd_tutorial.rst.txt  |    8 +-
 .../beginner/blitz/cifar10_tutorial.rst.txt   |   36 +-
 .../blitz/data_parallel_tutorial.rst.txt      |    2 +-
 .../blitz/neural_networks_tutorial.rst.txt    |    8 +-
 .../beginner/blitz/tensor_tutorial.rst.txt    |    2 +-
 _sources/beginner/chatbot_tutorial.rst.txt    |    2 +-
 .../beginner/data_loading_tutorial.rst.txt    |    4 +-
 .../beginner/dcgan_faces_tutorial.rst.txt     |   48 +-
 ...y_seq2seq_hybrid_frontend_tutorial.rst.txt |    2 +-
 .../examples_nn/polynomial_nn.rst.txt         |    2 +-
 .../examples_tensor/polynomial_numpy.rst.txt  |    2 +-
 _sources/beginner/fgsm_tutorial.rst.txt       |   12 +-
 .../flava_finetuning_tutorial.rst.txt         | 2248 +++++++--------
 .../former_torchies/nnft_tutorial.rst.txt     |    2 +-
 ..._frontend_through_example_tutorial.rst.txt |    2 +-
 .../hyperparameter_tuning_tutorial.rst.txt    | 1531 +++++-----
 .../introyt/autogradyt_tutorial.rst.txt       |   32 +-
 .../introyt/introyt1_tutorial.rst.txt         |   34 +-
 .../introyt/tensorboardyt_tutorial.rst.txt    |   69 +-
 .../introyt/tensors_deeper_tutorial.rst.txt   |   24 +-
 _sources/beginner/introyt/trainingyt.rst.txt  |  120 +-
 .../knowledge_distillation_tutorial.rst.txt   |  136 +-
 .../beginner/nlp/advanced_tutorial.rst.txt    |    4 +-
 .../nlp/deep_learning_tutorial.rst.txt        |    2 +-
 .../beginner/nlp/pytorch_tutorial.rst.txt     |    8 +-
 .../nlp/sequence_models_tutorial.rst.txt      |    4 +-
 .../nlp/word_embeddings_tutorial.rst.txt      |   12 +-
 _sources/beginner/nn_tutorial.rst.txt         |    6 +-
 ...port_simple_model_to_onnx_tutorial.rst.txt |    2 +-
 .../onnx/onnx_registry_tutorial.rst.txt       |  107 +-
 _sources/beginner/template_tutorial.rst.txt   |    2 +-
 .../transfer_learning_tutorial.rst.txt        |  120 +-
 _sources/beginner/vt_tutorial.rst.txt         |   71 +-
 ...ograd_saved_tensors_hooks_tutorial.rst.txt |    2 +-
 .../ax_multiobjective_nas_tutorial.rst.txt    | 1171 ++++----
 .../char_rnn_classification_tutorial.rst.txt  |   42 +-
 .../char_rnn_generation_tutorial.rst.txt      |   44 +-
 .../custom_function_conv_bn_tutorial.rst.txt  |   34 +-
 .../dqn_with_rnn_tutorial.rst.txt             |   20 +-
 _sources/intermediate/ensembling.rst.txt      |   10 +-
 .../intermediate/forward_ad_usage.rst.txt     |    2 +-
 .../fx_profiling_tutorial.rst.txt             |  144 +-
 .../intermediate/inductor_debug_cpu.rst.txt   |  101 +-
 .../intermediate/jacobians_hessians.rst.txt   |   32 +-
 .../intermediate/mario_rl_tutorial.rst.txt    |    8 +-
 .../memory_format_tutorial.rst.txt            |    2 +-
 .../model_parallel_tutorial.rst.txt           |    2 +-
 .../neural_tangent_kernels.rst.txt            |    2 +-
 ...ptimizer_step_in_backward_tutorial.rst.txt |  151 +-
 .../intermediate/parametrizations.rst.txt     |    2 +-
 .../intermediate/per_sample_grads.rst.txt     |   12 +-
 .../intermediate/pruning_tutorial.rst.txt     |    8 +-
 .../intermediate/reinforcement_ppo.rst.txt    |  202 +-
 .../reinforcement_q_learning.rst.txt          |    2 +-
 ...led_dot_product_attention_tutorial.rst.txt |   66 +-
 .../seq2seq_translation_tutorial.rst.txt      |   34 +-
 .../spatial_transformer_tutorial.rst.txt      |  130 +-
 .../torch_compile_tutorial.rst.txt            |  344 +--
 .../torch_export_tutorial.rst.txt             | 1024 +------
 .../intermediate/torchvision_tutorial.rst.txt |  166 +-
 _sources/prototype/sg_execution_times.rst.txt |    6 +-
 .../compiling_optimizer_lr_scheduler.rst.txt  |   52 +-
 .../recipes/changing_default_device.rst.txt   |    2 +-
 .../module_load_state_dict_tips.rst.txt       |    6 +-
 .../recipes/reasoning_about_shapes.rst.txt    |    6 +-
 _sources/recipes/recipes/swap_tensors.rst.txt |   14 +-
 ...ser_defined_triton_kernel_tutorial.rst.txt |    2 +-
 _sources/recipes/torch_logs.rst.txt           |    2 +-
 advanced/coding_ddpg.html                     |   42 +-
 advanced/dynamic_quantization_tutorial.html   |    6 +-
 advanced/neural_style_tutorial.html           |   82 +-
 advanced/numpy_extensions_tutorial.html       |    2 +-
 advanced/pendulum.html                        | 2454 ++++++++---------
 advanced/semi_structured_sparse.html          | 1036 ++++---
 beginner/Intro_to_TorchScript_tutorial.html   |    4 +-
 beginner/basics/autogradqs_tutorial.html      |    4 +-
 beginner/basics/buildmodel_tutorial.html      |    2 +-
 beginner/basics/data_tutorial.html            |   59 +-
 beginner/basics/optimization_tutorial.html    |   95 +-
 beginner/basics/quickstart_tutorial.html      |   53 +-
 beginner/basics/saveloadrun_tutorial.html     |   65 +-
 beginner/basics/transforms_tutorial.html      |   47 +-
 beginner/blitz/autograd_tutorial.html         |    8 +-
 beginner/blitz/cifar10_tutorial.html          |   36 +-
 beginner/blitz/data_parallel_tutorial.html    |    2 +-
 beginner/blitz/neural_networks_tutorial.html  |    8 +-
 beginner/blitz/tensor_tutorial.html           |    2 +-
 beginner/chatbot_tutorial.html                |    2 +-
 beginner/data_loading_tutorial.html           |    4 +-
 beginner/dcgan_faces_tutorial.html            |   46 +-
 ...ploy_seq2seq_hybrid_frontend_tutorial.html |    2 +-
 beginner/examples_nn/polynomial_nn.html       |    2 +-
 .../examples_tensor/polynomial_numpy.html     |    2 +-
 beginner/fgsm_tutorial.html                   |   12 +-
 beginner/flava_finetuning_tutorial.html       | 2248 +++++++--------
 beginner/former_torchies/nnft_tutorial.html   |    2 +-
 ...rid_frontend_through_example_tutorial.html |    2 +-
 beginner/hyperparameter_tuning_tutorial.html  | 1531 +++++-----
 beginner/introyt/autogradyt_tutorial.html     |   32 +-
 beginner/introyt/introyt1_tutorial.html       |   34 +-
 beginner/introyt/tensorboardyt_tutorial.html  |   69 +-
 beginner/introyt/tensors_deeper_tutorial.html |   24 +-
 beginner/introyt/trainingyt.html              |  120 +-
 beginner/knowledge_distillation_tutorial.html |  136 +-
 beginner/nlp/advanced_tutorial.html           |    4 +-
 beginner/nlp/deep_learning_tutorial.html      |    2 +-
 beginner/nlp/pytorch_tutorial.html            |    8 +-
 beginner/nlp/sequence_models_tutorial.html    |    4 +-
 beginner/nlp/word_embeddings_tutorial.html    |   12 +-
 beginner/nn_tutorial.html                     |    6 +-
 .../export_simple_model_to_onnx_tutorial.html |    2 +-
 beginner/onnx/onnx_registry_tutorial.html     |  156 +-
 beginner/template_tutorial.html               |    2 +-
 beginner/transfer_learning_tutorial.html      |  120 +-
 beginner/vt_tutorial.html                     |   71 +-
 ...autograd_saved_tensors_hooks_tutorial.html |    2 +-
 .../ax_multiobjective_nas_tutorial.html       | 1171 ++++----
 .../char_rnn_classification_tutorial.html     |   42 +-
 .../char_rnn_generation_tutorial.html         |   44 +-
 .../custom_function_conv_bn_tutorial.html     |   32 +-
 intermediate/dqn_with_rnn_tutorial.html       |   20 +-
 intermediate/ensembling.html                  |   10 +-
 intermediate/forward_ad_usage.html            |    2 +-
 intermediate/fx_profiling_tutorial.html       |  144 +-
 intermediate/inductor_debug_cpu.html          |  104 +-
 intermediate/jacobians_hessians.html          |   32 +-
 intermediate/mario_rl_tutorial.html           |    8 +-
 intermediate/memory_format_tutorial.html      |    2 +-
 intermediate/model_parallel_tutorial.html     |    2 +-
 intermediate/neural_tangent_kernels.html      |    2 +-
 .../optimizer_step_in_backward_tutorial.html  |  151 +-
 intermediate/parametrizations.html            |    2 +-
 intermediate/per_sample_grads.html            |   12 +-
 intermediate/pruning_tutorial.html            |    8 +-
 intermediate/reinforcement_ppo.html           |  202 +-
 intermediate/reinforcement_q_learning.html    |   30 +-
 ...scaled_dot_product_attention_tutorial.html |   66 +-
 .../seq2seq_translation_tutorial.html         |   34 +-
 .../spatial_transformer_tutorial.html         |  130 +-
 intermediate/torch_compile_tutorial.html      |  344 +--
 intermediate/torch_export_tutorial.html       | 1110 ++------
 intermediate/torchvision_tutorial.html        |  166 +-
 .../gpu_quantization_torchao_tutorial.html    |  510 ++--
 prototype/maskedtensor_adagrad.html           |    2 +-
 prototype/maskedtensor_overview.html          |    2 +-
 prototype/numeric_suite_tutorial.html         |    8 +-
 recipes/compiling_optimizer_lr_scheduler.html |   52 +-
 recipes/recipes/changing_default_device.html  |    2 +-
 .../recipes/module_load_state_dict_tips.html  |    6 +-
 recipes/recipes/reasoning_about_shapes.html   |    6 +-
 recipes/recipes/swap_tensors.html             |   14 +-
 ...e_user_defined_triton_kernel_tutorial.html |    2 +-
 recipes/torch_logs.html                       |    2 +-
 searchindex.js                                |    2 +-
 183 files changed, 11625 insertions(+), 14011 deletions(-)

diff --git a/_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb b/_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb
index 7c7c307175..c23d5542c0 100644
--- a/_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb
+++ b/_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb
@@ -34,7 +34,7 @@
   {
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    "execution_count": null,
-   "id": "7990a69c",
+   "id": "de9d7e17",
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@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd424fde",
+   "id": "38547d24",
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diff --git a/_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb b/_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb
index d5dd2551ee..4aaffb7973 100644
--- a/_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb
+++ b/_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb
@@ -31,7 +31,7 @@
   {
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@@ -47,7 +47,7 @@
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+   "id": "70db1f88",
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diff --git a/_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb b/_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb
index 821328bcb4..4f6ed15bad 100644
--- a/_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb
+++ b/_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb
@@ -34,7 +34,7 @@
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@@ -50,7 +50,7 @@
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diff --git a/_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb b/_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb
index 58fe4ee2ce..a57b4c1acd 100644
--- a/_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb
+++ b/_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb
@@ -35,7 +35,7 @@
   {
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@@ -51,7 +51,7 @@
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diff --git a/_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb b/_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb
index 16d9254cf1..941213c0b0 100644
--- a/_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb
+++ b/_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb
@@ -37,7 +37,7 @@
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@@ -53,7 +53,7 @@
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diff --git a/_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb b/_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb
index 98c5fa212b..96207f51b9 100644
--- a/_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb
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@@ -51,7 +51,7 @@
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diff --git a/_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb b/_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb
index 9df7e529f6..6fb3baf08b 100644
--- a/_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb
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index b188778e77..e508e626f9 100644
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diff --git a/_sources/advanced/coding_ddpg.rst.txt b/_sources/advanced/coding_ddpg.rst.txt
index d7db549d18..61be16b148 100644
--- a/_sources/advanced/coding_ddpg.rst.txt
+++ b/_sources/advanced/coding_ddpg.rst.txt
@@ -1632,26 +1632,26 @@ modules we need.
 
 
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-    reward: -2.56 (r0 = -2.20), reward eval: reward: -0.01, reward normalized=-3.21/6.37, grad norm= 44.83, loss_value= 395.90, loss_actor= 18.20, target value: -18.76:  56%|#####6    | 5600/10000 [00:08<00:05, 846.72it/s]
-    reward: -2.56 (r0 = -2.20), reward eval: reward: -0.01, reward normalized=-3.21/6.37, grad norm= 44.83, loss_value= 395.90, loss_actor= 18.20, target value: -18.76:  64%|######4   | 6400/10000 [00:09<00:05, 635.05it/s]
-    reward: -0.10 (r0 = -2.20), reward eval: reward: -0.01, reward normalized=-2.70/6.05, grad norm= 81.33, loss_value= 343.52, loss_actor= 14.75, target value: -16.15:  64%|######4   | 6400/10000 [00:10<00:05, 635.05it/s]
-    reward: -0.10 (r0 = -2.20), reward eval: reward: -0.01, reward normalized=-2.70/6.05, grad norm= 81.33, loss_value= 343.52, loss_actor= 14.75, target value: -16.15:  72%|#######2  | 7200/10000 [00:12<00:05, 486.42it/s]
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-    reward: -4.83 (r0 = -2.20), reward eval: reward: -0.01, reward normalized=-2.87/5.11, grad norm= 241.11, loss_value= 259.33, loss_actor= 16.64, target value: -19.13:  80%|########  | 8000/10000 [00:15<00:04, 418.73it/s]
-    reward: -4.83 (r0 = -2.20), reward eval: reward: -0.01, reward normalized=-2.87/5.11, grad norm= 241.11, loss_value= 259.33, loss_actor= 16.64, target value: -19.13:  88%|########8 | 8800/10000 [00:17<00:03, 382.18it/s]
-    reward: -5.14 (r0 = -2.20), reward eval: reward:  0.53, reward normalized=-2.44/4.86, grad norm= 150.89, loss_value= 188.20, loss_actor= 18.66, target value: -16.22:  88%|########8 | 8800/10000 [00:20<00:03, 382.18it/s]
-    reward: -5.14 (r0 = -2.20), reward eval: reward:  0.53, reward normalized=-2.44/4.86, grad norm= 150.89, loss_value= 188.20, loss_actor= 18.66, target value: -16.22:  96%|#########6| 9600/10000 [00:21<00:01, 284.56it/s]
-    reward: -5.13 (r0 = -2.20), reward eval: reward:  0.53, reward normalized=-2.81/5.41, grad norm= 125.30, loss_value= 268.30, loss_actor= 16.89, target value: -19.89:  96%|#########6| 9600/10000 [00:22<00:01, 284.56it/s]
-    reward: -5.13 (r0 = -2.20), reward eval: reward:  0.53, reward normalized=-2.81/5.41, grad norm= 125.30, loss_value= 268.30, loss_actor= 16.89, target value: -19.89: : 10400it [00:25, 264.83it/s]                        
-    reward: -3.58 (r0 = -2.20), reward eval: reward:  0.53, reward normalized=-3.82/5.60, grad norm= 87.65, loss_value= 267.84, loss_actor= 23.16, target value: -27.36: : 10400it [00:26, 264.83it/s] 
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+    reward: -2.33 (r0 = -2.76), reward eval: reward:  0.00, reward normalized=-2.94/6.04, grad norm= 39.94, loss_value= 309.89, loss_actor= 16.81, target value: -18.49:  56%|#####6    | 5600/10000 [00:08<00:05, 863.13it/s]
+    reward: -2.33 (r0 = -2.76), reward eval: reward:  0.00, reward normalized=-2.94/6.04, grad norm= 39.94, loss_value= 309.89, loss_actor= 16.81, target value: -18.49:  64%|######4   | 6400/10000 [00:09<00:05, 638.43it/s]
+    reward: -0.12 (r0 = -2.76), reward eval: reward:  0.00, reward normalized=-2.67/5.59, grad norm= 90.14, loss_value= 235.72, loss_actor= 14.76, target value: -16.42:  64%|######4   | 6400/10000 [00:10<00:05, 638.43it/s]
+    reward: -0.12 (r0 = -2.76), reward eval: reward:  0.00, reward normalized=-2.67/5.59, grad norm= 90.14, loss_value= 235.72, loss_actor= 14.76, target value: -16.42:  72%|#######2  | 7200/10000 [00:12<00:05, 491.19it/s]
+    reward: -2.76 (r0 = -2.76), reward eval: reward:  0.00, reward normalized=-2.44/5.94, grad norm= 201.55, loss_value= 271.25, loss_actor= 13.79, target value: -15.96:  72%|#######2  | 7200/10000 [00:12<00:05, 491.19it/s]
+    reward: -2.76 (r0 = -2.76), reward eval: reward:  0.00, reward normalized=-2.44/5.94, grad norm= 201.55, loss_value= 271.25, loss_actor= 13.79, target value: -15.96:  80%|########  | 8000/10000 [00:14<00:04, 423.48it/s]
+    reward: -4.90 (r0 = -2.76), reward eval: reward:  0.00, reward normalized=-2.51/4.70, grad norm= 34.93, loss_value= 161.79, loss_actor= 15.98, target value: -16.57:  80%|########  | 8000/10000 [00:15<00:04, 423.48it/s] 
+    reward: -4.90 (r0 = -2.76), reward eval: reward:  0.00, reward normalized=-2.51/4.70, grad norm= 34.93, loss_value= 161.79, loss_actor= 15.98, target value: -16.57:  88%|########8 | 8800/10000 [00:16<00:03, 388.29it/s]
+    reward: -4.41 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-2.43/5.13, grad norm= 72.99, loss_value= 241.28, loss_actor= 13.02, target value: -16.27:  88%|########8 | 8800/10000 [00:19<00:03, 388.29it/s]
+    reward: -4.41 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-2.43/5.13, grad norm= 72.99, loss_value= 241.28, loss_actor= 13.02, target value: -16.27:  96%|#########6| 9600/10000 [00:21<00:01, 288.47it/s]
+    reward: -4.65 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-2.92/5.14, grad norm= 147.22, loss_value= 251.85, loss_actor= 11.03, target value: -20.73:  96%|#########6| 9600/10000 [00:22<00:01, 288.47it/s]
+    reward: -4.65 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-2.92/5.14, grad norm= 147.22, loss_value= 251.85, loss_actor= 11.03, target value: -20.73: : 10400it [00:24, 268.73it/s]                        
+    reward: -4.72 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-3.49/4.19, grad norm= 133.24, loss_value= 169.25, loss_actor= 14.39, target value: -24.45: : 10400it [00:25, 268.73it/s]
 
 
 
@@ -1721,7 +1721,7 @@ To iterate further on this loss module we might consider:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  41.549 seconds)
+   **Total running time of the script:** ( 0 minutes  40.906 seconds)
 
 
 .. _sphx_glr_download_advanced_coding_ddpg.py:
diff --git a/_sources/advanced/dynamic_quantization_tutorial.rst.txt b/_sources/advanced/dynamic_quantization_tutorial.rst.txt
index 7c1625be60..3e6343bdab 100644
--- a/_sources/advanced/dynamic_quantization_tutorial.rst.txt
+++ b/_sources/advanced/dynamic_quantization_tutorial.rst.txt
@@ -516,9 +516,9 @@ models run single threaded.
  .. code-block:: none
 
     loss: 5.167
-    elapsed time (seconds): 206.3
+    elapsed time (seconds): 205.3
     loss: 5.168
-    elapsed time (seconds): 116.9
+    elapsed time (seconds): 117.6
 
 
 
@@ -540,7 +540,7 @@ Thanks for reading! As always, we welcome any feedback, so please create an issu
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  32.168 seconds)
+   **Total running time of the script:** ( 5 minutes  31.684 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:
diff --git a/_sources/advanced/neural_style_tutorial.rst.txt b/_sources/advanced/neural_style_tutorial.rst.txt
index 4f332a437e..32af985940 100644
--- a/_sources/advanced/neural_style_tutorial.rst.txt
+++ b/_sources/advanced/neural_style_tutorial.rst.txt
@@ -410,41 +410,39 @@ network to evaluation mode using ``.eval()``.
     Downloading: "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth
 
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+     62%|######1   | 337M/548M [00:02<00:01, 175MB/s]
+     65%|######4   | 354M/548M [00:02<00:01, 176MB/s]
+     68%|######7   | 371M/548M [00:02<00:01, 175MB/s]
+     71%|#######   | 388M/548M [00:02<00:00, 175MB/s]
+     74%|#######3  | 405M/548M [00:02<00:00, 176MB/s]
+     77%|#######7  | 422M/548M [00:02<00:00, 176MB/s]
+     80%|########  | 439M/548M [00:02<00:00, 176MB/s]
+     83%|########3 | 456M/548M [00:02<00:00, 176MB/s]
+     86%|########6 | 473M/548M [00:02<00:00, 176MB/s]
+     89%|########9 | 490M/548M [00:02<00:00, 175MB/s]
+     92%|#########2| 506M/548M [00:03<00:00, 175MB/s]
+     95%|#########5| 523M/548M [00:03<00:00, 175MB/s]
+     99%|#########8| 540M/548M [00:03<00:00, 176MB/s]
+    100%|##########| 548M/548M [00:03<00:00, 175MB/s]
 
 
 
@@ -765,22 +763,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 4.037464 Content Loss: 4.144108
+    Style Loss : 4.108783 Content Loss: 4.076407
 
     run [100]:
-    Style Loss : 1.150719 Content Loss: 3.040204
+    Style Loss : 1.131557 Content Loss: 3.022170
 
     run [150]:
-    Style Loss : 0.710417 Content Loss: 2.655678
+    Style Loss : 0.701424 Content Loss: 2.643672
 
     run [200]:
-    Style Loss : 0.479242 Content Loss: 2.487361
+    Style Loss : 0.470905 Content Loss: 2.488043
 
     run [250]:
-    Style Loss : 0.344841 Content Loss: 2.403615
+    Style Loss : 0.342489 Content Loss: 2.402129
 
     run [300]:
-    Style Loss : 0.262127 Content Loss: 2.350151
+    Style Loss : 0.262571 Content Loss: 2.348458
 
 
 
@@ -789,7 +787,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  37.689 seconds)
+   **Total running time of the script:** ( 0 minutes  37.426 seconds)
 
 
 .. _sphx_glr_download_advanced_neural_style_tutorial.py:
diff --git a/_sources/advanced/numpy_extensions_tutorial.rst.txt b/_sources/advanced/numpy_extensions_tutorial.rst.txt
index 1934c04ae9..21b0b0a93c 100644
--- a/_sources/advanced/numpy_extensions_tutorial.rst.txt
+++ b/_sources/advanced/numpy_extensions_tutorial.rst.txt
@@ -303,7 +303,7 @@ The backward pass computes the gradient ``wrt`` the input and the gradient ``wrt
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.560 seconds)
+   **Total running time of the script:** ( 0 minutes  0.619 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py:
diff --git a/_sources/advanced/pendulum.rst.txt b/_sources/advanced/pendulum.rst.txt
index f19f71a566..1c829f92d9 100644
--- a/_sources/advanced/pendulum.rst.txt
+++ b/_sources/advanced/pendulum.rst.txt
@@ -1568,1256 +1568,1256 @@ which demonstrates that the pendulum is upward and still as desired.
 
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-    reward: -4.4212, last reward: -3.0260, gradient norm:  26.01:  23%|##3       | 146/625 [00:23<01:16,  6.25it/s]
-    reward: -4.4212, last reward: -3.0260, gradient norm:  26.01:  24%|##3       | 147/625 [00:23<01:16,  6.27it/s]
-    reward: -4.0939, last reward: -4.6478, gradient norm:  9.605:  24%|##3       | 147/625 [00:23<01:16,  6.27it/s]
-    reward: -4.0939, last reward: -4.6478, gradient norm:  9.605:  24%|##3       | 148/625 [00:23<01:15,  6.28it/s]
-    reward: -4.6606, last reward: -4.7289, gradient norm:  11.19:  24%|##3       | 148/625 [00:23<01:15,  6.28it/s]
-    reward: -4.6606, last reward: -4.7289, gradient norm:  11.19:  24%|##3       | 149/625 [00:23<01:15,  6.27it/s]
-    reward: -4.9300, last reward: -4.7193, gradient norm:  8.563:  24%|##3       | 149/625 [00:23<01:15,  6.27it/s]
-    reward: -4.9300, last reward: -4.7193, gradient norm:  8.563:  24%|##4       | 150/625 [00:23<01:15,  6.29it/s]
-    reward: -5.1166, last reward: -4.8514, gradient norm:  8.384:  24%|##4       | 150/625 [00:23<01:15,  6.29it/s]
-    reward: -5.1166, last reward: -4.8514, gradient norm:  8.384:  24%|##4       | 151/625 [00:23<01:15,  6.31it/s]
-    reward: -4.9108, last reward: -5.0672, gradient norm:  9.292:  24%|##4       | 151/625 [00:24<01:15,  6.31it/s]
-    reward: -4.9108, last reward: -5.0672, gradient norm:  9.292:  24%|##4       | 152/625 [00:24<01:14,  6.31it/s]
-    reward: -4.8591, last reward: -4.3768, gradient norm:  9.72:  24%|##4       | 152/625 [00:24<01:14,  6.31it/s] 
-    reward: -4.8591, last reward: -4.3768, gradient norm:  9.72:  24%|##4       | 153/625 [00:24<01:14,  6.32it/s]
-    reward: -4.2721, last reward: -3.9976, gradient norm:  10.37:  24%|##4       | 153/625 [00:24<01:14,  6.32it/s]
-    reward: -4.2721, last reward: -3.9976, gradient norm:  10.37:  25%|##4       | 154/625 [00:24<01:14,  6.34it/s]
-    reward: -4.0576, last reward: -2.0067, gradient norm:  8.935:  25%|##4       | 154/625 [00:24<01:14,  6.34it/s]
-    reward: -4.0576, last reward: -2.0067, gradient norm:  8.935:  25%|##4       | 155/625 [00:24<01:14,  6.33it/s]
-    reward: -4.4199, last reward: -5.1722, gradient norm:  18.7:  25%|##4       | 155/625 [00:24<01:14,  6.33it/s] 
-    reward: -4.4199, last reward: -5.1722, gradient norm:  18.7:  25%|##4       | 156/625 [00:24<01:14,  6.33it/s]
-    reward: -4.8310, last reward: -7.3466, gradient norm:  28.52:  25%|##4       | 156/625 [00:24<01:14,  6.33it/s]
-    reward: -4.8310, last reward: -7.3466, gradient norm:  28.52:  25%|##5       | 157/625 [00:24<01:13,  6.34it/s]
-    reward: -4.8631, last reward: -6.2492, gradient norm:  89.17:  25%|##5       | 157/625 [00:25<01:13,  6.34it/s]
-    reward: -4.8631, last reward: -6.2492, gradient norm:  89.17:  25%|##5       | 158/625 [00:25<01:13,  6.35it/s]
-    reward: -4.8763, last reward: -6.1277, gradient norm:  24.43:  25%|##5       | 158/625 [00:25<01:13,  6.35it/s]
-    reward: -4.8763, last reward: -6.1277, gradient norm:  24.43:  25%|##5       | 159/625 [00:25<01:13,  6.36it/s]
-    reward: -4.5562, last reward: -5.7446, gradient norm:  23.35:  25%|##5       | 159/625 [00:25<01:13,  6.36it/s]
-    reward: -4.5562, last reward: -5.7446, gradient norm:  23.35:  26%|##5       | 160/625 [00:25<01:13,  6.35it/s]
-    reward: -4.1082, last reward: -4.9830, gradient norm:  22.14:  26%|##5       | 160/625 [00:25<01:13,  6.35it/s]
-    reward: -4.1082, last reward: -4.9830, gradient norm:  22.14:  26%|##5       | 161/625 [00:25<01:13,  6.33it/s]
-    reward: -4.0946, last reward: -2.5229, gradient norm:  10.47:  26%|##5       | 161/625 [00:25<01:13,  6.33it/s]
-    reward: -4.0946, last reward: -2.5229, gradient norm:  10.47:  26%|##5       | 162/625 [00:25<01:13,  6.30it/s]
-    reward: -4.4574, last reward: -4.6900, gradient norm:  112.6:  26%|##5       | 162/625 [00:25<01:13,  6.30it/s]
-    reward: -4.4574, last reward: -4.6900, gradient norm:  112.6:  26%|##6       | 163/625 [00:25<01:13,  6.31it/s]
-    reward: -5.2229, last reward: -4.0318, gradient norm:  6.482:  26%|##6       | 163/625 [00:25<01:13,  6.31it/s]
-    reward: -5.2229, last reward: -4.0318, gradient norm:  6.482:  26%|##6       | 164/625 [00:25<01:13,  6.31it/s]
-    reward: -5.0543, last reward: -4.0817, gradient norm:  5.761:  26%|##6       | 164/625 [00:26<01:13,  6.31it/s]
-    reward: -5.0543, last reward: -4.0817, gradient norm:  5.761:  26%|##6       | 165/625 [00:26<01:12,  6.32it/s]
-    reward: -5.2809, last reward: -4.5118, gradient norm:  5.366:  26%|##6       | 165/625 [00:26<01:12,  6.32it/s]
-    reward: -5.2809, last reward: -4.5118, gradient norm:  5.366:  27%|##6       | 166/625 [00:26<01:12,  6.32it/s]
-    reward: -5.1142, last reward: -4.5635, gradient norm:  5.04:  27%|##6       | 166/625 [00:26<01:12,  6.32it/s] 
-    reward: -5.1142, last reward: -4.5635, gradient norm:  5.04:  27%|##6       | 167/625 [00:26<01:12,  6.31it/s]
-    reward: -5.1949, last reward: -4.2327, gradient norm:  4.982:  27%|##6       | 167/625 [00:26<01:12,  6.31it/s]
-    reward: -5.1949, last reward: -4.2327, gradient norm:  4.982:  27%|##6       | 168/625 [00:26<01:12,  6.30it/s]
-    reward: -5.0967, last reward: -5.0387, gradient norm:  7.457:  27%|##6       | 168/625 [00:26<01:12,  6.30it/s]
-    reward: -5.0967, last reward: -5.0387, gradient norm:  7.457:  27%|##7       | 169/625 [00:26<01:12,  6.30it/s]
-    reward: -5.0782, last reward: -5.2150, gradient norm:  10.54:  27%|##7       | 169/625 [00:26<01:12,  6.30it/s]
-    reward: -5.0782, last reward: -5.2150, gradient norm:  10.54:  27%|##7       | 170/625 [00:26<01:11,  6.32it/s]
-    reward: -4.5222, last reward: -4.3725, gradient norm:  22.63:  27%|##7       | 170/625 [00:27<01:11,  6.32it/s]
-    reward: -4.5222, last reward: -4.3725, gradient norm:  22.63:  27%|##7       | 171/625 [00:27<01:11,  6.34it/s]
-    reward: -3.9288, last reward: -3.9837, gradient norm:  83.59:  27%|##7       | 171/625 [00:27<01:11,  6.34it/s]
-    reward: -3.9288, last reward: -3.9837, gradient norm:  83.59:  28%|##7       | 172/625 [00:27<01:11,  6.33it/s]
-    reward: -4.1416, last reward: -4.1099, gradient norm:  30.57:  28%|##7       | 172/625 [00:27<01:11,  6.33it/s]
-    reward: -4.1416, last reward: -4.1099, gradient norm:  30.57:  28%|##7       | 173/625 [00:27<01:11,  6.32it/s]
-    reward: -4.8620, last reward: -6.8475, gradient norm:  18.91:  28%|##7       | 173/625 [00:27<01:11,  6.32it/s]
-    reward: -4.8620, last reward: -6.8475, gradient norm:  18.91:  28%|##7       | 174/625 [00:27<01:11,  6.34it/s]
-    reward: -5.1807, last reward: -6.4375, gradient norm:  18.48:  28%|##7       | 174/625 [00:27<01:11,  6.34it/s]
-    reward: -5.1807, last reward: -6.4375, gradient norm:  18.48:  28%|##8       | 175/625 [00:27<01:10,  6.34it/s]
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-    reward: -5.2751, last reward: -4.8313, gradient norm:  15.32:  28%|##8       | 176/625 [00:28<01:10,  6.35it/s]
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-    reward: -4.9286, last reward: -6.9770, gradient norm:  24.75:  28%|##8       | 177/625 [00:28<01:10,  6.34it/s]
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+    reward: -4.0690, last reward: -2.5140, gradient norm:  22.5:  22%|##1       | 136/625 [00:21<01:16,  6.36it/s]
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+    reward: -2.1253, last reward: -0.0001, gradient norm:  0.6622: 100%|##########| 625/625 [01:38<00:00,  6.35it/s]
 
 
 
@@ -2844,7 +2844,7 @@ scratch. We touched the subjects of:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  39.914 seconds)
+   **Total running time of the script:** ( 1 minutes  38.707 seconds)
 
 
 .. _sphx_glr_download_advanced_pendulum.py:
diff --git a/_sources/advanced/semi_structured_sparse.rst.txt b/_sources/advanced/semi_structured_sparse.rst.txt
index 49b0776784..8c6d3ae1a2 100644
--- a/_sources/advanced/semi_structured_sparse.rst.txt
+++ b/_sources/advanced/semi_structured_sparse.rst.txt
@@ -112,7 +112,7 @@ accelerating ``nn.Linear`` layers for inference with
 
  .. code-block:: none
 
-    Dense: 2.921ms Sparse: 1.660ms | Speedup: 1.760x
+    Dense: 2.922ms Sparse: 1.649ms | Speedup: 1.772x
 
 
 
@@ -263,7 +263,7 @@ Let’s start by importing all the packages we need.
  .. code-block:: none
 
 
-    <torch._C.Generator object at 0x7f083c737510>
+    <torch._C.Generator object at 0x7f3c2d47b510>
 
 
 
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+    Map: 100%|##########| 87599/87599 [00:51<00:00, 1715.59 examples/s]
 
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+    Map: 100%|##########| 10570/10570 [00:04<00:00, 2077.13 examples/s]
+    Map: 100%|##########| 10570/10570 [00:05<00:00, 2111.39 examples/s]
 
 
 
@@ -794,46 +762,46 @@ vs. BERT-large or fine-tuning hyperparameters.
     Detected kernel version 4.14.336, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
 
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       9%|8         | 43/500 [00:25<04:29,  1.70it/s]
@@ -856,43 +824,43 @@ vs. BERT-large or fine-tuning hyperparameters.
      11%|#1        | 56/500 [00:32<04:21,  1.70it/s]
      11%|#1        | 57/500 [00:33<04:21,  1.70it/s]
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      17%|#7        | 86/500 [00:50<04:04,  1.69it/s]
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+     17%|#7        | 87/500 [00:51<04:03,  1.69it/s]
      18%|#7        | 88/500 [00:51<04:03,  1.69it/s]
-     18%|#7        | 89/500 [00:52<04:03,  1.69it/s]
+     18%|#7        | 89/500 [00:52<04:02,  1.69it/s]
      18%|#8        | 90/500 [00:53<04:02,  1.69it/s]
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-     19%|#8        | 94/500 [00:55<04:00,  1.69it/s]
-     19%|#9        | 95/500 [00:56<03:59,  1.69it/s]
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      20%|#9        | 98/500 [00:57<03:57,  1.69it/s]
@@ -904,7 +872,7 @@ vs. BERT-large or fine-tuning hyperparameters.
      20%|##        | 100/500 [00:58<03:56,  1.69it/s]
      20%|##        | 101/500 [00:59<03:56,  1.69it/s]
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      21%|##        | 104/500 [01:01<03:54,  1.69it/s]
      21%|##1       | 105/500 [01:01<03:53,  1.69it/s]
      21%|##1       | 106/500 [01:02<03:53,  1.69it/s]
@@ -916,9 +884,9 @@ vs. BERT-large or fine-tuning hyperparameters.
      22%|##2       | 112/500 [01:06<03:49,  1.69it/s]
      23%|##2       | 113/500 [01:06<03:49,  1.69it/s]
      23%|##2       | 114/500 [01:07<03:48,  1.69it/s]
-     23%|##3       | 115/500 [01:07<03:47,  1.69it/s]
+     23%|##3       | 115/500 [01:07<03:48,  1.69it/s]
      23%|##3       | 116/500 [01:08<03:47,  1.69it/s]
-     23%|##3       | 117/500 [01:09<03:46,  1.69it/s]
+     23%|##3       | 117/500 [01:08<03:46,  1.69it/s]
      24%|##3       | 118/500 [01:09<03:46,  1.69it/s]
      24%|##3       | 119/500 [01:10<03:45,  1.69it/s]
      24%|##4       | 120/500 [01:10<03:45,  1.69it/s]
@@ -945,7 +913,7 @@ vs. BERT-large or fine-tuning hyperparameters.
      28%|##8       | 141/500 [01:23<03:32,  1.69it/s]
      28%|##8       | 142/500 [01:23<03:32,  1.69it/s]
      29%|##8       | 143/500 [01:24<03:31,  1.69it/s]
-     29%|##8       | 144/500 [01:25<03:30,  1.69it/s]
+     29%|##8       | 144/500 [01:24<03:31,  1.69it/s]
      29%|##9       | 145/500 [01:25<03:30,  1.69it/s]
      29%|##9       | 146/500 [01:26<03:29,  1.69it/s]
      29%|##9       | 147/500 [01:26<03:29,  1.69it/s]
@@ -961,173 +929,173 @@ vs. BERT-large or fine-tuning hyperparameters.
      31%|###       | 153/500 [01:30<03:25,  1.69it/s]
      31%|###       | 154/500 [01:30<03:25,  1.69it/s]
      31%|###1      | 155/500 [01:31<03:24,  1.69it/s]
-     31%|###1      | 156/500 [01:32<03:23,  1.69it/s]
+     31%|###1      | 156/500 [01:32<03:24,  1.69it/s]
      31%|###1      | 157/500 [01:32<03:23,  1.69it/s]
      32%|###1      | 158/500 [01:33<03:22,  1.69it/s]
      32%|###1      | 159/500 [01:33<03:22,  1.69it/s]
      32%|###2      | 160/500 [01:34<03:21,  1.69it/s]
-     32%|###2      | 161/500 [01:35<03:20,  1.69it/s]
-     32%|###2      | 162/500 [01:35<03:20,  1.69it/s]
+     32%|###2      | 161/500 [01:35<03:21,  1.69it/s]
+     32%|###2      | 162/500 [01:35<03:20,  1.68it/s]
      33%|###2      | 163/500 [01:36<03:19,  1.69it/s]
-     33%|###2      | 164/500 [01:36<03:19,  1.69it/s]
-     33%|###3      | 165/500 [01:37<03:18,  1.69it/s]
-     33%|###3      | 166/500 [01:38<03:17,  1.69it/s]
-     33%|###3      | 167/500 [01:38<03:17,  1.69it/s]
-     34%|###3      | 168/500 [01:39<03:16,  1.69it/s]
-     34%|###3      | 169/500 [01:39<03:16,  1.69it/s]
-     34%|###4      | 170/500 [01:40<03:15,  1.69it/s]
-     34%|###4      | 171/500 [01:41<03:15,  1.69it/s]
-     34%|###4      | 172/500 [01:41<03:14,  1.69it/s]
-     35%|###4      | 173/500 [01:42<03:13,  1.69it/s]
-     35%|###4      | 174/500 [01:42<03:13,  1.69it/s]
-     35%|###5      | 175/500 [01:43<03:12,  1.69it/s]
-     35%|###5      | 176/500 [01:44<03:12,  1.69it/s]
-     35%|###5      | 177/500 [01:44<03:11,  1.69it/s]
-     36%|###5      | 178/500 [01:45<03:10,  1.69it/s]
-     36%|###5      | 179/500 [01:45<03:10,  1.69it/s]
-     36%|###6      | 180/500 [01:46<03:09,  1.69it/s]
-     36%|###6      | 181/500 [01:46<03:09,  1.69it/s]
-     36%|###6      | 182/500 [01:47<03:08,  1.69it/s]
-     37%|###6      | 183/500 [01:48<03:08,  1.69it/s]
+     33%|###2      | 164/500 [01:36<03:19,  1.68it/s]
+     33%|###3      | 165/500 [01:37<03:18,  1.68it/s]
+     33%|###3      | 166/500 [01:38<03:18,  1.69it/s]
+     33%|###3      | 167/500 [01:38<03:17,  1.68it/s]
+     34%|###3      | 168/500 [01:39<03:17,  1.68it/s]
+     34%|###3      | 169/500 [01:39<03:16,  1.68it/s]
+     34%|###4      | 170/500 [01:40<03:15,  1.68it/s]
+     34%|###4      | 171/500 [01:41<03:15,  1.68it/s]
+     34%|###4      | 172/500 [01:41<03:14,  1.68it/s]
+     35%|###4      | 173/500 [01:42<03:14,  1.68it/s]
+     35%|###4      | 174/500 [01:42<03:13,  1.68it/s]
+     35%|###5      | 175/500 [01:43<03:12,  1.68it/s]
+     35%|###5      | 176/500 [01:43<03:12,  1.68it/s]
+     35%|###5      | 177/500 [01:44<03:11,  1.68it/s]
+     36%|###5      | 178/500 [01:45<03:11,  1.68it/s]
+     36%|###5      | 179/500 [01:45<03:10,  1.68it/s]
+     36%|###6      | 180/500 [01:46<03:09,  1.68it/s]
+     36%|###6      | 181/500 [01:46<03:09,  1.68it/s]
+     36%|###6      | 182/500 [01:47<03:08,  1.68it/s]
+     37%|###6      | 183/500 [01:48<03:08,  1.68it/s]
      37%|###6      | 184/500 [01:48<03:07,  1.68it/s]
      37%|###7      | 185/500 [01:49<03:06,  1.68it/s]
-     37%|###7      | 186/500 [01:49<03:06,  1.69it/s]
+     37%|###7      | 186/500 [01:49<03:06,  1.68it/s]
      37%|###7      | 187/500 [01:50<03:05,  1.68it/s]
      38%|###7      | 188/500 [01:51<03:05,  1.68it/s]
      38%|###7      | 189/500 [01:51<03:04,  1.68it/s]
-     38%|###8      | 190/500 [01:52<03:03,  1.68it/s]
+     38%|###8      | 190/500 [01:52<03:04,  1.68it/s]
      38%|###8      | 191/500 [01:52<03:03,  1.68it/s]
-     38%|###8      | 192/500 [01:53<03:02,  1.68it/s]
-     39%|###8      | 193/500 [01:54<03:02,  1.69it/s]
-     39%|###8      | 194/500 [01:54<03:01,  1.69it/s]
-     39%|###9      | 195/500 [01:55<03:00,  1.69it/s]
-     39%|###9      | 196/500 [01:55<03:00,  1.69it/s]
-     39%|###9      | 197/500 [01:56<02:59,  1.69it/s]
-     40%|###9      | 198/500 [01:57<02:59,  1.69it/s]
-     40%|###9      | 199/500 [01:57<02:58,  1.69it/s]
+     38%|###8      | 192/500 [01:53<03:03,  1.68it/s]
+     39%|###8      | 193/500 [01:54<03:02,  1.68it/s]
+     39%|###8      | 194/500 [01:54<03:01,  1.68it/s]
+     39%|###9      | 195/500 [01:55<03:01,  1.68it/s]
+     39%|###9      | 196/500 [01:55<03:00,  1.68it/s]
+     39%|###9      | 197/500 [01:56<03:00,  1.68it/s]
+     40%|###9      | 198/500 [01:57<02:59,  1.68it/s]
+     40%|###9      | 199/500 [01:57<02:59,  1.68it/s]
      40%|####      | 200/500 [01:58<02:58,  1.68it/s]
                                                  
     {'loss': 1.6519, 'learning_rate': 5e-05, 'epoch': 0.07}
 
      40%|####      | 200/500 [01:58<02:58,  1.68it/s]
      40%|####      | 201/500 [01:58<02:57,  1.68it/s]
-     40%|####      | 202/500 [01:59<02:56,  1.68it/s]
-     41%|####      | 203/500 [02:00<02:56,  1.69it/s]
-     41%|####      | 204/500 [02:00<02:55,  1.69it/s]
+     40%|####      | 202/500 [01:59<02:57,  1.68it/s]
+     41%|####      | 203/500 [02:00<02:56,  1.68it/s]
+     41%|####      | 204/500 [02:00<02:56,  1.68it/s]
      41%|####1     | 205/500 [02:01<02:55,  1.68it/s]
      41%|####1     | 206/500 [02:01<02:54,  1.68it/s]
-     41%|####1     | 207/500 [02:02<02:53,  1.69it/s]
-     42%|####1     | 208/500 [02:02<02:53,  1.68it/s]
-     42%|####1     | 209/500 [02:03<02:52,  1.68it/s]
+     41%|####1     | 207/500 [02:02<02:54,  1.68it/s]
+     42%|####1     | 208/500 [02:03<02:53,  1.68it/s]
+     42%|####1     | 209/500 [02:03<02:53,  1.68it/s]
      42%|####2     | 210/500 [02:04<02:52,  1.68it/s]
      42%|####2     | 211/500 [02:04<02:51,  1.68it/s]
      42%|####2     | 212/500 [02:05<02:51,  1.68it/s]
      43%|####2     | 213/500 [02:05<02:50,  1.68it/s]
-     43%|####2     | 214/500 [02:06<02:49,  1.68it/s]
+     43%|####2     | 214/500 [02:06<02:50,  1.68it/s]
      43%|####3     | 215/500 [02:07<02:49,  1.68it/s]
-     43%|####3     | 216/500 [02:07<02:48,  1.68it/s]
+     43%|####3     | 216/500 [02:07<02:49,  1.68it/s]
      43%|####3     | 217/500 [02:08<02:48,  1.68it/s]
      44%|####3     | 218/500 [02:08<02:47,  1.68it/s]
-     44%|####3     | 219/500 [02:09<02:46,  1.68it/s]
+     44%|####3     | 219/500 [02:09<02:47,  1.68it/s]
      44%|####4     | 220/500 [02:10<02:46,  1.68it/s]
      44%|####4     | 221/500 [02:10<02:45,  1.68it/s]
      44%|####4     | 222/500 [02:11<02:45,  1.68it/s]
      45%|####4     | 223/500 [02:11<02:44,  1.68it/s]
-     45%|####4     | 224/500 [02:12<02:43,  1.68it/s]
+     45%|####4     | 224/500 [02:12<02:44,  1.68it/s]
      45%|####5     | 225/500 [02:13<02:43,  1.68it/s]
-     45%|####5     | 226/500 [02:13<02:42,  1.68it/s]
+     45%|####5     | 226/500 [02:13<02:43,  1.68it/s]
      45%|####5     | 227/500 [02:14<02:42,  1.68it/s]
      46%|####5     | 228/500 [02:14<02:41,  1.68it/s]
      46%|####5     | 229/500 [02:15<02:41,  1.68it/s]
      46%|####6     | 230/500 [02:16<02:40,  1.68it/s]
-     46%|####6     | 231/500 [02:16<02:39,  1.68it/s]
+     46%|####6     | 231/500 [02:16<02:40,  1.68it/s]
      46%|####6     | 232/500 [02:17<02:39,  1.68it/s]
      47%|####6     | 233/500 [02:17<02:38,  1.68it/s]
-     47%|####6     | 234/500 [02:18<02:37,  1.68it/s]
+     47%|####6     | 234/500 [02:18<02:38,  1.68it/s]
      47%|####6     | 235/500 [02:19<02:37,  1.68it/s]
-     47%|####7     | 236/500 [02:19<02:36,  1.68it/s]
+     47%|####7     | 236/500 [02:19<02:37,  1.68it/s]
      47%|####7     | 237/500 [02:20<02:36,  1.68it/s]
      48%|####7     | 238/500 [02:20<02:35,  1.68it/s]
      48%|####7     | 239/500 [02:21<02:35,  1.68it/s]
      48%|####8     | 240/500 [02:22<02:34,  1.68it/s]
-     48%|####8     | 241/500 [02:22<02:33,  1.68it/s]
+     48%|####8     | 241/500 [02:22<02:34,  1.68it/s]
      48%|####8     | 242/500 [02:23<02:33,  1.68it/s]
-     49%|####8     | 243/500 [02:23<02:32,  1.68it/s]
+     49%|####8     | 243/500 [02:23<02:33,  1.68it/s]
      49%|####8     | 244/500 [02:24<02:32,  1.68it/s]
-     49%|####9     | 245/500 [02:24<02:31,  1.68it/s]
-     49%|####9     | 246/500 [02:25<02:30,  1.68it/s]
+     49%|####9     | 245/500 [02:25<02:31,  1.68it/s]
+     49%|####9     | 246/500 [02:25<02:31,  1.68it/s]
      49%|####9     | 247/500 [02:26<02:30,  1.68it/s]
      50%|####9     | 248/500 [02:26<02:29,  1.68it/s]
      50%|####9     | 249/500 [02:27<02:29,  1.68it/s]
-     50%|#####     | 250/500 [02:27<02:28,  1.68it/s]
+     50%|#####     | 250/500 [02:28<02:28,  1.68it/s]
                                                  
     {'loss': 1.5305, 'learning_rate': 5e-05, 'epoch': 0.09}
 
-     50%|#####     | 250/500 [02:27<02:28,  1.68it/s]
+     50%|#####     | 250/500 [02:28<02:28,  1.68it/s]
      50%|#####     | 251/500 [02:28<02:28,  1.68it/s]
      50%|#####     | 252/500 [02:29<02:27,  1.68it/s]
-     51%|#####     | 253/500 [02:29<02:26,  1.68it/s]
+     51%|#####     | 253/500 [02:29<02:27,  1.68it/s]
      51%|#####     | 254/500 [02:30<02:26,  1.68it/s]
      51%|#####1    | 255/500 [02:30<02:25,  1.68it/s]
      51%|#####1    | 256/500 [02:31<02:25,  1.68it/s]
      51%|#####1    | 257/500 [02:32<02:24,  1.68it/s]
-     52%|#####1    | 258/500 [02:32<02:23,  1.68it/s]
+     52%|#####1    | 258/500 [02:32<02:24,  1.68it/s]
      52%|#####1    | 259/500 [02:33<02:23,  1.68it/s]
      52%|#####2    | 260/500 [02:33<02:22,  1.68it/s]
-     52%|#####2    | 261/500 [02:34<02:21,  1.68it/s]
+     52%|#####2    | 261/500 [02:34<02:22,  1.68it/s]
      52%|#####2    | 262/500 [02:35<02:21,  1.68it/s]
-     53%|#####2    | 263/500 [02:35<02:20,  1.68it/s]
+     53%|#####2    | 263/500 [02:35<02:21,  1.68it/s]
      53%|#####2    | 264/500 [02:36<02:20,  1.68it/s]
      53%|#####3    | 265/500 [02:36<02:19,  1.68it/s]
      53%|#####3    | 266/500 [02:37<02:19,  1.68it/s]
      53%|#####3    | 267/500 [02:38<02:18,  1.68it/s]
-     54%|#####3    | 268/500 [02:38<02:17,  1.68it/s]
+     54%|#####3    | 268/500 [02:38<02:18,  1.68it/s]
      54%|#####3    | 269/500 [02:39<02:17,  1.68it/s]
      54%|#####4    | 270/500 [02:39<02:16,  1.68it/s]
      54%|#####4    | 271/500 [02:40<02:16,  1.68it/s]
      54%|#####4    | 272/500 [02:41<02:15,  1.68it/s]
-     55%|#####4    | 273/500 [02:41<02:14,  1.68it/s]
+     55%|#####4    | 273/500 [02:41<02:15,  1.68it/s]
      55%|#####4    | 274/500 [02:42<02:14,  1.68it/s]
      55%|#####5    | 275/500 [02:42<02:13,  1.68it/s]
      55%|#####5    | 276/500 [02:43<02:13,  1.68it/s]
      55%|#####5    | 277/500 [02:44<02:12,  1.68it/s]
-     56%|#####5    | 278/500 [02:44<02:11,  1.68it/s]
+     56%|#####5    | 278/500 [02:44<02:12,  1.68it/s]
      56%|#####5    | 279/500 [02:45<02:11,  1.68it/s]
      56%|#####6    | 280/500 [02:45<02:10,  1.68it/s]
      56%|#####6    | 281/500 [02:46<02:10,  1.68it/s]
-     56%|#####6    | 282/500 [02:46<02:09,  1.68it/s]
-     57%|#####6    | 283/500 [02:47<02:08,  1.68it/s]
+     56%|#####6    | 282/500 [02:47<02:09,  1.68it/s]
+     57%|#####6    | 283/500 [02:47<02:09,  1.68it/s]
      57%|#####6    | 284/500 [02:48<02:08,  1.68it/s]
-     57%|#####6    | 285/500 [02:48<02:07,  1.68it/s]
+     57%|#####6    | 285/500 [02:48<02:08,  1.68it/s]
      57%|#####7    | 286/500 [02:49<02:07,  1.68it/s]
-     57%|#####7    | 287/500 [02:49<02:06,  1.68it/s]
-     58%|#####7    | 288/500 [02:50<02:05,  1.68it/s]
+     57%|#####7    | 287/500 [02:50<02:06,  1.68it/s]
+     58%|#####7    | 288/500 [02:50<02:06,  1.68it/s]
      58%|#####7    | 289/500 [02:51<02:05,  1.68it/s]
-     58%|#####8    | 290/500 [02:51<02:04,  1.68it/s]
+     58%|#####8    | 290/500 [02:51<02:05,  1.68it/s]
      58%|#####8    | 291/500 [02:52<02:04,  1.68it/s]
-     58%|#####8    | 292/500 [02:52<02:03,  1.68it/s]
+     58%|#####8    | 292/500 [02:53<02:03,  1.68it/s]
      59%|#####8    | 293/500 [02:53<02:03,  1.68it/s]
      59%|#####8    | 294/500 [02:54<02:02,  1.68it/s]
-     59%|#####8    | 295/500 [02:54<02:01,  1.68it/s]
+     59%|#####8    | 295/500 [02:54<02:02,  1.68it/s]
      59%|#####9    | 296/500 [02:55<02:01,  1.68it/s]
      59%|#####9    | 297/500 [02:55<02:00,  1.68it/s]
      60%|#####9    | 298/500 [02:56<02:00,  1.68it/s]
      60%|#####9    | 299/500 [02:57<01:59,  1.68it/s]
-     60%|######    | 300/500 [02:57<01:58,  1.68it/s]
+     60%|######    | 300/500 [02:57<01:59,  1.68it/s]
                                                  
     {'loss': 1.5315, 'learning_rate': 5e-05, 'epoch': 0.11}
 
-     60%|######    | 300/500 [02:57<01:58,  1.68it/s]
+     60%|######    | 300/500 [02:57<01:59,  1.68it/s]
      60%|######    | 301/500 [02:58<01:58,  1.68it/s]
      60%|######    | 302/500 [02:58<01:57,  1.68it/s]
      61%|######    | 303/500 [02:59<01:57,  1.68it/s]
      61%|######    | 304/500 [03:00<01:56,  1.68it/s]
-     61%|######1   | 305/500 [03:00<01:55,  1.68it/s]
+     61%|######1   | 305/500 [03:00<01:56,  1.68it/s]
      61%|######1   | 306/500 [03:01<01:55,  1.68it/s]
      61%|######1   | 307/500 [03:01<01:54,  1.68it/s]
      62%|######1   | 308/500 [03:02<01:54,  1.68it/s]
      62%|######1   | 309/500 [03:03<01:53,  1.68it/s]
-     62%|######2   | 310/500 [03:03<01:52,  1.68it/s]
+     62%|######2   | 310/500 [03:03<01:53,  1.68it/s]
      62%|######2   | 311/500 [03:04<01:52,  1.68it/s]
      62%|######2   | 312/500 [03:04<01:51,  1.68it/s]
      63%|######2   | 313/500 [03:05<01:51,  1.68it/s]
@@ -1136,35 +1104,35 @@ vs. BERT-large or fine-tuning hyperparameters.
      63%|######3   | 316/500 [03:07<01:49,  1.68it/s]
      63%|######3   | 317/500 [03:07<01:48,  1.68it/s]
      64%|######3   | 318/500 [03:08<01:48,  1.68it/s]
-     64%|######3   | 319/500 [03:08<01:47,  1.68it/s]
+     64%|######3   | 319/500 [03:09<01:47,  1.68it/s]
      64%|######4   | 320/500 [03:09<01:47,  1.68it/s]
      64%|######4   | 321/500 [03:10<01:46,  1.68it/s]
      64%|######4   | 322/500 [03:10<01:45,  1.68it/s]
      65%|######4   | 323/500 [03:11<01:45,  1.68it/s]
-     65%|######4   | 324/500 [03:11<01:44,  1.68it/s]
+     65%|######4   | 324/500 [03:12<01:44,  1.68it/s]
      65%|######5   | 325/500 [03:12<01:44,  1.68it/s]
      65%|######5   | 326/500 [03:13<01:43,  1.68it/s]
      65%|######5   | 327/500 [03:13<01:42,  1.68it/s]
      66%|######5   | 328/500 [03:14<01:42,  1.68it/s]
-     66%|######5   | 329/500 [03:14<01:41,  1.68it/s]
+     66%|######5   | 329/500 [03:15<01:41,  1.68it/s]
      66%|######6   | 330/500 [03:15<01:41,  1.68it/s]
      66%|######6   | 331/500 [03:16<01:40,  1.68it/s]
-     66%|######6   | 332/500 [03:16<01:39,  1.68it/s]
+     66%|######6   | 332/500 [03:16<01:40,  1.68it/s]
      67%|######6   | 333/500 [03:17<01:39,  1.68it/s]
-     67%|######6   | 334/500 [03:17<01:38,  1.68it/s]
+     67%|######6   | 334/500 [03:18<01:38,  1.68it/s]
      67%|######7   | 335/500 [03:18<01:38,  1.68it/s]
      67%|######7   | 336/500 [03:19<01:37,  1.68it/s]
-     67%|######7   | 337/500 [03:19<01:36,  1.68it/s]
+     67%|######7   | 337/500 [03:19<01:37,  1.68it/s]
      68%|######7   | 338/500 [03:20<01:36,  1.68it/s]
      68%|######7   | 339/500 [03:20<01:35,  1.68it/s]
      68%|######8   | 340/500 [03:21<01:35,  1.68it/s]
      68%|######8   | 341/500 [03:22<01:34,  1.68it/s]
-     68%|######8   | 342/500 [03:22<01:33,  1.68it/s]
+     68%|######8   | 342/500 [03:22<01:34,  1.68it/s]
      69%|######8   | 343/500 [03:23<01:33,  1.68it/s]
      69%|######8   | 344/500 [03:23<01:32,  1.68it/s]
      69%|######9   | 345/500 [03:24<01:32,  1.68it/s]
      69%|######9   | 346/500 [03:25<01:31,  1.68it/s]
-     69%|######9   | 347/500 [03:25<01:30,  1.68it/s]
+     69%|######9   | 347/500 [03:25<01:31,  1.68it/s]
      70%|######9   | 348/500 [03:26<01:30,  1.68it/s]
      70%|######9   | 349/500 [03:26<01:29,  1.68it/s]
      70%|#######   | 350/500 [03:27<01:29,  1.68it/s]
@@ -1177,30 +1145,30 @@ vs. BERT-large or fine-tuning hyperparameters.
      71%|#######   | 353/500 [03:29<01:27,  1.68it/s]
      71%|#######   | 354/500 [03:29<01:26,  1.68it/s]
      71%|#######1  | 355/500 [03:30<01:26,  1.68it/s]
-     71%|#######1  | 356/500 [03:30<01:25,  1.68it/s]
+     71%|#######1  | 356/500 [03:31<01:25,  1.68it/s]
      71%|#######1  | 357/500 [03:31<01:25,  1.68it/s]
      72%|#######1  | 358/500 [03:32<01:24,  1.68it/s]
      72%|#######1  | 359/500 [03:32<01:23,  1.68it/s]
      72%|#######2  | 360/500 [03:33<01:23,  1.68it/s]
-     72%|#######2  | 361/500 [03:33<01:22,  1.68it/s]
+     72%|#######2  | 361/500 [03:34<01:22,  1.68it/s]
      72%|#######2  | 362/500 [03:34<01:22,  1.68it/s]
      73%|#######2  | 363/500 [03:35<01:21,  1.68it/s]
      73%|#######2  | 364/500 [03:35<01:20,  1.68it/s]
      73%|#######3  | 365/500 [03:36<01:20,  1.68it/s]
-     73%|#######3  | 366/500 [03:36<01:19,  1.68it/s]
+     73%|#######3  | 366/500 [03:37<01:19,  1.68it/s]
      73%|#######3  | 367/500 [03:37<01:19,  1.68it/s]
      74%|#######3  | 368/500 [03:38<01:18,  1.68it/s]
-     74%|#######3  | 369/500 [03:38<01:17,  1.68it/s]
+     74%|#######3  | 369/500 [03:38<01:18,  1.68it/s]
      74%|#######4  | 370/500 [03:39<01:17,  1.68it/s]
-     74%|#######4  | 371/500 [03:39<01:16,  1.68it/s]
+     74%|#######4  | 371/500 [03:40<01:16,  1.68it/s]
      74%|#######4  | 372/500 [03:40<01:16,  1.68it/s]
      75%|#######4  | 373/500 [03:41<01:15,  1.68it/s]
-     75%|#######4  | 374/500 [03:41<01:14,  1.68it/s]
+     75%|#######4  | 374/500 [03:41<01:15,  1.68it/s]
      75%|#######5  | 375/500 [03:42<01:14,  1.68it/s]
-     75%|#######5  | 376/500 [03:42<01:13,  1.68it/s]
+     75%|#######5  | 376/500 [03:43<01:13,  1.68it/s]
      75%|#######5  | 377/500 [03:43<01:13,  1.68it/s]
      76%|#######5  | 378/500 [03:44<01:12,  1.68it/s]
-     76%|#######5  | 379/500 [03:44<01:11,  1.68it/s]
+     76%|#######5  | 379/500 [03:44<01:12,  1.68it/s]
      76%|#######6  | 380/500 [03:45<01:11,  1.68it/s]
      76%|#######6  | 381/500 [03:45<01:10,  1.68it/s]
      76%|#######6  | 382/500 [03:46<01:10,  1.68it/s]
@@ -1210,16 +1178,16 @@ vs. BERT-large or fine-tuning hyperparameters.
      77%|#######7  | 386/500 [03:48<01:07,  1.68it/s]
      77%|#######7  | 387/500 [03:49<01:07,  1.68it/s]
      78%|#######7  | 388/500 [03:50<01:06,  1.68it/s]
-     78%|#######7  | 389/500 [03:50<01:05,  1.68it/s]
+     78%|#######7  | 389/500 [03:50<01:06,  1.68it/s]
      78%|#######8  | 390/500 [03:51<01:05,  1.68it/s]
      78%|#######8  | 391/500 [03:51<01:04,  1.68it/s]
      78%|#######8  | 392/500 [03:52<01:04,  1.68it/s]
-     79%|#######8  | 393/500 [03:52<01:03,  1.68it/s]
+     79%|#######8  | 393/500 [03:53<01:03,  1.68it/s]
      79%|#######8  | 394/500 [03:53<01:03,  1.68it/s]
      79%|#######9  | 395/500 [03:54<01:02,  1.68it/s]
      79%|#######9  | 396/500 [03:54<01:01,  1.68it/s]
      79%|#######9  | 397/500 [03:55<01:01,  1.68it/s]
-     80%|#######9  | 398/500 [03:55<01:00,  1.68it/s]
+     80%|#######9  | 398/500 [03:56<01:00,  1.68it/s]
      80%|#######9  | 399/500 [03:56<01:00,  1.68it/s]
      80%|########  | 400/500 [03:57<00:59,  1.68it/s]
                                                  
@@ -1228,105 +1196,105 @@ vs. BERT-large or fine-tuning hyperparameters.
      80%|########  | 400/500 [03:57<00:59,  1.68it/s]
      80%|########  | 401/500 [03:57<00:58,  1.68it/s]
      80%|########  | 402/500 [03:58<00:58,  1.68it/s]
-     81%|########  | 403/500 [03:58<00:57,  1.68it/s]
+     81%|########  | 403/500 [03:59<00:57,  1.68it/s]
      81%|########  | 404/500 [03:59<00:57,  1.68it/s]
      81%|########1 | 405/500 [04:00<00:56,  1.68it/s]
      81%|########1 | 406/500 [04:00<00:55,  1.68it/s]
      81%|########1 | 407/500 [04:01<00:55,  1.68it/s]
-     82%|########1 | 408/500 [04:01<00:54,  1.68it/s]
+     82%|########1 | 408/500 [04:02<00:54,  1.68it/s]
      82%|########1 | 409/500 [04:02<00:54,  1.68it/s]
      82%|########2 | 410/500 [04:03<00:53,  1.68it/s]
-     82%|########2 | 411/500 [04:03<00:52,  1.68it/s]
+     82%|########2 | 411/500 [04:03<00:53,  1.68it/s]
      82%|########2 | 412/500 [04:04<00:52,  1.68it/s]
-     83%|########2 | 413/500 [04:04<00:51,  1.68it/s]
+     83%|########2 | 413/500 [04:05<00:51,  1.68it/s]
      83%|########2 | 414/500 [04:05<00:51,  1.68it/s]
      83%|########2 | 415/500 [04:06<00:50,  1.68it/s]
-     83%|########3 | 416/500 [04:06<00:49,  1.68it/s]
+     83%|########3 | 416/500 [04:06<00:50,  1.68it/s]
      83%|########3 | 417/500 [04:07<00:49,  1.68it/s]
-     84%|########3 | 418/500 [04:07<00:48,  1.68it/s]
+     84%|########3 | 418/500 [04:08<00:48,  1.68it/s]
      84%|########3 | 419/500 [04:08<00:48,  1.68it/s]
      84%|########4 | 420/500 [04:09<00:47,  1.68it/s]
-     84%|########4 | 421/500 [04:09<00:46,  1.68it/s]
+     84%|########4 | 421/500 [04:09<00:47,  1.68it/s]
      84%|########4 | 422/500 [04:10<00:46,  1.68it/s]
-     85%|########4 | 423/500 [04:10<00:45,  1.68it/s]
+     85%|########4 | 423/500 [04:11<00:45,  1.68it/s]
      85%|########4 | 424/500 [04:11<00:45,  1.68it/s]
      85%|########5 | 425/500 [04:12<00:44,  1.68it/s]
      85%|########5 | 426/500 [04:12<00:44,  1.68it/s]
      85%|########5 | 427/500 [04:13<00:43,  1.68it/s]
      86%|########5 | 428/500 [04:13<00:42,  1.68it/s]
      86%|########5 | 429/500 [04:14<00:42,  1.68it/s]
-     86%|########6 | 430/500 [04:14<00:41,  1.68it/s]
+     86%|########6 | 430/500 [04:15<00:41,  1.68it/s]
      86%|########6 | 431/500 [04:15<00:41,  1.68it/s]
      86%|########6 | 432/500 [04:16<00:40,  1.68it/s]
      87%|########6 | 433/500 [04:16<00:39,  1.68it/s]
      87%|########6 | 434/500 [04:17<00:39,  1.68it/s]
-     87%|########7 | 435/500 [04:17<00:38,  1.68it/s]
+     87%|########7 | 435/500 [04:18<00:38,  1.68it/s]
      87%|########7 | 436/500 [04:18<00:38,  1.68it/s]
      87%|########7 | 437/500 [04:19<00:37,  1.68it/s]
      88%|########7 | 438/500 [04:19<00:36,  1.68it/s]
      88%|########7 | 439/500 [04:20<00:36,  1.68it/s]
-     88%|########8 | 440/500 [04:20<00:35,  1.68it/s]
+     88%|########8 | 440/500 [04:21<00:35,  1.68it/s]
      88%|########8 | 441/500 [04:21<00:35,  1.68it/s]
      88%|########8 | 442/500 [04:22<00:34,  1.68it/s]
      89%|########8 | 443/500 [04:22<00:33,  1.68it/s]
      89%|########8 | 444/500 [04:23<00:33,  1.68it/s]
-     89%|########9 | 445/500 [04:23<00:32,  1.68it/s]
+     89%|########9 | 445/500 [04:24<00:32,  1.68it/s]
      89%|########9 | 446/500 [04:24<00:32,  1.68it/s]
      89%|########9 | 447/500 [04:25<00:31,  1.68it/s]
      90%|########9 | 448/500 [04:25<00:30,  1.68it/s]
      90%|########9 | 449/500 [04:26<00:30,  1.68it/s]
-     90%|######### | 450/500 [04:26<00:29,  1.68it/s]
+     90%|######### | 450/500 [04:27<00:29,  1.68it/s]
                                                  
     {'loss': 1.3616, 'learning_rate': 5e-05, 'epoch': 0.16}
 
-     90%|######### | 450/500 [04:26<00:29,  1.68it/s]
+     90%|######### | 450/500 [04:27<00:29,  1.68it/s]
      90%|######### | 451/500 [04:27<00:29,  1.68it/s]
      90%|######### | 452/500 [04:28<00:28,  1.68it/s]
-     91%|######### | 453/500 [04:28<00:27,  1.68it/s]
+     91%|######### | 453/500 [04:28<00:28,  1.68it/s]
      91%|######### | 454/500 [04:29<00:27,  1.68it/s]
-     91%|#########1| 455/500 [04:29<00:26,  1.68it/s]
+     91%|#########1| 455/500 [04:30<00:26,  1.68it/s]
      91%|#########1| 456/500 [04:30<00:26,  1.68it/s]
      91%|#########1| 457/500 [04:31<00:25,  1.68it/s]
-     92%|#########1| 458/500 [04:31<00:24,  1.68it/s]
+     92%|#########1| 458/500 [04:31<00:25,  1.68it/s]
      92%|#########1| 459/500 [04:32<00:24,  1.68it/s]
-     92%|#########2| 460/500 [04:32<00:23,  1.68it/s]
+     92%|#########2| 460/500 [04:33<00:23,  1.68it/s]
      92%|#########2| 461/500 [04:33<00:23,  1.68it/s]
      92%|#########2| 462/500 [04:34<00:22,  1.68it/s]
      93%|#########2| 463/500 [04:34<00:22,  1.68it/s]
      93%|#########2| 464/500 [04:35<00:21,  1.68it/s]
-     93%|#########3| 465/500 [04:35<00:20,  1.68it/s]
+     93%|#########3| 465/500 [04:36<00:20,  1.68it/s]
      93%|#########3| 466/500 [04:36<00:20,  1.68it/s]
-     93%|#########3| 467/500 [04:36<00:19,  1.68it/s]
+     93%|#########3| 467/500 [04:37<00:19,  1.68it/s]
      94%|#########3| 468/500 [04:37<00:19,  1.68it/s]
      94%|#########3| 469/500 [04:38<00:18,  1.68it/s]
-     94%|#########3| 470/500 [04:38<00:17,  1.68it/s]
+     94%|#########3| 470/500 [04:39<00:17,  1.68it/s]
      94%|#########4| 471/500 [04:39<00:17,  1.68it/s]
-     94%|#########4| 472/500 [04:39<00:16,  1.68it/s]
+     94%|#########4| 472/500 [04:40<00:16,  1.68it/s]
      95%|#########4| 473/500 [04:40<00:16,  1.68it/s]
      95%|#########4| 474/500 [04:41<00:15,  1.68it/s]
      95%|#########5| 475/500 [04:41<00:14,  1.68it/s]
      95%|#########5| 476/500 [04:42<00:14,  1.68it/s]
-     95%|#########5| 477/500 [04:42<00:13,  1.68it/s]
+     95%|#########5| 477/500 [04:43<00:13,  1.68it/s]
      96%|#########5| 478/500 [04:43<00:13,  1.68it/s]
      96%|#########5| 479/500 [04:44<00:12,  1.68it/s]
      96%|#########6| 480/500 [04:44<00:11,  1.68it/s]
      96%|#########6| 481/500 [04:45<00:11,  1.68it/s]
-     96%|#########6| 482/500 [04:45<00:10,  1.68it/s]
+     96%|#########6| 482/500 [04:46<00:10,  1.68it/s]
      97%|#########6| 483/500 [04:46<00:10,  1.68it/s]
      97%|#########6| 484/500 [04:47<00:09,  1.68it/s]
      97%|#########7| 485/500 [04:47<00:08,  1.68it/s]
      97%|#########7| 486/500 [04:48<00:08,  1.68it/s]
-     97%|#########7| 487/500 [04:48<00:07,  1.68it/s]
+     97%|#########7| 487/500 [04:49<00:07,  1.68it/s]
      98%|#########7| 488/500 [04:49<00:07,  1.68it/s]
      98%|#########7| 489/500 [04:50<00:06,  1.68it/s]
      98%|#########8| 490/500 [04:50<00:05,  1.68it/s]
      98%|#########8| 491/500 [04:51<00:05,  1.68it/s]
-     98%|#########8| 492/500 [04:51<00:04,  1.68it/s]
+     98%|#########8| 492/500 [04:52<00:04,  1.68it/s]
      99%|#########8| 493/500 [04:52<00:04,  1.68it/s]
      99%|#########8| 494/500 [04:53<00:03,  1.68it/s]
      99%|#########9| 495/500 [04:53<00:02,  1.68it/s]
      99%|#########9| 496/500 [04:54<00:02,  1.68it/s]
-     99%|#########9| 497/500 [04:54<00:01,  1.68it/s]
+     99%|#########9| 497/500 [04:55<00:01,  1.68it/s]
     100%|#########9| 498/500 [04:55<00:01,  1.68it/s]
     100%|#########9| 499/500 [04:56<00:00,  1.68it/s]
     100%|##########| 500/500 [04:56<00:00,  1.68it/s]
@@ -1335,44 +1303,44 @@ vs. BERT-large or fine-tuning hyperparameters.
 
     100%|##########| 500/500 [04:56<00:00,  1.68it/s]
                                                  
-    {'train_runtime': 297.6, 'train_samples_per_second': 53.763, 'train_steps_per_second': 1.68, 'train_loss': 1.7749846496582031, 'epoch': 0.18}
+    {'train_runtime': 297.7951, 'train_samples_per_second': 53.728, 'train_steps_per_second': 1.679, 'train_loss': 1.7749846496582031, 'epoch': 0.18}
 
     100%|##########| 500/500 [04:57<00:00,  1.68it/s]
     100%|##########| 500/500 [04:57<00:00,  1.68it/s]
 
       0%|          | 0/43 [00:00<?, ?it/s]
-      5%|4         | 2/43 [00:00<00:19,  2.13it/s]
-      7%|6         | 3/43 [00:01<00:26,  1.50it/s]
-      9%|9         | 4/43 [00:02<00:29,  1.30it/s]
+      5%|4         | 2/43 [00:00<00:19,  2.14it/s]
+      7%|6         | 3/43 [00:01<00:26,  1.51it/s]
+      9%|9         | 4/43 [00:02<00:29,  1.31it/s]
      12%|#1        | 5/43 [00:03<00:31,  1.21it/s]
      14%|#3        | 6/43 [00:04<00:31,  1.16it/s]
      16%|#6        | 7/43 [00:05<00:31,  1.13it/s]
      19%|#8        | 8/43 [00:06<00:31,  1.11it/s]
-     21%|##        | 9/43 [00:07<00:31,  1.09it/s]
-     23%|##3       | 10/43 [00:08<00:30,  1.08it/s]
+     21%|##        | 9/43 [00:07<00:30,  1.10it/s]
+     23%|##3       | 10/43 [00:08<00:30,  1.09it/s]
      26%|##5       | 11/43 [00:09<00:29,  1.08it/s]
-     28%|##7       | 12/43 [00:10<00:28,  1.07it/s]
-     30%|###       | 13/43 [00:11<00:27,  1.07it/s]
-     33%|###2      | 14/43 [00:12<00:27,  1.07it/s]
+     28%|##7       | 12/43 [00:10<00:28,  1.08it/s]
+     30%|###       | 13/43 [00:11<00:27,  1.08it/s]
+     33%|###2      | 14/43 [00:12<00:26,  1.08it/s]
      35%|###4      | 15/43 [00:13<00:26,  1.07it/s]
      37%|###7      | 16/43 [00:14<00:25,  1.07it/s]
-     40%|###9      | 17/43 [00:15<00:24,  1.07it/s]
+     40%|###9      | 17/43 [00:14<00:24,  1.07it/s]
      42%|####1     | 18/43 [00:15<00:23,  1.07it/s]
      44%|####4     | 19/43 [00:16<00:22,  1.07it/s]
-     47%|####6     | 20/43 [00:17<00:21,  1.06it/s]
-     49%|####8     | 21/43 [00:18<00:20,  1.06it/s]
-     51%|#####1    | 22/43 [00:19<00:19,  1.06it/s]
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-     58%|#####8    | 25/43 [00:22<00:16,  1.06it/s]
-     60%|######    | 26/43 [00:23<00:15,  1.06it/s]
-     63%|######2   | 27/43 [00:24<00:15,  1.07it/s]
+     47%|####6     | 20/43 [00:17<00:21,  1.07it/s]
+     49%|####8     | 21/43 [00:18<00:20,  1.07it/s]
+     51%|#####1    | 22/43 [00:19<00:19,  1.07it/s]
+     53%|#####3    | 23/43 [00:20<00:18,  1.07it/s]
+     56%|#####5    | 24/43 [00:21<00:17,  1.07it/s]
+     58%|#####8    | 25/43 [00:22<00:16,  1.07it/s]
+     60%|######    | 26/43 [00:23<00:15,  1.07it/s]
+     63%|######2   | 27/43 [00:24<00:14,  1.07it/s]
      65%|######5   | 28/43 [00:25<00:14,  1.07it/s]
      67%|######7   | 29/43 [00:26<00:13,  1.07it/s]
      70%|######9   | 30/43 [00:27<00:12,  1.07it/s]
      72%|#######2  | 31/43 [00:28<00:11,  1.07it/s]
-     74%|#######4  | 32/43 [00:29<00:10,  1.07it/s]
-     77%|#######6  | 33/43 [00:30<00:09,  1.07it/s]
+     74%|#######4  | 32/43 [00:28<00:10,  1.07it/s]
+     77%|#######6  | 33/43 [00:29<00:09,  1.07it/s]
      79%|#######9  | 34/43 [00:30<00:08,  1.07it/s]
      81%|########1 | 35/43 [00:31<00:07,  1.07it/s]
      84%|########3 | 36/43 [00:32<00:06,  1.07it/s]
@@ -1381,17 +1349,17 @@ vs. BERT-large or fine-tuning hyperparameters.
      91%|######### | 39/43 [00:35<00:03,  1.07it/s]
      93%|#########3| 40/43 [00:36<00:02,  1.07it/s]
      95%|#########5| 41/43 [00:37<00:01,  1.07it/s]
-     98%|#########7| 42/43 [00:38<00:00,  1.11it/s]
-    100%|##########| 43/43 [00:38<00:00,  1.48it/s]
+     98%|#########7| 42/43 [00:38<00:00,  1.12it/s]
+    100%|##########| 43/43 [00:38<00:00,  1.49it/s]
     100%|##########| 43/43 [00:38<00:00,  1.12it/s]
 
     Downloading builder script:   0%|          | 0.00/4.53k [00:00<?, ?B/s]
-    Downloading builder script: 100%|##########| 4.53k/4.53k [00:00<00:00, 32.4MB/s]
+    Downloading builder script: 100%|##########| 4.53k/4.53k [00:00<00:00, 32.6MB/s]
 
     Downloading extra modules:   0%|          | 0.00/3.32k [00:00<?, ?B/s]
-    Downloading extra modules: 100%|##########| 3.32k/3.32k [00:00<00:00, 24.4MB/s]
+    Downloading extra modules: 100%|##########| 3.32k/3.32k [00:00<00:00, 33.0MB/s]
     fp16 {'exact_match': 71.77861873226112, 'f1': 81.35189141414303}
-    cuda_fp16 time {4: 14.114074000099208, '4_compile': 9.624771002563648, 16: 51.17472790007014, '16_compile': 36.57838400067703, 64: 201.03624100011075, '64_compile': 123.16050250046828, 256: 785.2311540009396, '256_compile': 474.52999999950407}
+    cuda_fp16 time {4: 14.086745750137197, '4_compile': 9.647232000133954, 16: 51.1473719998321, '16_compile': 36.34630699889385, 64: 200.96267699773307, '64_compile': 123.04909100021177, 256: 785.1076100014325, '256_compile': 474.5083670022723}
 
     <Axes: title={'center': 'Loss vs. # steps'}, xlabel='step', ylabel='loss'>
 
@@ -1530,49 +1498,49 @@ zero-shot pruning, or pruning without fine-tuning / retraining.
 
 
       0%|          | 0/43 [00:00<?, ?it/s]
-      5%|4         | 2/43 [00:00<00:19,  2.11it/s]
+      5%|4         | 2/43 [00:00<00:19,  2.13it/s]
       7%|6         | 3/43 [00:01<00:26,  1.50it/s]
-      9%|9         | 4/43 [00:02<00:30,  1.30it/s]
+      9%|9         | 4/43 [00:02<00:29,  1.30it/s]
      12%|#1        | 5/43 [00:03<00:31,  1.21it/s]
-     14%|#3        | 6/43 [00:04<00:32,  1.15it/s]
-     16%|#6        | 7/43 [00:05<00:32,  1.12it/s]
-     19%|#8        | 8/43 [00:06<00:31,  1.10it/s]
+     14%|#3        | 6/43 [00:04<00:31,  1.16it/s]
+     16%|#6        | 7/43 [00:05<00:31,  1.13it/s]
+     19%|#8        | 8/43 [00:06<00:31,  1.11it/s]
      21%|##        | 9/43 [00:07<00:31,  1.09it/s]
-     23%|##3       | 10/43 [00:08<00:30,  1.08it/s]
-     26%|##5       | 11/43 [00:09<00:29,  1.07it/s]
-     28%|##7       | 12/43 [00:10<00:28,  1.07it/s]
-     30%|###       | 13/43 [00:11<00:28,  1.07it/s]
+     23%|##3       | 10/43 [00:08<00:30,  1.09it/s]
+     26%|##5       | 11/43 [00:09<00:29,  1.08it/s]
+     28%|##7       | 12/43 [00:10<00:28,  1.08it/s]
+     30%|###       | 13/43 [00:11<00:27,  1.07it/s]
      33%|###2      | 14/43 [00:12<00:27,  1.07it/s]
-     35%|###4      | 15/43 [00:13<00:26,  1.06it/s]
-     37%|###7      | 16/43 [00:14<00:25,  1.06it/s]
-     40%|###9      | 17/43 [00:15<00:24,  1.06it/s]
-     42%|####1     | 18/43 [00:16<00:23,  1.06it/s]
-     44%|####4     | 19/43 [00:16<00:22,  1.06it/s]
-     47%|####6     | 20/43 [00:17<00:21,  1.06it/s]
-     49%|####8     | 21/43 [00:18<00:20,  1.06it/s]
-     51%|#####1    | 22/43 [00:19<00:19,  1.06it/s]
-     53%|#####3    | 23/43 [00:20<00:18,  1.06it/s]
-     56%|#####5    | 24/43 [00:21<00:17,  1.06it/s]
-     58%|#####8    | 25/43 [00:22<00:16,  1.06it/s]
-     60%|######    | 26/43 [00:23<00:16,  1.06it/s]
+     35%|###4      | 15/43 [00:13<00:26,  1.07it/s]
+     37%|###7      | 16/43 [00:14<00:25,  1.07it/s]
+     40%|###9      | 17/43 [00:15<00:24,  1.07it/s]
+     42%|####1     | 18/43 [00:15<00:23,  1.07it/s]
+     44%|####4     | 19/43 [00:16<00:22,  1.07it/s]
+     47%|####6     | 20/43 [00:17<00:21,  1.07it/s]
+     49%|####8     | 21/43 [00:18<00:20,  1.07it/s]
+     51%|#####1    | 22/43 [00:19<00:19,  1.07it/s]
+     53%|#####3    | 23/43 [00:20<00:18,  1.07it/s]
+     56%|#####5    | 24/43 [00:21<00:17,  1.07it/s]
+     58%|#####8    | 25/43 [00:22<00:16,  1.07it/s]
+     60%|######    | 26/43 [00:23<00:15,  1.07it/s]
      63%|######2   | 27/43 [00:24<00:15,  1.06it/s]
      65%|######5   | 28/43 [00:25<00:14,  1.06it/s]
      67%|######7   | 29/43 [00:26<00:13,  1.06it/s]
-     70%|######9   | 30/43 [00:27<00:12,  1.06it/s]
-     72%|#######2  | 31/43 [00:28<00:11,  1.06it/s]
-     74%|#######4  | 32/43 [00:29<00:10,  1.06it/s]
-     77%|#######6  | 33/43 [00:30<00:09,  1.06it/s]
-     79%|#######9  | 34/43 [00:31<00:08,  1.06it/s]
-     81%|########1 | 35/43 [00:32<00:07,  1.06it/s]
-     84%|########3 | 36/43 [00:33<00:06,  1.06it/s]
-     86%|########6 | 37/43 [00:33<00:05,  1.06it/s]
-     88%|########8 | 38/43 [00:34<00:04,  1.06it/s]
-     91%|######### | 39/43 [00:35<00:03,  1.06it/s]
-     93%|#########3| 40/43 [00:36<00:02,  1.06it/s]
-     95%|#########5| 41/43 [00:37<00:01,  1.06it/s]
+     70%|######9   | 30/43 [00:27<00:12,  1.07it/s]
+     72%|#######2  | 31/43 [00:28<00:11,  1.07it/s]
+     74%|#######4  | 32/43 [00:29<00:10,  1.07it/s]
+     77%|#######6  | 33/43 [00:30<00:09,  1.07it/s]
+     79%|#######9  | 34/43 [00:30<00:08,  1.07it/s]
+     81%|########1 | 35/43 [00:31<00:07,  1.07it/s]
+     84%|########3 | 36/43 [00:32<00:06,  1.07it/s]
+     86%|########6 | 37/43 [00:33<00:05,  1.07it/s]
+     88%|########8 | 38/43 [00:34<00:04,  1.07it/s]
+     91%|######### | 39/43 [00:35<00:03,  1.07it/s]
+     93%|#########3| 40/43 [00:36<00:02,  1.07it/s]
+     95%|#########5| 41/43 [00:37<00:01,  1.07it/s]
      98%|#########7| 42/43 [00:38<00:00,  1.11it/s]
-    100%|##########| 43/43 [00:38<00:00,  1.47it/s]
-    100%|##########| 43/43 [00:38<00:00,  1.11it/s]
+    100%|##########| 43/43 [00:38<00:00,  1.48it/s]
+    100%|##########| 43/43 [00:38<00:00,  1.12it/s]
     pruned eval metrics: {'exact_match': 31.069063386944183, 'f1': 43.162708225870595}
 
 
@@ -1620,49 +1588,49 @@ we are left with a zeroed-out 2:4 dense model.
       0%|          | 2/500 [00:01<05:00,  1.66it/s]
       1%|          | 3/500 [00:01<04:57,  1.67it/s]
       1%|          | 4/500 [00:02<04:55,  1.68it/s]
-      1%|1         | 5/500 [00:02<04:54,  1.68it/s]
-      1%|1         | 6/500 [00:03<04:53,  1.69it/s]
-      1%|1         | 7/500 [00:04<04:52,  1.69it/s]
+      1%|1         | 5/500 [00:02<04:53,  1.69it/s]
+      1%|1         | 6/500 [00:03<04:52,  1.69it/s]
+      1%|1         | 7/500 [00:04<04:51,  1.69it/s]
       2%|1         | 8/500 [00:04<04:51,  1.69it/s]
-      2%|1         | 9/500 [00:05<04:51,  1.69it/s]
-      2%|2         | 10/500 [00:05<04:50,  1.69it/s]
+      2%|1         | 9/500 [00:05<04:50,  1.69it/s]
+      2%|2         | 10/500 [00:05<04:49,  1.69it/s]
       2%|2         | 11/500 [00:06<04:49,  1.69it/s]
-      2%|2         | 12/500 [00:07<04:49,  1.69it/s]
-      3%|2         | 13/500 [00:07<04:48,  1.69it/s]
-      3%|2         | 14/500 [00:08<04:48,  1.69it/s]
-      3%|3         | 15/500 [00:08<04:47,  1.69it/s]
-      3%|3         | 16/500 [00:09<04:47,  1.69it/s]
-      3%|3         | 17/500 [00:10<04:46,  1.69it/s]
+      2%|2         | 12/500 [00:07<04:48,  1.69it/s]
+      3%|2         | 13/500 [00:07<04:47,  1.69it/s]
+      3%|2         | 14/500 [00:08<04:47,  1.69it/s]
+      3%|3         | 15/500 [00:08<04:46,  1.69it/s]
+      3%|3         | 16/500 [00:09<04:46,  1.69it/s]
+      3%|3         | 17/500 [00:10<04:45,  1.69it/s]
       4%|3         | 18/500 [00:10<04:45,  1.69it/s]
-      4%|3         | 19/500 [00:11<04:45,  1.69it/s]
+      4%|3         | 19/500 [00:11<04:44,  1.69it/s]
       4%|4         | 20/500 [00:11<04:44,  1.69it/s]
-      4%|4         | 21/500 [00:12<04:44,  1.69it/s]
-      4%|4         | 22/500 [00:13<04:43,  1.68it/s]
-      5%|4         | 23/500 [00:13<04:43,  1.69it/s]
+      4%|4         | 21/500 [00:12<04:43,  1.69it/s]
+      4%|4         | 22/500 [00:13<04:43,  1.69it/s]
+      5%|4         | 23/500 [00:13<04:42,  1.69it/s]
       5%|4         | 24/500 [00:14<04:42,  1.69it/s]
       5%|5         | 25/500 [00:14<04:41,  1.69it/s]
-      5%|5         | 26/500 [00:15<04:41,  1.69it/s]
+      5%|5         | 26/500 [00:15<04:40,  1.69it/s]
       5%|5         | 27/500 [00:16<04:40,  1.69it/s]
       6%|5         | 28/500 [00:16<04:39,  1.69it/s]
       6%|5         | 29/500 [00:17<04:39,  1.69it/s]
       6%|6         | 30/500 [00:17<04:38,  1.69it/s]
       6%|6         | 31/500 [00:18<04:38,  1.69it/s]
-      6%|6         | 32/500 [00:19<04:37,  1.69it/s]
-      7%|6         | 33/500 [00:19<04:37,  1.68it/s]
-      7%|6         | 34/500 [00:20<04:36,  1.68it/s]
-      7%|7         | 35/500 [00:20<04:36,  1.68it/s]
-      7%|7         | 36/500 [00:21<04:35,  1.68it/s]
-      7%|7         | 37/500 [00:21<04:34,  1.68it/s]
-      8%|7         | 38/500 [00:22<04:34,  1.68it/s]
-      8%|7         | 39/500 [00:23<04:33,  1.68it/s]
-      8%|8         | 40/500 [00:23<04:33,  1.68it/s]
-      8%|8         | 41/500 [00:24<04:32,  1.68it/s]
-      8%|8         | 42/500 [00:24<04:31,  1.68it/s]
-      9%|8         | 43/500 [00:25<04:31,  1.68it/s]
-      9%|8         | 44/500 [00:26<04:30,  1.68it/s]
+      6%|6         | 32/500 [00:18<04:37,  1.69it/s]
+      7%|6         | 33/500 [00:19<04:36,  1.69it/s]
+      7%|6         | 34/500 [00:20<04:36,  1.69it/s]
+      7%|7         | 35/500 [00:20<04:35,  1.69it/s]
+      7%|7         | 36/500 [00:21<04:35,  1.69it/s]
+      7%|7         | 37/500 [00:21<04:34,  1.69it/s]
+      8%|7         | 38/500 [00:22<04:33,  1.69it/s]
+      8%|7         | 39/500 [00:23<04:33,  1.69it/s]
+      8%|8         | 40/500 [00:23<04:32,  1.69it/s]
+      8%|8         | 41/500 [00:24<04:32,  1.69it/s]
+      8%|8         | 42/500 [00:24<04:31,  1.69it/s]
+      9%|8         | 43/500 [00:25<04:31,  1.69it/s]
+      9%|8         | 44/500 [00:26<04:30,  1.69it/s]
       9%|9         | 45/500 [00:26<04:30,  1.68it/s]
       9%|9         | 46/500 [00:27<04:29,  1.68it/s]
-      9%|9         | 47/500 [00:27<04:29,  1.68it/s]
+      9%|9         | 47/500 [00:27<04:28,  1.68it/s]
      10%|9         | 48/500 [00:28<04:28,  1.68it/s]
      10%|9         | 49/500 [00:29<04:27,  1.68it/s]
      10%|#         | 50/500 [00:29<04:27,  1.68it/s]
@@ -1673,7 +1641,7 @@ we are left with a zeroed-out 2:4 dense model.
      10%|#         | 51/500 [00:30<04:26,  1.68it/s]
      10%|#         | 52/500 [00:30<04:26,  1.68it/s]
      11%|#         | 53/500 [00:31<04:25,  1.68it/s]
-     11%|#         | 54/500 [00:32<04:25,  1.68it/s]
+     11%|#         | 54/500 [00:32<04:24,  1.68it/s]
      11%|#1        | 55/500 [00:32<04:24,  1.68it/s]
      11%|#1        | 56/500 [00:33<04:23,  1.68it/s]
      11%|#1        | 57/500 [00:33<04:23,  1.68it/s]
@@ -1683,50 +1651,50 @@ we are left with a zeroed-out 2:4 dense model.
      12%|#2        | 61/500 [00:36<04:20,  1.68it/s]
      12%|#2        | 62/500 [00:36<04:20,  1.68it/s]
      13%|#2        | 63/500 [00:37<04:19,  1.68it/s]
-     13%|#2        | 64/500 [00:38<04:19,  1.68it/s]
+     13%|#2        | 64/500 [00:37<04:18,  1.68it/s]
      13%|#3        | 65/500 [00:38<04:18,  1.68it/s]
-     13%|#3        | 66/500 [00:39<04:18,  1.68it/s]
+     13%|#3        | 66/500 [00:39<04:17,  1.68it/s]
      13%|#3        | 67/500 [00:39<04:17,  1.68it/s]
-     14%|#3        | 68/500 [00:40<04:17,  1.68it/s]
+     14%|#3        | 68/500 [00:40<04:16,  1.68it/s]
      14%|#3        | 69/500 [00:40<04:16,  1.68it/s]
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+     14%|#4        | 70/500 [00:41<04:15,  1.68it/s]
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+     14%|#4        | 72/500 [00:42<04:14,  1.68it/s]
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+     15%|#5        | 77/500 [00:45<04:11,  1.68it/s]
      16%|#5        | 78/500 [00:46<04:11,  1.68it/s]
-     16%|#5        | 79/500 [00:46<04:11,  1.68it/s]
+     16%|#5        | 79/500 [00:46<04:10,  1.68it/s]
      16%|#6        | 80/500 [00:47<04:10,  1.68it/s]
-     16%|#6        | 81/500 [00:48<04:10,  1.68it/s]
+     16%|#6        | 81/500 [00:48<04:09,  1.68it/s]
      16%|#6        | 82/500 [00:48<04:09,  1.68it/s]
      17%|#6        | 83/500 [00:49<04:08,  1.68it/s]
-     17%|#6        | 84/500 [00:49<04:08,  1.68it/s]
+     17%|#6        | 84/500 [00:49<04:07,  1.68it/s]
      17%|#7        | 85/500 [00:50<04:07,  1.68it/s]
-     17%|#7        | 86/500 [00:51<04:07,  1.68it/s]
+     17%|#7        | 86/500 [00:51<04:06,  1.68it/s]
      17%|#7        | 87/500 [00:51<04:06,  1.68it/s]
      18%|#7        | 88/500 [00:52<04:05,  1.68it/s]
-     18%|#7        | 89/500 [00:52<04:05,  1.68it/s]
+     18%|#7        | 89/500 [00:52<04:04,  1.68it/s]
      18%|#8        | 90/500 [00:53<04:04,  1.68it/s]
-     18%|#8        | 91/500 [00:54<04:04,  1.68it/s]
+     18%|#8        | 91/500 [00:54<04:03,  1.68it/s]
      18%|#8        | 92/500 [00:54<04:03,  1.68it/s]
      19%|#8        | 93/500 [00:55<04:02,  1.68it/s]
      19%|#8        | 94/500 [00:55<04:02,  1.68it/s]
-     19%|#9        | 95/500 [00:56<04:01,  1.67it/s]
-     19%|#9        | 96/500 [00:57<04:01,  1.68it/s]
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+     19%|#9        | 95/500 [00:56<04:01,  1.68it/s]
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      20%|#9        | 98/500 [00:58<03:59,  1.68it/s]
      20%|#9        | 99/500 [00:58<03:59,  1.68it/s]
-     20%|##        | 100/500 [00:59<03:58,  1.67it/s]
+     20%|##        | 100/500 [00:59<03:58,  1.68it/s]
                                                  
     {'loss': 1.4599, 'learning_rate': 5e-05, 'epoch': 0.04}
 
-     20%|##        | 100/500 [00:59<03:58,  1.67it/s]
-     20%|##        | 101/500 [01:00<03:58,  1.67it/s]
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      21%|##1       | 106/500 [01:03<03:55,  1.68it/s]
@@ -1734,31 +1702,31 @@ we are left with a zeroed-out 2:4 dense model.
      22%|##1       | 108/500 [01:04<03:53,  1.68it/s]
      22%|##1       | 109/500 [01:04<03:53,  1.68it/s]
      22%|##2       | 110/500 [01:05<03:52,  1.68it/s]
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+     23%|##3       | 115/500 [01:08<03:49,  1.68it/s]
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      27%|##6       | 134/500 [01:19<03:38,  1.67it/s]
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      27%|##7       | 137/500 [01:21<03:36,  1.67it/s]
      28%|##7       | 138/500 [01:22<03:36,  1.67it/s]
@@ -1779,29 +1747,29 @@ we are left with a zeroed-out 2:4 dense model.
 
      30%|###       | 150/500 [01:29<03:29,  1.67it/s]
      30%|###       | 151/500 [01:29<03:28,  1.67it/s]
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      33%|###2      | 163/500 [01:37<03:21,  1.67it/s]
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+     33%|###2      | 164/500 [01:37<03:21,  1.67it/s]
      33%|###3      | 165/500 [01:38<03:20,  1.67it/s]
      33%|###3      | 166/500 [01:38<03:19,  1.67it/s]
      33%|###3      | 167/500 [01:39<03:19,  1.67it/s]
      34%|###3      | 168/500 [01:40<03:18,  1.67it/s]
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+     34%|###3      | 169/500 [01:40<03:18,  1.67it/s]
      34%|###4      | 170/500 [01:41<03:17,  1.67it/s]
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      35%|###4      | 173/500 [01:43<03:15,  1.67it/s]
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      35%|###5      | 176/500 [01:44<03:13,  1.67it/s]
      35%|###5      | 177/500 [01:45<03:13,  1.67it/s]
@@ -1811,7 +1779,7 @@ we are left with a zeroed-out 2:4 dense model.
      36%|###6      | 181/500 [01:47<03:10,  1.67it/s]
      36%|###6      | 182/500 [01:48<03:10,  1.67it/s]
      37%|###6      | 183/500 [01:49<03:09,  1.67it/s]
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      37%|###7      | 185/500 [01:50<03:08,  1.67it/s]
      37%|###7      | 186/500 [01:50<03:07,  1.67it/s]
      37%|###7      | 187/500 [01:51<03:07,  1.67it/s]
@@ -1823,7 +1791,7 @@ we are left with a zeroed-out 2:4 dense model.
      39%|###8      | 193/500 [01:55<03:03,  1.67it/s]
      39%|###8      | 194/500 [01:55<03:03,  1.67it/s]
      39%|###9      | 195/500 [01:56<03:02,  1.67it/s]
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      39%|###9      | 197/500 [01:57<03:01,  1.67it/s]
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      40%|###9      | 199/500 [01:58<03:00,  1.67it/s]
@@ -1839,47 +1807,47 @@ we are left with a zeroed-out 2:4 dense model.
      41%|####1     | 205/500 [02:02<02:56,  1.67it/s]
      41%|####1     | 206/500 [02:02<02:56,  1.67it/s]
      41%|####1     | 207/500 [02:03<02:55,  1.67it/s]
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      47%|####7     | 237/500 [02:21<02:37,  1.67it/s]
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      48%|####7     | 239/500 [02:22<02:36,  1.67it/s]
      48%|####8     | 240/500 [02:23<02:35,  1.67it/s]
      48%|####8     | 241/500 [02:23<02:35,  1.67it/s]
      48%|####8     | 242/500 [02:24<02:34,  1.67it/s]
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      50%|#####     | 250/500 [02:29<02:29,  1.67it/s]
                                                  
@@ -1888,27 +1856,27 @@ we are left with a zeroed-out 2:4 dense model.
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      53%|#####2    | 264/500 [02:37<02:21,  1.67it/s]
      53%|#####3    | 265/500 [02:38<02:20,  1.67it/s]
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      53%|#####3    | 267/500 [02:39<02:19,  1.67it/s]
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      54%|#####3    | 269/500 [02:40<02:18,  1.67it/s]
      54%|#####4    | 270/500 [02:41<02:17,  1.67it/s]
      54%|#####4    | 271/500 [02:41<02:17,  1.67it/s]
      54%|#####4    | 272/500 [02:42<02:16,  1.67it/s]
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      55%|#####4    | 274/500 [02:43<02:15,  1.67it/s]
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      55%|#####5    | 276/500 [02:44<02:14,  1.67it/s]
@@ -1918,12 +1886,12 @@ we are left with a zeroed-out 2:4 dense model.
      56%|#####6    | 280/500 [02:47<02:11,  1.67it/s]
      56%|#####6    | 281/500 [02:47<02:11,  1.67it/s]
      56%|#####6    | 282/500 [02:48<02:10,  1.67it/s]
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      57%|#####7    | 286/500 [02:50<02:08,  1.67it/s]
      57%|#####7    | 287/500 [02:51<02:07,  1.67it/s]
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      58%|#####7    | 289/500 [02:52<02:06,  1.67it/s]
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      58%|#####8    | 291/500 [02:53<02:05,  1.67it/s]
@@ -1952,7 +1920,7 @@ we are left with a zeroed-out 2:4 dense model.
      62%|######2   | 310/500 [03:05<01:53,  1.67it/s]
      62%|######2   | 311/500 [03:05<01:53,  1.67it/s]
      62%|######2   | 312/500 [03:06<01:52,  1.67it/s]
-     63%|######2   | 313/500 [03:06<01:51,  1.67it/s]
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      63%|######2   | 314/500 [03:07<01:51,  1.67it/s]
      63%|######3   | 315/500 [03:08<01:50,  1.67it/s]
      63%|######3   | 316/500 [03:08<01:50,  1.67it/s]
@@ -2157,7 +2125,7 @@ we are left with a zeroed-out 2:4 dense model.
 
     100%|##########| 500/500 [04:59<00:00,  1.67it/s]
                                                  
-    {'train_runtime': 308.189, 'train_samples_per_second': 51.916, 'train_steps_per_second': 1.622, 'train_loss': 1.402785385131836, 'epoch': 0.18}
+    {'train_runtime': 308.1732, 'train_samples_per_second': 51.919, 'train_steps_per_second': 1.622, 'train_loss': 1.402785385131836, 'epoch': 0.18}
 
     100%|##########| 500/500 [05:08<00:00,  1.67it/s]
     100%|##########| 500/500 [05:08<00:00,  1.62it/s]
@@ -2223,50 +2191,50 @@ inference just like in the QuickStart Guide.
 
 
       0%|          | 0/43 [00:00<?, ?it/s]
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-      7%|6         | 3/43 [00:01<00:25,  1.56it/s]
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-     21%|##        | 9/43 [00:07<00:30,  1.13it/s]
-     23%|##3       | 10/43 [00:08<00:29,  1.12it/s]
-     26%|##5       | 11/43 [00:09<00:28,  1.12it/s]
-     28%|##7       | 12/43 [00:09<00:27,  1.11it/s]
-     30%|###       | 13/43 [00:10<00:27,  1.11it/s]
-     33%|###2      | 14/43 [00:11<00:26,  1.11it/s]
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+    100%|##########| 43/43 [00:36<00:00,  1.17it/s]
     sparse eval metrics:  {'exact_match': 71.69347209082308, 'f1': 81.04133166079365}
-    sparse perf metrics:  {4: 15.225212249970355, '4_compile': 7.838782999897376, 16: 33.27060020019417, '16_compile': 25.207834400134743, 64: 129.30342100116832, '64_compile': 98.90185570002359, 256: 751.4368839983945, '256_compile': 412.98149100111914}
+    sparse perf metrics:  {4: 15.75757575010357, '4_compile': 7.653417000256013, 16: 33.264404800138436, '16_compile': 25.207538699760335, 64: 128.67821350118902, '64_compile': 98.73939639983291, 256: 748.8537140015978, '256_compile': 410.3211130022828}
 
 
 
@@ -2325,7 +2293,7 @@ fine-tuning BERT to recover any lost F1 (86.92 dense vs 86.48 sparse).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 21 minutes  7.733 seconds)
+   **Total running time of the script:** ( 17 minutes  23.687 seconds)
 
 
 .. _sphx_glr_download_advanced_semi_structured_sparse.py:
diff --git a/_sources/beginner/Intro_to_TorchScript_tutorial.rst.txt b/_sources/beginner/Intro_to_TorchScript_tutorial.rst.txt
index 34652f3644..4b0bbce3d9 100644
--- a/_sources/beginner/Intro_to_TorchScript_tutorial.rst.txt
+++ b/_sources/beginner/Intro_to_TorchScript_tutorial.rst.txt
@@ -68,7 +68,7 @@ model from C++.
 
     2.3.0+cu121
 
-    <torch._C.Generator object at 0x7f4b55b2f510>
+    <torch._C.Generator object at 0x7f3bed1d7510>
 
 
 
@@ -806,7 +806,7 @@ https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.219 seconds)
+   **Total running time of the script:** ( 0 minutes  0.224 seconds)
 
 
 .. _sphx_glr_download_beginner_Intro_to_TorchScript_tutorial.py:
diff --git a/_sources/beginner/basics/autogradqs_tutorial.rst.txt b/_sources/beginner/basics/autogradqs_tutorial.rst.txt
index b7d4dab5a0..28bb181e62 100644
--- a/_sources/beginner/basics/autogradqs_tutorial.rst.txt
+++ b/_sources/beginner/basics/autogradqs_tutorial.rst.txt
@@ -113,8 +113,8 @@ documentation <https://pytorch.org/docs/stable/autograd.html#function>`__.
 
  .. code-block:: none
 
-    Gradient function for z = <AddBackward0 object at 0x7f9ebc3e3970>
-    Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward0 object at 0x7f9ebebc32e0>
+    Gradient function for z = <AddBackward0 object at 0x7fedc54b9510>
+    Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward0 object at 0x7fedc54b9bd0>
 
 
 
diff --git a/_sources/beginner/basics/buildmodel_tutorial.rst.txt b/_sources/beginner/basics/buildmodel_tutorial.rst.txt
index 2f0fef9a00..56e8c50d53 100644
--- a/_sources/beginner/basics/buildmodel_tutorial.rst.txt
+++ b/_sources/beginner/basics/buildmodel_tutorial.rst.txt
@@ -482,7 +482,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.322 seconds)
+   **Total running time of the script:** ( 0 minutes  0.309 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_buildmodel_tutorial.py:
diff --git a/_sources/beginner/basics/data_tutorial.rst.txt b/_sources/beginner/basics/data_tutorial.rst.txt
index ec9fc72954..8d308dc647 100644
--- a/_sources/beginner/basics/data_tutorial.rst.txt
+++ b/_sources/beginner/basics/data_tutorial.rst.txt
@@ -103,43 +103,62 @@ We load the `FashionMNIST Dataset <https://pytorch.org/vision/stable/datasets.ht
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
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     Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
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+    100%|##########| 29515/29515 [00:00<00:00, 327533.48it/s]
     Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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+    100%|##########| 4422102/4422102 [00:00<00:00, 6223488.96it/s]
     Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
       0%|          | 0/5148 [00:00<?, ?it/s]
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+    100%|##########| 5148/5148 [00:00<00:00, 40209081.92it/s]
     Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
 
@@ -440,7 +459,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  5.375 seconds)
+   **Total running time of the script:** ( 0 minutes  8.455 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_data_tutorial.py:
diff --git a/_sources/beginner/basics/optimization_tutorial.rst.txt b/_sources/beginner/basics/optimization_tutorial.rst.txt
index d51ae5a6b0..69bac208eb 100644
--- a/_sources/beginner/basics/optimization_tutorial.rst.txt
+++ b/_sources/beginner/basics/optimization_tutorial.rst.txt
@@ -102,49 +102,92 @@ and `Build Model  <buildmodel_tutorial.html>`_.
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
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+    100%|##########| 26421880/26421880 [00:08<00:00, 3072146.60it/s] 
     Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
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     Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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     Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
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+    100%|##########| 5148/5148 [00:00<00:00, 38014572.17it/s]
     Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
 
@@ -521,7 +564,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  4.865 seconds)
+   **Total running time of the script:** ( 2 minutes  12.018 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_optimization_tutorial.py:
diff --git a/_sources/beginner/basics/quickstart_tutorial.rst.txt b/_sources/beginner/basics/quickstart_tutorial.rst.txt
index 089a783077..ac54b296d9 100644
--- a/_sources/beginner/basics/quickstart_tutorial.rst.txt
+++ b/_sources/beginner/basics/quickstart_tutorial.rst.txt
@@ -101,57 +101,42 @@ use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two argumen
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
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     Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
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+    100%|##########| 29515/29515 [00:00<00:00, 325277.82it/s]
     Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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     Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
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     Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
 
@@ -598,7 +583,7 @@ Read more about `Saving & Loading your model <saveloadrun_tutorial.html>`_.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  3.247 seconds)
+   **Total running time of the script:** ( 1 minutes  6.083 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_quickstart_tutorial.py:
diff --git a/_sources/beginner/basics/saveloadrun_tutorial.rst.txt b/_sources/beginner/basics/saveloadrun_tutorial.rst.txt
index b17bb4646b..66f7a01ccc 100644
--- a/_sources/beginner/basics/saveloadrun_tutorial.rst.txt
+++ b/_sources/beginner/basics/saveloadrun_tutorial.rst.txt
@@ -76,38 +76,37 @@ method:
     Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vgg16-397923af.pth
 
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@@ -241,7 +240,7 @@ Related Tutorials
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  8.752 seconds)
+   **Total running time of the script:** ( 0 minutes  8.645 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_saveloadrun_tutorial.py:
diff --git a/_sources/beginner/basics/transforms_tutorial.rst.txt b/_sources/beginner/basics/transforms_tutorial.rst.txt
index 3305f84205..9dcb112272 100644
--- a/_sources/beginner/basics/transforms_tutorial.rst.txt
+++ b/_sources/beginner/basics/transforms_tutorial.rst.txt
@@ -73,53 +73,40 @@ To make these transformations, we use ``ToTensor`` and ``Lambda``.
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
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+    100%|##########| 26421880/26421880 [00:01<00:00, 18683791.68it/s]
     Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
       0%|          | 0/29515 [00:00<?, ?it/s]
-    100%|##########| 29515/29515 [00:00<00:00, 328506.94it/s]
+    100%|##########| 29515/29515 [00:00<00:00, 328478.17it/s]
     Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
       0%|          | 0/4422102 [00:00<?, ?it/s]
-      1%|1         | 65536/4422102 [00:00<00:11, 364832.94it/s]
-      5%|5         | 229376/4422102 [00:00<00:06, 685589.79it/s]
-     19%|#8        | 819200/4422102 [00:00<00:01, 2333202.23it/s]
-     34%|###4      | 1507328/4422102 [00:00<00:00, 3089497.82it/s]
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+      1%|1         | 65536/4422102 [00:00<00:12, 360130.05it/s]
+      5%|5         | 229376/4422102 [00:00<00:06, 676468.10it/s]
+     19%|#9        | 851968/4422102 [00:00<00:01, 2412922.89it/s]
+     42%|####2     | 1867776/4422102 [00:00<00:00, 3932564.50it/s]
+    100%|##########| 4422102/4422102 [00:00<00:00, 6046597.03it/s]
     Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
       0%|          | 0/5148 [00:00<?, ?it/s]
-    100%|##########| 5148/5148 [00:00<00:00, 46335358.35it/s]
+    100%|##########| 5148/5148 [00:00<00:00, 33270072.41it/s]
     Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
 
 
@@ -176,7 +163,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  8.616 seconds)
+   **Total running time of the script:** ( 0 minutes  4.433 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_transforms_tutorial.py:
diff --git a/_sources/beginner/blitz/autograd_tutorial.rst.txt b/_sources/beginner/blitz/autograd_tutorial.rst.txt
index 1b0e0f2024..2e02dc30b1 100644
--- a/_sources/beginner/blitz/autograd_tutorial.rst.txt
+++ b/_sources/beginner/blitz/autograd_tutorial.rst.txt
@@ -81,9 +81,9 @@ shape (1,1000).
     Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
 
       0%|          | 0.00/44.7M [00:00<?, ?B/s]
-     38%|###7      | 16.8M/44.7M [00:00<00:00, 175MB/s]
-     75%|#######5  | 33.5M/44.7M [00:00<00:00, 175MB/s]
-    100%|##########| 44.7M/44.7M [00:00<00:00, 175MB/s]
+     37%|###7      | 16.6M/44.7M [00:00<00:00, 174MB/s]
+     76%|#######5  | 33.8M/44.7M [00:00<00:00, 177MB/s]
+    100%|##########| 44.7M/44.7M [00:00<00:00, 177MB/s]
 
 
 
@@ -537,7 +537,7 @@ Further readings:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.822 seconds)
+   **Total running time of the script:** ( 0 minutes  0.850 seconds)
 
 
 .. _sphx_glr_download_beginner_blitz_autograd_tutorial.py:
diff --git a/_sources/beginner/blitz/cifar10_tutorial.rst.txt b/_sources/beginner/blitz/cifar10_tutorial.rst.txt
index addb44c354..825aeb7260 100644
--- a/_sources/beginner/blitz/cifar10_tutorial.rst.txt
+++ b/_sources/beginner/blitz/cifar10_tutorial.rst.txt
@@ -134,22 +134,24 @@ We transform them to Tensors of normalized range [-1, 1].
     Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
 
       0%|          | 0/170498071 [00:00<?, ?it/s]
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-      5%|4         | 8093696/170498071 [00:00<00:03, 46545344.08it/s]
-     12%|#1        | 19791872/170498071 [00:00<00:01, 78572917.37it/s]
-     18%|#8        | 31490048/170498071 [00:00<00:01, 93704738.50it/s]
-     25%|##5       | 43220992/170498071 [00:00<00:01, 102195234.95it/s]
-     32%|###2      | 54919168/170498071 [00:00<00:01, 107199554.08it/s]
-     39%|###9      | 66584576/170498071 [00:00<00:00, 110220795.60it/s]
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-     59%|#####9    | 101285888/170498071 [00:01<00:00, 111561581.39it/s]
-     66%|######6   | 112984064/170498071 [00:01<00:00, 113149863.63it/s]
-     73%|#######3  | 124682240/170498071 [00:01<00:00, 114294924.37it/s]
-     80%|########  | 136413184/170498071 [00:01<00:00, 115188234.60it/s]
-     87%|########6 | 147947520/170498071 [00:01<00:00, 113844360.44it/s]
-     94%|#########3| 159645696/170498071 [00:01<00:00, 114750149.53it/s]
-    100%|##########| 170498071/170498071 [00:01<00:00, 105907193.16it/s]
+      0%|          | 622592/170498071 [00:00<00:27, 6208016.63it/s]
+      5%|4         | 7733248/170498071 [00:00<00:03, 44313670.04it/s]
+     11%|#1        | 18776064/170498071 [00:00<00:02, 74391918.37it/s]
+     17%|#7        | 29032448/170498071 [00:00<00:01, 85354523.94it/s]
+     23%|##2       | 38731776/170498071 [00:00<00:01, 89477633.38it/s]
+     29%|##8       | 49250304/170498071 [00:00<00:01, 94781345.11it/s]
+     35%|###4      | 59342848/170498071 [00:00<00:01, 96737893.07it/s]
+     41%|####      | 69206016/170498071 [00:00<00:01, 97317692.17it/s]
+     47%|####6     | 79462400/170498071 [00:00<00:00, 98904427.35it/s]
+     52%|#####2    | 89358336/170498071 [00:01<00:00, 98712514.94it/s]
+     58%|#####8    | 99352576/170498071 [00:01<00:00, 99062298.85it/s]
+     64%|######4   | 109805568/170498071 [00:01<00:00, 100561714.73it/s]
+     70%|#######   | 119865344/170498071 [00:01<00:00, 100315157.62it/s]
+     76%|#######6  | 130383872/170498071 [00:01<00:00, 101773817.02it/s]
+     82%|########2 | 140574720/170498071 [00:01<00:00, 101638657.01it/s]
+     88%|########8 | 150765568/170498071 [00:01<00:00, 100893396.72it/s]
+     95%|#########4| 161447936/170498071 [00:01<00:00, 102619235.74it/s]
+    100%|##########| 170498071/170498071 [00:01<00:00, 94600749.22it/s] 
     Extracting ./data/cifar-10-python.tar.gz to ./data
     Files already downloaded and verified
 
@@ -682,7 +684,7 @@ Where do I go next?
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  5.426 seconds)
+   **Total running time of the script:** ( 2 minutes  7.344 seconds)
 
 
 .. _sphx_glr_download_beginner_blitz_cifar10_tutorial.py:
diff --git a/_sources/beginner/blitz/data_parallel_tutorial.rst.txt b/_sources/beginner/blitz/data_parallel_tutorial.rst.txt
index bf7ffb4a9e..7ad5a608e0 100644
--- a/_sources/beginner/blitz/data_parallel_tutorial.rst.txt
+++ b/_sources/beginner/blitz/data_parallel_tutorial.rst.txt
@@ -393,7 +393,7 @@ https://pytorch.org/tutorials/beginner/former\_torchies/parallelism\_tutorial.ht
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.428 seconds)
+   **Total running time of the script:** ( 0 minutes  1.445 seconds)
 
 
 .. _sphx_glr_download_beginner_blitz_data_parallel_tutorial.py:
diff --git a/_sources/beginner/blitz/neural_networks_tutorial.rst.txt b/_sources/beginner/blitz/neural_networks_tutorial.rst.txt
index 5437d92642..03d5045aeb 100644
--- a/_sources/beginner/blitz/neural_networks_tutorial.rst.txt
+++ b/_sources/beginner/blitz/neural_networks_tutorial.rst.txt
@@ -323,9 +323,9 @@ For illustration, let us follow a few steps backward:
 
  .. code-block:: none
 
-    <MseLossBackward0 object at 0x7f05db0b2f80>
-    <AddmmBackward0 object at 0x7f05db0b0d90>
-    <AccumulateGrad object at 0x7f05db0b2f20>
+    <MseLossBackward0 object at 0x7f67bc851000>
+    <AddmmBackward0 object at 0x7f67bc850e20>
+    <AccumulateGrad object at 0x7f67bc853100>
 
 
 
@@ -436,7 +436,7 @@ implements all these methods. Using it is very simple:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.062 seconds)
+   **Total running time of the script:** ( 0 minutes  0.068 seconds)
 
 
 .. _sphx_glr_download_beginner_blitz_neural_networks_tutorial.py:
diff --git a/_sources/beginner/blitz/tensor_tutorial.rst.txt b/_sources/beginner/blitz/tensor_tutorial.rst.txt
index 49ba66e34b..91709a6c9d 100644
--- a/_sources/beginner/blitz/tensor_tutorial.rst.txt
+++ b/_sources/beginner/blitz/tensor_tutorial.rst.txt
@@ -555,7 +555,7 @@ Changes in the NumPy array reflects in the tensor.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.036 seconds)
+   **Total running time of the script:** ( 0 minutes  0.035 seconds)
 
 
 .. _sphx_glr_download_beginner_blitz_tensor_tutorial.py:
diff --git a/_sources/beginner/chatbot_tutorial.rst.txt b/_sources/beginner/chatbot_tutorial.rst.txt
index b928539f34..1803f2a7ca 100644
--- a/_sources/beginner/chatbot_tutorial.rst.txt
+++ b/_sources/beginner/chatbot_tutorial.rst.txt
@@ -5767,7 +5767,7 @@ in PyTorch!
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  51.748 seconds)
+   **Total running time of the script:** ( 5 minutes  53.106 seconds)
 
 
 .. _sphx_glr_download_beginner_chatbot_tutorial.py:
diff --git a/_sources/beginner/data_loading_tutorial.rst.txt b/_sources/beginner/data_loading_tutorial.rst.txt
index 7968d2cf65..d4a8cba249 100644
--- a/_sources/beginner/data_loading_tutorial.rst.txt
+++ b/_sources/beginner/data_loading_tutorial.rst.txt
@@ -63,7 +63,7 @@ installed:
  .. code-block:: none
 
 
-    <contextlib.ExitStack object at 0x7f8be2c38d60>
+    <contextlib.ExitStack object at 0x7fc8b70a4a00>
 
 
 
@@ -661,7 +661,7 @@ For an example with training code, please see
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.734 seconds)
+   **Total running time of the script:** ( 0 minutes  2.829 seconds)
 
 
 .. _sphx_glr_download_beginner_data_loading_tutorial.py:
diff --git a/_sources/beginner/dcgan_faces_tutorial.rst.txt b/_sources/beginner/dcgan_faces_tutorial.rst.txt
index 8e760aa809..0c9b8549a8 100644
--- a/_sources/beginner/dcgan_faces_tutorial.rst.txt
+++ b/_sources/beginner/dcgan_faces_tutorial.rst.txt
@@ -1284,42 +1284,42 @@ animation.
     </style>
 
     <div class="animation">
-      <img id="_anim_img5c4a5c884f6a4b178d66187683458b26">
+      <img id="_anim_img50f3b675e3ef4e27b2eaabe76970a12c">
       <div class="anim-controls">
-        <input id="_anim_slider5c4a5c884f6a4b178d66187683458b26" type="range" class="anim-slider"
+        <input id="_anim_slider50f3b675e3ef4e27b2eaabe76970a12c" type="range" class="anim-slider"
                name="points" min="0" max="1" step="1" value="0"
-               oninput="anim5c4a5c884f6a4b178d66187683458b26.set_frame(parseInt(this.value));">
+               oninput="anim50f3b675e3ef4e27b2eaabe76970a12c.set_frame(parseInt(this.value));">
         <div class="anim-buttons">
-          <button title="Decrease speed" aria-label="Decrease speed" onclick="anim5c4a5c884f6a4b178d66187683458b26.slower()">
+          <button title="Decrease speed" aria-label="Decrease speed" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.slower()">
               <i class="fa fa-minus"></i></button>
-          <button title="First frame" aria-label="First frame" onclick="anim5c4a5c884f6a4b178d66187683458b26.first_frame()">
+          <button title="First frame" aria-label="First frame" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.first_frame()">
             <i class="fa fa-fast-backward"></i></button>
-          <button title="Previous frame" aria-label="Previous frame" onclick="anim5c4a5c884f6a4b178d66187683458b26.previous_frame()">
+          <button title="Previous frame" aria-label="Previous frame" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.previous_frame()">
               <i class="fa fa-step-backward"></i></button>
-          <button title="Play backwards" aria-label="Play backwards" onclick="anim5c4a5c884f6a4b178d66187683458b26.reverse_animation()">
+          <button title="Play backwards" aria-label="Play backwards" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.reverse_animation()">
               <i class="fa fa-play fa-flip-horizontal"></i></button>
-          <button title="Pause" aria-label="Pause" onclick="anim5c4a5c884f6a4b178d66187683458b26.pause_animation()">
+          <button title="Pause" aria-label="Pause" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.pause_animation()">
               <i class="fa fa-pause"></i></button>
-          <button title="Play" aria-label="Play" onclick="anim5c4a5c884f6a4b178d66187683458b26.play_animation()">
+          <button title="Play" aria-label="Play" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.play_animation()">
               <i class="fa fa-play"></i></button>
-          <button title="Next frame" aria-label="Next frame" onclick="anim5c4a5c884f6a4b178d66187683458b26.next_frame()">
+          <button title="Next frame" aria-label="Next frame" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.next_frame()">
               <i class="fa fa-step-forward"></i></button>
-          <button title="Last frame" aria-label="Last frame" onclick="anim5c4a5c884f6a4b178d66187683458b26.last_frame()">
+          <button title="Last frame" aria-label="Last frame" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.last_frame()">
               <i class="fa fa-fast-forward"></i></button>
-          <button title="Increase speed" aria-label="Increase speed" onclick="anim5c4a5c884f6a4b178d66187683458b26.faster()">
+          <button title="Increase speed" aria-label="Increase speed" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.faster()">
               <i class="fa fa-plus"></i></button>
         </div>
-        <form title="Repetition mode" aria-label="Repetition mode" action="#n" name="_anim_loop_select5c4a5c884f6a4b178d66187683458b26"
+        <form title="Repetition mode" aria-label="Repetition mode" action="#n" name="_anim_loop_select50f3b675e3ef4e27b2eaabe76970a12c"
               class="anim-state">
-          <input type="radio" name="state" value="once" id="_anim_radio1_5c4a5c884f6a4b178d66187683458b26"
+          <input type="radio" name="state" value="once" id="_anim_radio1_50f3b675e3ef4e27b2eaabe76970a12c"
                  >
-          <label for="_anim_radio1_5c4a5c884f6a4b178d66187683458b26">Once</label>
-          <input type="radio" name="state" value="loop" id="_anim_radio2_5c4a5c884f6a4b178d66187683458b26"
+          <label for="_anim_radio1_50f3b675e3ef4e27b2eaabe76970a12c">Once</label>
+          <input type="radio" name="state" value="loop" id="_anim_radio2_50f3b675e3ef4e27b2eaabe76970a12c"
                  checked>
-          <label for="_anim_radio2_5c4a5c884f6a4b178d66187683458b26">Loop</label>
-          <input type="radio" name="state" value="reflect" id="_anim_radio3_5c4a5c884f6a4b178d66187683458b26"
+          <label for="_anim_radio2_50f3b675e3ef4e27b2eaabe76970a12c">Loop</label>
+          <input type="radio" name="state" value="reflect" id="_anim_radio3_50f3b675e3ef4e27b2eaabe76970a12c"
                  >
-          <label for="_anim_radio3_5c4a5c884f6a4b178d66187683458b26">Reflect</label>
+          <label for="_anim_radio3_50f3b675e3ef4e27b2eaabe76970a12c">Reflect</label>
         </form>
       </div>
     </div>
@@ -1329,9 +1329,9 @@ animation.
       /* Instantiate the Animation class. */
       /* The IDs given should match those used in the template above. */
       (function() {
-        var img_id = "_anim_img5c4a5c884f6a4b178d66187683458b26";
-        var slider_id = "_anim_slider5c4a5c884f6a4b178d66187683458b26";
-        var loop_select_id = "_anim_loop_select5c4a5c884f6a4b178d66187683458b26";
+        var img_id = "_anim_img50f3b675e3ef4e27b2eaabe76970a12c";
+        var slider_id = "_anim_slider50f3b675e3ef4e27b2eaabe76970a12c";
+        var loop_select_id = "_anim_loop_select50f3b675e3ef4e27b2eaabe76970a12c";
         var frames = new Array(17);
     
       frames[0] = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAMgCAYAAADbcAZoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\
@@ -233662,7 +233662,7 @@ animation.
         /* set a timeout to make sure all the above elements are created before
            the object is initialized. */
         setTimeout(function() {
-            anim5c4a5c884f6a4b178d66187683458b26 = new Animation(frames, img_id, slider_id, 1000.0,
+            anim50f3b675e3ef4e27b2eaabe76970a12c = new Animation(frames, img_id, slider_id, 1000.0,
                                      loop_select_id);
         }, 0);
       })()
@@ -233735,7 +233735,7 @@ could go from here. You could:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 6 minutes  57.402 seconds)
+   **Total running time of the script:** ( 7 minutes  1.022 seconds)
 
 
 .. _sphx_glr_download_beginner_dcgan_faces_tutorial.py:
diff --git a/_sources/beginner/deploy_seq2seq_hybrid_frontend_tutorial.rst.txt b/_sources/beginner/deploy_seq2seq_hybrid_frontend_tutorial.rst.txt
index 9d1500e70d..0dfb6c9db5 100644
--- a/_sources/beginner/deploy_seq2seq_hybrid_frontend_tutorial.rst.txt
+++ b/_sources/beginner/deploy_seq2seq_hybrid_frontend_tutorial.rst.txt
@@ -1135,7 +1135,7 @@ of torch.save(model, PATH).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.733 seconds)
+   **Total running time of the script:** ( 0 minutes  0.725 seconds)
 
 
 .. _sphx_glr_download_beginner_deploy_seq2seq_hybrid_frontend_tutorial.py:
diff --git a/_sources/beginner/examples_nn/polynomial_nn.rst.txt b/_sources/beginner/examples_nn/polynomial_nn.rst.txt
index 001b137c4b..b7404e8404 100644
--- a/_sources/beginner/examples_nn/polynomial_nn.rst.txt
+++ b/_sources/beginner/examples_nn/polynomial_nn.rst.txt
@@ -144,7 +144,7 @@ input and may have some trainable weights.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.849 seconds)
+   **Total running time of the script:** ( 0 minutes  0.883 seconds)
 
 
 .. _sphx_glr_download_beginner_examples_nn_polynomial_nn.py:
diff --git a/_sources/beginner/examples_tensor/polynomial_numpy.rst.txt b/_sources/beginner/examples_tensor/polynomial_numpy.rst.txt
index 1e19f8f9b8..74951431f8 100644
--- a/_sources/beginner/examples_tensor/polynomial_numpy.rst.txt
+++ b/_sources/beginner/examples_tensor/polynomial_numpy.rst.txt
@@ -113,7 +113,7 @@ generic numeric computations.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.353 seconds)
+   **Total running time of the script:** ( 0 minutes  0.350 seconds)
 
 
 .. _sphx_glr_download_beginner_examples_tensor_polynomial_numpy.py:
diff --git a/_sources/beginner/fgsm_tutorial.rst.txt b/_sources/beginner/fgsm_tutorial.rst.txt
index c3eee78706..82c749822c 100644
--- a/_sources/beginner/fgsm_tutorial.rst.txt
+++ b/_sources/beginner/fgsm_tutorial.rst.txt
@@ -180,7 +180,7 @@ follows:
  .. code-block:: none
 
 
-    <torch._C.Generator object at 0x7f4b55b2f510>
+    <torch._C.Generator object at 0x7f3bed1d7510>
 
 
 
@@ -267,7 +267,7 @@ pretrained weights.
     Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz
 
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     Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw
 
     Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
@@ -278,7 +278,7 @@ pretrained weights.
     Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz
 
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     Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw
 
     Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
@@ -289,7 +289,7 @@ pretrained weights.
     Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz
 
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     Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw
 
     Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
@@ -300,7 +300,7 @@ pretrained weights.
     Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz
 
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     Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw
 
     CUDA Available:  True
@@ -662,7 +662,7 @@ vs working on one attack at a time in the above for each ``epsilon test()`` loop
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 3 minutes  51.890 seconds)
+   **Total running time of the script:** ( 3 minutes  53.781 seconds)
 
 
 .. _sphx_glr_download_beginner_fgsm_tutorial.py:
diff --git a/_sources/beginner/flava_finetuning_tutorial.rst.txt b/_sources/beginner/flava_finetuning_tutorial.rst.txt
index 7e896bb498..4b3677b6cb 100644
--- a/_sources/beginner/flava_finetuning_tutorial.rst.txt
+++ b/_sources/beginner/flava_finetuning_tutorial.rst.txt
@@ -116,2063 +116,1956 @@ unknown answers.
     ['<unk>\n', 'nokia\n', 'ec\n', 'virgin\n', '2011\n']
 
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-    Downloading data files: 100%|##########| 5/5 [01:39<00:00, 26.23s/it]
-    Downloading data files: 100%|##########| 5/5 [01:39<00:00, 19.86s/it]
+    Downloading data files: 100%|##########| 5/5 [01:33<00:00, 24.74s/it]
+    Downloading data files: 100%|##########| 5/5 [01:33<00:00, 18.70s/it]
 
     Extracting data files:   0%|          | 0/5 [00:00<?, ?it/s]
-    Extracting data files:  80%|########  | 4/5 [00:27<00:06,  6.82s/it]
+    Extracting data files:  80%|########  | 4/5 [00:27<00:06,  6.81s/it]
     Extracting data files: 100%|##########| 5/5 [00:31<00:00,  6.03s/it]
     Extracting data files: 100%|##########| 5/5 [00:31<00:00,  6.22s/it]
 
     Generating train split:   0%|          | 0/34602 [00:00<?, ? examples/s]
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-    Generating train split:   1%|1         | 495/34602 [00:00<00:27, 1231.51 examples/s]
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-    Generating train split: 100%|##########| 34602/34602 [00:07<00:00, 4382.63 examples/s]
+    Generating train split:   0%|          | 1/34602 [00:00<4:13:27,  2.28 examples/s]
+    Generating train split:   1%|1         | 493/34602 [00:00<00:28, 1208.24 examples/s]
+    Generating train split:   3%|2         | 1000/34602 [00:00<00:25, 1330.67 examples/s]
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+    flava_for_pretraining_unified_text_encoder.pt:  39%|###8      | 552M/1.43G [00:02<00:03, 288MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  41%|####      | 581M/1.43G [00:02<00:02, 288MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  43%|####2     | 609M/1.43G [00:02<00:02, 288MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  45%|####4     | 638M/1.43G [00:02<00:02, 288MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  47%|####6     | 667M/1.43G [00:02<00:02, 288MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  49%|####8     | 696M/1.43G [00:02<00:02, 289MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  51%|#####     | 725M/1.43G [00:02<00:02, 289MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  53%|#####2    | 754M/1.43G [00:02<00:02, 289MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  54%|#####3    | 772M/1.43G [00:02<00:02, 257MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  56%|#####5    | 794M/1.43G [00:02<00:02, 247MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  58%|#####7    | 823M/1.43G [00:03<00:02, 259MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  60%|#####9    | 852M/1.43G [00:03<00:02, 268MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  62%|######1   | 881M/1.43G [00:03<00:02, 274MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  64%|######3   | 910M/1.43G [00:03<00:01, 278MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  66%|######5   | 938M/1.43G [00:03<00:01, 281MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  68%|######7   | 967M/1.43G [00:03<00:01, 284MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  70%|######9   | 996M/1.43G [00:03<00:01, 285MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  72%|#######1  | 1.03G/1.43G [00:03<00:01, 286MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  74%|#######3  | 1.05G/1.43G [00:03<00:01, 287MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  76%|#######5  | 1.08G/1.43G [00:03<00:01, 287MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  78%|#######7  | 1.11G/1.43G [00:04<00:01, 287MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  80%|#######9  | 1.14G/1.43G [00:04<00:01, 286MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  82%|########1 | 1.17G/1.43G [00:04<00:00, 286MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  84%|########3 | 1.20G/1.43G [00:04<00:00, 286MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  86%|########5 | 1.23G/1.43G [00:04<00:00, 285MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  88%|########7 | 1.25G/1.43G [00:04<00:00, 284MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  90%|########9 | 1.28G/1.43G [00:04<00:00, 285MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  91%|######### | 1.30G/1.43G [00:04<00:00, 259MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  93%|#########2| 1.33G/1.43G [00:04<00:00, 260MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  95%|#########4| 1.36G/1.43G [00:04<00:00, 267MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  97%|#########6| 1.38G/1.43G [00:05<00:00, 271MB/s]
+    flava_for_pretraining_unified_text_encoder.pt:  99%|#########8| 1.41G/1.43G [00:05<00:00, 273MB/s]
+    flava_for_pretraining_unified_text_encoder.pt: 1.43GB [00:05, 277MB/s]                            
 
 
 
@@ -2447,7 +2331,7 @@ which is multitask model spanning image, video and 3d classification.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  42.433 seconds)
+   **Total running time of the script:** ( 2 minutes  35.105 seconds)
 
 
 .. _sphx_glr_download_beginner_flava_finetuning_tutorial.py:
diff --git a/_sources/beginner/former_torchies/nnft_tutorial.rst.txt b/_sources/beginner/former_torchies/nnft_tutorial.rst.txt
index 21922c96cd..55eff84999 100644
--- a/_sources/beginner/former_torchies/nnft_tutorial.rst.txt
+++ b/_sources/beginner/former_torchies/nnft_tutorial.rst.txt
@@ -489,7 +489,7 @@ Recurrent Nets
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.027 seconds)
+   **Total running time of the script:** ( 0 minutes  0.031 seconds)
 
 
 .. _sphx_glr_download_beginner_former_torchies_nnft_tutorial.py:
diff --git a/_sources/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.rst.txt b/_sources/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.rst.txt
index 049c84c76d..93bf9a76d1 100644
--- a/_sources/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.rst.txt
+++ b/_sources/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.rst.txt
@@ -453,7 +453,7 @@ However, such a model may not be exported to ONNX.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.106 seconds)
+   **Total running time of the script:** ( 0 minutes  0.114 seconds)
 
 
 .. _sphx_glr_download_beginner_hybrid_frontend_learning_hybrid_frontend_through_example_tutorial.py:
diff --git a/_sources/beginner/hyperparameter_tuning_tutorial.rst.txt b/_sources/beginner/hyperparameter_tuning_tutorial.rst.txt
index 679f0add70..55bd9f0d89 100644
--- a/_sources/beginner/hyperparameter_tuning_tutorial.rst.txt
+++ b/_sources/beginner/hyperparameter_tuning_tutorial.rst.txt
@@ -569,72 +569,83 @@ The full main function looks like this:
     Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /var/lib/workspace/beginner_source/data/cifar-10-python.tar.gz
 
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-     94% 160497664/170498071 [00:01<00:00, 116825655.92it/s]
-    100% 170498071/170498071 [00:01<00:00, 107260206.10it/s]
+      0% 655360/170498071 [00:00<00:25, 6541130.02it/s]
+      5% 8224768/170498071 [00:00<00:03, 47109422.99it/s]
+     12% 19759104/170498071 [00:00<00:01, 78182628.97it/s]
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+     31% 53149696/170498071 [00:00<00:01, 102940157.97it/s]
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+     44% 75038720/170498071 [00:00<00:00, 106248986.17it/s]
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+     96% 164331520/170498071 [00:01<00:00, 109579324.61it/s]
+    100% 170498071/170498071 [00:01<00:00, 102120833.13it/s]
     Extracting /var/lib/workspace/beginner_source/data/cifar-10-python.tar.gz to /var/lib/workspace/beginner_source/data
     Files already downloaded and verified
-    2024-06-25 20:32:09,767 WARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 2147479552 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=10.24gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.
-    2024-06-25 20:32:09,901 INFO worker.py:1642 -- Started a local Ray instance.
-    2024-06-25 20:32:11,013 INFO tune.py:228 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `tune.run(...)`.
-    2024-06-25 20:32:11,015 INFO tune.py:654 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see https://github.com/ray-project/ray/issues/36949
+    2024-06-25 21:07:32,044 WARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 2147479552 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=10.24gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.
+    2024-06-25 21:07:33,182 INFO worker.py:1642 -- Started a local Ray instance.
+    2024-06-25 21:07:34,259 INFO tune.py:228 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `tune.run(...)`.
+    2024-06-25 21:07:34,261 INFO tune.py:654 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see https://github.com/ray-project/ray/issues/36949
     +--------------------------------------------------------------------+
-    | Configuration for experiment     train_cifar_2024-06-25_20-32-11   |
+    | Configuration for experiment     train_cifar_2024-06-25_21-07-34   |
     +--------------------------------------------------------------------+
     | Search algorithm                 BasicVariantGenerator             |
     | Scheduler                        AsyncHyperBandScheduler           |
     | Number of trials                 10                                |
     +--------------------------------------------------------------------+
 
-    View detailed results here: /var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11
-    To visualize your results with TensorBoard, run: `tensorboard --logdir /var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11`
+    View detailed results here: /var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34
+    To visualize your results with TensorBoard, run: `tensorboard --logdir /var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34`
 
     Trial status: 10 PENDING
-    Current time: 2024-06-25 20:32:11. Total running time: 0s
+    Current time: 2024-06-25 21:07:34. Total running time: 0s
     Logical resource usage: 0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +-------------------------------------------------------------------------------+
     | Trial name                status       l1     l2            lr     batch_size |
     +-------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00000   PENDING      16      1   0.00213327               2 |
-    | train_cifar_ffd52_00001   PENDING       1      2   0.013416                 4 |
-    | train_cifar_ffd52_00002   PENDING     256     64   0.0113784                2 |
-    | train_cifar_ffd52_00003   PENDING      64    256   0.0274071                8 |
-    | train_cifar_ffd52_00004   PENDING      16      2   0.056666                 4 |
-    | train_cifar_ffd52_00005   PENDING       8     64   0.000353097              4 |
-    | train_cifar_ffd52_00006   PENDING      16      4   0.000147684              8 |
-    | train_cifar_ffd52_00007   PENDING     256    256   0.00477469               8 |
-    | train_cifar_ffd52_00008   PENDING     128    256   0.0306227                8 |
-    | train_cifar_ffd52_00009   PENDING       2     16   0.0286986                2 |
+    | train_cifar_f162b_00000   PENDING      16      1   0.00213327               2 |
+    | train_cifar_f162b_00001   PENDING       1      2   0.013416                 4 |
+    | train_cifar_f162b_00002   PENDING     256     64   0.0113784                2 |
+    | train_cifar_f162b_00003   PENDING      64    256   0.0274071                8 |
+    | train_cifar_f162b_00004   PENDING      16      2   0.056666                 4 |
+    | train_cifar_f162b_00005   PENDING       8     64   0.000353097              4 |
+    | train_cifar_f162b_00006   PENDING      16      4   0.000147684              8 |
+    | train_cifar_f162b_00007   PENDING     256    256   0.00477469               8 |
+    | train_cifar_f162b_00008   PENDING     128    256   0.0306227                8 |
+    | train_cifar_f162b_00009   PENDING       2     16   0.0286986                2 |
     +-------------------------------------------------------------------------------+
-    (pid=5288) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-    (pid=5288)   _torch_pytree._register_pytree_node(
+    (pid=5286) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    (pid=5286)   _torch_pytree._register_pytree_node(
 
-    Trial train_cifar_ffd52_00005 started with configuration:
+    Trial train_cifar_f162b_00000 started with configuration:
     +--------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 config             |
+    | Trial train_cifar_f162b_00000 config             |
+    +--------------------------------------------------+
+    | batch_size                                     2 |
+    | l1                                            16 |
+    | l2                                             1 |
+    | lr                                       0.00213 |
+    +--------------------------------------------------+
+
+    Trial train_cifar_f162b_00001 started with configuration:
+    +--------------------------------------------------+
+    | Trial train_cifar_f162b_00001 config             |
     +--------------------------------------------------+
     | batch_size                                     4 |
-    | l1                                             8 |
-    | l2                                            64 |
-    | lr                                       0.00035 |
+    | l1                                             1 |
+    | l2                                             2 |
+    | lr                                       0.01342 |
     +--------------------------------------------------+
 
-    Trial train_cifar_ffd52_00006 started with configuration:
+    Trial train_cifar_f162b_00006 started with configuration:
     +--------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 config             |
+    | Trial train_cifar_f162b_00006 config             |
     +--------------------------------------------------+
     | batch_size                                     8 |
     | l1                                            16 |
@@ -642,9 +653,9 @@ The full main function looks like this:
     | lr                                       0.00015 |
     +--------------------------------------------------+
 
-    Trial train_cifar_ffd52_00004 started with configuration:
+    Trial train_cifar_f162b_00004 started with configuration:
     +--------------------------------------------------+
-    | Trial train_cifar_ffd52_00004 config             |
+    | Trial train_cifar_f162b_00004 config             |
     +--------------------------------------------------+
     | batch_size                                     4 |
     | l1                                            16 |
@@ -652,19 +663,19 @@ The full main function looks like this:
     | lr                                       0.05667 |
     +--------------------------------------------------+
 
-    Trial train_cifar_ffd52_00001 started with configuration:
+    Trial train_cifar_f162b_00002 started with configuration:
     +--------------------------------------------------+
-    | Trial train_cifar_ffd52_00001 config             |
+    | Trial train_cifar_f162b_00002 config             |
     +--------------------------------------------------+
-    | batch_size                                     4 |
-    | l1                                             1 |
-    | l2                                             2 |
-    | lr                                       0.01342 |
+    | batch_size                                     2 |
+    | l1                                           256 |
+    | l2                                            64 |
+    | lr                                       0.01138 |
     +--------------------------------------------------+
 
-    Trial train_cifar_ffd52_00007 started with configuration:
+    Trial train_cifar_f162b_00007 started with configuration:
     +--------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 config             |
+    | Trial train_cifar_f162b_00007 config             |
     +--------------------------------------------------+
     | batch_size                                     8 |
     | l1                                           256 |
@@ -672,125 +683,116 @@ The full main function looks like this:
     | lr                                       0.00477 |
     +--------------------------------------------------+
 
-    Trial train_cifar_ffd52_00000 started with configuration:
+    Trial train_cifar_f162b_00005 started with configuration:
     +--------------------------------------------------+
-    | Trial train_cifar_ffd52_00000 config             |
+    | Trial train_cifar_f162b_00005 config             |
     +--------------------------------------------------+
-    | batch_size                                     2 |
-    | l1                                            16 |
-    | l2                                             1 |
-    | lr                                       0.00213 |
+    | batch_size                                     4 |
+    | l1                                             8 |
+    | l2                                            64 |
+    | lr                                       0.00035 |
     +--------------------------------------------------+
 
-    Trial train_cifar_ffd52_00003 started with configuration:
+    Trial train_cifar_f162b_00003 started with configuration:
     +--------------------------------------------------+
-    | Trial train_cifar_ffd52_00003 config             |
+    | Trial train_cifar_f162b_00003 config             |
     +--------------------------------------------------+
     | batch_size                                     8 |
     | l1                                            64 |
     | l2                                           256 |
     | lr                                       0.02741 |
     +--------------------------------------------------+
-
-    Trial train_cifar_ffd52_00002 started with configuration:
-    +--------------------------------------------------+
-    | Trial train_cifar_ffd52_00002 config             |
-    +--------------------------------------------------+
-    | batch_size                                     2 |
-    | l1                                           256 |
-    | l2                                            64 |
-    | lr                                       0.01138 |
-    +--------------------------------------------------+
-    (func pid=5287) Files already downloaded and verified
-    (func pid=5276) [1,  2000] loss: 2.328
-    (func pid=5284) Files already downloaded and verified [repeated 15x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)
+    (func pid=5288) Files already downloaded and verified
+    (func pid=5274) [1,  2000] loss: 2.328
+    (func pid=5285) Files already downloaded and verified [repeated 15x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)
 
     Trial status: 8 RUNNING | 2 PENDING
-    Current time: 2024-06-25 20:32:41. Total running time: 30s
+    Current time: 2024-06-25 21:08:04. Total running time: 30s
     Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +-------------------------------------------------------------------------------+
     | Trial name                status       l1     l2            lr     batch_size |
     +-------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00000   RUNNING      16      1   0.00213327               2 |
-    | train_cifar_ffd52_00001   RUNNING       1      2   0.013416                 4 |
-    | train_cifar_ffd52_00002   RUNNING     256     64   0.0113784                2 |
-    | train_cifar_ffd52_00003   RUNNING      64    256   0.0274071                8 |
-    | train_cifar_ffd52_00004   RUNNING      16      2   0.056666                 4 |
-    | train_cifar_ffd52_00005   RUNNING       8     64   0.000353097              4 |
-    | train_cifar_ffd52_00006   RUNNING      16      4   0.000147684              8 |
-    | train_cifar_ffd52_00007   RUNNING     256    256   0.00477469               8 |
-    | train_cifar_ffd52_00008   PENDING     128    256   0.0306227                8 |
-    | train_cifar_ffd52_00009   PENDING       2     16   0.0286986                2 |
-    +-------------------------------------------------------------------------------+
-    (func pid=5276) [1,  4000] loss: 1.152 [repeated 8x across cluster]
-    (func pid=5288) [1,  4000] loss: 1.157 [repeated 5x across cluster]
-    (func pid=5276) [1,  6000] loss: 0.768 [repeated 3x across cluster]
-    (func pid=5284) [1,  6000] loss: 0.738 [repeated 4x across cluster]
-    Trial status: 8 RUNNING | 2 PENDING
-    Current time: 2024-06-25 20:33:11. Total running time: 1min 0s
-    Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-    +-------------------------------------------------------------------------------+
-    | Trial name                status       l1     l2            lr     batch_size |
-    +-------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00000   RUNNING      16      1   0.00213327               2 |
-    | train_cifar_ffd52_00001   RUNNING       1      2   0.013416                 4 |
-    | train_cifar_ffd52_00002   RUNNING     256     64   0.0113784                2 |
-    | train_cifar_ffd52_00003   RUNNING      64    256   0.0274071                8 |
-    | train_cifar_ffd52_00004   RUNNING      16      2   0.056666                 4 |
-    | train_cifar_ffd52_00005   RUNNING       8     64   0.000353097              4 |
-    | train_cifar_ffd52_00006   RUNNING      16      4   0.000147684              8 |
-    | train_cifar_ffd52_00007   RUNNING     256    256   0.00477469               8 |
-    | train_cifar_ffd52_00008   PENDING     128    256   0.0306227                8 |
-    | train_cifar_ffd52_00009   PENDING       2     16   0.0286986                2 |
+    | train_cifar_f162b_00000   RUNNING      16      1   0.00213327               2 |
+    | train_cifar_f162b_00001   RUNNING       1      2   0.013416                 4 |
+    | train_cifar_f162b_00002   RUNNING     256     64   0.0113784                2 |
+    | train_cifar_f162b_00003   RUNNING      64    256   0.0274071                8 |
+    | train_cifar_f162b_00004   RUNNING      16      2   0.056666                 4 |
+    | train_cifar_f162b_00005   RUNNING       8     64   0.000353097              4 |
+    | train_cifar_f162b_00006   RUNNING      16      4   0.000147684              8 |
+    | train_cifar_f162b_00007   RUNNING     256    256   0.00477469               8 |
+    | train_cifar_f162b_00008   PENDING     128    256   0.0306227                8 |
+    | train_cifar_f162b_00009   PENDING       2     16   0.0286986                2 |
     +-------------------------------------------------------------------------------+
+    (func pid=5274) [1,  4000] loss: 1.113 [repeated 8x across cluster]
+    (func pid=5289) [1,  4000] loss: 0.791 [repeated 6x across cluster]
+    (func pid=5274) [1,  6000] loss: 0.699 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00006 finished iteration 1 at 2024-06-25 20:33:12. Total running time: 1min 1s
+    Trial train_cifar_f162b_00006 finished iteration 1 at 2024-06-25 21:08:34. Total running time: 1min 0s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                  55.87247 |
-    | time_total_s                                      55.87247 |
+    | time_this_iter_s                                  54.28015 |
+    | time_total_s                                      54.28015 |
     | training_iteration                                       1 |
-    | accuracy                                            0.1452 |
-    | loss                                                2.2988 |
+    | accuracy                                            0.1018 |
+    | loss                                               2.30517 |
     +------------------------------------------------------------+
-    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000000)
-    (pid=5284) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead. [repeated 7x across cluster]
-    (pid=5284)   _torch_pytree._register_pytree_node( [repeated 7x across cluster]
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000000
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000000
+    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000000)
+    (pid=5275) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead. [repeated 7x across cluster]
+    (pid=5275)   _torch_pytree._register_pytree_node( [repeated 7x across cluster]
 
-    Trial train_cifar_ffd52_00007 finished iteration 1 at 2024-06-25 20:33:14. Total running time: 1min 3s
-    +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
+    Trial status: 8 RUNNING | 2 PENDING
+    Current time: 2024-06-25 21:08:34. Total running time: 1min 0s
+    Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+    +----------------------------------------------------------------------------------------------------------------------------------+
+    | Trial name                status       l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+    +----------------------------------------------------------------------------------------------------------------------------------+
+    | train_cifar_f162b_00000   RUNNING      16      1   0.00213327               2                                                    |
+    | train_cifar_f162b_00001   RUNNING       1      2   0.013416                 4                                                    |
+    | train_cifar_f162b_00002   RUNNING     256     64   0.0113784                2                                                    |
+    | train_cifar_f162b_00003   RUNNING      64    256   0.0274071                8                                                    |
+    | train_cifar_f162b_00004   RUNNING      16      2   0.056666                 4                                                    |
+    | train_cifar_f162b_00005   RUNNING       8     64   0.000353097              4                                                    |
+    | train_cifar_f162b_00006   RUNNING      16      4   0.000147684              8        1            54.2801   2.30517       0.1018 |
+    | train_cifar_f162b_00007   RUNNING     256    256   0.00477469               8                                                    |
+    | train_cifar_f162b_00008   PENDING     128    256   0.0306227                8                                                    |
+    | train_cifar_f162b_00009   PENDING       2     16   0.0286986                2                                                    |
+    +----------------------------------------------------------------------------------------------------------------------------------+
+    (func pid=5284) [1,  6000] loss: 0.732 [repeated 4x across cluster]
+
+    Trial train_cifar_f162b_00007 finished iteration 1 at 2024-06-25 21:08:36. Total running time: 1min 1s
+    +------------------------------------------------------------+
+    | Trial train_cifar_f162b_00007 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                   57.7987 |
-    | time_total_s                                       57.7987 |
+    | time_this_iter_s                                  55.98108 |
+    | time_total_s                                      55.98108 |
     | training_iteration                                       1 |
-    | accuracy                                            0.4914 |
-    | loss                                               1.41195 |
+    | accuracy                                            0.4231 |
+    | loss                                               1.54688 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000000
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000000
 
-    Trial train_cifar_ffd52_00003 finished iteration 1 at 2024-06-25 20:33:15. Total running time: 1min 4s
+    Trial train_cifar_f162b_00003 finished iteration 1 at 2024-06-25 21:08:37. Total running time: 1min 3s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00003 result                       |
+    | Trial train_cifar_f162b_00003 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                  58.11498 |
-    | time_total_s                                      58.11498 |
+    | time_this_iter_s                                  57.11161 |
+    | time_total_s                                      57.11161 |
     | training_iteration                                       1 |
-    | accuracy                                            0.2241 |
-    | loss                                               2.05448 |
+    | accuracy                                            0.2199 |
+    | loss                                               2.10497 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00003 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00003_3_batch_size=8,l1=64,l2=256,lr=0.0274_2024-06-25_20-32-11/checkpoint_000000
+    Trial train_cifar_f162b_00003 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00003_3_batch_size=8,l1=64,l2=256,lr=0.0274_2024-06-25_21-07-34/checkpoint_000000
 
-    Trial train_cifar_ffd52_00003 completed after 1 iterations at 2024-06-25 20:33:15. Total running time: 1min 4s
+    Trial train_cifar_f162b_00003 completed after 1 iterations at 2024-06-25 21:08:37. Total running time: 1min 3s
 
-    Trial train_cifar_ffd52_00008 started with configuration:
+    Trial train_cifar_f162b_00008 started with configuration:
     +--------------------------------------------------+
-    | Trial train_cifar_ffd52_00008 config             |
+    | Trial train_cifar_f162b_00008 config             |
     +--------------------------------------------------+
     | batch_size                                     8 |
     | l1                                           128 |
@@ -799,935 +801,934 @@ The full main function looks like this:
     +--------------------------------------------------+
     (func pid=5285) Files already downloaded and verified
     (func pid=5285) Files already downloaded and verified
-    (func pid=5276) [1,  8000] loss: 0.576
-    (func pid=5284) [1,  8000] loss: 0.547 [repeated 4x across cluster]
-    (func pid=5285) [1,  2000] loss: 2.121 [repeated 3x across cluster]
+    (func pid=5274) [1,  8000] loss: 0.497
+    (func pid=5275) [1,  8000] loss: 0.577
+    (func pid=5284) [1,  8000] loss: 0.600 [repeated 3x across cluster]
+    (func pid=5285) [1,  2000] loss: 2.116 [repeated 3x across cluster]
 
     Trial status: 8 RUNNING | 1 TERMINATED | 1 PENDING
-    Current time: 2024-06-25 20:33:41. Total running time: 1min 30s
+    Current time: 2024-06-25 21:09:04. Total running time: 1min 30s
     Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00000   RUNNING        16      1   0.00213327               2                                                    |
-    | train_cifar_ffd52_00001   RUNNING         1      2   0.013416                 4                                                    |
-    | train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2                                                    |
-    | train_cifar_ffd52_00004   RUNNING        16      2   0.056666                 4                                                    |
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4                                                    |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        1            55.8725   2.2988        0.1452 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        1            57.7987   1.41195       0.4914 |
-    | train_cifar_ffd52_00008   RUNNING       128    256   0.0306227                8                                                    |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00009   PENDING         2     16   0.0286986                2                                                    |
+    | train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2                                                    |
+    | train_cifar_f162b_00001   RUNNING         1      2   0.013416                 4                                                    |
+    | train_cifar_f162b_00002   RUNNING       256     64   0.0113784                2                                                    |
+    | train_cifar_f162b_00004   RUNNING        16      2   0.056666                 4                                                    |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4                                                    |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        1            54.2801   2.30517       0.1018 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        1            55.9811   1.54688       0.4231 |
+    | train_cifar_f162b_00008   RUNNING       128    256   0.0306227                8                                                    |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00009   PENDING         2     16   0.0286986                2                                                    |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5283) [1, 10000] loss: 0.462 [repeated 4x across cluster]
-    (func pid=5284) [1, 10000] loss: 0.452
-    (func pid=5288) [2,  4000] loss: 1.122
+    (func pid=5286) [1, 10000] loss: 0.467 [repeated 4x across cluster]
+    (func pid=5284) [1, 10000] loss: 0.463 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00004 finished iteration 1 at 2024-06-25 20:33:55. Total running time: 1min 43s
+    Trial train_cifar_f162b_00001 finished iteration 1 at 2024-06-25 21:09:16. Total running time: 1min 41s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00004 result                       |
+    | Trial train_cifar_f162b_00001 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                  97.86952 |
-    | time_total_s                                      97.86952 |
+    | time_this_iter_s                                  95.96261 |
+    | time_total_s                                      95.96261 |
     | training_iteration                                       1 |
-    | accuracy                                            0.1023 |
-    | loss                                               2.34534 |
+    | accuracy                                            0.0997 |
+    | loss                                               2.30698 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00004 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00004_4_batch_size=4,l1=16,l2=2,lr=0.0567_2024-06-25_20-32-11/checkpoint_000000
+    Trial train_cifar_f162b_00001 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00001_1_batch_size=4,l1=1,l2=2,lr=0.0134_2024-06-25_21-07-34/checkpoint_000000
+
+    Trial train_cifar_f162b_00001 completed after 1 iterations at 2024-06-25 21:09:16. Total running time: 1min 41s
 
-    Trial train_cifar_ffd52_00004 completed after 1 iterations at 2024-06-25 20:33:55. Total running time: 1min 43s
+    Trial train_cifar_f162b_00005 finished iteration 1 at 2024-06-25 21:09:16. Total running time: 1min 41s
+    +------------------------------------------------------------+
+    | Trial train_cifar_f162b_00005 result                       |
+    +------------------------------------------------------------+
+    | checkpoint_dir_name                      checkpoint_000000 |
+    | time_this_iter_s                                  95.87316 |
+    | time_total_s                                      95.87316 |
+    | training_iteration                                       1 |
+    | accuracy                                            0.3733 |
+    | loss                                               1.65649 |
+    +------------------------------------------------------------+
 
-    Trial train_cifar_ffd52_00009 started with configuration:
+    Trial train_cifar_f162b_00009 started with configuration:
     +-------------------------------------------------+
-    | Trial train_cifar_ffd52_00009 config            |
+    | Trial train_cifar_f162b_00009 config            |
     +-------------------------------------------------+
     | batch_size                                    2 |
     | l1                                            2 |
     | l2                                           16 |
     | lr                                       0.0287 |
     +-------------------------------------------------+
-    (func pid=5286) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00004_4_batch_size=4,l1=16,l2=2,lr=0.0567_2024-06-25_20-32-11/checkpoint_000000) [repeated 3x across cluster]
 
-    Trial train_cifar_ffd52_00005 finished iteration 1 at 2024-06-25 20:33:55. Total running time: 1min 44s
-    +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
-    +------------------------------------------------------------+
-    | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                  98.30064 |
-    | time_total_s                                      98.30064 |
-    | training_iteration                                       1 |
-    | accuracy                                            0.3493 |
-    | loss                                               1.76885 |
-    +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000000
-    (func pid=5286) Files already downloaded and verified
-    (func pid=5276) [1, 12000] loss: 0.384 [repeated 2x across cluster]
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000000
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000000) [repeated 3x across cluster]
+    (func pid=5275) Files already downloaded and verified
+    (func pid=5274) [1, 12000] loss: 0.327 [repeated 2x across cluster]
+    (func pid=5275) Files already downloaded and verified
 
-    Trial train_cifar_ffd52_00001 finished iteration 1 at 2024-06-25 20:33:56. Total running time: 1min 45s
+    Trial train_cifar_f162b_00004 finished iteration 1 at 2024-06-25 21:09:20. Total running time: 1min 45s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00001 result                       |
+    | Trial train_cifar_f162b_00004 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                  99.25883 |
-    | time_total_s                                      99.25883 |
+    | time_this_iter_s                                  99.76729 |
+    | time_total_s                                      99.76729 |
     | training_iteration                                       1 |
-    | accuracy                                            0.1022 |
-    | loss                                               2.31335 |
+    | accuracy                                            0.0939 |
+    | loss                                               2.36081 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00001 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00001_1_batch_size=4,l1=1,l2=2,lr=0.0134_2024-06-25_20-32-11/checkpoint_000000
+    Trial train_cifar_f162b_00004 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00004_4_batch_size=4,l1=16,l2=2,lr=0.0567_2024-06-25_21-07-34/checkpoint_000000
 
-    Trial train_cifar_ffd52_00001 completed after 1 iterations at 2024-06-25 20:33:56. Total running time: 1min 45s
-    (func pid=5286) Files already downloaded and verified
+    Trial train_cifar_f162b_00004 completed after 1 iterations at 2024-06-25 21:09:20. Total running time: 1min 45s
 
-    Trial train_cifar_ffd52_00006 finished iteration 2 at 2024-06-25 20:34:05. Total running time: 1min 54s
+    Trial train_cifar_f162b_00006 finished iteration 2 at 2024-06-25 21:09:26. Total running time: 1min 51s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000001 |
-    | time_this_iter_s                                  52.81679 |
-    | time_total_s                                     108.68926 |
+    | time_this_iter_s                                  51.49323 |
+    | time_total_s                                     105.77338 |
     | training_iteration                                       2 |
-    | accuracy                                            0.1768 |
-    | loss                                               2.13897 |
+    | accuracy                                            0.1361 |
+    | loss                                               2.29136 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000001
-    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000001) [repeated 3x across cluster]
-    (func pid=5284) [1, 12000] loss: 0.368 [repeated 2x across cluster]
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000001
+    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000001) [repeated 3x across cluster]
+    (func pid=5274) [1, 14000] loss: 0.277 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00007 finished iteration 2 at 2024-06-25 20:34:09. Total running time: 1min 58s
+    Trial train_cifar_f162b_00007 finished iteration 2 at 2024-06-25 21:09:29. Total running time: 1min 54s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
+    | Trial train_cifar_f162b_00007 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000001 |
-    | time_this_iter_s                                  54.29292 |
-    | time_total_s                                     112.09162 |
+    | time_this_iter_s                                  52.92892 |
+    | time_total_s                                        108.91 |
     | training_iteration                                       2 |
-    | accuracy                                            0.5402 |
-    | loss                                               1.30084 |
+    | accuracy                                            0.4883 |
+    | loss                                               1.45272 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000001
-    (func pid=5286) [1,  2000] loss: 2.334 [repeated 3x across cluster]
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000001
 
-    Trial status: 7 RUNNING | 3 TERMINATED
-    Current time: 2024-06-25 20:34:11. Total running time: 2min 0s
-    Logical resource usage: 14.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00000   RUNNING        16      1   0.00213327               2                                                    |
-    | train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2                                                    |
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        1            98.3006   1.76885       0.3493 |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        2           108.689    2.13897       0.1768 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        2           112.092    1.30084       0.5402 |
-    | train_cifar_ffd52_00008   RUNNING       128    256   0.0306227                8                                                    |
-    | train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    +------------------------------------------------------------------------------------------------------------------------------------+
-
-    Trial train_cifar_ffd52_00008 finished iteration 1 at 2024-06-25 20:34:12. Total running time: 2min 1s
+    Trial train_cifar_f162b_00008 finished iteration 1 at 2024-06-25 21:09:33. Total running time: 1min 59s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00008 result                       |
+    | Trial train_cifar_f162b_00008 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                  57.37668 |
-    | time_total_s                                      57.37668 |
+    | time_this_iter_s                                  56.15682 |
+    | time_total_s                                      56.15682 |
     | training_iteration                                       1 |
-    | accuracy                                            0.1941 |
-    | loss                                               2.14956 |
+    | accuracy                                            0.2268 |
+    | loss                                               2.01125 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00008 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_20-32-11/checkpoint_000000
-    (func pid=5285) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_20-32-11/checkpoint_000000) [repeated 2x across cluster]
-    (func pid=5276) [1, 16000] loss: 0.288
-    (func pid=5288) [3,  2000] loss: 2.079
-    (func pid=5285) [2,  2000] loss: 2.102 [repeated 5x across cluster]
-    (func pid=5288) [3,  4000] loss: 0.988 [repeated 2x across cluster]
+    Trial train_cifar_f162b_00008 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_21-07-34/checkpoint_000000
+    (func pid=5285) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_21-07-34/checkpoint_000000) [repeated 2x across cluster]
 
     Trial status: 7 RUNNING | 3 TERMINATED
-    Current time: 2024-06-25 20:34:41. Total running time: 2min 30s
+    Current time: 2024-06-25 21:09:34. Total running time: 2min 0s
     Logical resource usage: 14.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00000   RUNNING        16      1   0.00213327               2                                                    |
-    | train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2                                                    |
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        1            98.3006   1.76885       0.3493 |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        2           108.689    2.13897       0.1768 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        2           112.092    1.30084       0.5402 |
-    | train_cifar_ffd52_00008   RUNNING       128    256   0.0306227                8        1            57.3767   2.14956       0.1941 |
-    | train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
+    | train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2                                                    |
+    | train_cifar_f162b_00002   RUNNING       256     64   0.0113784                2                                                    |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        1            95.8732   1.65649       0.3733 |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        2           105.773    2.29136       0.1361 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        2           108.91     1.45272       0.4883 |
+    | train_cifar_f162b_00008   RUNNING       128    256   0.0306227                8        1            56.1568   2.01125       0.2268 |
+    | train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    (func pid=5274) [1, 16000] loss: 0.242 [repeated 4x across cluster]
+    (func pid=5285) [2,  2000] loss: 2.071 [repeated 6x across cluster]
+    (func pid=5288) [3,  4000] loss: 1.131 [repeated 2x across cluster]
+    Trial status: 7 RUNNING | 3 TERMINATED
+    Current time: 2024-06-25 21:10:04. Total running time: 2min 30s
+    Logical resource usage: 14.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5276) [1, 20000] loss: 0.230 [repeated 5x across cluster]
+    | train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2                                                    |
+    | train_cifar_f162b_00002   RUNNING       256     64   0.0113784                2                                                    |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        1            95.8732   1.65649       0.3733 |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        2           105.773    2.29136       0.1361 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        2           108.91     1.45272       0.4883 |
+    | train_cifar_f162b_00008   RUNNING       128    256   0.0306227                8        1            56.1568   2.01125       0.2268 |
+    | train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    (func pid=5274) [1, 20000] loss: 0.196 [repeated 5x across cluster]
 
-    Trial train_cifar_ffd52_00006 finished iteration 3 at 2024-06-25 20:34:52. Total running time: 2min 41s
+    Trial train_cifar_f162b_00006 finished iteration 3 at 2024-06-25 21:10:11. Total running time: 2min 37s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000002 |
-    | time_this_iter_s                                   46.6738 |
-    | time_total_s                                     155.36306 |
+    | time_this_iter_s                                   45.3082 |
+    | time_total_s                                     151.08158 |
     | training_iteration                                       3 |
-    | accuracy                                            0.2232 |
-    | loss                                               1.92145 |
+    | accuracy                                             0.174 |
+    | loss                                               2.24346 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000002
-    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000002)
-    (func pid=5284) [1, 18000] loss: 0.241 [repeated 4x across cluster]
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000002
+    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000002)
+    (func pid=5275) [1,  8000] loss: 0.583 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00007 finished iteration 3 at 2024-06-25 20:34:56. Total running time: 2min 45s
+    Trial train_cifar_f162b_00007 finished iteration 3 at 2024-06-25 21:10:15. Total running time: 2min 41s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
+    | Trial train_cifar_f162b_00007 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000002 |
-    | time_this_iter_s                                  47.31363 |
-    | time_total_s                                     159.40525 |
+    | time_this_iter_s                                  46.53087 |
+    | time_total_s                                     155.44087 |
     | training_iteration                                       3 |
-    | accuracy                                            0.5642 |
-    | loss                                               1.22132 |
+    | accuracy                                            0.5147 |
+    | loss                                               1.42976 |
     +------------------------------------------------------------+
-    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000002)
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000002
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000002
+    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000002)
 
-    Trial train_cifar_ffd52_00008 finished iteration 2 at 2024-06-25 20:35:04. Total running time: 2min 53s
+    Trial train_cifar_f162b_00008 finished iteration 2 at 2024-06-25 21:10:25. Total running time: 2min 50s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00008 result                       |
+    | Trial train_cifar_f162b_00008 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000001 |
-    | time_this_iter_s                                  52.03223 |
-    | time_total_s                                     109.40892 |
+    | time_this_iter_s                                  51.44847 |
+    | time_total_s                                     107.60529 |
     | training_iteration                                       2 |
-    | accuracy                                            0.2231 |
-    | loss                                               2.07936 |
+    | accuracy                                            0.1834 |
+    | loss                                               2.09518 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00008 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_20-32-11/checkpoint_000001
+    Trial train_cifar_f162b_00008 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_21-07-34/checkpoint_000001
 
-    Trial train_cifar_ffd52_00008 completed after 2 iterations at 2024-06-25 20:35:04. Total running time: 2min 53s
-    (func pid=5285) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_20-32-11/checkpoint_000001)
-    (func pid=5286) [1, 10000] loss: 0.467
-    (func pid=5287) [2, 10000] loss: 0.301
+    Trial train_cifar_f162b_00008 completed after 2 iterations at 2024-06-25 21:10:25. Total running time: 2min 50s
+    (func pid=5285) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_21-07-34/checkpoint_000001)
+    (func pid=5275) [1, 10000] loss: 0.466 [repeated 3x across cluster]
 
-    Trial train_cifar_ffd52_00000 finished iteration 1 at 2024-06-25 20:35:09. Total running time: 2min 58s
+    Trial train_cifar_f162b_00000 finished iteration 1 at 2024-06-25 21:10:28. Total running time: 2min 53s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00000 result                       |
+    | Trial train_cifar_f162b_00000 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                 172.11847 |
-    | time_total_s                                     172.11847 |
+    | time_this_iter_s                                 168.05027 |
+    | time_total_s                                     168.05027 |
     | training_iteration                                       1 |
-    | accuracy                                            0.1011 |
-    | loss                                                2.3056 |
+    | accuracy                                            0.1994 |
+    | loss                                               1.93221 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00000 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_20-32-11/checkpoint_000000
-
-    (func pid=5276) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_20-32-11/checkpoint_000000)
-    Trial train_cifar_ffd52_00000 completed after 1 iterations at 2024-06-25 20:35:09. Total running time: 2min 58s
+    Trial train_cifar_f162b_00000 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_21-07-34/checkpoint_000000
+    (func pid=5274) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_21-07-34/checkpoint_000000)
+    (func pid=5289) [4,  2000] loss: 1.194 [repeated 3x across cluster]
 
-    Trial status: 5 TERMINATED | 5 RUNNING
-    Current time: 2024-06-25 20:35:11. Total running time: 3min 0s
-    Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+    Trial status: 6 RUNNING | 4 TERMINATED
+    Current time: 2024-06-25 21:10:34. Total running time: 3min 0s
+    Logical resource usage: 12.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2                                                    |
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        1            98.3006   1.76885       0.3493 |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        3           155.363    1.92145       0.2232 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        3           159.405    1.22132       0.5642 |
-    | train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
+    | train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2        1           168.05     1.93221       0.1994 |
+    | train_cifar_f162b_00002   RUNNING       256     64   0.0113784                2                                                    |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        1            95.8732   1.65649       0.3733 |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        3           151.082    2.24346       0.174  |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        3           155.441    1.42976       0.5147 |
+    | train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5289) [4,  2000] loss: 1.122 [repeated 3x across cluster]
 
-    Trial train_cifar_ffd52_00005 finished iteration 2 at 2024-06-25 20:35:15. Total running time: 3min 4s
+    Trial train_cifar_f162b_00005 finished iteration 2 at 2024-06-25 21:10:36. Total running time: 3min 2s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
+    | Trial train_cifar_f162b_00005 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000001 |
-    | time_this_iter_s                                  80.62054 |
-    | time_total_s                                     178.92117 |
+    | time_this_iter_s                                  80.48369 |
+    | time_total_s                                     176.35684 |
     | training_iteration                                       2 |
-    | accuracy                                             0.438 |
-    | loss                                                1.5187 |
+    | accuracy                                            0.4429 |
+    | loss                                               1.49254 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000001
-    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000001)
-    (func pid=5288) [4,  4000] loss: 0.936 [repeated 2x across cluster]
-    (func pid=5286) [1, 14000] loss: 0.334 [repeated 2x across cluster]
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000001
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000001)
+    (func pid=5275) [1, 12000] loss: 0.389 [repeated 2x across cluster]
+    (func pid=5289) [4,  4000] loss: 0.609 [repeated 3x across cluster]
 
-    Trial train_cifar_ffd52_00002 finished iteration 1 at 2024-06-25 20:35:27. Total running time: 3min 16s
+    Trial train_cifar_f162b_00002 finished iteration 1 at 2024-06-25 21:10:50. Total running time: 3min 16s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00002 result                       |
+    | Trial train_cifar_f162b_00002 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                 189.89642 |
-    | time_total_s                                     189.89642 |
+    | time_this_iter_s                                 190.49445 |
+    | time_total_s                                     190.49445 |
     | training_iteration                                       1 |
-    | accuracy                                            0.1695 |
-    | loss                                               2.14381 |
+    | accuracy                                            0.1002 |
+    | loss                                               2.31482 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00002 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_20-32-11/checkpoint_000000
-    (func pid=5284) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_20-32-11/checkpoint_000000)
+    Trial train_cifar_f162b_00002 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_21-07-34/checkpoint_000000
 
-    Trial train_cifar_ffd52_00006 finished iteration 4 at 2024-06-25 20:35:31. Total running time: 3min 20s
+    Trial train_cifar_f162b_00002 completed after 1 iterations at 2024-06-25 21:10:50. Total running time: 3min 16s
+    (func pid=5284) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_21-07-34/checkpoint_000000)
+    (func pid=5274) [2,  4000] loss: 0.962 [repeated 3x across cluster]
+
+    Trial train_cifar_f162b_00006 finished iteration 4 at 2024-06-25 21:10:52. Total running time: 3min 17s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000003 |
-    | time_this_iter_s                                  39.09685 |
-    | time_total_s                                     194.45991 |
+    | time_this_iter_s                                  40.77518 |
+    | time_total_s                                     191.85676 |
     | training_iteration                                       4 |
-    | accuracy                                            0.2643 |
-    | loss                                               1.84947 |
+    | accuracy                                            0.2204 |
+    | loss                                                 2.133 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000003
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000003
 
-    Trial train_cifar_ffd52_00007 finished iteration 4 at 2024-06-25 20:35:36. Total running time: 3min 25s
+    Trial train_cifar_f162b_00007 finished iteration 4 at 2024-06-25 21:10:56. Total running time: 3min 22s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
+    | Trial train_cifar_f162b_00007 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000003 |
-    | time_this_iter_s                                  40.05575 |
-    | time_total_s                                     199.46101 |
+    | time_this_iter_s                                  41.20969 |
+    | time_total_s                                     196.65056 |
     | training_iteration                                       4 |
-    | accuracy                                            0.5966 |
-    | loss                                               1.17434 |
+    | accuracy                                            0.5547 |
+    | loss                                               1.26787 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000003
-    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000003) [repeated 2x across cluster]
-    (func pid=5286) [1, 16000] loss: 0.292 [repeated 2x across cluster]
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000003
+    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000003) [repeated 2x across cluster]
+    (func pid=5275) [1, 16000] loss: 0.292
+    (func pid=5287) [3,  4000] loss: 0.723
 
-    Trial status: 5 TERMINATED | 5 RUNNING
-    Current time: 2024-06-25 20:35:41. Total running time: 3min 30s
+    Trial status: 5 RUNNING | 5 TERMINATED
+    Current time: 2024-06-25 21:11:04. Total running time: 3min 30s
     Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        2           178.921    1.5187        0.438  |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        4           194.46     1.84947       0.2643 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        4           199.461    1.17434       0.5966 |
-    | train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
+    | train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2        1           168.05     1.93221       0.1994 |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        2           176.357    1.49254       0.4429 |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        4           191.857    2.133         0.2204 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        4           196.651    1.26787       0.5547 |
+    | train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5288) [5,  2000] loss: 1.829 [repeated 3x across cluster]
-    (func pid=5287) [3,  6000] loss: 0.485 [repeated 2x across cluster]
-    (func pid=5288) [5,  4000] loss: 0.898 [repeated 3x across cluster]
-    (func pid=5289) [5,  4000] loss: 0.542 [repeated 3x across cluster]
+    (func pid=5275) [1, 18000] loss: 0.260 [repeated 3x across cluster]
+    (func pid=5288) [5,  4000] loss: 1.037 [repeated 4x across cluster]
+    (func pid=5289) [5,  4000] loss: 0.581 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00006 finished iteration 5 at 2024-06-25 20:36:06. Total running time: 3min 55s
+    Trial train_cifar_f162b_00006 finished iteration 5 at 2024-06-25 21:11:26. Total running time: 3min 51s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000004 |
-    | time_this_iter_s                                  34.93373 |
-    | time_total_s                                     229.39364 |
+    | time_this_iter_s                                  34.14598 |
+    | time_total_s                                     226.00273 |
     | training_iteration                                       5 |
-    | accuracy                                            0.3267 |
-    | loss                                               1.77951 |
+    | accuracy                                            0.2423 |
+    | loss                                               2.04993 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000004
-    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000004)
-    (func pid=5287) [3, 10000] loss: 0.277 [repeated 2x across cluster]
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000004
+    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000004)
+    (func pid=5274) [2, 12000] loss: 0.320 [repeated 3x across cluster]
+
+    Trial train_cifar_f162b_00007 finished iteration 5 at 2024-06-25 21:11:33. Total running time: 3min 59s
+    +------------------------------------------------------------+
+    | Trial train_cifar_f162b_00007 result                       |
+    +------------------------------------------------------------+
+    | checkpoint_dir_name                      checkpoint_000004 |
+    | time_this_iter_s                                  36.37413 |
+    | time_total_s                                     233.02469 |
+    | training_iteration                                       5 |
+    | accuracy                                            0.5505 |
+    | loss                                               1.31606 |
+    +------------------------------------------------------------+(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000004)
 
-    Trial status: 5 TERMINATED | 5 RUNNING
-    Current time: 2024-06-25 20:36:11. Total running time: 4min 0s
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000004
+
+    Trial status: 5 RUNNING | 5 TERMINATED
+    Current time: 2024-06-25 21:11:34. Total running time: 4min 0s
     Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        2           178.921    1.5187        0.438  |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        5           229.394    1.77951       0.3267 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        4           199.461    1.17434       0.5966 |
-    | train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
+    | train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2        1           168.05     1.93221       0.1994 |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        2           176.357    1.49254       0.4429 |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        5           226.003    2.04993       0.2423 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        5           233.025    1.31606       0.5505 |
+    | train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
     +------------------------------------------------------------------------------------------------------------------------------------+
 
-    Trial train_cifar_ffd52_00007 finished iteration 5 at 2024-06-25 20:36:13. Total running time: 4min 2s
-    +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
-    +------------------------------------------------------------+
-    | checkpoint_dir_name                      checkpoint_000004 |
-    | time_this_iter_s                                  37.25813 |
-    | time_total_s                                     236.71913 |
-    | training_iteration                                       5 |
-    | accuracy                                            0.5748 |
-    | loss                                               1.25535 |
-    +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000004
-    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000004)
-
-    Trial train_cifar_ffd52_00009 finished iteration 1 at 2024-06-25 20:36:14. Total running time: 4min 3s
+    Trial train_cifar_f162b_00009 finished iteration 1 at 2024-06-25 21:11:35. Total running time: 4min 1s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00009 result                       |
+    | Trial train_cifar_f162b_00009 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000000 |
-    | time_this_iter_s                                 139.19683 |
-    | time_total_s                                     139.19683 |
+    | time_this_iter_s                                 139.18021 |
+    | time_total_s                                     139.18021 |
     | training_iteration                                       1 |
-    | accuracy                                            0.1016 |
-    | loss                                               2.33735 |
+    | accuracy                                            0.0962 |
+    | loss                                               2.36893 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00009 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00009_9_batch_size=2,l1=2,l2=16,lr=0.0287_2024-06-25_20-32-11/checkpoint_000000
+    Trial train_cifar_f162b_00009 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00009_9_batch_size=2,l1=2,l2=16,lr=0.0287_2024-06-25_21-07-34/checkpoint_000000
 
-    Trial train_cifar_ffd52_00009 completed after 1 iterations at 2024-06-25 20:36:14. Total running time: 4min 3s
-    (func pid=5288) [6,  2000] loss: 1.758
+    Trial train_cifar_f162b_00009 completed after 1 iterations at 2024-06-25 21:11:35. Total running time: 4min 1s
+    (func pid=5288) [6,  2000] loss: 2.023 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00005 finished iteration 3 at 2024-06-25 20:36:19. Total running time: 4min 8s
+    Trial train_cifar_f162b_00005 finished iteration 3 at 2024-06-25 21:11:41. Total running time: 4min 6s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
+    | Trial train_cifar_f162b_00005 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000002 |
-    | time_this_iter_s                                  63.42164 |
-    | time_total_s                                     242.34282 |
+    | time_this_iter_s                                  64.30477 |
+    | time_total_s                                     240.66161 |
     | training_iteration                                       3 |
-    | accuracy                                            0.5075 |
-    | loss                                               1.34967 |
+    | accuracy                                            0.4872 |
+    | loss                                               1.39914 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000002
-    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000002) [repeated 2x across cluster]
-    (func pid=5284) [2,  8000] loss: 0.559
-    (func pid=5289) [6,  2000] loss: 0.994
-    (func pid=5287) [4,  2000] loss: 1.390
-    (func pid=5289) [6,  4000] loss: 0.520 [repeated 3x across cluster]
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000002
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000002) [repeated 2x across cluster]
+    (func pid=5289) [6,  2000] loss: 1.059 [repeated 2x across cluster]
+    (func pid=5287) [4,  2000] loss: 1.362 [repeated 2x across cluster]
+    (func pid=5289) [6,  4000] loss: 0.564 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00006 finished iteration 6 at 2024-06-25 20:36:39. Total running time: 4min 28s
+    Trial train_cifar_f162b_00006 finished iteration 6 at 2024-06-25 21:11:59. Total running time: 4min 24s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000005 |
-    | time_this_iter_s                                  33.11123 |
-    | time_total_s                                     262.50487 |
+    | time_this_iter_s                                  32.75475 |
+    | time_total_s                                     258.75748 |
     | training_iteration                                       6 |
-    | accuracy                                            0.3527 |
-    | loss                                               1.72881 |
+    | accuracy                                            0.2521 |
+    | loss                                               1.97434 |
     +------------------------------------------------------------+
-    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000005)
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000005
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000005
+    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000005)
 
-    Trial status: 6 TERMINATED | 4 RUNNING
-    Current time: 2024-06-25 20:36:41. Total running time: 4min 30s
+    Trial status: 4 RUNNING | 6 TERMINATED
+    Current time: 2024-06-25 21:12:04. Total running time: 4min 30s
     Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        3           242.343    1.34967       0.5075 |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        6           262.505    1.72881       0.3527 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        5           236.719    1.25535       0.5748 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+    | train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2        1           168.05     1.93221       0.1994 |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        3           240.662    1.39914       0.4872 |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        6           258.757    1.97434       0.2521 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        5           233.025    1.31606       0.5505 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5284) [2, 12000] loss: 0.370 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00007 finished iteration 6 at 2024-06-25 20:36:48. Total running time: 4min 37s
+    Trial train_cifar_f162b_00007 finished iteration 6 at 2024-06-25 21:12:07. Total running time: 4min 32s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
+    | Trial train_cifar_f162b_00007 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000005 |
-    | time_this_iter_s                                  34.28092 |
-    | time_total_s                                     271.00006 |
+    | time_this_iter_s                                  33.75116 |
+    | time_total_s                                     266.77585 |
     | training_iteration                                       6 |
-    | accuracy                                            0.5616 |
-    | loss                                               1.31087 |
+    | accuracy                                             0.566 |
+    | loss                                               1.25734 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000005
-    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000005)
-    (func pid=5287) [4,  6000] loss: 0.449
-    (func pid=5288) [7,  2000] loss: 1.710
-    (func pid=5284) [2, 14000] loss: 0.315
-    (func pid=5287) [4,  8000] loss: 0.335
-    (func pid=5284) [2, 16000] loss: 0.276 [repeated 3x across cluster]
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000005
+    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000005)
+    (func pid=5288) [7,  2000] loss: 1.963 [repeated 3x across cluster]
+    (func pid=5289) [7,  2000] loss: 1.027 [repeated 3x across cluster]
 
-    Trial train_cifar_ffd52_00006 finished iteration 7 at 2024-06-25 20:37:11. Total running time: 5min 0s
+    Trial train_cifar_f162b_00000 finished iteration 2 at 2024-06-25 21:12:24. Total running time: 4min 50s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00000 result                       |
     +------------------------------------------------------------+
-    | checkpoint_dir_name                      checkpoint_000006 |
-    | time_this_iter_s                                  32.06512 |
-    | time_total_s                                        294.57 |
-    | training_iteration                                       7 |
-    | accuracy                                            0.3767 |
-    | loss                                                1.6804 |
+    | checkpoint_dir_name                      checkpoint_000001 |
+    | time_this_iter_s                                 116.14539 |
+    | time_total_s                                     284.19566 |
+    | training_iteration                                       2 |
+    | accuracy                                            0.2374 |
+    | loss                                               1.89564 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000006
-    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000006)
-
-    Trial status: 6 TERMINATED | 4 RUNNING
-    Current time: 2024-06-25 20:37:11. Total running time: 5min 0s
-    Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        3           242.343    1.34967       0.5075 |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        7           294.57     1.6804        0.3767 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        6           271        1.31087       0.5616 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
-    +------------------------------------------------------------------------------------------------------------------------------------+
+    Trial train_cifar_f162b_00000 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_21-07-34/checkpoint_000001
 
-    Trial train_cifar_ffd52_00005 finished iteration 4 at 2024-06-25 20:37:17. Total running time: 5min 6s
-    +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
-    +------------------------------------------------------------+
-    | checkpoint_dir_name                      checkpoint_000003 |
-    | time_this_iter_s                                  58.04506 |
-    | time_total_s                                     300.38788 |
-    | training_iteration                                       4 |
-    | accuracy                                            0.5308 |
-    | loss                                               1.29584 |
-    +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000003
-    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000003)
-    (func pid=5284) [2, 18000] loss: 0.244 [repeated 3x across cluster]
+    Trial train_cifar_f162b_00000 completed after 2 iterations at 2024-06-25 21:12:24. Total running time: 4min 50s
+    (func pid=5274) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_21-07-34/checkpoint_000001)
 
-    Trial train_cifar_ffd52_00007 finished iteration 7 at 2024-06-25 20:37:22. Total running time: 5min 11s
+    Trial train_cifar_f162b_00006 finished iteration 7 at 2024-06-25 21:12:30. Total running time: 4min 56s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000006 |
-    | time_this_iter_s                                  34.36209 |
-    | time_total_s                                     305.36215 |
+    | time_this_iter_s                                  31.28651 |
+    | time_total_s                                       290.044 |
     | training_iteration                                       7 |
-    | accuracy                                            0.5518 |
-    | loss                                               1.35099 |
-    +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000006
-    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000006)
-    (func pid=5287) [5,  2000] loss: 1.305 [repeated 2x across cluster]
-    (func pid=5284) [2, 20000] loss: 0.223
-    (func pid=5288) [8,  4000] loss: 0.829
+    | accuracy                                            0.2675 |
+    | loss                                               1.89649 |
+    +------------------------------------------------------------+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000006)
 
-    Trial status: 6 TERMINATED | 4 RUNNING
-    Current time: 2024-06-25 20:37:41. Total running time: 5min 30s
-    Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000006
+    (func pid=5287) [4, 10000] loss: 0.269 [repeated 3x across cluster]
+
+    Trial status: 7 TERMINATED | 3 RUNNING
+    Current time: 2024-06-25 21:12:35. Total running time: 5min 0s
+    Logical resource usage: 6.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        4           300.388    1.29584       0.5308 |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        7           294.57     1.6804        0.3767 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        7           305.362    1.35099       0.5518 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        3           240.662    1.39914       0.4872 |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        7           290.044    1.89649       0.2675 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        6           266.776    1.25734       0.566  |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
     +------------------------------------------------------------------------------------------------------------------------------------+
 
-    Trial train_cifar_ffd52_00006 finished iteration 8 at 2024-06-25 20:37:44. Total running time: 5min 32s
+    Trial train_cifar_f162b_00005 finished iteration 4 at 2024-06-25 21:12:37. Total running time: 5min 3s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00005 result                       |
     +------------------------------------------------------------+
-    | checkpoint_dir_name                      checkpoint_000007 |
-    | time_this_iter_s                                  32.38367 |
-    | time_total_s                                     326.95366 |
-    | training_iteration                                       8 |
-    | accuracy                                            0.3772 |
-    | loss                                               1.66349 |
+    | checkpoint_dir_name                      checkpoint_000003 |
+    | time_this_iter_s                                  56.43809 |
+    | time_total_s                                      297.0997 |
+    | training_iteration                                       4 |
+    | accuracy                                            0.4976 |
+    | loss                                               1.35867 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000007
-    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000007)
-    (func pid=5289) [8,  4000] loss: 0.490 [repeated 3x across cluster]
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000003
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000003)
 
-    Trial train_cifar_ffd52_00002 finished iteration 2 at 2024-06-25 20:37:48. Total running time: 5min 37s
+    Trial train_cifar_f162b_00007 finished iteration 7 at 2024-06-25 21:12:40. Total running time: 5min 5s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00002 result                       |
+    | Trial train_cifar_f162b_00007 result                       |
     +------------------------------------------------------------+
-    | checkpoint_dir_name                      checkpoint_000001 |
-    | time_this_iter_s                                 141.05097 |
-    | time_total_s                                     330.94739 |
-    | training_iteration                                       2 |
-    | accuracy                                            0.1713 |
-    | loss                                               2.18945 |
+    | checkpoint_dir_name                      checkpoint_000006 |
+    | time_this_iter_s                                  32.88982 |
+    | time_total_s                                     299.66567 |
+    | training_iteration                                       7 |
+    | accuracy                                            0.5503 |
+    | loss                                               1.32877 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00002 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_20-32-11/checkpoint_000001
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000006
+    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000006)
+    (func pid=5288) [8,  2000] loss: 1.885 [repeated 2x across cluster]
+    (func pid=5287) [5,  2000] loss: 1.301
+    (func pid=5288) [8,  4000] loss: 0.932
+    (func pid=5287) [5,  4000] loss: 0.651 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00002 completed after 2 iterations at 2024-06-25 20:37:48. Total running time: 5min 37s
-    (func pid=5288) [9,  2000] loss: 1.630 [repeated 2x across cluster]
-
-    Trial train_cifar_ffd52_00007 finished iteration 8 at 2024-06-25 20:37:56. Total running time: 5min 45s
+    Trial train_cifar_f162b_00006 finished iteration 8 at 2024-06-25 21:12:59. Total running time: 5min 25s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000007 |
-    | time_this_iter_s                                  33.90971 |
-    | time_total_s                                     339.27186 |
+    | time_this_iter_s                                   29.4743 |
+    | time_total_s                                      319.5183 |
     | training_iteration                                       8 |
-    | accuracy                                            0.5812 |
-    | loss                                               1.26995 |
+    | accuracy                                            0.2794 |
+    | loss                                               1.83365 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000007
-    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000007) [repeated 2x across cluster]
-    (func pid=5288) [9,  4000] loss: 0.805 [repeated 2x across cluster]
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000007
+    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000007)
+    (func pid=5289) [8,  4000] loss: 0.528
 
     Trial status: 7 TERMINATED | 3 RUNNING
-    Current time: 2024-06-25 20:38:11. Total running time: 6min 0s
+    Current time: 2024-06-25 21:13:05. Total running time: 5min 30s
     Logical resource usage: 6.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        4           300.388    1.29584       0.5308 |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        8           326.954    1.66349       0.3772 |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        8           339.272    1.26995       0.5812 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        4           297.1      1.35867       0.4976 |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        8           319.518    1.83365       0.2794 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        7           299.666    1.32877       0.5503 |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
     +------------------------------------------------------------------------------------------------------------------------------------+
+    (func pid=5287) [5,  6000] loss: 0.445
+    (func pid=5288) [9,  2000] loss: 1.825
 
-    Trial train_cifar_ffd52_00005 finished iteration 5 at 2024-06-25 20:38:13. Total running time: 6min 2s
+    Trial train_cifar_f162b_00007 finished iteration 8 at 2024-06-25 21:13:10. Total running time: 5min 36s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
+    | Trial train_cifar_f162b_00007 result                       |
     +------------------------------------------------------------+
-    | checkpoint_dir_name                      checkpoint_000004 |
-    | time_this_iter_s                                  56.50784 |
-    | time_total_s                                     356.89571 |
-    | training_iteration                                       5 |
-    | accuracy                                            0.5478 |
-    | loss                                               1.25522 |
+    | checkpoint_dir_name                      checkpoint_000007 |
+    | time_this_iter_s                                  30.88169 |
+    | time_total_s                                     330.54736 |
+    | training_iteration                                       8 |
+    | accuracy                                             0.554 |
+    | loss                                               1.35308 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000004
-    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000004)
+    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000007)
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000007
+    (func pid=5287) [5,  8000] loss: 0.324
+    (func pid=5288) [9,  4000] loss: 0.902
+    (func pid=5289) [9,  2000] loss: 0.963
 
-    Trial train_cifar_ffd52_00006 finished iteration 9 at 2024-06-25 20:38:14. Total running time: 6min 3s
+    Trial train_cifar_f162b_00006 finished iteration 9 at 2024-06-25 21:13:29. Total running time: 5min 55s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000008 |
-    | time_this_iter_s                                  30.33792 |
-    | time_total_s                                     357.29159 |
+    | time_this_iter_s                                  29.63863 |
+    | time_total_s                                     349.15693 |
     | training_iteration                                       9 |
-    | accuracy                                               0.4 |
-    | loss                                               1.61052 |
+    | accuracy                                            0.3127 |
+    | loss                                               1.77556 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000008
-    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000008)
-    (func pid=5289) [9,  4000] loss: 0.484 [repeated 3x across cluster]
-    (func pid=5288) [10,  2000] loss: 1.586 [repeated 2x across cluster]
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000008
+    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000008)
 
-    Trial train_cifar_ffd52_00007 finished iteration 9 at 2024-06-25 20:38:28. Total running time: 6min 17s
+    Trial train_cifar_f162b_00005 finished iteration 5 at 2024-06-25 21:13:30. Total running time: 5min 56s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
+    | Trial train_cifar_f162b_00005 result                       |
     +------------------------------------------------------------+
-    | checkpoint_dir_name                      checkpoint_000008 |
-    | time_this_iter_s                                  32.01444 |
-    | time_total_s                                     371.28629 |
-    | training_iteration                                       9 |
-    | accuracy                                             0.594 |
-    | loss                                               1.25797 |
+    | checkpoint_dir_name                      checkpoint_000004 |
+    | time_this_iter_s                                  53.22804 |
+    | time_total_s                                     350.32774 |
+    | training_iteration                                       5 |
+    | accuracy                                            0.5108 |
+    | loss                                               1.33224 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000008
-    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000008)
-    (func pid=5287) [6,  4000] loss: 0.617
-    (func pid=5288) [10,  4000] loss: 0.792
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000004
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000004)
+    (func pid=5289) [9,  4000] loss: 0.518 [repeated 2x across cluster]
 
     Trial status: 7 TERMINATED | 3 RUNNING
-    Current time: 2024-06-25 20:38:42. Total running time: 6min 30s
+    Current time: 2024-06-25 21:13:35. Total running time: 6min 0s
     Logical resource usage: 6.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        5           356.896    1.25522       0.5478 |
-    | train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        9           357.292    1.61052       0.4    |
-    | train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        9           371.286    1.25797       0.594  |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        5           350.328    1.33224       0.5108 |
+    | train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        9           349.157    1.77556       0.3127 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        8           330.547    1.35308       0.554  |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5287) [6,  6000] loss: 0.418 [repeated 2x across cluster]
+    (func pid=5288) [10,  2000] loss: 1.772
+    (func pid=5287) [6,  2000] loss: 1.257
+
+    Trial train_cifar_f162b_00007 finished iteration 9 at 2024-06-25 21:13:42. Total running time: 6min 8s
+    +------------------------------------------------------------+
+    | Trial train_cifar_f162b_00007 result                       |
+    +------------------------------------------------------------+
+    | checkpoint_dir_name                      checkpoint_000008 |
+    | time_this_iter_s                                  31.42566 |
+    | time_total_s                                     361.97302 |
+    | training_iteration                                       9 |
+    | accuracy                                            0.5507 |
+    | loss                                               1.38221 |
+    +------------------------------------------------------------+
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000008
+    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000008)
+    (func pid=5287) [6,  4000] loss: 0.644
+    (func pid=5288) [10,  4000] loss: 0.880
+    (func pid=5287) [6,  6000] loss: 0.423 [repeated 2x across cluster]
 
-    Trial train_cifar_ffd52_00006 finished iteration 10 at 2024-06-25 20:38:43. Total running time: 6min 32s
+    Trial train_cifar_f162b_00006 finished iteration 10 at 2024-06-25 21:13:58. Total running time: 6min 24s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00006 result                       |
+    | Trial train_cifar_f162b_00006 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000009 |
-    | time_this_iter_s                                  29.54813 |
-    | time_total_s                                     386.83972 |
+    | time_this_iter_s                                   29.2616 |
+    | time_total_s                                     378.41852 |
     | training_iteration                                      10 |
-    | accuracy                                            0.4213 |
-    | loss                                               1.57777 |
+    | accuracy                                            0.3241 |
+    | loss                                               1.73982 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000009
+    Trial train_cifar_f162b_00006 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000009
 
-    Trial train_cifar_ffd52_00006 completed after 10 iterations at 2024-06-25 20:38:43. Total running time: 6min 32s
-    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000009)
-    (func pid=5289) [10,  4000] loss: 0.473
-    (func pid=5287) [6,  8000] loss: 0.307
+    Trial train_cifar_f162b_00006 completed after 10 iterations at 2024-06-25 21:13:58. Total running time: 6min 24s
+    (func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000009)
+    (func pid=5289) [10,  4000] loss: 0.502
 
-    Trial train_cifar_ffd52_00007 finished iteration 10 at 2024-06-25 20:38:58. Total running time: 6min 47s
+    Trial status: 8 TERMINATED | 2 RUNNING
+    Current time: 2024-06-25 21:14:05. Total running time: 6min 30s
+    Logical resource usage: 4.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        5           350.328    1.33224       0.5108 |
+    | train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        9           361.973    1.38221       0.5507 |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    (func pid=5287) [6,  8000] loss: 0.321
+
+    Trial train_cifar_f162b_00007 finished iteration 10 at 2024-06-25 21:14:11. Total running time: 6min 37s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00007 result                       |
+    | Trial train_cifar_f162b_00007 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000009 |
-    | time_this_iter_s                                  29.95987 |
-    | time_total_s                                     401.24617 |
+    | time_this_iter_s                                  29.31768 |
+    | time_total_s                                      391.2907 |
     | training_iteration                                      10 |
-    | accuracy                                            0.5766 |
-    | loss                                               1.36315 |
+    | accuracy                                             0.562 |
+    | loss                                               1.42098 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000009
+    Trial train_cifar_f162b_00007 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000009
 
-    Trial train_cifar_ffd52_00007 completed after 10 iterations at 2024-06-25 20:38:58. Total running time: 6min 47s
-    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000009)
-    (func pid=5287) [6, 10000] loss: 0.245
+    Trial train_cifar_f162b_00007 completed after 10 iterations at 2024-06-25 21:14:11. Total running time: 6min 37s
+    (func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000009)
+    (func pid=5287) [6, 10000] loss: 0.252
 
-    Trial train_cifar_ffd52_00005 finished iteration 6 at 2024-06-25 20:39:04. Total running time: 6min 53s
+    Trial train_cifar_f162b_00005 finished iteration 6 at 2024-06-25 21:14:20. Total running time: 6min 45s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
+    | Trial train_cifar_f162b_00005 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000005 |
-    | time_this_iter_s                                  50.71725 |
-    | time_total_s                                     407.61296 |
+    | time_this_iter_s                                  49.44468 |
+    | time_total_s                                     399.77242 |
     | training_iteration                                       6 |
-    | accuracy                                            0.5493 |
-    | loss                                                1.2737 |
+    | accuracy                                            0.5258 |
+    | loss                                               1.32112 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000005
-    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000005)
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000005
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000005)
+    (func pid=5287) [7,  2000] loss: 1.244
+    (func pid=5287) [7,  4000] loss: 0.634
 
     Trial status: 9 TERMINATED | 1 RUNNING
-    Current time: 2024-06-25 20:39:12. Total running time: 7min 0s
+    Current time: 2024-06-25 21:14:35. Total running time: 7min 0s
     Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        6           407.613    1.2737        0.5493 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-    | train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5287) [7,  2000] loss: 1.199
-    (func pid=5287) [7,  4000] loss: 0.609
-    (func pid=5287) [7,  6000] loss: 0.398
-    (func pid=5287) [7,  8000] loss: 0.299
-    Trial status: 9 TERMINATED | 1 RUNNING
-    Current time: 2024-06-25 20:39:42. Total running time: 7min 30s
-    Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        6           399.772    1.32112       0.5258 |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+    | train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        6           407.613    1.2737        0.5493 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-    | train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5287) [7, 10000] loss: 0.239
+    (func pid=5287) [7,  6000] loss: 0.415
+    (func pid=5287) [7,  8000] loss: 0.306
+    (func pid=5287) [7, 10000] loss: 0.246
 
-    Trial train_cifar_ffd52_00005 finished iteration 7 at 2024-06-25 20:39:49. Total running time: 7min 38s
+    Trial train_cifar_f162b_00005 finished iteration 7 at 2024-06-25 21:15:03. Total running time: 7min 28s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
+    | Trial train_cifar_f162b_00005 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000006 |
-    | time_this_iter_s                                  44.67267 |
-    | time_total_s                                     452.28563 |
+    | time_this_iter_s                                  43.15288 |
+    | time_total_s                                      442.9253 |
     | training_iteration                                       7 |
-    | accuracy                                            0.5668 |
-    | loss                                               1.20639 |
+    | accuracy                                            0.5389 |
+    | loss                                               1.27488 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000006
-    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000006)
-    (func pid=5287) [8,  2000] loss: 1.179
-    (func pid=5287) [8,  4000] loss: 0.575
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000006
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000006)
 
     Trial status: 9 TERMINATED | 1 RUNNING
-    Current time: 2024-06-25 20:40:12. Total running time: 8min 0s
+    Current time: 2024-06-25 21:15:05. Total running time: 7min 31s
+    Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        7           442.925    1.27488       0.5389 |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+    | train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    (func pid=5287) [8,  2000] loss: 1.195
+    (func pid=5287) [8,  4000] loss: 0.606
+    (func pid=5287) [8,  6000] loss: 0.412
+    (func pid=5287) [8,  8000] loss: 0.307
+    Trial status: 9 TERMINATED | 1 RUNNING
+    Current time: 2024-06-25 21:15:35. Total running time: 8min 1s
     Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        7           452.286    1.20639       0.5668 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-    | train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        7           442.925    1.27488       0.5389 |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+    | train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5287) [8,  6000] loss: 0.393
-    (func pid=5287) [8,  8000] loss: 0.293
-    (func pid=5287) [8, 10000] loss: 0.236
+    (func pid=5287) [8, 10000] loss: 0.243
 
-    Trial train_cifar_ffd52_00005 finished iteration 8 at 2024-06-25 20:40:33. Total running time: 8min 22s
+    Trial train_cifar_f162b_00005 finished iteration 8 at 2024-06-25 21:15:46. Total running time: 8min 12s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
+    | Trial train_cifar_f162b_00005 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000007 |
-    | time_this_iter_s                                   44.3303 |
-    | time_total_s                                     496.61593 |
+    | time_this_iter_s                                  43.62678 |
+    | time_total_s                                     486.55208 |
     | training_iteration                                       8 |
-    | accuracy                                            0.5922 |
-    | loss                                               1.16386 |
+    | accuracy                                             0.558 |
+    | loss                                               1.23456 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000007
-    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000007)
-    (func pid=5287) [9,  2000] loss: 1.136
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000007
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000007)
+    (func pid=5287) [9,  2000] loss: 1.185
+    (func pid=5287) [9,  4000] loss: 0.599
 
     Trial status: 9 TERMINATED | 1 RUNNING
-    Current time: 2024-06-25 20:40:42. Total running time: 8min 31s
+    Current time: 2024-06-25 21:16:05. Total running time: 8min 31s
     Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        8           496.616    1.16386       0.5922 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-    | train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5287) [9,  4000] loss: 0.578
-    (func pid=5287) [9,  6000] loss: 0.377
-    (func pid=5287) [9,  8000] loss: 0.290
-    (func pid=5287) [9, 10000] loss: 0.231
-    Trial status: 9 TERMINATED | 1 RUNNING
-    Current time: 2024-06-25 20:41:12. Total running time: 9min 1s
-    Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
-    +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        8           496.616    1.16386       0.5922 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-    | train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        8           486.552    1.23456       0.558  |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+    | train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
     +------------------------------------------------------------------------------------------------------------------------------------+
+    (func pid=5287) [9,  6000] loss: 0.398
+    (func pid=5287) [9,  8000] loss: 0.300
+    (func pid=5287) [9, 10000] loss: 0.239
 
-    Trial train_cifar_ffd52_00005 finished iteration 9 at 2024-06-25 20:41:18. Total running time: 9min 6s
+    Trial train_cifar_f162b_00005 finished iteration 9 at 2024-06-25 21:16:30. Total running time: 8min 56s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
+    | Trial train_cifar_f162b_00005 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000008 |
-    | time_this_iter_s                                  44.30344 |
-    | time_total_s                                     540.91937 |
+    | time_this_iter_s                                  44.00693 |
+    | time_total_s                                     530.55901 |
     | training_iteration                                       9 |
-    | accuracy                                            0.5751 |
-    | loss                                               1.18888 |
+    | accuracy                                            0.5576 |
+    | loss                                               1.24916 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000008
-    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000008)
-    (func pid=5287) [10,  2000] loss: 1.123
-    (func pid=5287) [10,  4000] loss: 0.573
-    (func pid=5287) [10,  6000] loss: 0.379
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000008
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000008)
 
     Trial status: 9 TERMINATED | 1 RUNNING
-    Current time: 2024-06-25 20:41:42. Total running time: 9min 31s
+    Current time: 2024-06-25 21:16:35. Total running time: 9min 1s
+    Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        9           530.559    1.24916       0.5576 |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+    | train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
+    +------------------------------------------------------------------------------------------------------------------------------------+
+    (func pid=5287) [10,  2000] loss: 1.186
+    (func pid=5287) [10,  4000] loss: 0.595
+    (func pid=5287) [10,  6000] loss: 0.397
+    (func pid=5287) [10,  8000] loss: 0.296
+    Trial status: 9 TERMINATED | 1 RUNNING
+    Current time: 2024-06-25 21:17:05. Total running time: 9min 31s
     Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        9           540.919    1.18888       0.5751 |
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-    | train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+    | train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        9           530.559    1.24916       0.5576 |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+    | train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    (func pid=5287) [10,  8000] loss: 0.283
-    (func pid=5287) [10, 10000] loss: 0.225
+    (func pid=5287) [10, 10000] loss: 0.230
 
-    Trial train_cifar_ffd52_00005 finished iteration 10 at 2024-06-25 20:42:02. Total running time: 9min 51s
+    Trial train_cifar_f162b_00005 finished iteration 10 at 2024-06-25 21:17:14. Total running time: 9min 39s
     +------------------------------------------------------------+
-    | Trial train_cifar_ffd52_00005 result                       |
+    | Trial train_cifar_f162b_00005 result                       |
     +------------------------------------------------------------+
     | checkpoint_dir_name                      checkpoint_000009 |
-    | time_this_iter_s                                  44.58802 |
-    | time_total_s                                     585.50738 |
+    | time_this_iter_s                                  43.34379 |
+    | time_total_s                                      573.9028 |
     | training_iteration                                      10 |
-    | accuracy                                            0.5827 |
-    | loss                                               1.19666 |
+    | accuracy                                            0.5445 |
+    | loss                                                1.3022 |
     +------------------------------------------------------------+
-    Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000009
+    Trial train_cifar_f162b_00005 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000009
 
-    Trial train_cifar_ffd52_00005 completed after 10 iterations at 2024-06-25 20:42:02. Total running time: 9min 51s
+    Trial train_cifar_f162b_00005 completed after 10 iterations at 2024-06-25 21:17:14. Total running time: 9min 39s
 
     Trial status: 10 TERMINATED
-    Current time: 2024-06-25 20:42:02. Total running time: 9min 51s
+    Current time: 2024-06-25 21:17:14. Total running time: 9min 39s
     Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
     +------------------------------------------------------------------------------------------------------------------------------------+
     | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
     +------------------------------------------------------------------------------------------------------------------------------------+
-    | train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-    | train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-    | train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-    | train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-    | train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-    | train_cifar_ffd52_00005   TERMINATED      8     64   0.000353097              4       10           585.507    1.19666       0.5827 |
-    | train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-    | train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-    | train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-    | train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+    | train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+    | train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+    | train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+    | train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+    | train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+    | train_cifar_f162b_00005   TERMINATED      8     64   0.000353097              4       10           573.903    1.3022        0.5445 |
+    | train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+    | train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+    | train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+    | train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
     +------------------------------------------------------------------------------------------------------------------------------------+
 
+    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000009)
     Best trial config: {'l1': 8, 'l2': 64, 'lr': 0.0003530972286268149, 'batch_size': 4}
-    Best trial final validation loss: 1.196658158802986
-    Best trial final validation accuracy: 0.5827
-    (func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000009)
+    Best trial final validation loss: 1.302202902083099
+    Best trial final validation accuracy: 0.5445
     Files already downloaded and verified
     Files already downloaded and verified
-    Best trial test set accuracy: 0.5918
+    Best trial test set accuracy: 0.5611
 
 
 
@@ -1768,7 +1769,7 @@ So that's it! You can now tune the parameters of your PyTorch models.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 10 minutes  7.553 seconds)
+   **Total running time of the script:** ( 9 minutes  56.863 seconds)
 
 
 .. _sphx_glr_download_beginner_hyperparameter_tuning_tutorial.py:
diff --git a/_sources/beginner/introyt/autogradyt_tutorial.rst.txt b/_sources/beginner/introyt/autogradyt_tutorial.rst.txt
index 92a94ce7c2..cf9a7c1471 100644
--- a/_sources/beginner/introyt/autogradyt_tutorial.rst.txt
+++ b/_sources/beginner/introyt/autogradyt_tutorial.rst.txt
@@ -214,7 +214,7 @@ inputs:
  .. code-block:: none
 
 
-    [<matplotlib.lines.Line2D object at 0x7f4695b2c040>]
+    [<matplotlib.lines.Line2D object at 0x7f2100c80880>]
 
 
 
@@ -363,17 +363,17 @@ to the function with no history of its own.
  .. code-block:: none
 
     d:
-    <AddBackward0 object at 0x7f467c1db9d0>
-    ((<MulBackward0 object at 0x7f467c1db880>, 0), (None, 0))
-    ((<SinBackward0 object at 0x7f467c1db880>, 0), (None, 0))
-    ((<AccumulateGrad object at 0x7f467c1db9d0>, 0),)
+    <AddBackward0 object at 0x7f21004c76a0>
+    ((<MulBackward0 object at 0x7f21004c7550>, 0), (None, 0))
+    ((<SinBackward0 object at 0x7f21004c7550>, 0), (None, 0))
+    ((<AccumulateGrad object at 0x7f21004c76a0>, 0),)
     ()
 
     c:
-    <MulBackward0 object at 0x7f467c1db880>
+    <MulBackward0 object at 0x7f21004c7550>
 
     b:
-    <SinBackward0 object at 0x7f467c1db880>
+    <SinBackward0 object at 0x7f21004c7550>
 
     a:
     None
@@ -417,7 +417,7 @@ call the ``backward()`` method on the output, and check the input’s
             -1.4142e+00, -1.0000e+00, -5.1764e-01,  2.3850e-08,  5.1764e-01,
              1.0000e+00,  1.4142e+00,  1.7321e+00,  1.9319e+00,  2.0000e+00])
 
-    [<matplotlib.lines.Line2D object at 0x7f467c20cb80>]
+    [<matplotlib.lines.Line2D object at 0x7f21004f5630>]
 
 
 
@@ -928,17 +928,17 @@ example usage:
     ------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
                                     Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls  
     ------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
-                         cudaEventRecord        45.07%       9.215ms        45.07%       9.215ms       2.304us       0.000us         0.00%       0.000us       0.000us          4000  
-                               aten::div        27.27%       5.575ms        27.27%       5.575ms       5.575us      16.286ms        50.14%      16.286ms      16.286us          1000  
-                               aten::mul        26.58%       5.433ms        26.58%       5.433ms       5.433us      16.193ms        49.86%      16.193ms      16.193us          1000  
-              cudaGetDeviceProperties_v2         0.96%     196.000us         0.96%     196.000us     196.000us       0.000us         0.00%       0.000us       0.000us             1  
+                         cudaEventRecord        46.67%       9.570ms        46.67%       9.570ms       2.393us       0.000us         0.00%       0.000us       0.000us          4000  
+                               aten::div        26.28%       5.389ms        26.28%       5.389ms       5.389us      16.622ms        50.62%      16.622ms      16.622us          1000  
+                               aten::mul        26.07%       5.346ms        26.07%       5.346ms       5.346us      16.216ms        49.38%      16.216ms      16.216us          1000  
+              cudaGetDeviceProperties_v2         0.85%     174.000us         0.85%     174.000us     174.000us       0.000us         0.00%       0.000us       0.000us             1  
                    cudaDeviceSynchronize         0.06%      12.000us         0.06%      12.000us      12.000us       0.000us         0.00%       0.000us       0.000us             1  
-                   cudaStreamIsCapturing         0.04%       8.000us         0.04%       8.000us       2.000us       0.000us         0.00%       0.000us       0.000us             4  
+                   cudaStreamIsCapturing         0.04%       9.000us         0.04%       9.000us       2.250us       0.000us         0.00%       0.000us       0.000us             4  
         cudaDeviceGetStreamPriorityRange         0.01%       3.000us         0.01%       3.000us       3.000us       0.000us         0.00%       0.000us       0.000us             1  
                       cudaGetDeviceCount         0.01%       2.000us         0.01%       2.000us       1.000us       0.000us         0.00%       0.000us       0.000us             2  
     ------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
-    Self CPU time total: 20.444ms
-    Self CUDA time total: 32.479ms
+    Self CPU time total: 20.505ms
+    Self CUDA time total: 32.838ms
 
 
 
@@ -1214,7 +1214,7 @@ API <https://pytorch.org/docs/stable/autograd.html#functional-higher-level-api>`
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.803 seconds)
+   **Total running time of the script:** ( 0 minutes  0.778 seconds)
 
 
 .. _sphx_glr_download_beginner_introyt_autogradyt_tutorial.py:
diff --git a/_sources/beginner/introyt/introyt1_tutorial.rst.txt b/_sources/beginner/introyt/introyt1_tutorial.rst.txt
index 5760ef0c92..32a873c8f7 100644
--- a/_sources/beginner/introyt/introyt1_tutorial.rst.txt
+++ b/_sources/beginner/introyt/introyt1_tutorial.rst.txt
@@ -598,22 +598,22 @@ automobile, ship, truck):
     Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
 
       0%|          | 0/170498071 [00:00<?, ?it/s]
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+    100%|##########| 170498071/170498071 [00:01<00:00, 106488593.38it/s]
     Extracting ./data/cifar-10-python.tar.gz to ./data
 
 
@@ -1092,7 +1092,7 @@ demonstrates that some general learning did happen in the model.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  58.852 seconds)
+   **Total running time of the script:** ( 1 minutes  59.366 seconds)
 
 
 .. _sphx_glr_download_beginner_introyt_introyt1_tutorial.py:
diff --git a/_sources/beginner/introyt/tensorboardyt_tutorial.rst.txt b/_sources/beginner/introyt/tensorboardyt_tutorial.rst.txt
index 7ba945890f..2d6d1a83c4 100644
--- a/_sources/beginner/introyt/tensorboardyt_tutorial.rst.txt
+++ b/_sources/beginner/introyt/tensorboardyt_tutorial.rst.txt
@@ -185,75 +185,40 @@ Let’s start by adding sample images from our dataset to TensorBoard:
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
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+    100%|##########| 26421880/26421880 [00:01<00:00, 18278844.19it/s]
     Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
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     Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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     Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
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+    100%|##########| 5148/5148 [00:00<00:00, 41364515.31it/s]
     Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
 
 
@@ -544,7 +509,7 @@ For more information, have a look at:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  42.301 seconds)
+   **Total running time of the script:** ( 2 minutes  40.903 seconds)
 
 
 .. _sphx_glr_download_beginner_introyt_tensorboardyt_tutorial.py:
diff --git a/_sources/beginner/introyt/tensors_deeper_tutorial.rst.txt b/_sources/beginner/introyt/tensors_deeper_tutorial.rst.txt
index d47679934a..e87bd86533 100644
--- a/_sources/beginner/introyt/tensors_deeper_tutorial.rst.txt
+++ b/_sources/beginner/introyt/tensors_deeper_tutorial.rst.txt
@@ -87,9 +87,9 @@ The simplest way to create a tensor is with the ``torch.empty()`` call:
  .. code-block:: none
 
     <class 'torch.Tensor'>
-    tensor([[-9.6404e-31,  4.5629e-41,  2.4354e-05,  0.0000e+00],
-            [ 2.0735e-37,  0.0000e+00,  0.0000e+00,  0.0000e+00],
-            [ 5.5904e+07,  0.0000e+00, -6.3661e-37,  4.5629e-41]])
+    tensor([[ 0.0000e+00,  0.0000e+00,  1.4013e-45,  1.4013e-45],
+            [ 1.7331e-37,  0.0000e+00,  0.0000e+00,  0.0000e+00],
+            [ 6.6902e-29,  0.0000e+00, -2.4643e-31,  4.5907e-41]])
 
 
 
@@ -271,17 +271,17 @@ have the ``torch.*_like()`` methods:
  .. code-block:: none
 
     torch.Size([2, 2, 3])
-    tensor([[[ 9.5868e-10,  0.0000e+00,  0.0000e+00],
-             [ 0.0000e+00,  8.9683e-44,  0.0000e+00]],
+    tensor([[[-3.6793e-25,  4.5907e-41,  7.1389e-14],
+             [ 0.0000e+00,  1.7331e-37,  0.0000e+00]],
 
-            [[ 1.5695e-43,  0.0000e+00,  2.0005e+03],
-             [ 0.0000e+00, -6.3661e-37,  4.5629e-41]]])
+            [[ 0.0000e+00,  0.0000e+00,  7.1043e-28],
+             [ 0.0000e+00, -2.4643e-31,  4.5907e-41]]])
     torch.Size([2, 2, 3])
-    tensor([[[ 1.5769e-31,  0.0000e+00,  7.4182e-13],
-             [ 0.0000e+00,  4.4842e-44,  0.0000e+00]],
+    tensor([[[ 2.0829e-07,  0.0000e+00,  4.5751e-29],
+             [ 0.0000e+00,  8.9683e-44,  0.0000e+00]],
 
-            [[ 1.5695e-43,  0.0000e+00,  1.3849e-31],
-             [ 0.0000e+00, -6.3661e-37,  4.5629e-41]]])
+            [[ 1.5695e-43,  0.0000e+00,  2.4362e-27],
+             [ 0.0000e+00, -2.4643e-31,  4.5907e-41]]])
     torch.Size([2, 2, 3])
     tensor([[[0., 0., 0.],
              [0., 0., 0.]],
@@ -1777,7 +1777,7 @@ are reflected in the other:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.275 seconds)
+   **Total running time of the script:** ( 0 minutes  0.299 seconds)
 
 
 .. _sphx_glr_download_beginner_introyt_tensors_deeper_tutorial.py:
diff --git a/_sources/beginner/introyt/trainingyt.rst.txt b/_sources/beginner/introyt/trainingyt.rst.txt
index a87e8ea1c0..d8e3d9e65b 100644
--- a/_sources/beginner/introyt/trainingyt.rst.txt
+++ b/_sources/beginner/introyt/trainingyt.rst.txt
@@ -127,88 +127,78 @@ and download both training and validation data splits.
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
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+     83%|########2 | 21823488/26421880 [00:06<00:00, 9492431.98it/s]
+     87%|########6 | 22970368/26421880 [00:06<00:00, 9885286.93it/s]
+     96%|#########5| 25264128/26421880 [00:06<00:00, 12662382.26it/s]
+    100%|##########| 26421880/26421880 [00:06<00:00, 3864805.39it/s] 
     Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
       0%|          | 0/29515 [00:00<?, ?it/s]
-    100%|##########| 29515/29515 [00:00<00:00, 328370.13it/s]
+    100%|##########| 29515/29515 [00:00<00:00, 334714.65it/s]
     Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
       0%|          | 0/4422102 [00:00<?, ?it/s]
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+    100%|##########| 4422102/4422102 [00:00<00:00, 5968878.75it/s]
     Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
       0%|          | 0/5148 [00:00<?, ?it/s]
-    100%|##########| 5148/5148 [00:00<00:00, 45941014.88it/s]
+    100%|##########| 5148/5148 [00:00<00:00, 33218887.68it/s]
     Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
 
     Training set has 60000 instances
@@ -689,7 +679,7 @@ Other Resources
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  16.953 seconds)
+   **Total running time of the script:** ( 5 minutes  26.753 seconds)
 
 
 .. _sphx_glr_download_beginner_introyt_trainingyt.py:
diff --git a/_sources/beginner/knowledge_distillation_tutorial.rst.txt b/_sources/beginner/knowledge_distillation_tutorial.rst.txt
index ce08b3fc34..77422b2be1 100644
--- a/_sources/beginner/knowledge_distillation_tutorial.rst.txt
+++ b/_sources/beginner/knowledge_distillation_tutorial.rst.txt
@@ -335,17 +335,17 @@ Start by training the teacher network using cross-entropy:
 
  .. code-block:: none
 
-    Epoch 1/10, Loss: 1.3250710057175679
-    Epoch 2/10, Loss: 0.8651102480985929
-    Epoch 3/10, Loss: 0.6783160822623221
-    Epoch 4/10, Loss: 0.5429468773819907
-    Epoch 5/10, Loss: 0.4233940201418479
-    Epoch 6/10, Loss: 0.3186772309453286
-    Epoch 7/10, Loss: 0.23109134231382014
-    Epoch 8/10, Loss: 0.1697552327514457
-    Epoch 9/10, Loss: 0.14379946459704043
-    Epoch 10/10, Loss: 0.12531204655995148
-    Test Accuracy: 75.62%
+    Epoch 1/10, Loss: 1.3316447629648096
+    Epoch 2/10, Loss: 0.8751732992089313
+    Epoch 3/10, Loss: 0.6836497868265947
+    Epoch 4/10, Loss: 0.5458234034078505
+    Epoch 5/10, Loss: 0.4359907577257327
+    Epoch 6/10, Loss: 0.3299281927554504
+    Epoch 7/10, Loss: 0.24488897051881342
+    Epoch 8/10, Loss: 0.18661382771513957
+    Epoch 9/10, Loss: 0.15769847224244987
+    Epoch 10/10, Loss: 0.12847331230578674
+    Test Accuracy: 74.97%
 
 
 
@@ -446,17 +446,17 @@ Train and test the lightweight network with cross entropy loss:
 
  .. code-block:: none
 
-    Epoch 1/10, Loss: 1.4690716739200875
-    Epoch 2/10, Loss: 1.1584134421994925
-    Epoch 3/10, Loss: 1.0249660551700446
-    Epoch 4/10, Loss: 0.9242221932581929
-    Epoch 5/10, Loss: 0.8470086699251629
-    Epoch 6/10, Loss: 0.7781770118057271
-    Epoch 7/10, Loss: 0.7141712661594382
-    Epoch 8/10, Loss: 0.6576351702518171
-    Epoch 9/10, Loss: 0.6013266375607542
-    Epoch 10/10, Loss: 0.5511761150701576
-    Test Accuracy: 70.30%
+    Epoch 1/10, Loss: 1.467257319508916
+    Epoch 2/10, Loss: 1.1540557117108494
+    Epoch 3/10, Loss: 1.0237963839869975
+    Epoch 4/10, Loss: 0.9230599127462148
+    Epoch 5/10, Loss: 0.8483210123713364
+    Epoch 6/10, Loss: 0.781327926289395
+    Epoch 7/10, Loss: 0.7165115344554872
+    Epoch 8/10, Loss: 0.6570775745927221
+    Epoch 9/10, Loss: 0.6063216873599441
+    Epoch 10/10, Loss: 0.5517354763834678
+    Test Accuracy: 71.13%
 
 
 
@@ -482,8 +482,8 @@ The metrics so far can be seen with the following lines:
 
  .. code-block:: none
 
-    Teacher accuracy: 75.62%
-    Student accuracy: 70.30%
+    Teacher accuracy: 74.97%
+    Student accuracy: 71.13%
 
 
 
@@ -582,20 +582,20 @@ As we continue defining our first helper function that introduces a teacher-stud
 
  .. code-block:: none
 
-    Epoch 1/10, Loss: 2.3669486673896576
-    Epoch 2/10, Loss: 1.8551302818995912
-    Epoch 3/10, Loss: 1.6341343560182224
-    Epoch 4/10, Loss: 1.4716220581927872
-    Epoch 5/10, Loss: 1.3448759957652567
-    Epoch 6/10, Loss: 1.230848588754454
-    Epoch 7/10, Loss: 1.137130338212718
-    Epoch 8/10, Loss: 1.0464744631896543
-    Epoch 9/10, Loss: 0.9703757310157541
-    Epoch 10/10, Loss: 0.9071777760220305
-    Test Accuracy: 71.17%
-    Teacher accuracy: 75.62%
-    Student accuracy without teacher: 70.30%
-    Student accuracy with CE + KD: 71.17%
+    Epoch 1/10, Loss: 2.363848817927758
+    Epoch 2/10, Loss: 1.846368370458598
+    Epoch 3/10, Loss: 1.6295966614237831
+    Epoch 4/10, Loss: 1.470853830847289
+    Epoch 5/10, Loss: 1.3469416916827717
+    Epoch 6/10, Loss: 1.23279900700235
+    Epoch 7/10, Loss: 1.1376631096805758
+    Epoch 8/10, Loss: 1.050427379053267
+    Epoch 9/10, Loss: 0.9787890670244651
+    Epoch 10/10, Loss: 0.9075392502957903
+    Test Accuracy: 70.41%
+    Teacher accuracy: 74.97%
+    Student accuracy without teacher: 71.13%
+    Student accuracy with CE + KD: 70.41%
 
 
 
@@ -715,8 +715,8 @@ Now, the forward function returns not only the logits of the network but also th
 
  .. code-block:: none
 
-    Norm of 1st layer for deep_nn: 7.500669956207275
-    Norm of 1st layer for modified_deep_nn: 7.500669956207275
+    Norm of 1st layer for deep_nn: 7.507744312286377
+    Norm of 1st layer for modified_deep_nn: 7.507744312286377
     Norm of 1st layer: 2.327361822128296
 
 
@@ -889,17 +889,17 @@ For now, we can run a simple train-test session.
 
  .. code-block:: none
 
-    Epoch 1/10, Loss: 1.306663755565653
-    Epoch 2/10, Loss: 1.0738007182355427
-    Epoch 3/10, Loss: 0.9744930055440234
-    Epoch 4/10, Loss: 0.8975373378495122
-    Epoch 5/10, Loss: 0.8423850412868783
-    Epoch 6/10, Loss: 0.7968010483190532
-    Epoch 7/10, Loss: 0.7563447217502253
-    Epoch 8/10, Loss: 0.7203674510007014
-    Epoch 9/10, Loss: 0.6835664493958359
-    Epoch 10/10, Loss: 0.6579263746890875
-    Test Accuracy: 70.91%
+    Epoch 1/10, Loss: 1.3032945272562755
+    Epoch 2/10, Loss: 1.0667807447635913
+    Epoch 3/10, Loss: 0.9640445730570332
+    Epoch 4/10, Loss: 0.88822200917222
+    Epoch 5/10, Loss: 0.833785516526693
+    Epoch 6/10, Loss: 0.7883921777805709
+    Epoch 7/10, Loss: 0.7487654428347907
+    Epoch 8/10, Loss: 0.7142888545380224
+    Epoch 9/10, Loss: 0.6748507934458116
+    Epoch 10/10, Loss: 0.6500329192337173
+    Test Accuracy: 70.92%
 
 
 
@@ -1106,17 +1106,17 @@ in addition to the regular cross entropy loss of the classification task.
 
  .. code-block:: none
 
-    Epoch 1/10, Loss: 1.692887835185546
-    Epoch 2/10, Loss: 1.3222829390059956
-    Epoch 3/10, Loss: 1.1805361286758462
-    Epoch 4/10, Loss: 1.0865643943667107
-    Epoch 5/10, Loss: 1.0102771583115657
-    Epoch 6/10, Loss: 0.9460458064932957
-    Epoch 7/10, Loss: 0.8926568333145297
-    Epoch 8/10, Loss: 0.842905539990691
-    Epoch 9/10, Loss: 0.798999123865991
-    Epoch 10/10, Loss: 0.7600122499648873
-    Test Accuracy: 70.84%
+    Epoch 1/10, Loss: 1.7142535137093586
+    Epoch 2/10, Loss: 1.3228392171128023
+    Epoch 3/10, Loss: 1.1758271349055687
+    Epoch 4/10, Loss: 1.079089686541301
+    Epoch 5/10, Loss: 1.0010888690838728
+    Epoch 6/10, Loss: 0.9352552056922327
+    Epoch 7/10, Loss: 0.8804437144638022
+    Epoch 8/10, Loss: 0.828696703362038
+    Epoch 9/10, Loss: 0.7858170161161886
+    Epoch 10/10, Loss: 0.7487682099537472
+    Test Accuracy: 71.06%
 
 
 
@@ -1146,11 +1146,11 @@ Including the extra network is the idea behind hint-based distillation.
 
  .. code-block:: none
 
-    Teacher accuracy: 75.62%
-    Student accuracy without teacher: 70.30%
-    Student accuracy with CE + KD: 71.17%
-    Student accuracy with CE + CosineLoss: 70.91%
-    Student accuracy with CE + RegressorMSE: 70.84%
+    Teacher accuracy: 74.97%
+    Student accuracy without teacher: 71.13%
+    Student accuracy with CE + KD: 70.41%
+    Student accuracy with CE + CosineLoss: 70.92%
+    Student accuracy with CE + RegressorMSE: 71.06%
 
 
 
@@ -1176,7 +1176,7 @@ For more information, see:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 7 minutes  35.866 seconds)
+   **Total running time of the script:** ( 7 minutes  33.687 seconds)
 
 
 .. _sphx_glr_download_beginner_knowledge_distillation_tutorial.py:
diff --git a/_sources/beginner/nlp/advanced_tutorial.rst.txt b/_sources/beginner/nlp/advanced_tutorial.rst.txt
index 31cbe90615..fc2a91d6fb 100644
--- a/_sources/beginner/nlp/advanced_tutorial.rst.txt
+++ b/_sources/beginner/nlp/advanced_tutorial.rst.txt
@@ -154,7 +154,7 @@ you could probably use this tagger for real tasks.
  .. code-block:: none
 
 
-    <torch._C.Generator object at 0x7f8c10b3b510>
+    <torch._C.Generator object at 0x7fc8e4fff510>
 
 
 
@@ -458,7 +458,7 @@ Pick up some real data and do a comparison!
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  10.515 seconds)
+   **Total running time of the script:** ( 0 minutes  11.222 seconds)
 
 
 .. _sphx_glr_download_beginner_nlp_advanced_tutorial.py:
diff --git a/_sources/beginner/nlp/deep_learning_tutorial.rst.txt b/_sources/beginner/nlp/deep_learning_tutorial.rst.txt
index ddc798e54d..fa31475bbb 100644
--- a/_sources/beginner/nlp/deep_learning_tutorial.rst.txt
+++ b/_sources/beginner/nlp/deep_learning_tutorial.rst.txt
@@ -73,7 +73,7 @@ output below is the mapping of the :math:`i`'th row of the input under
  .. code-block:: none
 
 
-    <torch._C.Generator object at 0x7f46a2fdf510>
+    <torch._C.Generator object at 0x7f210e81b510>
 
 
 
diff --git a/_sources/beginner/nlp/pytorch_tutorial.rst.txt b/_sources/beginner/nlp/pytorch_tutorial.rst.txt
index be55f56c53..0cc5e947e1 100644
--- a/_sources/beginner/nlp/pytorch_tutorial.rst.txt
+++ b/_sources/beginner/nlp/pytorch_tutorial.rst.txt
@@ -49,7 +49,7 @@ let's look what we can do with tensors.
  .. code-block:: none
 
 
-    <torch._C.Generator object at 0x7f4b55b2f510>
+    <torch._C.Generator object at 0x7f3bed1d7510>
 
 
 
@@ -386,7 +386,7 @@ created. Let's see it in action.
  .. code-block:: none
 
     tensor([5., 7., 9.], grad_fn=<AddBackward0>)
-    <AddBackward0 object at 0x7f4b20d59bd0>
+    <AddBackward0 object at 0x7f3bc42bee30>
 
 
 
@@ -421,7 +421,7 @@ But how does that help us compute a gradient?
  .. code-block:: none
 
     tensor(21., grad_fn=<SumBackward0>)
-    <SumBackward0 object at 0x7f4b20d58970>
+    <SumBackward0 object at 0x7f3bc42bc460>
 
 
 
@@ -536,7 +536,7 @@ successful programmer in deep learning.
 
     False False
     None
-    <AddBackward0 object at 0x7f4b20d58af0>
+    <AddBackward0 object at 0x7f3bc42bc190>
     True
     None
 
diff --git a/_sources/beginner/nlp/sequence_models_tutorial.rst.txt b/_sources/beginner/nlp/sequence_models_tutorial.rst.txt
index 3e78e31c70..0dfa902762 100644
--- a/_sources/beginner/nlp/sequence_models_tutorial.rst.txt
+++ b/_sources/beginner/nlp/sequence_models_tutorial.rst.txt
@@ -90,7 +90,7 @@ Let's see a quick example.
  .. code-block:: none
 
 
-    <torch._C.Generator object at 0x7f9f17287510>
+    <torch._C.Generator object at 0x7fee1e54b510>
 
 
 
@@ -376,7 +376,7 @@ this LSTM. Hints:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.845 seconds)
+   **Total running time of the script:** ( 0 minutes  0.809 seconds)
 
 
 .. _sphx_glr_download_beginner_nlp_sequence_models_tutorial.py:
diff --git a/_sources/beginner/nlp/word_embeddings_tutorial.rst.txt b/_sources/beginner/nlp/word_embeddings_tutorial.rst.txt
index 2ccb80ff4e..3a9105a42d 100644
--- a/_sources/beginner/nlp/word_embeddings_tutorial.rst.txt
+++ b/_sources/beginner/nlp/word_embeddings_tutorial.rst.txt
@@ -195,7 +195,7 @@ indices are integers, not floats).
  .. code-block:: none
 
 
-    <torch._C.Generator object at 0x7f6a5965b510>
+    <torch._C.Generator object at 0x7f2fdc7bf510>
 
 
 
@@ -343,9 +343,9 @@ examples and update the parameters with backpropagation.
  .. code-block:: none
 
     [(['forty', 'When'], 'winters'), (['winters', 'forty'], 'shall'), (['shall', 'winters'], 'besiege')]
-    [521.698427438736, 519.1963937282562, 516.7113490104675, 514.2416417598724, 511.7873330116272, 509.3473746776581, 506.92100834846497, 504.5057735443115, 502.10225200653076, 499.70783376693726]
-    tensor([-1.1033, -0.6981,  0.2359,  1.9145,  1.8357,  1.3259, -0.0693,  0.3463,
-            -0.6511,  1.5578], grad_fn=<SelectBackward0>)
+    [519.4899263381958, 516.9370996952057, 514.4005351066589, 511.87999200820923, 509.37452578544617, 506.88408493995667, 504.40851354599, 501.94547271728516, 499.4936594963074, 497.0529816150665]
+    tensor([ 0.6867,  0.4197, -1.0219,  0.9891,  0.7819, -2.2055, -1.4977, -0.9028,
+             0.8012, -0.6630], grad_fn=<SelectBackward0>)
 
 
 
@@ -439,14 +439,14 @@ tips:
 
     [(['are', 'We', 'to', 'study'], 'about'), (['about', 'are', 'study', 'the'], 'to'), (['to', 'about', 'the', 'idea'], 'study'), (['study', 'to', 'idea', 'of'], 'the'), (['the', 'study', 'of', 'a'], 'idea')]
 
-    tensor([48, 14, 34,  7])
+    tensor([35, 30, 27, 19])
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.738 seconds)
+   **Total running time of the script:** ( 0 minutes  0.784 seconds)
 
 
 .. _sphx_glr_download_beginner_nlp_word_embeddings_tutorial.py:
diff --git a/_sources/beginner/nn_tutorial.rst.txt b/_sources/beginner/nn_tutorial.rst.txt
index 1cc0a7bdc3..614b9f6684 100644
--- a/_sources/beginner/nn_tutorial.rst.txt
+++ b/_sources/beginner/nn_tutorial.rst.txt
@@ -1648,8 +1648,8 @@ You should find it runs faster now:
 
  .. code-block:: none
 
-    0 0.18058827521800994
-    1 0.1704494299888611
+    0 0.1840453363776207
+    1 0.1712360767841339
 
 
 
@@ -1692,7 +1692,7 @@ what we've seen:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  25.080 seconds)
+   **Total running time of the script:** ( 0 minutes  24.457 seconds)
 
 
 .. _sphx_glr_download_beginner_nn_tutorial.py:
diff --git a/_sources/beginner/onnx/export_simple_model_to_onnx_tutorial.rst.txt b/_sources/beginner/onnx/export_simple_model_to_onnx_tutorial.rst.txt
index 489a9aba6c..c460535e7b 100644
--- a/_sources/beginner/onnx/export_simple_model_to_onnx_tutorial.rst.txt
+++ b/_sources/beginner/onnx/export_simple_model_to_onnx_tutorial.rst.txt
@@ -344,7 +344,7 @@ sit tight and have fun going through all of them to learn all there is about the
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.014 seconds)
+   **Total running time of the script:** ( 0 minutes  1.084 seconds)
 
 
 .. _sphx_glr_download_beginner_onnx_export_simple_model_to_onnx_tutorial.py:
diff --git a/_sources/beginner/onnx/onnx_registry_tutorial.rst.txt b/_sources/beginner/onnx/onnx_registry_tutorial.rst.txt
index 24bab75dae..cb67ab087f 100644
--- a/_sources/beginner/onnx/onnx_registry_tutorial.rst.txt
+++ b/_sources/beginner/onnx/onnx_registry_tutorial.rst.txt
@@ -140,26 +140,6 @@ We will override the registration of ``aten::add.Tensor`` with our custom implem
         )
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:136: UserWarning:
-
-    torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
-
-    aten::add.Tensor is supported by ONNX registry:       True
-    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning:
-
-    torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 122-126
 
 Now let's inspect the model and verify the model has a ``custom_aten_add`` instead of ``aten::add.Tensor``.
@@ -184,12 +164,6 @@ operator, and one for constant attribute.
 
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 139-155
 
 This is how ``custom_aten_add_model`` looks in the ONNX graph using Netron:
@@ -237,12 +211,6 @@ As an additional step, we can use ONNX Runtime to run the model, and compare the
 
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 179-193
 
 Custom operators with existing ONNX Runtime support
@@ -271,22 +239,6 @@ Before we begin, let's check whether ``aten::gelu.default`` is really supported
 
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:136: UserWarning:
-
-    torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
-
-    aten::gelu.default is supported by ONNX registry:     True
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 200-202
 
 In our example, ``aten::gelu.default`` operator is supported by the ONNX registry,
@@ -328,22 +280,6 @@ so :meth:`onnx_registry.is_registered_op` returns ``True``.
 
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    'Gelu' is not a known op in 'com.microsoft'
-    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:136: UserWarning:
-
-    torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 234-239
 
 Let's inspect the model and verify the model uses :func:`custom_aten_gelu` instead of
@@ -368,12 +304,6 @@ node for ``Gelu`` with namespace ``com.microsoft``.
 
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 251-265
 
 The following diagram shows ``custom_aten_gelu_model`` ONNX graph using Netron:
@@ -416,12 +346,6 @@ and compare the results with PyTorch.
         torch.testing.assert_close(torch_output, torch.tensor(onnxruntime_output))
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 286-308
 
 Custom operators without ONNX Runtime support
@@ -478,12 +402,6 @@ This can be done by using ``torch._custom_op``.
 
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 335-344
 
 Custom Ops Registration in ONNX Registry
@@ -530,23 +448,6 @@ and ``CustomOpTwo``. These two ops are registered and built in
 
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    'CustomOpOne' is not a known op in 'test.customop'
-    'CustomOpTwo' is not a known op in 'test.customop'
-    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:136: UserWarning:
-
-    torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 375-379
 
 The ``onnx_program`` exposes the exported model as protobuf through ``onnx_program.model_proto``.
@@ -568,12 +469,6 @@ there are two function nodes, one for each operator.
 
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 389-469
 
 This is how ``custom_addandround_model`` ONNX graph looks using Netron:
@@ -660,7 +555,7 @@ sit tight and have fun going through all of them to learn all there is about the
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.920 seconds)
+   **Total running time of the script:** ( 0 minutes  0.000 seconds)
 
 
 .. _sphx_glr_download_beginner_onnx_onnx_registry_tutorial.py:
diff --git a/_sources/beginner/template_tutorial.rst.txt b/_sources/beginner/template_tutorial.rst.txt
index c4d16117d8..1962901a9f 100644
--- a/_sources/beginner/template_tutorial.rst.txt
+++ b/_sources/beginner/template_tutorial.rst.txt
@@ -134,7 +134,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.021 seconds)
+   **Total running time of the script:** ( 0 minutes  0.023 seconds)
 
 
 .. _sphx_glr_download_beginner_template_tutorial.py:
diff --git a/_sources/beginner/transfer_learning_tutorial.rst.txt b/_sources/beginner/transfer_learning_tutorial.rst.txt
index a894982002..0794f9630e 100644
--- a/_sources/beginner/transfer_learning_tutorial.rst.txt
+++ b/_sources/beginner/transfer_learning_tutorial.rst.txt
@@ -80,7 +80,7 @@ These two major transfer learning scenarios look as follows:
  .. code-block:: none
 
 
-    <contextlib.ExitStack object at 0x7f7f3a9de6b0>
+    <contextlib.ExitStack object at 0x7f495c89e650>
 
 
 
@@ -377,8 +377,8 @@ Load a pretrained model and reset final fully connected layer.
     Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
 
       0%|          | 0.00/44.7M [00:00<?, ?B/s]
-     94%|#########3| 41.9M/44.7M [00:00<00:00, 438MB/s]
-    100%|##########| 44.7M/44.7M [00:00<00:00, 433MB/s]
+     95%|#########5| 42.6M/44.7M [00:00<00:00, 447MB/s]
+    100%|##########| 44.7M/44.7M [00:00<00:00, 446MB/s]
 
 
 
@@ -410,131 +410,131 @@ minute.
 
     Epoch 0/24
     ----------
-    train Loss: 0.4767 Acc: 0.7623
-    val Loss: 0.2806 Acc: 0.8824
+    train Loss: 0.4800 Acc: 0.7500
+    val Loss: 0.3129 Acc: 0.8758
 
     Epoch 1/24
     ----------
-    train Loss: 0.5237 Acc: 0.8033
-    val Loss: 0.6379 Acc: 0.7255
+    train Loss: 0.5424 Acc: 0.7951
+    val Loss: 0.5827 Acc: 0.7712
 
     Epoch 2/24
     ----------
-    train Loss: 0.4181 Acc: 0.8320
-    val Loss: 0.2440 Acc: 0.9216
+    train Loss: 0.4197 Acc: 0.8361
+    val Loss: 0.1975 Acc: 0.9085
 
     Epoch 3/24
     ----------
-    train Loss: 0.5908 Acc: 0.7746
-    val Loss: 0.3013 Acc: 0.8758
+    train Loss: 0.5670 Acc: 0.7623
+    val Loss: 0.2927 Acc: 0.8693
 
     Epoch 4/24
     ----------
-    train Loss: 0.4045 Acc: 0.8566
-    val Loss: 0.1879 Acc: 0.9150
+    train Loss: 0.3947 Acc: 0.8443
+    val Loss: 0.3083 Acc: 0.8889
 
     Epoch 5/24
     ----------
-    train Loss: 0.4965 Acc: 0.7992
-    val Loss: 0.2498 Acc: 0.8889
+    train Loss: 0.4595 Acc: 0.8156
+    val Loss: 0.2687 Acc: 0.9150
 
     Epoch 6/24
     ----------
-    train Loss: 0.3785 Acc: 0.8279
-    val Loss: 0.2358 Acc: 0.9281
+    train Loss: 0.3799 Acc: 0.8115
+    val Loss: 0.2301 Acc: 0.9412
 
     Epoch 7/24
     ----------
-    train Loss: 0.3876 Acc: 0.8607
-    val Loss: 0.2037 Acc: 0.9150
+    train Loss: 0.3627 Acc: 0.8197
+    val Loss: 0.1722 Acc: 0.9477
 
     Epoch 8/24
     ----------
-    train Loss: 0.2167 Acc: 0.9057
-    val Loss: 0.2235 Acc: 0.9216
+    train Loss: 0.2120 Acc: 0.9098
+    val Loss: 0.1956 Acc: 0.9412
 
     Epoch 9/24
     ----------
-    train Loss: 0.2616 Acc: 0.8852
-    val Loss: 0.2264 Acc: 0.9216
+    train Loss: 0.2721 Acc: 0.8770
+    val Loss: 0.2000 Acc: 0.9346
 
     Epoch 10/24
     ----------
-    train Loss: 0.3352 Acc: 0.8607
-    val Loss: 0.1934 Acc: 0.9216
+    train Loss: 0.3438 Acc: 0.8402
+    val Loss: 0.1668 Acc: 0.9542
 
     Epoch 11/24
     ----------
-    train Loss: 0.3193 Acc: 0.8566
-    val Loss: 0.2572 Acc: 0.9216
+    train Loss: 0.3142 Acc: 0.8238
+    val Loss: 0.2695 Acc: 0.9020
 
     Epoch 12/24
     ----------
-    train Loss: 0.2434 Acc: 0.9016
-    val Loss: 0.2078 Acc: 0.9216
+    train Loss: 0.2165 Acc: 0.8975
+    val Loss: 0.2052 Acc: 0.9346
 
     Epoch 13/24
     ----------
-    train Loss: 0.2875 Acc: 0.8484
-    val Loss: 0.1963 Acc: 0.9150
+    train Loss: 0.2812 Acc: 0.8689
+    val Loss: 0.1586 Acc: 0.9542
 
     Epoch 14/24
     ----------
-    train Loss: 0.2617 Acc: 0.8852
-    val Loss: 0.2301 Acc: 0.9216
+    train Loss: 0.2480 Acc: 0.9098
+    val Loss: 0.2275 Acc: 0.9085
 
     Epoch 15/24
     ----------
-    train Loss: 0.3066 Acc: 0.8689
-    val Loss: 0.2791 Acc: 0.9085
+    train Loss: 0.3120 Acc: 0.8689
+    val Loss: 0.2508 Acc: 0.8954
 
     Epoch 16/24
     ----------
-    train Loss: 0.2018 Acc: 0.9262
-    val Loss: 0.2149 Acc: 0.9216
+    train Loss: 0.2068 Acc: 0.9221
+    val Loss: 0.1905 Acc: 0.9216
 
     Epoch 17/24
     ----------
-    train Loss: 0.2667 Acc: 0.8975
-    val Loss: 0.1902 Acc: 0.9150
+    train Loss: 0.2502 Acc: 0.8934
+    val Loss: 0.1703 Acc: 0.9542
 
     Epoch 18/24
     ----------
-    train Loss: 0.2705 Acc: 0.8975
-    val Loss: 0.2449 Acc: 0.9216
+    train Loss: 0.2655 Acc: 0.8934
+    val Loss: 0.2096 Acc: 0.9085
 
     Epoch 19/24
     ----------
-    train Loss: 0.1940 Acc: 0.9180
-    val Loss: 0.1963 Acc: 0.9150
+    train Loss: 0.1975 Acc: 0.9303
+    val Loss: 0.1733 Acc: 0.9346
 
     Epoch 20/24
     ----------
-    train Loss: 0.2379 Acc: 0.8893
-    val Loss: 0.2075 Acc: 0.9216
+    train Loss: 0.2412 Acc: 0.8975
+    val Loss: 0.1880 Acc: 0.9281
 
     Epoch 21/24
     ----------
-    train Loss: 0.2309 Acc: 0.8770
-    val Loss: 0.2516 Acc: 0.9150
+    train Loss: 0.2323 Acc: 0.8934
+    val Loss: 0.2280 Acc: 0.9150
 
     Epoch 22/24
     ----------
-    train Loss: 0.3238 Acc: 0.8730
-    val Loss: 0.2021 Acc: 0.9216
+    train Loss: 0.3288 Acc: 0.8566
+    val Loss: 0.1817 Acc: 0.9412
 
     Epoch 23/24
     ----------
-    train Loss: 0.2599 Acc: 0.8689
-    val Loss: 0.2126 Acc: 0.9216
+    train Loss: 0.2776 Acc: 0.8770
+    val Loss: 0.1916 Acc: 0.9281
 
     Epoch 24/24
     ----------
-    train Loss: 0.2999 Acc: 0.8689
-    val Loss: 0.1979 Acc: 0.9150
+    train Loss: 0.2940 Acc: 0.8811
+    val Loss: 0.1740 Acc: 0.9608
 
-    Training complete in 0m 30s
-    Best val Acc: 0.928105
+    Training complete in 0m 31s
+    Best val Acc: 0.960784
 
 
 
@@ -642,7 +642,7 @@ network. However, forward does need to be computed.
 
     Epoch 2/24
     ----------
-    train Loss: 0.4602 Acc: 0.7869
+    train Loss: 0.4603 Acc: 0.7869
     val Loss: 0.1847 Acc: 0.9477
 
     Epoch 3/24
@@ -658,7 +658,7 @@ network. However, forward does need to be computed.
     Epoch 5/24
     ----------
     train Loss: 0.5505 Acc: 0.7828
-    val Loss: 0.1642 Acc: 0.9477
+    val Loss: 0.1643 Acc: 0.9477
 
     Epoch 6/24
     ----------
@@ -747,7 +747,7 @@ network. However, forward does need to be computed.
 
     Epoch 23/24
     ----------
-    train Loss: 0.3625 Acc: 0.8361
+    train Loss: 0.3626 Acc: 0.8361
     val Loss: 0.1747 Acc: 0.9412
 
     Epoch 24/24
@@ -755,7 +755,7 @@ network. However, forward does need to be computed.
     train Loss: 0.3840 Acc: 0.8320
     val Loss: 0.1768 Acc: 0.9412
 
-    Training complete in 0m 23s
+    Training complete in 0m 22s
     Best val Acc: 0.954248
 
 
@@ -864,7 +864,7 @@ checkout our `Quantized Transfer Learning for Computer Vision Tutorial <https://
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  54.787 seconds)
+   **Total running time of the script:** ( 0 minutes  55.883 seconds)
 
 
 .. _sphx_glr_download_beginner_transfer_learning_tutorial.py:
diff --git a/_sources/beginner/vt_tutorial.rst.txt b/_sources/beginner/vt_tutorial.rst.txt
index 4b5116b25d..714fcea8f4 100644
--- a/_sources/beginner/vt_tutorial.rst.txt
+++ b/_sources/beginner/vt_tutorial.rst.txt
@@ -164,26 +164,37 @@ then run the script below:
     Downloading: "https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/deit_base_patch16_224-b5f2ef4d.pth
 
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     269
 
 
@@ -451,11 +462,11 @@ the optimized-quantized-and-scripted model - run the code below:
 
  .. code-block:: none
 
-    original model: 120.11ms
-    scripted model: 104.93ms
-    scripted & quantized model: 129.52ms
-    scripted & quantized & optimized model: 126.87ms
-    lite model: 116.97ms
+    original model: 102.06ms
+    scripted model: 107.77ms
+    scripted & quantized model: 133.82ms
+    scripted & quantized & optimized model: 129.41ms
+    lite model: 121.42ms
 
 
 
@@ -522,10 +533,10 @@ model.
 
                                         Model  ... Reduction
     0                          original model  ...        0%
-    1                          scripted model  ...    12.64%
-    2              scripted & quantized model  ...    -7.83%
-    3  scripted & quantized & optimized model  ...    -5.63%
-    4                              lite model  ...     2.62%
+    1                          scripted model  ...    -5.60%
+    2              scripted & quantized model  ...   -31.12%
+    3  scripted & quantized & optimized model  ...   -26.80%
+    4                              lite model  ...   -18.97%
 
     [5 rows x 3 columns]
 
@@ -545,7 +556,7 @@ Learn More
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  18.388 seconds)
+   **Total running time of the script:** ( 0 minutes  19.455 seconds)
 
 
 .. _sphx_glr_download_beginner_vt_tutorial.py:
diff --git a/_sources/intermediate/autograd_saved_tensors_hooks_tutorial.rst.txt b/_sources/intermediate/autograd_saved_tensors_hooks_tutorial.rst.txt
index e90a6bc879..7e761eebf8 100644
--- a/_sources/intermediate/autograd_saved_tensors_hooks_tutorial.rst.txt
+++ b/_sources/intermediate/autograd_saved_tensors_hooks_tutorial.rst.txt
@@ -811,7 +811,7 @@ tradeoff memory for compute.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.034 seconds)
+   **Total running time of the script:** ( 0 minutes  0.033 seconds)
 
 
 .. _sphx_glr_download_intermediate_autograd_saved_tensors_hooks_tutorial.py:
diff --git a/_sources/intermediate/ax_multiobjective_nas_tutorial.rst.txt b/_sources/intermediate/ax_multiobjective_nas_tutorial.rst.txt
index bb4f5558a9..449d858ac6 100644
--- a/_sources/intermediate/ax_multiobjective_nas_tutorial.rst.txt
+++ b/_sources/intermediate/ax_multiobjective_nas_tutorial.rst.txt
@@ -483,12 +483,12 @@ before starting a model-based Bayesian Optimization strategy.
 
  .. code-block:: none
 
-    [INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.
-    [INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=48 use_batch_trials=False
-    [INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=9
-    [INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=9
-    [INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.
-    [INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 9 trials, BoTorch for subsequent trials]). Iterations after 9 will take longer to generate due to model-fitting.
+    [INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.
+    [INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=48 use_batch_trials=False
+    [INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=9
+    [INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=9
+    [INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.
+    [INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 9 trials, BoTorch for subsequent trials]). Iterations after 9 will take longer to generate due to model-fitting.
 
 
 
@@ -544,8 +544,8 @@ of machines you want to use in parallel.
 
  .. code-block:: none
 
-    [WARNING 06-25 21:33:15] ax.service.utils.with_db_settings_base: Ax currently requires a sqlalchemy version below 2.0. This will be addressed in a future release. Disabling SQL storage in Ax for now, if you would like to use SQL storage please install Ax with mysql extras via `pip install ax-platform[mysql]`.
-    [INFO 06-25 21:33:15] Scheduler: `Scheduler` requires experiment to have immutable search space and optimization config. Setting property immutable_search_space_and_opt_config to `True` on experiment.
+    [WARNING 06-25 22:16:55] ax.service.utils.with_db_settings_base: Ax currently requires a sqlalchemy version below 2.0. This will be addressed in a future release. Disabling SQL storage in Ax for now, if you would like to use SQL storage please install Ax with mysql extras via `pip install ax-platform[mysql]`.
+    [INFO 06-25 22:16:55] Scheduler: `Scheduler` requires experiment to have immutable search space and optimization config. Setting property immutable_search_space_and_opt_config to `True` on experiment.
 
 
 
@@ -579,29 +579,29 @@ optimization algorithm.
 
  .. code-block:: none
 
-    [INFO 06-25 21:33:15] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:33:15] Scheduler: Running trials [0]...
-    [INFO 06-25 21:33:16] Scheduler: Running trials [1]...
-    [INFO 06-25 21:33:17] Scheduler: Running trials [2]...
-    [INFO 06-25 21:33:18] Scheduler: Running trials [3]...
-    [INFO 06-25 21:33:19] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:33:19] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-    [INFO 06-25 21:33:20] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:33:20] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-    [INFO 06-25 21:33:22] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:33:22] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-    [INFO 06-25 21:33:24] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:33:24] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
-    [INFO 06-25 21:33:27] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:33:27] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
-    [INFO 06-25 21:33:32] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:33:32] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 4).
-    [INFO 06-25 21:33:40] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:33:40] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 4).
-    [INFO 06-25 21:33:51] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:33:51] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 4).
-    [INFO 06-25 21:34:09] Scheduler: Fetching data for newly completed trials: [1].
-    [INFO 06-25 21:34:09] Scheduler: Retrieved COMPLETED trials: [1].
+    [INFO 06-25 22:16:55] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:16:55] Scheduler: Running trials [0]...
+    [INFO 06-25 22:16:56] Scheduler: Running trials [1]...
+    [INFO 06-25 22:16:57] Scheduler: Running trials [2]...
+    [INFO 06-25 22:16:58] Scheduler: Running trials [3]...
+    [INFO 06-25 22:16:59] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:16:59] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+    [INFO 06-25 22:17:00] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:00] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+    [INFO 06-25 22:17:01] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:01] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+    [INFO 06-25 22:17:03] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:03] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+    [INFO 06-25 22:17:07] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:07] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
+    [INFO 06-25 22:17:12] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:12] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 4).
+    [INFO 06-25 22:17:19] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:19] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 4).
+    [INFO 06-25 22:17:31] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:31] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 4).
+    [INFO 06-25 22:17:48] Scheduler: Fetching data for newly completed trials: [1].
+    [INFO 06-25 22:17:48] Scheduler: Retrieved COMPLETED trials: [1].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -610,11 +610,13 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:34:09] Scheduler: Running trials [4]...
-    [INFO 06-25 21:34:10] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:10] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-    [INFO 06-25 21:34:11] Scheduler: Fetching data for newly completed trials: [3].
-    [INFO 06-25 21:34:11] Scheduler: Retrieved COMPLETED trials: [3].
+    [INFO 06-25 22:17:48] Scheduler: Running trials [4]...
+    [INFO 06-25 22:17:49] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:49] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+    [INFO 06-25 22:17:50] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:50] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+    [INFO 06-25 22:17:52] Scheduler: Fetching data for newly completed trials: [3].
+    [INFO 06-25 22:17:52] Scheduler: Retrieved COMPLETED trials: [3].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -623,19 +625,17 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:34:11] Scheduler: Running trials [5]...
-    [INFO 06-25 21:34:11] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:11] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-    [INFO 06-25 21:34:12] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:12] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-    [INFO 06-25 21:34:13] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:13] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-    [INFO 06-25 21:34:16] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:16] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
-    [INFO 06-25 21:34:19] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:19] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
-    [INFO 06-25 21:34:24] Scheduler: Fetching data for newly completed trials: [2].
-    [INFO 06-25 21:34:24] Scheduler: Retrieved COMPLETED trials: [2].
+    [INFO 06-25 22:17:52] Scheduler: Running trials [5]...
+    [INFO 06-25 22:17:53] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:53] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+    [INFO 06-25 22:17:54] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:54] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+    [INFO 06-25 22:17:55] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:55] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+    [INFO 06-25 22:17:57] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:17:57] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+    [INFO 06-25 22:18:01] Scheduler: Fetching data for newly completed trials: [2].
+    [INFO 06-25 22:18:01] Scheduler: Retrieved COMPLETED trials: [2].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -644,17 +644,19 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:34:24] Scheduler: Running trials [6]...
-    [INFO 06-25 21:34:25] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:25] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-    [INFO 06-25 21:34:26] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:26] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-    [INFO 06-25 21:34:28] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:28] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-    [INFO 06-25 21:34:30] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:30] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
-    [INFO 06-25 21:34:33] Scheduler: Fetching data for newly completed trials: [0].
-    [INFO 06-25 21:34:33] Scheduler: Retrieved COMPLETED trials: [0].
+    [INFO 06-25 22:18:01] Scheduler: Running trials [6]...
+    [INFO 06-25 22:18:02] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:02] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+    [INFO 06-25 22:18:03] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:03] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+    [INFO 06-25 22:18:04] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:04] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+    [INFO 06-25 22:18:07] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:07] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+    [INFO 06-25 22:18:10] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:10] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
+    [INFO 06-25 22:18:15] Scheduler: Fetching data for newly completed trials: [0].
+    [INFO 06-25 22:18:15] Scheduler: Retrieved COMPLETED trials: [0].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -663,17 +665,15 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:34:33] Scheduler: Running trials [7]...
-    [INFO 06-25 21:34:34] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:34] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-    [INFO 06-25 21:34:35] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:35] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-    [INFO 06-25 21:34:37] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:37] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-    [INFO 06-25 21:34:39] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:39] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
-    [INFO 06-25 21:34:43] Scheduler: Fetching data for newly completed trials: [4].
-    [INFO 06-25 21:34:43] Scheduler: Retrieved COMPLETED trials: [4].
+    [INFO 06-25 22:18:15] Scheduler: Running trials [7]...
+    [INFO 06-25 22:18:16] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:16] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+    [INFO 06-25 22:18:17] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:17] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+    [INFO 06-25 22:18:19] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:19] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+    [INFO 06-25 22:18:21] Scheduler: Fetching data for newly completed trials: [4].
+    [INFO 06-25 22:18:21] Scheduler: Retrieved COMPLETED trials: [4].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -682,15 +682,17 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:34:43] Scheduler: Running trials [8]...
-    [INFO 06-25 21:34:44] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:44] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-    [INFO 06-25 21:34:45] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:45] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-    [INFO 06-25 21:34:46] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:46] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-    [INFO 06-25 21:34:48] Scheduler: Fetching data for newly completed trials: [5].
-    [INFO 06-25 21:34:48] Scheduler: Retrieved COMPLETED trials: [5].
+    [INFO 06-25 22:18:21] Scheduler: Running trials [8]...
+    [INFO 06-25 22:18:22] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:22] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+    [INFO 06-25 22:18:23] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:23] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+    [INFO 06-25 22:18:25] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:25] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+    [INFO 06-25 22:18:27] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:27] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+    [INFO 06-25 22:18:30] Scheduler: Fetching data for newly completed trials: [5].
+    [INFO 06-25 22:18:30] Scheduler: Retrieved COMPLETED trials: [5].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -699,15 +701,9 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:34:51] Scheduler: Running trials [9]...
-    [INFO 06-25 21:34:51] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:51] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-    [INFO 06-25 21:34:52] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:52] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-    [INFO 06-25 21:34:54] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:34:54] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-    [INFO 06-25 21:34:56] Scheduler: Fetching data for newly completed trials: [6].
-    [INFO 06-25 21:34:56] Scheduler: Retrieved COMPLETED trials: [6].
+    [INFO 06-25 22:18:33] Scheduler: Running trials [9]...
+    [INFO 06-25 22:18:35] Scheduler: Fetching data for newly completed trials: [6].
+    [INFO 06-25 22:18:35] Scheduler: Retrieved COMPLETED trials: [6].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -716,19 +712,17 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:34:59] Scheduler: Running trials [10]...
-    [INFO 06-25 21:35:00] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:00] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-    [INFO 06-25 21:35:01] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:01] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-    [INFO 06-25 21:35:02] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:02] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-    [INFO 06-25 21:35:04] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:04] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
-    [INFO 06-25 21:35:08] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:08] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
-    [INFO 06-25 21:35:13] Scheduler: Fetching data for newly completed trials: [8].
-    [INFO 06-25 21:35:13] Scheduler: Retrieved COMPLETED trials: [8].
+    [INFO 06-25 22:18:39] Scheduler: Running trials [10]...
+    [INFO 06-25 22:18:40] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+    [INFO 06-25 22:18:41] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+    [INFO 06-25 22:18:43] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:43] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+    [INFO 06-25 22:18:45] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:45] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+    [INFO 06-25 22:18:48] Scheduler: Fetching data for newly completed trials: [8].
+    [INFO 06-25 22:18:48] Scheduler: Retrieved COMPLETED trials: [8].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -737,19 +731,21 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:35:15] Scheduler: Running trials [11]...
-    [INFO 06-25 21:35:16] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:16] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-    [INFO 06-25 21:35:17] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:17] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-    [INFO 06-25 21:35:19] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:19] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-    [INFO 06-25 21:35:21] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:21] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
-    [INFO 06-25 21:35:25] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:25] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
-    [INFO 06-25 21:35:30] Scheduler: Fetching data for newly completed trials: [7].
-    [INFO 06-25 21:35:30] Scheduler: Retrieved COMPLETED trials: [7].
+    [INFO 06-25 22:18:52] Scheduler: Running trials [11]...
+    [INFO 06-25 22:18:53] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:53] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+    [INFO 06-25 22:18:54] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:54] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+    [INFO 06-25 22:18:55] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:55] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+    [INFO 06-25 22:18:57] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:18:57] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+    [INFO 06-25 22:19:01] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:01] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
+    [INFO 06-25 22:19:06] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:06] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 4).
+    [INFO 06-25 22:19:13] Scheduler: Fetching data for newly completed trials: [7].
+    [INFO 06-25 22:19:13] Scheduler: Retrieved COMPLETED trials: [7].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -758,18 +754,18 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:35:30] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:30] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:35:31] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:31] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:35:32] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:32] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:35:35] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:35] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:35:38] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:38] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:35:43] Scheduler: Fetching data for newly completed trials: [9].
-    [INFO 06-25 21:35:43] Scheduler: Retrieved COMPLETED trials: [9].
+    [INFO 06-25 22:19:14] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:14] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:19:15] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:15] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:19:16] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:16] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:19:18] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:18] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:19:22] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:22] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:19:27] Scheduler: Fetching data for newly completed trials: [9].
+    [INFO 06-25 22:19:27] Scheduler: Retrieved COMPLETED trials: [9].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -778,27 +774,23 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:35:45] Scheduler: Running trials [12]...
+    [INFO 06-25 22:19:29] Scheduler: Running trials [12]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:35:46] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:35:46] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:35:46] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:46] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:35:47] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:47] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:35:49] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:49] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:35:51] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:51] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:35:54] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:54] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:35:59] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:35:59] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:36:07] Scheduler: Fetching data for newly completed trials: [10].
-    [INFO 06-25 21:36:07] Scheduler: Retrieved COMPLETED trials: [10].
+    [INFO 06-25 22:19:30] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:19:30] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:19:30] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:30] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:19:31] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:31] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:19:32] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:32] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:19:35] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:35] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:19:38] Scheduler: Fetching data for newly completed trials: [10].
+    [INFO 06-25 22:19:38] Scheduler: Retrieved COMPLETED trials: [10].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -807,15 +799,27 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:36:10] Scheduler: Running trials [13]...
+    [INFO 06-25 22:19:41] Scheduler: Running trials [13]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:36:12] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:36:12] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:36:12] Scheduler: Fetching data for newly completed trials: [11].
-    [INFO 06-25 21:36:12] Scheduler: Retrieved COMPLETED trials: [11].
+    [INFO 06-25 22:19:42] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:19:42] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:19:42] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:42] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:19:43] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:43] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:19:44] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:44] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:19:47] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:47] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:19:50] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:50] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:19:55] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:19:55] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:20:03] Scheduler: Fetching data for newly completed trials: [11].
+    [INFO 06-25 22:20:03] Scheduler: Retrieved COMPLETED trials: [11].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -824,27 +828,15 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:36:15] Scheduler: Running trials [14]...
+    [INFO 06-25 22:20:05] Scheduler: Running trials [14]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:36:16] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:36:16] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:36:16] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:16] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:36:17] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:17] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:36:18] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:18] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:36:20] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:20] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:36:24] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:24] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:36:29] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:29] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:36:37] Scheduler: Fetching data for newly completed trials: [12].
-    [INFO 06-25 21:36:37] Scheduler: Retrieved COMPLETED trials: [12].
+    [INFO 06-25 22:20:06] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:20:06] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:20:06] Scheduler: Fetching data for newly completed trials: [12].
+    [INFO 06-25 22:20:06] Scheduler: Retrieved COMPLETED trials: [12].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -853,23 +845,27 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:36:39] Scheduler: Running trials [15]...
+    [INFO 06-25 22:20:09] Scheduler: Running trials [15]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:36:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:36:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:36:40] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:36:41] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:36:42] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:42] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:36:45] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:45] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:36:48] Scheduler: Fetching data for newly completed trials: [13].
-    [INFO 06-25 21:36:48] Scheduler: Retrieved COMPLETED trials: [13].
+    [INFO 06-25 22:20:10] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:20:10] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:20:10] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:10] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:20:11] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:11] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:20:12] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:12] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:20:15] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:15] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:20:18] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:18] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:20:23] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:23] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:20:31] Scheduler: Fetching data for newly completed trials: [13].
+    [INFO 06-25 22:20:31] Scheduler: Retrieved COMPLETED trials: [13].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -878,23 +874,27 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:36:51] Scheduler: Running trials [16]...
+    [INFO 06-25 22:20:33] Scheduler: Running trials [16]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:36:52] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:36:52] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:36:52] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:52] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:36:53] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:53] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:36:55] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:55] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:36:57] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:36:57] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:37:00] Scheduler: Fetching data for newly completed trials: [14].
-    [INFO 06-25 21:37:00] Scheduler: Retrieved COMPLETED trials: [14].
+    [INFO 06-25 22:20:34] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:20:34] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:20:34] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:34] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:20:35] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:35] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:20:36] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:36] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:20:38] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:38] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:20:42] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:42] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:20:47] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:20:47] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:20:54] Scheduler: Fetching data for newly completed trials: [15].
+    [INFO 06-25 22:20:54] Scheduler: Retrieved COMPLETED trials: [15].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -903,27 +903,19 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:37:03] Scheduler: Running trials [17]...
+    [INFO 06-25 22:20:59] Scheduler: Running trials [17]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:37:04] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:37:04] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:37:04] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:04] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:37:05] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:05] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:37:06] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:06] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:37:08] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:08] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:37:12] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:12] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:37:17] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:17] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:37:24] Scheduler: Fetching data for newly completed trials: [15].
-    [INFO 06-25 21:37:24] Scheduler: Retrieved COMPLETED trials: [15].
+    [INFO 06-25 22:21:00] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:21:00] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:21:00] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:00] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:21:01] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:01] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:21:02] Scheduler: Fetching data for newly completed trials: [14].
+    [INFO 06-25 22:21:02] Scheduler: Retrieved COMPLETED trials: [14].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -932,23 +924,25 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:37:27] Scheduler: Running trials [18]...
+    [INFO 06-25 22:21:07] Scheduler: Running trials [18]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:37:28] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:37:28] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:37:28] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:28] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:37:29] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:29] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:37:30] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:30] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:37:33] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:33] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:37:36] Scheduler: Fetching data for newly completed trials: [16].
-    [INFO 06-25 21:37:36] Scheduler: Retrieved COMPLETED trials: [16].
+    [INFO 06-25 22:21:08] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:21:08] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:21:08] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:08] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:21:09] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:09] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:21:11] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:11] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:21:13] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:13] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:21:16] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:16] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:21:21] Scheduler: Fetching data for newly completed trials: [16].
+    [INFO 06-25 22:21:21] Scheduler: Retrieved COMPLETED trials: [16].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -957,25 +951,29 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:37:39] Scheduler: Running trials [19]...
+    [INFO 06-25 22:21:26] Scheduler: Running trials [19]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:37:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:37:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:37:40] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:37:41] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:37:43] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:43] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:37:45] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:45] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:37:48] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:48] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:37:53] Scheduler: Fetching data for newly completed trials: [17].
-    [INFO 06-25 21:37:53] Scheduler: Retrieved COMPLETED trials: [17].
+    [INFO 06-25 22:21:27] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:21:27] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:21:27] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:27] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:21:28] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:28] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:21:30] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:30] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:21:32] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:32] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:21:36] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:36] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:21:41] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:41] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:21:48] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:21:48] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:22:00] Scheduler: Fetching data for newly completed trials: [17].
+    [INFO 06-25 22:22:00] Scheduler: Retrieved COMPLETED trials: [17].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -984,29 +982,15 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:37:56] Scheduler: Running trials [20]...
+    [INFO 06-25 22:22:04] Scheduler: Running trials [20]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:37:57] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:37:57] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:37:57] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:57] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:37:58] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:37:58] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:38:00] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:00] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:38:02] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:02] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:38:06] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:06] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:38:11] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:11] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:38:18] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:18] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-    [INFO 06-25 21:38:30] Scheduler: Fetching data for newly completed trials: [18, 20].
-    [INFO 06-25 21:38:30] Scheduler: Retrieved COMPLETED trials: [18, 20].
+    [INFO 06-25 22:22:05] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:22:05] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:22:05] Scheduler: Fetching data for newly completed trials: [18].
+    [INFO 06-25 22:22:05] Scheduler: Retrieved COMPLETED trials: [18].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1015,75 +999,73 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:38:32] Scheduler: Running trials [21]...
+    [INFO 06-25 22:22:11] Scheduler: Running trials [21]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:38:33] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:38:36] Scheduler: Running trials [22]...
+    [INFO 06-25 22:22:12] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:22:12] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:22:12] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:22:12] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:22:13] Scheduler: Fetching data for newly completed trials: [19].
+    [INFO 06-25 22:22:13] Scheduler: Retrieved COMPLETED trials: [19].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:38:37] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:38:37] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:38:37] Scheduler: Fetching data for newly completed trials: [19].
-    [INFO 06-25 21:38:37] Scheduler: Retrieved COMPLETED trials: [19].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:22:19] Scheduler: Running trials [22]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:38:41] Scheduler: Running trials [23]...
+    [INFO 06-25 22:22:20] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:22:20] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:22:20] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:22:20] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:22:21] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:22:21] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:22:22] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:22:22] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:22:25] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:22:25] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:22:28] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:22:28] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:22:33] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:22:33] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:22:41] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:22:41] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:22:52] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:22:52] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+    [INFO 06-25 22:23:09] Scheduler: Fetching data for newly completed trials: 20 - 21.
+    [INFO 06-25 22:23:09] Scheduler: Retrieved COMPLETED trials: 20 - 21.
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:38:42] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:38:42] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:38:42] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:42] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:38:43] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:43] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:38:44] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:44] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:38:47] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:47] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:38:50] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:50] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:38:55] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:38:55] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:39:03] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:39:03] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-    [INFO 06-25 21:39:14] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:39:14] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-    [INFO 06-25 21:39:31] Scheduler: Fetching data for newly completed trials: [21].
-    [INFO 06-25 21:39:31] Scheduler: Retrieved COMPLETED trials: [21].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:23:14] Scheduler: Running trials [23]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:39:35] Scheduler: Running trials [24]...
+    [INFO 06-25 22:23:15] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:23:18] Scheduler: Running trials [24]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:39:36] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:39:36] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:39:36] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:39:36] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:39:37] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:39:37] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:39:39] Scheduler: Fetching data for newly completed trials: [22].
-    [INFO 06-25 21:39:39] Scheduler: Retrieved COMPLETED trials: [22].
+    [INFO 06-25 22:23:19] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:23:19] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:23:19] Scheduler: Fetching data for newly completed trials: [22].
+    [INFO 06-25 22:23:19] Scheduler: Retrieved COMPLETED trials: [22].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1092,15 +1074,31 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:39:43] Scheduler: Running trials [25]...
+    [INFO 06-25 22:23:23] Scheduler: Running trials [25]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:39:44] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:39:44] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:39:44] Scheduler: Fetching data for newly completed trials: [23].
-    [INFO 06-25 21:39:44] Scheduler: Retrieved COMPLETED trials: [23].
+    [INFO 06-25 22:23:24] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:23:24] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:23:24] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:23:24] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:23:25] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:23:25] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:23:27] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:23:27] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:23:29] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:23:29] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:23:33] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:23:33] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:23:38] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:23:38] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:23:45] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:23:45] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:23:57] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:23:57] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+    [INFO 06-25 22:24:14] Scheduler: Fetching data for newly completed trials: [23].
+    [INFO 06-25 22:24:14] Scheduler: Retrieved COMPLETED trials: [23].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1109,31 +1107,15 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:39:48] Scheduler: Running trials [26]...
+    [INFO 06-25 22:24:18] Scheduler: Running trials [26]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:39:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:39:49] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:39:49] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:39:49] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:39:50] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:39:50] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:39:52] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:39:52] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:39:54] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:39:54] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:39:58] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:39:58] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:40:03] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:40:03] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:40:10] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:40:10] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-    [INFO 06-25 21:40:22] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:40:22] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-    [INFO 06-25 21:40:39] Scheduler: Fetching data for newly completed trials: 24 - 25.
-    [INFO 06-25 21:40:39] Scheduler: Retrieved COMPLETED trials: 24 - 25.
+    [INFO 06-25 22:24:18] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:24:18] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:24:18] Scheduler: Fetching data for newly completed trials: 24 - 25.
+    [INFO 06-25 22:24:18] Scheduler: Retrieved COMPLETED trials: 24 - 25.
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1142,21 +1124,37 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:40:43] Scheduler: Running trials [27]...
+    [INFO 06-25 22:24:24] Scheduler: Running trials [27]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:40:44] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:40:48] Scheduler: Running trials [28]...
+    [INFO 06-25 22:24:24] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:24:28] Scheduler: Running trials [28]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:40:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:40:49] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:40:49] Scheduler: Fetching data for newly completed trials: [26].
-    [INFO 06-25 21:40:49] Scheduler: Retrieved COMPLETED trials: [26].
+    [INFO 06-25 22:24:29] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:24:29] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:24:29] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:24:29] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:24:30] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:24:30] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:24:31] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:24:31] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:24:33] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:24:33] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:24:37] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:24:37] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:24:42] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:24:42] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:24:49] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:24:49] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:25:01] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:25:01] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+    [INFO 06-25 22:25:18] Scheduler: Fetching data for newly completed trials: [26].
+    [INFO 06-25 22:25:18] Scheduler: Retrieved COMPLETED trials: [26].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1165,31 +1163,15 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:40:54] Scheduler: Running trials [29]...
+    [INFO 06-25 22:25:22] Scheduler: Running trials [29]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:40:55] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:40:55] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:40:55] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:40:55] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:40:56] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:40:56] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:40:58] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:40:58] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:41:00] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:41:00] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:41:03] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:41:03] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:41:08] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:41:08] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:41:16] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:41:16] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-    [INFO 06-25 21:41:27] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:41:27] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-    [INFO 06-25 21:41:45] Scheduler: Fetching data for newly completed trials: 27 - 28.
-    [INFO 06-25 21:41:45] Scheduler: Retrieved COMPLETED trials: 27 - 28.
+    [INFO 06-25 22:25:23] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:25:23] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:25:23] Scheduler: Fetching data for newly completed trials: [27].
+    [INFO 06-25 22:25:23] Scheduler: Retrieved COMPLETED trials: [27].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1198,338 +1180,429 @@ optimization algorithm.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:41:53] Scheduler: Running trials [30]...
+    [INFO 06-25 22:25:29] Scheduler: Running trials [30]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:41:54] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:42:02] Scheduler: Running trials [31]...
+    [INFO 06-25 22:25:30] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:25:30] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:25:30] Scheduler: Fetching data for newly completed trials: [28].
+    [INFO 06-25 22:25:30] Scheduler: Retrieved COMPLETED trials: [28].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:42:03] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:42:03] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:42:04] Scheduler: Fetching data for newly completed trials: [29].
-    [INFO 06-25 21:42:04] Scheduler: Retrieved COMPLETED trials: [29].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:25:39] Scheduler: Running trials [31]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:42:14] Scheduler: Running trials [32]...
+    [INFO 06-25 22:25:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:25:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:25:40] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:25:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:25:41] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:25:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:25:42] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:25:42] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:25:45] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:25:45] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:25:48] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:25:48] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:25:53] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:25:53] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:26:01] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:26:01] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:26:12] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:26:12] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+    [INFO 06-25 22:26:29] Scheduler: Fetching data for newly completed trials: 29 - 30.
+    [INFO 06-25 22:26:29] Scheduler: Retrieved COMPLETED trials: 29 - 30.
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:42:15] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:42:15] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:42:15] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:42:15] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:42:16] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:42:16] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:42:17] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:42:17] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:42:20] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:42:20] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:42:23] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:42:23] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:42:28] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:42:28] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:42:36] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:42:36] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-    [INFO 06-25 21:42:47] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:42:47] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-    [INFO 06-25 21:43:04] Scheduler: Fetching data for newly completed trials: 30 - 31.
-    [INFO 06-25 21:43:04] Scheduler: Retrieved COMPLETED trials: 30 - 31.
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:26:35] Scheduler: Running trials [32]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:43:09] Scheduler: Running trials [33]...
+    [INFO 06-25 22:26:36] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:26:40] Scheduler: Running trials [33]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:43:10] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:43:14] Scheduler: Running trials [34]...
+    [INFO 06-25 22:26:41] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:26:41] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:26:41] Scheduler: Fetching data for newly completed trials: [31].
+    [INFO 06-25 22:26:41] Scheduler: Retrieved COMPLETED trials: [31].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:43:15] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:43:15] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:43:16] Scheduler: Fetching data for newly completed trials: [32].
-    [INFO 06-25 21:43:16] Scheduler: Retrieved COMPLETED trials: [32].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:26:46] Scheduler: Running trials [34]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:43:20] Scheduler: Running trials [35]...
+    [INFO 06-25 22:26:47] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:26:47] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:26:47] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:26:47] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:26:48] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:26:48] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:26:50] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:26:50] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:26:52] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:26:52] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:26:55] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:26:55] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:27:00] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:27:00] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:27:08] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:27:08] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:27:19] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:27:19] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+    [INFO 06-25 22:27:36] Scheduler: Fetching data for newly completed trials: [32].
+    [INFO 06-25 22:27:36] Scheduler: Retrieved COMPLETED trials: [32].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:43:22] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:43:22] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:43:22] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:43:22] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:43:23] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:43:23] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:43:24] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:43:24] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
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-    [INFO 06-25 21:43:26] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
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-    [INFO 06-25 21:43:54] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-    [INFO 06-25 21:44:11] Scheduler: Fetching data for newly completed trials: 33 - 34.
-    [INFO 06-25 21:44:11] Scheduler: Retrieved COMPLETED trials: 33 - 34.
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:27:42] Scheduler: Running trials [35]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:44:17] Scheduler: Running trials [36]...
+    [INFO 06-25 22:27:43] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:27:43] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:27:43] Scheduler: Fetching data for newly completed trials: [34].
+    [INFO 06-25 22:27:43] Scheduler: Retrieved COMPLETED trials: [34].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:44:17] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:44:21] Scheduler: Running trials [37]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:44:22] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:44:22] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:44:22] Scheduler: Fetching data for newly completed trials: [35].
-    [INFO 06-25 21:44:22] Scheduler: Retrieved COMPLETED trials: [35].
+    [INFO 06-25 22:27:49] Scheduler: Running trials [36]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:27:50] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:27:50] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:27:50] Scheduler: Fetching data for newly completed trials: [33].
+    [INFO 06-25 22:27:50] Scheduler: Retrieved COMPLETED trials: [33].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:44:27] Scheduler: Running trials [38]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:44:27] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:44:27] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:44:27] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:44:27] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
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-    [INFO 06-25 21:44:28] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:44:29] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:44:29] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
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-    [INFO 06-25 21:44:32] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
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-    [INFO 06-25 21:44:35] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
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-    [INFO 06-25 21:44:40] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:44:48] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:44:48] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-    [INFO 06-25 21:44:59] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:44:59] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-    [INFO 06-25 21:45:16] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:45:16] Scheduler: Waiting for completed trials (for 25 sec, currently running trials: 3).
-    [INFO 06-25 21:45:42] Scheduler: Fetching data for newly completed trials: 36 - 38.
-    [INFO 06-25 21:45:42] Scheduler: Retrieved COMPLETED trials: 36 - 38.
+    [INFO 06-25 22:27:55] Scheduler: Running trials [37]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:27:56] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:27:56] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:27:56] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:27:56] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:27:57] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:27:57] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:27:59] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:27:59] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:28:01] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:28:01] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:28:04] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:28:04] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:28:09] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:28:09] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:28:17] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:28:17] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:28:28] Scheduler: Fetching data for newly completed trials: [37].
+    [INFO 06-25 22:28:28] Scheduler: Retrieved COMPLETED trials: [37].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:45:48] Scheduler: Running trials [39]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:45:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:45:53] Scheduler: Running trials [40]...
+    [INFO 06-25 22:28:34] Scheduler: Running trials [38]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:45:54] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:45:59] Scheduler: Running trials [41]...
+    [INFO 06-25 22:28:35] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:28:35] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:28:35] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:28:35] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:28:36] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:28:36] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:28:37] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:28:37] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:28:39] Scheduler: Fetching data for newly completed trials: [35].
+    [INFO 06-25 22:28:39] Scheduler: Retrieved COMPLETED trials: [35].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:46:00] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:46:00] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:46:00] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:46:00] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:46:01] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:46:01] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:46:02] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:46:02] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:46:04] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:46:04] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:46:08] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:46:08] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:46:13] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:46:13] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:46:20] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:46:20] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-    [INFO 06-25 21:46:32] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:46:32] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-    [INFO 06-25 21:46:49] Scheduler: Fetching data for newly completed trials: 39 - 40.
-    [INFO 06-25 21:46:49] Scheduler: Retrieved COMPLETED trials: 39 - 40.
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:28:46] Scheduler: Running trials [39]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:46:54] Scheduler: Running trials [42]...
+    [INFO 06-25 22:28:47] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:28:47] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:28:47] Scheduler: Fetching data for newly completed trials: [36].
+    [INFO 06-25 22:28:47] Scheduler: Retrieved COMPLETED trials: [36].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:46:55] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:47:00] Scheduler: Running trials [43]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:47:01] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:47:01] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:47:01] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:01] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:47:02] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:02] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:47:04] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:04] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:47:06] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:06] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:47:09] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:09] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:47:14] Scheduler: Fetching data for newly completed trials: [41].
-    [INFO 06-25 21:47:14] Scheduler: Retrieved COMPLETED trials: [41].
+    [INFO 06-25 22:28:56] Scheduler: Running trials [40]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:28:57] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:28:57] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:28:57] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:28:57] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:28:58] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:28:58] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:29:00] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:29:00] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:29:02] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:29:02] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:29:05] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:29:05] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:29:10] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:29:10] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:29:18] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:29:18] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:29:29] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:29:29] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+    [INFO 06-25 22:29:46] Scheduler: Fetching data for newly completed trials: 38 - 39.
+    [INFO 06-25 22:29:46] Scheduler: Retrieved COMPLETED trials: 38 - 39.
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:47:20] Scheduler: Running trials [44]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:47:21] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:47:21] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:47:21] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:21] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:47:22] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:22] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:47:24] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:24] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:47:26] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:26] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:47:29] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:29] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:47:34] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:34] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:47:42] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:42] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-    [INFO 06-25 21:47:53] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:47:53] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-    [INFO 06-25 21:48:10] Scheduler: Fetching data for newly completed trials: 42 - 43.
-    [INFO 06-25 21:48:10] Scheduler: Retrieved COMPLETED trials: 42 - 43.
+    [INFO 06-25 22:29:54] Scheduler: Running trials [41]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:29:55] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:29:59] Scheduler: Running trials [42]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:48:17] Scheduler: Running trials [45]...
+    [INFO 06-25 22:30:00] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:30:00] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:30:00] Scheduler: Fetching data for newly completed trials: [40].
+    [INFO 06-25 22:30:00] Scheduler: Retrieved COMPLETED trials: [40].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:48:18] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:48:24] Scheduler: Running trials [46]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:48:25] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-    [INFO 06-25 21:48:25] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-    [INFO 06-25 21:48:25] Scheduler: Fetching data for newly completed trials: [44].
-    [INFO 06-25 21:48:25] Scheduler: Retrieved COMPLETED trials: [44].
+    [INFO 06-25 22:30:05] Scheduler: Running trials [43]...
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+    [INFO 06-25 22:30:06] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:30:06] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:30:06] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:06] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:30:07] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:07] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:30:08] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:08] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:30:10] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:10] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:30:14] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:14] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:30:19] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:19] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:30:26] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:26] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:30:38] Scheduler: Fetching data for newly completed trials: [42].
+    [INFO 06-25 22:30:38] Scheduler: Retrieved COMPLETED trials: [42].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:48:31] Scheduler: Running trials [47]...
-    [INFO 06-25 21:48:32] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:48:32] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-    [INFO 06-25 21:48:33] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:48:33] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-    [INFO 06-25 21:48:34] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:48:34] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-    [INFO 06-25 21:48:37] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:48:37] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-    [INFO 06-25 21:48:40] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:48:40] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-    [INFO 06-25 21:48:45] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:48:45] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-    [INFO 06-25 21:48:53] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:48:53] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-    [INFO 06-25 21:49:04] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:49:04] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-    [INFO 06-25 21:49:21] Scheduler: Fetching data for newly completed trials: 46 - 47.
-    [INFO 06-25 21:49:21] Scheduler: Retrieved COMPLETED trials: 46 - 47.
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [INFO 06-25 21:49:21] Scheduler: Done submitting trials, waiting for remaining 1 running trials...
-    [INFO 06-25 21:49:21] Scheduler: Fetching data for newly completed trials: [].
-    [INFO 06-25 21:49:21] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 1).
-    [INFO 06-25 21:49:23] Scheduler: Fetching data for newly completed trials: [45].
-    [INFO 06-25 21:49:23] Scheduler: Retrieved COMPLETED trials: [45].
+    [INFO 06-25 22:30:43] Scheduler: Running trials [44]...
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    [INFO 06-25 22:30:43] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:30:43] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:30:43] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:43] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:30:44] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:44] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:30:46] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:46] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:30:48] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:30:48] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:30:52] Scheduler: Fetching data for newly completed trials: [41].
+    [INFO 06-25 22:30:52] Scheduler: Retrieved COMPLETED trials: [41].
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    [INFO 06-25 22:30:59] Scheduler: Running trials [45]...
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    [INFO 06-25 22:31:00] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:31:00] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:31:00] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:00] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:31:01] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:01] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:31:02] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:02] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:31:05] Scheduler: Fetching data for newly completed trials: [43].
+    [INFO 06-25 22:31:05] Scheduler: Retrieved COMPLETED trials: [43].
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    [INFO 06-25 22:31:10] Scheduler: Running trials [46]...
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    [INFO 06-25 22:31:11] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+    [INFO 06-25 22:31:11] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+    [INFO 06-25 22:31:11] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:11] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:31:12] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:12] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:31:13] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:13] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+    [INFO 06-25 22:31:15] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:15] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+    [INFO 06-25 22:31:19] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:19] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+    [INFO 06-25 22:31:24] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:24] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+    [INFO 06-25 22:31:31] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:31] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+    [INFO 06-25 22:31:43] Scheduler: Fetching data for newly completed trials: [44].
+    [INFO 06-25 22:31:43] Scheduler: Retrieved COMPLETED trials: [44].
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    [INFO 06-25 22:31:50] Scheduler: Running trials [47]...
+    [INFO 06-25 22:31:51] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:51] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+    [INFO 06-25 22:31:52] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:52] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+    [INFO 06-25 22:31:53] Scheduler: Fetching data for newly completed trials: [45].
+    [INFO 06-25 22:31:53] Scheduler: Retrieved COMPLETED trials: [45].
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    [INFO 06-25 22:31:53] Scheduler: Done submitting trials, waiting for remaining 2 running trials...
+    [INFO 06-25 22:31:53] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:53] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 2).
+    [INFO 06-25 22:31:54] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:54] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 2).
+    [INFO 06-25 22:31:56] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:56] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 2).
+    [INFO 06-25 22:31:58] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:31:58] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 2).
+    [INFO 06-25 22:32:01] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:32:01] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 2).
+    [INFO 06-25 22:32:06] Scheduler: Fetching data for newly completed trials: [46].
+    [INFO 06-25 22:32:06] Scheduler: Retrieved COMPLETED trials: [46].
+    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+    The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+    [INFO 06-25 22:32:06] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:32:07] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 1).
+    [INFO 06-25 22:32:08] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:32:08] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 1).
+    [INFO 06-25 22:32:09] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:32:09] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 1).
+    [INFO 06-25 22:32:11] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:32:11] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 1).
+    [INFO 06-25 22:32:15] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:32:15] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 1).
+    [INFO 06-25 22:32:20] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:32:20] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 1).
+    [INFO 06-25 22:32:27] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:32:27] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 1).
+    [INFO 06-25 22:32:39] Scheduler: Fetching data for newly completed trials: [].
+    [INFO 06-25 22:32:39] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 1).
+    [INFO 06-25 22:32:56] Scheduler: Fetching data for newly completed trials: [47].
+    [INFO 06-25 22:32:56] Scheduler: Retrieved COMPLETED trials: [47].
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1580,7 +1653,7 @@ an easy way to sanity check the optimization.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [WARNING 06-25 21:49:23] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.
+    [WARNING 06-25 22:32:56] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.
 
 
 .. raw:: html
@@ -1628,7 +1701,7 @@ an easy way to sanity check the optimization.
           <td>Sobol</td>
           <td>False</td>
           <td>16810.0</td>
-          <td>0.897621</td>
+          <td>0.911009</td>
           <td>19</td>
           <td>66</td>
           <td>0.003182</td>
@@ -1644,7 +1717,7 @@ an easy way to sanity check the optimization.
           <td>Sobol</td>
           <td>False</td>
           <td>21926.0</td>
-          <td>0.879206</td>
+          <td>0.883842</td>
           <td>23</td>
           <td>118</td>
           <td>0.000145</td>
@@ -1660,7 +1733,7 @@ an easy way to sanity check the optimization.
           <td>Sobol</td>
           <td>True</td>
           <td>37560.0</td>
-          <td>0.954121</td>
+          <td>0.949112</td>
           <td>40</td>
           <td>124</td>
           <td>0.002745</td>
@@ -1676,7 +1749,7 @@ an easy way to sanity check the optimization.
           <td>Sobol</td>
           <td>False</td>
           <td>14756.0</td>
-          <td>0.892503</td>
+          <td>0.899586</td>
           <td>18</td>
           <td>23</td>
           <td>0.000166</td>
@@ -1692,7 +1765,7 @@ an easy way to sanity check the optimization.
           <td>Sobol</td>
           <td>True</td>
           <td>71630.0</td>
-          <td>0.952770</td>
+          <td>0.948751</td>
           <td>80</td>
           <td>99</td>
           <td>0.000642</td>
@@ -1708,7 +1781,7 @@ an easy way to sanity check the optimization.
           <td>Sobol</td>
           <td>False</td>
           <td>13948.0</td>
-          <td>0.925682</td>
+          <td>0.923867</td>
           <td>16</td>
           <td>54</td>
           <td>0.000444</td>
@@ -1724,7 +1797,7 @@ an easy way to sanity check the optimization.
           <td>Sobol</td>
           <td>False</td>
           <td>24686.0</td>
-          <td>0.855924</td>
+          <td>0.862653</td>
           <td>29</td>
           <td>50</td>
           <td>0.000177</td>
@@ -1740,7 +1813,7 @@ an easy way to sanity check the optimization.
           <td>Sobol</td>
           <td>False</td>
           <td>18290.0</td>
-          <td>0.877489</td>
+          <td>0.875520</td>
           <td>20</td>
           <td>87</td>
           <td>0.000119</td>
@@ -1756,7 +1829,7 @@ an easy way to sanity check the optimization.
           <td>Sobol</td>
           <td>False</td>
           <td>20996.0</td>
-          <td>0.723215</td>
+          <td>0.841835</td>
           <td>26</td>
           <td>17</td>
           <td>0.005245</td>
@@ -1771,13 +1844,13 @@ an easy way to sanity check the optimization.
           <td>COMPLETED</td>
           <td>BoTorch</td>
           <td>True</td>
-          <td>41030.0</td>
-          <td>0.960493</td>
-          <td>44</td>
-          <td>121</td>
-          <td>0.001528</td>
+          <td>52088.0</td>
+          <td>0.954132</td>
+          <td>56</td>
+          <td>124</td>
+          <td>0.002046</td>
           <td>3</td>
-          <td>0.089977</td>
+          <td>0.168286</td>
           <td>64</td>
         </tr>
       </tbody>
@@ -1829,14 +1902,14 @@ validation accuracy.
 
     The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-    [WARNING 06-25 21:49:23] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.
+    [WARNING 06-25 22:32:56] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.
 
 
 .. raw:: html
 
     <div class="output_subarea output_html rendered_html output_result">
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@@ -1881,7 +1954,7 @@ much easier to model than the validation accuracy (``val_acc``) metric.
 
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nan]\u003cbr\u003ePredicted Outcome: 37782.98 [34839.636, 40726.325]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.002744711802661285\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19660000363364816\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 3_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 14756.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 14803.773 [4006.540, 25601.006]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.00016592709310927874\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16949627175927162\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 4_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 71630.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 78284.493 [67595.635, 88973.351]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0006421366253755414\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2912765285000205\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 5_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 13948.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 14380.322 [5041.726, 23718.919]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.00044353892687835314\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.05755229713395238\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 6_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 24686.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 24236.927 [14867.791, 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60514.432]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002408052603520359\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.2510863887315102\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 11_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 20800.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 20662.752 [16835.653, 24489.851]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.001470133144187864\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21407980749240713\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 12_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 23520.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 23525.89 [19842.453, 27209.327]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245468034556\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.15340543119063924\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 32288.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 32161.228 [27213.832, 37108.624]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001994300938129328\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.21499235767270763\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 28658.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 28337.644 [25890.893, 30784.395]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.0008764775313422482\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.09728717173603192\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 20122.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 19795.554 [16161.168, 23429.941]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.0022267758514678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.058372345242382266\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 38672.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 38307.769 [35463.851, 41151.687]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237371701334\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.19449495772619033\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 115808.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 82800.604 [68565.568, 97035.641]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.0012017670755849175\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.13474598990397513\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 25904.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 25871.522 [24791.152, 26951.891]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012018965795618222\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.035270633844164935\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 34736.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 34788.758 [33510.965, 36066.551]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.0026075764359321427\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08965967639634911\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 27960.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 28848.862 [24012.134, 33685.590]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0009987284102201518\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 16800.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 16810.408 [15849.765, 17771.051]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.0010052246187516813\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 45673.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 45331.739 [43773.994, 46889.484]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.0010740830978628849\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08962285567515688\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 45968.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 46002.758 [45543.092, 46462.423]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0023031161968662373\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.07332857890409054\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21344.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21401.498 [18621.239, 24181.757]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0039016676642448078\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 50075.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 50608.467 [48806.372, 52410.561]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0012272060004844662\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10591940160087167\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 39584.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 39558.149 [38466.502, 40649.796]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0023557560376612557\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.013262435603067895\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 50528.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 50474.403 [50240.027, 50708.779]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0018959916188280854\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06769325117594031\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 49616.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 49570.345 [49338.109, 49802.582]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019116412526868863\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06882646818321762\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 49616.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 49621.102 [49341.551, 49900.653]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001896813466012091\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.047524591711452374\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 51440.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 51496.968 [51089.843, 51904.094]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002051537058037546\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.04398768200255323\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 23590.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 23645.386 [22896.294, 24394.478]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.0015065283662359535\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.002151874266435718\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 18110.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 17972.946 [16875.985, 19069.906]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.001448955255377498\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 43232.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 43242.989 [42741.968, 43744.010]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019186606244828539\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.07176473293061604\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 15872.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 16097.978 [14080.716, 18115.239]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0009895788143775668\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.029928865179771662\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 14104.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 14590.406 [12061.078, 17119.735]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.0024350447173329265\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 36_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 49175.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 49172.02 [48781.284, 49562.757]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0018066351219625165\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06526323924987067\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 37_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 50080.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 50073.438 [49694.783, 50452.093]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0018035678195813015\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06840431994084485\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 38_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 31276.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 31346.56 [30441.968, 32251.152]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762026758842844\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.028217772221820595\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 39_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 14728.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 14549.155 [13313.338, 15784.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.001289584449813646\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03270213326253294\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 40_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 16568.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 17598.368 [12654.782, 22541.953]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.0017306487601014113\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 41_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 12960.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 18009.735 [2212.615, 33806.856]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 42_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 59648.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 60476.205 [58722.373, 62230.037]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0015988439912958453\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.11538931071540572\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 43_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 56912.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 56882.199 [55632.346, 58132.051]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002192914937837011\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09898427583037986\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 44_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 26948.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 26896.183 [26033.799, 27758.566]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.001969449854904104\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.025489989406061484\u003cbr\u003ebatch_size: 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0_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.898 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.927 [0.905, 0.950]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816019941386738\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1909700185060501\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 1_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.879 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.907 [0.893, 0.921]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00014466425599951336\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.46575378254055977\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 2_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.951 [0.944, 0.959]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.002744711802661285\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19660000363364816\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 3_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.893 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.915 [0.888, 0.942]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.00016592709310927874\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16949627175927162\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 4_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.953 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.941 [0.923, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0006421366253755414\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2912765285000205\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 5_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.926 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.925 [0.904, 0.945]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.00044353892687835314\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.05755229713395238\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 6_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.856 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.908 [0.886, 0.930]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.00017699389919183936\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.4350298522040248\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 7_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.877 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.902 [0.887, 0.917]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.00011908174276707618\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.46274396777153015\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 8_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.723 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.912 [0.880, 0.944]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.005244787716785168\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.45581294130533934\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 9_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.96 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.952, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0015283801386478438\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.08997717482673702\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 10_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.959 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.949, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002408052603520359\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.2510863887315102\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 11_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.944 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.937, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.001470133144187864\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21407980749240713\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 12_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.945 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.944 [0.932, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245468034556\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.15340543119063924\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.941 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.945 [0.932, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001994300938129328\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.21499235767270763\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.952 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.95 [0.942, 0.959]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.0008764775313422482\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.09728717173603192\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.952 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.940, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.0022267758514678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.058372345242382266\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.953 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.956 [0.947, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237371701334\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.19449495772619033\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.97 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.962 [0.945, 0.978]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.0012017670755849175\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.13474598990397513\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.959 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.957 [0.953, 0.962]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012018965795618222\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.035270633844164935\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.962 [0.956, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.0026075764359321427\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08965967639634911\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.944 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.95 [0.937, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0009987284102201518\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.95 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.955 [0.950, 0.959]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.0010052246187516813\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.966 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.963 [0.956, 0.970]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.0010740830978628849\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08962285567515688\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.969 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.964, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0023031161968662373\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.07332857890409054\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.949 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.953 [0.942, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0039016676642448078\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.965 [0.958, 0.971]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0012272060004844662\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10591940160087167\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.968 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0023557560376612557\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.013262435603067895\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.968 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.965, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0018959916188280854\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06769325117594031\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.969 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.965, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019116412526868863\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06882646818321762\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.967 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.965, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001896813466012091\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.047524591711452374\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.968 [0.966, 0.970]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002051537058037546\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.04398768200255323\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.957 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.957, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.0015065283662359535\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.002151874266435718\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.958 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.948, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.001448955255377498\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.965, 0.970]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019186606244828539\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.07176473293061604\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.95 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.947 [0.940, 0.954]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0009895788143775668\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.029928865179771662\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.94 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.938, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.0024350447173329265\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 36_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0018066351219625165\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06526323924987067\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 37_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.963, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0018035678195813015\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06840431994084485\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 38_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.963 [0.960, 0.966]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762026758842844\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.028217772221820595\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 39_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.951 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.95 [0.945, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.001289584449813646\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03270213326253294\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 40_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.952 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.946 [0.930, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.0017306487601014113\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 41_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.912 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.915 [0.885, 0.946]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 42_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.969 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.963, 0.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0015988439912958453\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.11538931071540572\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 43_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.968 [0.964, 0.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002192914937837011\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09898427583037986\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 44_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.96 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.962 [0.959, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.001969449854904104\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.025489989406061484\u003cbr\u003ebatch_size: 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32631.107]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0015836311118018933\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2050776715403858\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 30792.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 30669.976 [28497.180, 32842.771]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030924955346424113\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.15425080038036754\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 35024.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 35203.43 [33938.688, 36468.172]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000892939773706933\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15377686031759935\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21344.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21136.647 [17769.926, 24503.367]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019122437399990496\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.11648592327558678\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 34112.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 34058.052 [32483.707, 35632.397]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588023688155876\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.20693801163974998\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21695.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21669.082 [19793.036, 23545.129]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.001428892513377131\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21430085808858387\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 46267.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 46121.589 [45379.665, 46863.513]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.001435040428753664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15967120130756182\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 40390.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 40552.374 [38408.298, 42696.450]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.0007441019683474586\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.1588948086860703\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 24498.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 24461.116 [23536.777, 25385.455]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.0008458653529265809\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08591265458457016\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 60560.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 59662.281 [57846.557, 61478.004]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0017804197438138333\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16868798731941295\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 38544.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 38531.59 [37820.736, 39242.444]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.001550017812211206\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.12840093075010264\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 18290.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 18109.863 [16412.513, 19807.213]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.0008563855724641313\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0788539938881646\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 44611.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 44642.676 [44082.406, 45202.946]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0014959402865701906\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09098484746501616\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 44729.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 44145.835 [41711.023, 46580.646]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0019242342360913701\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13264347854218717\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 29204.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 29182.626 [28348.227, 30017.026]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.0012059060451300881\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554013848979968\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 48545.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 48590.181 [48122.257, 49058.105]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0016147538184019287\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1240463086440828\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 20544.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 20394.164 [17696.083, 23092.245]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.0006538314475705554\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 112772.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 81782.272 [68274.375, 95290.169]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185006900815838\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13265672941473142\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21167.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21185.222 [20386.478, 21983.966]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.0012526312430088306\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09609853986651441\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 26360.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 26397.009 [25818.325, 26975.693]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0011707340084095397\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875025543199334\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 57576.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 57741.722 [56784.042, 58699.402]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0015427188728687946\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1294948811057008\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 15872.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 16824.721 [11469.780, 22179.662]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001066663459652777\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.043526285037224215\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 30254.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 30255.76 [29913.634, 30597.886]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.0013965235130177253\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03764783823261509\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 22964.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 23036.958 [21886.346, 24187.570]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.0013783106638542864\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.004069116579453136\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 36_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 34112.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 34098.252 [33627.857, 34568.647]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0016649690964076766\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03710676543833276\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 37_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 15872.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 20355.606 [7902.620, 32808.593]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 38_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 60956.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 60902.426 [60519.563, 61285.289]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.002003639791986216\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16358667872355087\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 39_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 61917.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 63621.52 [60681.618, 66561.421]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0019158548962992022\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16659106176553426\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 40_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 32288.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 32262.155 [31948.900, 32575.411]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014188925033155881\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.04812832697870953\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 41_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 62800.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 62958.154 [62378.440, 63537.868]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.002291288856676227\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156049268867353\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 42_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 42320.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 43106.393 [34832.601, 51380.186]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.28284327697220896\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 43_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 39277.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 39438.787 [38345.090, 40532.485]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.002311018317884243\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0031601680139129963\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 44_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 48736.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 48420.349 [46908.512, 49932.186]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.002697593907691842\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.01955848196111164\u003cbr\u003ebatch_size: 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0_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.911 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.93 [0.909, 0.951]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816019941386738\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1909700185060501\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 1_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.884 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.907 [0.893, 0.921]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00014466425599951336\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.46575378254055977\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 2_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.949 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.940, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.002744711802661285\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19660000363364816\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 3_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.9 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.915 [0.888, 0.943]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.00016592709310927874\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16949627175927162\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 4_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.949 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.944 [0.925, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0006421366253755414\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2912765285000205\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 5_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.924 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.92 [0.899, 0.941]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.00044353892687835314\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.05755229713395238\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 6_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.863 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.909 [0.887, 0.931]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.00017699389919183936\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.4350298522040248\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 7_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.876 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.903 [0.887, 0.918]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.00011908174276707618\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.46274396777153015\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 8_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.842 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.915 [0.883, 0.947]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.005244787716785168\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.45581294130533934\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 9_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.957 [0.947, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.002046487133026912\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.16828606332032023\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 10_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.955 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.953 [0.946, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.003139271388033848\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16521781376806371\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 11_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.949 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.948 [0.938, 0.957]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.0008286510325344759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15904629532238485\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 12_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.945 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.937 [0.923, 0.950]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0015836311118018933\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2050776715403858\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.951 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.95 [0.941, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030924955346424113\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.15425080038036754\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.959 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.954, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000892939773706933\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15377686031759935\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.942 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.951 [0.943, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019122437399990496\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.11648592327558678\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.956 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.945, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588023688155876\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.20693801163974998\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.942 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.948 [0.940, 0.956]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.001428892513377131\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21430085808858387\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.001435040428753664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15967120130756182\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.957 [0.949, 0.965]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.0007441019683474586\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.1588948086860703\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.955 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.955 [0.951, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.0008458653529265809\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08591265458457016\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.966 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.962, 0.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0017804197438138333\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16868798731941295\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.964 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.962, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.001550017812211206\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.12840093075010264\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.948 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.944, 0.954]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.0008563855724641313\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0788539938881646\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.964, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0014959402865701906\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09098484746501616\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.962 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.961 [0.954, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0019242342360913701\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13264347854218717\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.96 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.957, 0.962]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.0012059060451300881\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554013848979968\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.967 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.965, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0016147538184019287\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1240463086440828\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.952 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.942, 0.962]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.0006538314475705554\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.972 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.946, 0.973]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185006900815838\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13265672941473142\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.949, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.0012526312430088306\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09609853986651441\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.961 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.961 [0.958, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0011707340084095397\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875025543199334\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.966 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.964, 0.970]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0015427188728687946\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1294948811057008\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.95 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.946 [0.936, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001066663459652777\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.043526285037224215\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.962 [0.961, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.0013965235130177253\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03764783823261509\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.958 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.958 [0.954, 0.962]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.0013783106638542864\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.004069116579453136\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 36_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.963 [0.960, 0.965]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0016649690964076766\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03710676543833276\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 37_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.89 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.923 [0.894, 0.951]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 38_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.968 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.963, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.002003639791986216\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16358667872355087\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 39_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.964 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.961, 0.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0019158548962992022\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16659106176553426\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 40_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.962 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.965 [0.963, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014188925033155881\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.04812832697870953\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 41_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.968 [0.964, 0.971]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.002291288856676227\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156049268867353\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 42_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.933 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.938 [0.913, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.28284327697220896\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 43_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.958 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.002311018317884243\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0031601680139129963\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 44_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.954, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.002697593907691842\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.01955848196111164\u003cbr\u003ebatch_size: 128"],"visible":false,"x":[0.9110090136528015,0.8838416934013367,0.949111819267273,0.8995856642723083,0.9487510919570923,0.9238665103912354,0.8626527190208435,0.8755195140838623,0.841835081577301,0.9541319608688354,0.9554736614227295,0.9485170841217041,0.9454241991043091,0.9510927796363831,0.9593484401702881,0.9415989518165588,0.9559780955314636,0.9424334764480591,0.965461790561676,0.9542149305343628,0.9547032117843628,0.9659853577613831,0.9639917016029358,0.9475871920585632,0.9646998047828674,0.9615045785903931,0.9603331089019775,0.9668697118759155,0.9517737030982971,0.9717813730239868,0.9538366198539734,0.9607595801353455,0.9664375185966492,0.950124204158783,0.9649875164031982,0.958263099193573,0.9654144644737244,0.890011727809906,0.9678809642791748,0.9641238451004028,0.961800754070282,0.9649840593338013,0.9334069490432739,0.958203136920929,0.9648357629776001],"y":[0.9302869338684696,0.9068851772723312,0.9492996048089083,0.9153267640320253,0.9440346187276889,0.9196338531633792,0.9087370977191331,0.9026026163174592,0.9151194215494712,0.9572354181645552,0.9528517713785435,0.9476897091619653,0.93653768498881,0.949654632170069,0.9591645340494209,0.9514429451237414,0.951562656674562,0.9478116549562078,0.96409759569689,0.9571556229174486,0.9547147852762838,0.9668779333717318,0.9644084850132388,0.9489648735906442,0.9664131889728305,0.9605767941644683,0.9597463871792846,0.9664066445599353,0.9517655036143808,0.9596350147814909,0.95212191185513,0.9606549707842262,0.967321595509326,0.9457039394866652,0.96238654163058,0.9582810232201464,0.9628552836994768,0.9226201313265748,0.9655613792361888,0.9662937956799548,0.9647381873941672,0.967746511424018,0.937955439040303,0.9644348312707393,0.958591468745544],"type":"scatter"}], 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0_0\u003cbr\u003eval_acc: 0.911009 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003eval_acc: 0.955474 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003eval_acc: 0.948517 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003eval_acc: 0.945424 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003eval_acc: 0.951093 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003eval_acc: 0.959348 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003eval_acc: 0.941599 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003eval_acc: 0.955978 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003eval_acc: 0.942433 (SEM: None)\u003cbr\u003ehidden_size_1: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012019\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0352706\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 34736 (SEM: None)\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00260758\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896597\u003cbr\u003ebatch_size: 128","Arm 20_0\u003cbr\u003enum_params: 27960 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.000998728\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 16800 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.00100522\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 45673 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00107408\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896229\u003cbr\u003ebatch_size: 64","Arm 23_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00230312\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0733286\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00390167\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0475246\u003cbr\u003ebatch_size: 128","Arm 30_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00205154\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0439877\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.00150653\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00215187\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 18110 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.00144896\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 43232 (SEM: None)\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191866\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0717647\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000989579\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0299289\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 14104 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.00243504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 49175 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0652632\u003cbr\u003ebatch_size: 64","Arm 37_0\u003cbr\u003enum_params: 50080 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180357\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0684043\u003cbr\u003ebatch_size: 64","Arm 38_0\u003cbr\u003enum_params: 31276 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0282178\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 14728 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.00128958\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0327021\u003cbr\u003ebatch_size: 128","Arm 40_0\u003cbr\u003enum_params: 16568 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.00173065\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 41_0\u003cbr\u003enum_params: 12960 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 42_0\u003cbr\u003enum_params: 59648 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00159884\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.115389\u003cbr\u003ebatch_size: 64","Arm 43_0\u003cbr\u003enum_params: 56912 (SEM: None)\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00219291\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989843\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 26948 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00196945\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.02549\u003cbr\u003ebatch_size: 128"],"visible":false,"x":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"xaxis":"x","y":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 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None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012019\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0352706\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 34736 (SEM: None)\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00260758\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896597\u003cbr\u003ebatch_size: 128","Arm 20_0\u003cbr\u003enum_params: 27960 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.000998728\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 16800 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.00100522\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 45673 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00107408\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896229\u003cbr\u003ebatch_size: 64","Arm 23_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00230312\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0733286\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00390167\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0475246\u003cbr\u003ebatch_size: 128","Arm 30_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00205154\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0439877\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.00150653\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00215187\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 18110 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.00144896\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 43232 (SEM: None)\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191866\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0717647\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000989579\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0299289\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 14104 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.00243504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 49175 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0652632\u003cbr\u003ebatch_size: 64","Arm 37_0\u003cbr\u003enum_params: 50080 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180357\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0684043\u003cbr\u003ebatch_size: 64","Arm 38_0\u003cbr\u003enum_params: 31276 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0282178\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 14728 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.00128958\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0327021\u003cbr\u003ebatch_size: 128","Arm 40_0\u003cbr\u003enum_params: 16568 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.00173065\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 41_0\u003cbr\u003enum_params: 12960 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 42_0\u003cbr\u003enum_params: 59648 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00159884\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.115389\u003cbr\u003ebatch_size: 64","Arm 43_0\u003cbr\u003enum_params: 56912 (SEM: None)\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00219291\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989843\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 26948 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00196945\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.02549\u003cbr\u003ebatch_size: 128"],"visible":true,"x":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"xaxis":"x","y":[66,118,124,23,99,54,50,87,17,121,128,111,112,128,85,118,128,112,128,103,111,96,123,128,128,107,128,128,128,128,128,114,81,128,128,60,121,121,105,84,120,16,128,128,108],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 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112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012019\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0352706\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 34736 (SEM: None)\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00260758\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896597\u003cbr\u003ebatch_size: 128","Arm 20_0\u003cbr\u003enum_params: 27960 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.000998728\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 16800 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.00100522\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 45673 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00107408\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896229\u003cbr\u003ebatch_size: 64","Arm 23_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00230312\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0733286\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00390167\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0475246\u003cbr\u003ebatch_size: 128","Arm 30_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00205154\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0439877\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.00150653\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00215187\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 18110 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.00144896\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 43232 (SEM: None)\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191866\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0717647\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000989579\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0299289\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 14104 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.00243504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 49175 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0652632\u003cbr\u003ebatch_size: 64","Arm 37_0\u003cbr\u003enum_params: 50080 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180357\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0684043\u003cbr\u003ebatch_size: 64","Arm 38_0\u003cbr\u003enum_params: 31276 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0282178\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 14728 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.00128958\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0327021\u003cbr\u003ebatch_size: 128","Arm 40_0\u003cbr\u003enum_params: 16568 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.00173065\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 41_0\u003cbr\u003enum_params: 12960 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 42_0\u003cbr\u003enum_params: 59648 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00159884\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.115389\u003cbr\u003ebatch_size: 64","Arm 43_0\u003cbr\u003enum_params: 56912 (SEM: None)\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00219291\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989843\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 26948 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00196945\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.02549\u003cbr\u003ebatch_size: 128"],"visible":false,"x":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"xaxis":"x","y":[0.0031816019941386738,0.00014466425599951336,0.002744711802661285,0.00016592709310927874,0.0006421366253755414,0.00044353892687835314,0.00017699389919183936,0.00011908174276707618,0.005244787716785168,0.0015283801386478438,0.002408052603520359,0.001470133144187864,0.000780245468034556,0.001994300938129328,0.0008764775313422482,0.0022267758514678,0.00110237371701334,0.0012017670755849175,0.0012018965795618222,0.0026075764359321427,0.0009987284102201518,0.0010052246187516813,0.0010740830978628849,0.0023031161968662373,0.0039016676642448078,0.0012272060004844662,0.0023557560376612557,0.0018959916188280854,0.0019116412526868863,0.001896813466012091,0.002051537058037546,0.0015065283662359535,0.001448955255377498,0.0019186606244828539,0.0009895788143775668,0.0024350447173329265,0.0018066351219625165,0.0018035678195813015,0.0016762026758842844,0.001289584449813646,0.0017306487601014113,0.0001,0.0015988439912958453,0.002192914937837011,0.001969449854904104],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 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128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 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None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 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112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012019\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0352706\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 34736 (SEM: None)\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00260758\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896597\u003cbr\u003ebatch_size: 128","Arm 20_0\u003cbr\u003enum_params: 27960 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.000998728\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 16800 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.00100522\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 45673 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00107408\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896229\u003cbr\u003ebatch_size: 64","Arm 23_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00230312\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0733286\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00390167\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0475246\u003cbr\u003ebatch_size: 128","Arm 30_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00205154\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0439877\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.00150653\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00215187\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 18110 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.00144896\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 43232 (SEM: None)\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191866\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0717647\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000989579\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0299289\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 14104 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.00243504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 49175 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0652632\u003cbr\u003ebatch_size: 64","Arm 37_0\u003cbr\u003enum_params: 50080 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180357\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0684043\u003cbr\u003ebatch_size: 64","Arm 38_0\u003cbr\u003enum_params: 31276 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0282178\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 14728 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.00128958\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0327021\u003cbr\u003ebatch_size: 128","Arm 40_0\u003cbr\u003enum_params: 16568 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.00173065\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 41_0\u003cbr\u003enum_params: 12960 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 42_0\u003cbr\u003enum_params: 59648 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00159884\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.115389\u003cbr\u003ebatch_size: 64","Arm 43_0\u003cbr\u003enum_params: 56912 (SEM: None)\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00219291\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989843\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 26948 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00196945\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.02549\u003cbr\u003ebatch_size: 128"],"visible":false,"x":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"xaxis":"x","y":[0.1909700185060501,0.46575378254055977,0.19660000363364816,0.16949627175927162,0.2912765285000205,0.05755229713395238,0.4350298522040248,0.46274396777153015,0.45581294130533934,0.08997717482673702,0.2510863887315102,0.21407980749240713,0.15340543119063924,0.21499235767270763,0.09728717173603192,0.058372345242382266,0.19449495772619033,0.13474598990397513,0.035270633844164935,0.08965967639634911,0.0,0.0,0.08962285567515688,0.07332857890409054,0.0,0.10591940160087167,0.013262435603067895,0.06769325117594031,0.06882646818321762,0.047524591711452374,0.04398768200255323,0.002151874266435718,0.0,0.07176473293061604,0.029928865179771662,0.0,0.06526323924987067,0.06840431994084485,0.028217772221820595,0.03270213326253294,0.0,0.0,0.11538931071540572,0.09898427583037986,0.025489989406061484],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00120591\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 48545 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00161475\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.124046\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 20544 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.000653831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 64","Arm 29_0\u003cbr\u003enum_params: 112772 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185007\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132657\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 21167 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.00125263\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0960985\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 26360 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00117073\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 57576 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00154272\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.129495\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00106666\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0435263\u003cbr\u003ebatch_size: 64","Arm 34_0\u003cbr\u003enum_params: 30254 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00139652\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0376478\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 22964 (SEM: None)\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.00137831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00406912\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00141889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0481283\u003cbr\u003ebatch_size: 128","Arm 41_0\u003cbr\u003enum_params: 62800 (SEM: None)\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00229129\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 42320 (SEM: None)\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.282843\u003cbr\u003ebatch_size: 256","Arm 43_0\u003cbr\u003enum_params: 39277 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00231102\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00316017\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 48736 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00269759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0195585\u003cbr\u003ebatch_size: 128","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 52088 (SEM: None)\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00204649\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.168286\u003cbr\u003ebatch_size: 64"],"visible":false,"x":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"xaxis":"x","y":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00120591\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 48545 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00161475\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.124046\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 20544 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.000653831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 64","Arm 29_0\u003cbr\u003enum_params: 112772 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185007\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132657\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 21167 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.00125263\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0960985\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 26360 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00117073\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 57576 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00154272\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.129495\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00106666\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0435263\u003cbr\u003ebatch_size: 64","Arm 34_0\u003cbr\u003enum_params: 30254 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00139652\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0376478\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 22964 (SEM: None)\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.00137831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00406912\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00141889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0481283\u003cbr\u003ebatch_size: 128","Arm 41_0\u003cbr\u003enum_params: 62800 (SEM: None)\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00229129\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 42320 (SEM: None)\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.282843\u003cbr\u003ebatch_size: 256","Arm 43_0\u003cbr\u003enum_params: 39277 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00231102\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00316017\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 48736 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00269759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0195585\u003cbr\u003ebatch_size: 128","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 52088 (SEM: None)\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00204649\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.168286\u003cbr\u003ebatch_size: 64"],"visible":true,"x":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"xaxis":"x","y":[66,128,114,116,94,128,128,128,111,103,126,118,90,128,108,87,105,107,98,111,103,90,124,95,116,100,128,123,122,128,128,98,99,23,128,99,128,105,100,99,54,50,87,17,124],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00120591\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 48545 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00161475\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.124046\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 20544 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.000653831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 64","Arm 29_0\u003cbr\u003enum_params: 112772 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185007\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132657\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 21167 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.00125263\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0960985\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 26360 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00117073\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 57576 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00154272\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.129495\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00106666\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0435263\u003cbr\u003ebatch_size: 64","Arm 34_0\u003cbr\u003enum_params: 30254 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00139652\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0376478\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 22964 (SEM: None)\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.00137831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00406912\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00141889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0481283\u003cbr\u003ebatch_size: 128","Arm 41_0\u003cbr\u003enum_params: 62800 (SEM: None)\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00229129\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 42320 (SEM: None)\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.282843\u003cbr\u003ebatch_size: 256","Arm 43_0\u003cbr\u003enum_params: 39277 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00231102\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00316017\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 48736 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00269759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0195585\u003cbr\u003ebatch_size: 128","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 52088 (SEM: None)\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00204649\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.168286\u003cbr\u003ebatch_size: 64"],"visible":false,"x":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"xaxis":"x","y":[0.0031816019941386738,0.003139271388033848,0.0008286510325344759,0.0015836311118018933,0.0030924955346424113,0.000892939773706933,0.0019122437399990496,0.0014588023688155876,0.001428892513377131,0.001435040428753664,0.0007441019683474586,0.00014466425599951336,0.0008458653529265809,0.0017804197438138333,0.001550017812211206,0.0008563855724641313,0.0014959402865701906,0.0019242342360913701,0.0012059060451300881,0.0016147538184019287,0.0006538314475705554,0.00185006900815838,0.002744711802661285,0.0012526312430088306,0.0011707340084095397,0.0015427188728687946,0.001066663459652777,0.0013965235130177253,0.0013783106638542864,0.0016649690964076766,0.0001,0.002003639791986216,0.0019158548962992022,0.00016592709310927874,0.0014188925033155881,0.002291288856676227,0.01,0.002311018317884243,0.002697593907691842,0.0006421366253755414,0.00044353892687835314,0.00017699389919183936,0.00011908174276707618,0.005244787716785168,0.002046487133026912],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 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128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00120591\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 48545 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00161475\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.124046\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 20544 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.000653831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 64","Arm 29_0\u003cbr\u003enum_params: 112772 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185007\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132657\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 21167 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.00125263\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0960985\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 26360 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00117073\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 57576 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00154272\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.129495\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00106666\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0435263\u003cbr\u003ebatch_size: 64","Arm 34_0\u003cbr\u003enum_params: 30254 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00139652\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0376478\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 22964 (SEM: None)\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.00137831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00406912\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00141889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0481283\u003cbr\u003ebatch_size: 128","Arm 41_0\u003cbr\u003enum_params: 62800 (SEM: None)\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00229129\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 42320 (SEM: None)\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.282843\u003cbr\u003ebatch_size: 256","Arm 43_0\u003cbr\u003enum_params: 39277 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00231102\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00316017\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 48736 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00269759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0195585\u003cbr\u003ebatch_size: 128","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 52088 (SEM: None)\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00204649\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.168286\u003cbr\u003ebatch_size: 64"],"visible":false,"x":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"xaxis":"x","y":[0.1909700185060501,0.16521781376806371,0.15904629532238485,0.2050776715403858,0.15425080038036754,0.15377686031759935,0.11648592327558678,0.20693801163974998,0.21430085808858387,0.15967120130756182,0.1588948086860703,0.46575378254055977,0.08591265458457016,0.16868798731941295,0.12840093075010264,0.0788539938881646,0.09098484746501616,0.13264347854218717,0.10554013848979968,0.1240463086440828,0.0,0.13265672941473142,0.19660000363364816,0.09609853986651441,0.015875025543199334,0.1294948811057008,0.043526285037224215,0.03764783823261509,0.004069116579453136,0.03710676543833276,0.0,0.16358667872355087,0.16659106176553426,0.16949627175927162,0.04812832697870953,0.16156049268867353,0.28284327697220896,0.0031601680139129963,0.01955848196111164,0.2912765285000205,0.05755229713395238,0.4350298522040248,0.46274396777153015,0.45581294130533934,0.16828606332032023],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 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@@ -1960,7 +2033,7 @@ for their help with integrating TorchX with Ax.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 16 minutes  27.058 seconds)
+   **Total running time of the script:** ( 16 minutes  21.583 seconds)
 
 
 .. _sphx_glr_download_intermediate_ax_multiobjective_nas_tutorial.py:
diff --git a/_sources/intermediate/char_rnn_classification_tutorial.rst.txt b/_sources/intermediate/char_rnn_classification_tutorial.rst.txt
index 002210eefd..60ec06a47f 100644
--- a/_sources/intermediate/char_rnn_classification_tutorial.rst.txt
+++ b/_sources/intermediate/char_rnn_classification_tutorial.rst.txt
@@ -594,26 +594,26 @@ average of the loss.
 
  .. code-block:: none
 
-    5000 5% (0m 34s) 2.2208 Horigome / Japanese ✓
+    5000 5% (0m 35s) 2.2208 Horigome / Japanese ✓
     10000 10% (1m 10s) 1.6752 Miazga / Japanese ✗ (Polish)
-    15000 15% (1m 46s) 0.1778 Yukhvidov / Russian ✓
-    20000 20% (2m 21s) 1.5856 Mclaughlin / Irish ✗ (Scottish)
-    25000 25% (2m 57s) 0.6552 Banh / Vietnamese ✓
-    30000 30% (3m 32s) 1.5547 Machado / Japanese ✗ (Portuguese)
-    35000 35% (4m 8s) 0.0168 Fotopoulos / Greek ✓
-    40000 40% (4m 43s) 1.1464 Quirke / Irish ✓
-    45000 45% (5m 18s) 1.7532 Reier / French ✗ (German)
-    50000 50% (5m 54s) 0.8413 Hou / Chinese ✓
-    55000 55% (6m 29s) 0.8587 Duan / Vietnamese ✗ (Chinese)
-    60000 60% (7m 5s) 0.2047 Giang / Vietnamese ✓
-    65000 65% (7m 40s) 2.5534 Cober / French ✗ (Czech)
-    70000 70% (8m 16s) 1.5163 Mateus / Arabic ✗ (Portuguese)
-    75000 75% (8m 51s) 0.2217 Hamilton / Scottish ✓
-    80000 80% (9m 30s) 0.4456 Maessen / Dutch ✓
-    85000 85% (10m 6s) 0.0239 Gan / Chinese ✓
-    90000 90% (10m 41s) 0.0521 Bellomi / Italian ✓
-    95000 95% (11m 16s) 0.0867 Vozgov / Russian ✓
-    100000 100% (11m 52s) 0.2730 Tong / Vietnamese ✓
+    15000 15% (1m 47s) 0.1778 Yukhvidov / Russian ✓
+    20000 20% (2m 22s) 1.5856 Mclaughlin / Irish ✗ (Scottish)
+    25000 25% (2m 58s) 0.6552 Banh / Vietnamese ✓
+    30000 30% (3m 34s) 1.5547 Machado / Japanese ✗ (Portuguese)
+    35000 35% (4m 10s) 0.0168 Fotopoulos / Greek ✓
+    40000 40% (4m 49s) 1.1464 Quirke / Irish ✓
+    45000 45% (5m 24s) 1.7532 Reier / French ✗ (German)
+    50000 50% (6m 0s) 0.8413 Hou / Chinese ✓
+    55000 55% (6m 36s) 0.8587 Duan / Vietnamese ✗ (Chinese)
+    60000 60% (7m 12s) 0.2047 Giang / Vietnamese ✓
+    65000 65% (7m 48s) 2.5534 Cober / French ✗ (Czech)
+    70000 70% (8m 23s) 1.5163 Mateus / Arabic ✗ (Portuguese)
+    75000 75% (8m 59s) 0.2217 Hamilton / Scottish ✓
+    80000 80% (9m 34s) 0.4456 Maessen / Dutch ✓
+    85000 85% (10m 10s) 0.0239 Gan / Chinese ✓
+    90000 90% (10m 46s) 0.0521 Bellomi / Italian ✓
+    95000 95% (11m 21s) 0.0867 Vozgov / Russian ✓
+    100000 100% (11m 57s) 0.2730 Tong / Vietnamese ✓
 
 
 
@@ -653,7 +653,7 @@ learning:
  .. code-block:: none
 
 
-    [<matplotlib.lines.Line2D object at 0x7f8ba2ebbf40>]
+    [<matplotlib.lines.Line2D object at 0x7fc876581f60>]
 
 
 
@@ -844,7 +844,7 @@ Exercises
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 12 minutes  4.757 seconds)
+   **Total running time of the script:** ( 12 minutes  10.610 seconds)
 
 
 .. _sphx_glr_download_intermediate_char_rnn_classification_tutorial.py:
diff --git a/_sources/intermediate/char_rnn_generation_tutorial.rst.txt b/_sources/intermediate/char_rnn_generation_tutorial.rst.txt
index f0cbae6867..fcc42866de 100644
--- a/_sources/intermediate/char_rnn_generation_tutorial.rst.txt
+++ b/_sources/intermediate/char_rnn_generation_tutorial.rst.txt
@@ -464,26 +464,26 @@ in ``all_losses`` for plotting later.
 
  .. code-block:: none
 
-    0m 39s (5000 5%) 3.1506
-    1m 19s (10000 10%) 2.5070
-    1m 59s (15000 15%) 3.3047
-    2m 39s (20000 20%) 2.4247
-    3m 20s (25000 25%) 2.6406
-    4m 0s (30000 30%) 2.0266
-    4m 40s (35000 35%) 2.6520
-    5m 20s (40000 40%) 2.4261
-    6m 0s (45000 45%) 2.2302
-    6m 41s (50000 50%) 1.6496
-    7m 22s (55000 55%) 2.7101
-    8m 3s (60000 60%) 2.5396
-    8m 43s (65000 65%) 2.5978
-    9m 23s (70000 70%) 1.6029
-    10m 4s (75000 75%) 0.9634
-    10m 44s (80000 80%) 3.0950
-    11m 24s (85000 85%) 2.0512
-    12m 4s (90000 90%) 2.5302
-    12m 45s (95000 95%) 3.2365
-    13m 25s (100000 100%) 1.7113
+    0m 38s (5000 5%) 3.1506
+    1m 17s (10000 10%) 2.5070
+    1m 57s (15000 15%) 3.3047
+    2m 37s (20000 20%) 2.4247
+    3m 17s (25000 25%) 2.6406
+    3m 57s (30000 30%) 2.0266
+    4m 37s (35000 35%) 2.6520
+    5m 17s (40000 40%) 2.4261
+    5m 57s (45000 45%) 2.2302
+    6m 37s (50000 50%) 1.6496
+    7m 17s (55000 55%) 2.7101
+    7m 58s (60000 60%) 2.5396
+    8m 38s (65000 65%) 2.5978
+    9m 18s (70000 70%) 1.6029
+    9m 58s (75000 75%) 0.9634
+    10m 38s (80000 80%) 3.0950
+    11m 18s (85000 85%) 2.0512
+    11m 59s (90000 90%) 2.5302
+    12m 40s (95000 95%) 3.2365
+    13m 20s (100000 100%) 1.7113
 
 
 
@@ -522,7 +522,7 @@ learning:
  .. code-block:: none
 
 
-    [<matplotlib.lines.Line2D object at 0x7f46681c4070>]
+    [<matplotlib.lines.Line2D object at 0x7f20c0c74100>]
 
 
 
@@ -641,7 +641,7 @@ Exercises
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 13 minutes  25.694 seconds)
+   **Total running time of the script:** ( 13 minutes  21.102 seconds)
 
 
 .. _sphx_glr_download_intermediate_char_rnn_generation_tutorial.py:
diff --git a/_sources/intermediate/custom_function_conv_bn_tutorial.rst.txt b/_sources/intermediate/custom_function_conv_bn_tutorial.rst.txt
index fdf8b2c28e..1fe1d40700 100644
--- a/_sources/intermediate/custom_function_conv_bn_tutorial.rst.txt
+++ b/_sources/intermediate/custom_function_conv_bn_tutorial.rst.txt
@@ -568,22 +568,22 @@ allocate one fewer buffer per fused ``conv-bn`` pair.
     Test set: Average loss: 0.4197, Accuracy: 8681/10000 (87%)
 
     Train Epoch: 0 [0/60000 (0%)]   Loss: 2.349030
-    Train Epoch: 0 [4096/60000 (7%)]        Loss: 7.435157
-    Train Epoch: 0 [8192/60000 (13%)]       Loss: 5.443542
-    Train Epoch: 0 [12288/60000 (20%)]      Loss: 2.457858
-    Train Epoch: 0 [16384/60000 (27%)]      Loss: 1.739216
-    Train Epoch: 0 [20480/60000 (33%)]      Loss: 1.448291
-    Train Epoch: 0 [24576/60000 (40%)]      Loss: 1.312150
-    Train Epoch: 0 [28672/60000 (47%)]      Loss: 1.145356
-    Train Epoch: 0 [32768/60000 (53%)]      Loss: 1.496005
-    Train Epoch: 0 [36864/60000 (60%)]      Loss: 1.251128
-    Train Epoch: 0 [40960/60000 (67%)]      Loss: 1.076815
-    Train Epoch: 0 [45056/60000 (73%)]      Loss: 0.892333
-    Train Epoch: 0 [49152/60000 (80%)]      Loss: 0.829717
-    Train Epoch: 0 [53248/60000 (87%)]      Loss: 0.740473
-    Train Epoch: 0 [57344/60000 (93%)]      Loss: 0.789344
-
-    Test set: Average loss: 0.4183, Accuracy: 8869/10000 (89%)
+    Train Epoch: 0 [4096/60000 (7%)]        Loss: 7.435156
+    Train Epoch: 0 [8192/60000 (13%)]       Loss: 5.443526
+    Train Epoch: 0 [12288/60000 (20%)]      Loss: 2.457884
+    Train Epoch: 0 [16384/60000 (27%)]      Loss: 1.739214
+    Train Epoch: 0 [20480/60000 (33%)]      Loss: 1.448314
+    Train Epoch: 0 [24576/60000 (40%)]      Loss: 1.312073
+    Train Epoch: 0 [28672/60000 (47%)]      Loss: 1.144974
+    Train Epoch: 0 [32768/60000 (53%)]      Loss: 1.495681
+    Train Epoch: 0 [36864/60000 (60%)]      Loss: 1.249685
+    Train Epoch: 0 [40960/60000 (67%)]      Loss: 1.080824
+    Train Epoch: 0 [45056/60000 (73%)]      Loss: 0.894487
+    Train Epoch: 0 [49152/60000 (80%)]      Loss: 0.845663
+    Train Epoch: 0 [53248/60000 (87%)]      Loss: 0.700515
+    Train Epoch: 0 [57344/60000 (93%)]      Loss: 0.743951
+
+    Test set: Average loss: 0.4384, Accuracy: 8719/10000 (87%)
 
     cuDNN version: 8902
 
@@ -598,7 +598,7 @@ allocate one fewer buffer per fused ``conv-bn`` pair.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  36.520 seconds)
+   **Total running time of the script:** ( 0 minutes  37.817 seconds)
 
 
 .. _sphx_glr_download_intermediate_custom_function_conv_bn_tutorial.py:
diff --git a/_sources/intermediate/dqn_with_rnn_tutorial.rst.txt b/_sources/intermediate/dqn_with_rnn_tutorial.rst.txt
index 526a996746..9bd04e7471 100644
--- a/_sources/intermediate/dqn_with_rnn_tutorial.rst.txt
+++ b/_sources/intermediate/dqn_with_rnn_tutorial.rst.txt
@@ -803,15 +803,15 @@ every 50 data collection, and plot the results after training.
         device=cpu,
         is_shared=False)
 
-      0%|          | 50/1000000 [00:00<2:26:56, 113.41it/s]
-      0%|          | 50/1000000 [00:15<2:26:56, 113.41it/s]
-    steps: 16, loss_val:  0.0002, action_spread: tensor([ 8, 42]):   0%|          | 50/1000000 [00:25<2:26:56, 113.41it/s]
-    steps: 16, loss_val:  0.0002, action_spread: tensor([ 8, 42]):   0%|          | 100/1000000 [00:26<85:13:26,  3.26it/s]
-    steps: 16, loss_val:  0.0003, action_spread: tensor([40, 10]):   0%|          | 100/1000000 [00:50<85:13:26,  3.26it/s]
-    steps: 16, loss_val:  0.0003, action_spread: tensor([40, 10]):   0%|          | 150/1000000 [00:51<110:30:44,  2.51it/s]
-    steps: 16, loss_val:  0.0003, action_spread: tensor([ 8, 42]):   0%|          | 150/1000000 [01:16<110:30:44,  2.51it/s]
-    steps: 16, loss_val:  0.0003, action_spread: tensor([ 8, 42]):   0%|          | 200/1000000 [01:16<122:26:43,  2.27it/s]
-    steps: 16, loss_val:  0.0003, action_spread: tensor([43,  7]):   0%|          | 200/1000000 [01:41<122:26:43,  2.27it/s]
+      0%|          | 50/1000000 [00:00<2:24:15, 115.53it/s]
+      0%|          | 50/1000000 [00:10<2:24:15, 115.53it/s]
+    steps: 14, loss_val:  0.0005, action_spread: tensor([ 9, 41]):   0%|          | 50/1000000 [00:26<2:24:15, 115.53it/s]
+    steps: 14, loss_val:  0.0005, action_spread: tensor([ 9, 41]):   0%|          | 100/1000000 [00:27<87:57:07,  3.16it/s]
+    steps: 14, loss_val:  0.0006, action_spread: tensor([42,  8]):   0%|          | 100/1000000 [00:52<87:57:07,  3.16it/s]
+    steps: 14, loss_val:  0.0006, action_spread: tensor([42,  8]):   0%|          | 150/1000000 [00:53<114:57:29,  2.42it/s]
+    steps: 14, loss_val:  0.0007, action_spread: tensor([ 7, 43]):   0%|          | 150/1000000 [01:19<114:57:29,  2.42it/s]
+    steps: 14, loss_val:  0.0007, action_spread: tensor([ 7, 43]):   0%|          | 200/1000000 [01:20<127:54:56,  2.17it/s]
+    steps: 14, loss_val:  0.0007, action_spread: tensor([41,  9]):   0%|          | 200/1000000 [01:45<127:54:56,  2.17it/s]
 
 
 
@@ -867,7 +867,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  46.960 seconds)
+   **Total running time of the script:** ( 1 minutes  51.213 seconds)
 
 
 .. _sphx_glr_download_intermediate_dqn_with_rnn_tutorial.py:
diff --git a/_sources/intermediate/ensembling.rst.txt b/_sources/intermediate/ensembling.rst.txt
index bfef212f9c..f9f5cf5453 100644
--- a/_sources/intermediate/ensembling.rst.txt
+++ b/_sources/intermediate/ensembling.rst.txt
@@ -306,13 +306,13 @@ Curious about performance numbers? Here's how the numbers look.
 
  .. code-block:: none
 
-    Predictions without vmap <torch.utils.benchmark.utils.common.Measurement object at 0x7f6a06751600>
+    Predictions without vmap <torch.utils.benchmark.utils.common.Measurement object at 0x7f2f8a322890>
     [model(minibatch) for model, minibatch in zip(models, minibatches)]
-      2.08 ms
+      2.26 ms
       1 measurement, 100 runs , 1 thread
-    Predictions with vmap <torch.utils.benchmark.utils.common.Measurement object at 0x7f6a06752c80>
+    Predictions with vmap <torch.utils.benchmark.utils.common.Measurement object at 0x7f2f8a322710>
     vmap(fmodel)(params, buffers, minibatches)
-      795.37 us
+      858.11 us
       1 measurement, 100 runs , 1 thread
 
 
@@ -332,7 +332,7 @@ on GitHub.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.835 seconds)
+   **Total running time of the script:** ( 0 minutes  0.879 seconds)
 
 
 .. _sphx_glr_download_intermediate_ensembling.py:
diff --git a/_sources/intermediate/forward_ad_usage.rst.txt b/_sources/intermediate/forward_ad_usage.rst.txt
index d534b7cfcc..9797e2e59c 100644
--- a/_sources/intermediate/forward_ad_usage.rst.txt
+++ b/_sources/intermediate/forward_ad_usage.rst.txt
@@ -370,7 +370,7 @@ to the module.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.152 seconds)
+   **Total running time of the script:** ( 0 minutes  0.155 seconds)
 
 
 .. _sphx_glr_download_intermediate_forward_ad_usage.py:
diff --git a/_sources/intermediate/fx_profiling_tutorial.rst.txt b/_sources/intermediate/fx_profiling_tutorial.rst.txt
index 4d48d3daae..eb6c50b5dc 100644
--- a/_sources/intermediate/fx_profiling_tutorial.rst.txt
+++ b/_sources/intermediate/fx_profiling_tutorial.rst.txt
@@ -477,77 +477,77 @@ characteristics of our ResNet18 model;
 
     Op type        Op                       Average runtime (s)    Pct total runtime
     -------------  ---------------------  ---------------------  -------------------
-    call_module    maxpool                          0.00575376             8.70941
-    call_module    layer4_1_conv2                   0.00491071             7.4333
-    call_module    conv1                            0.00479746             7.26187
-    call_module    layer4_0_conv2                   0.00369048             5.58625
-    call_module    layer1_0_conv1                   0.00338578             5.12503
-    call_module    layer1_1_conv2                   0.00320458             4.85075
-    call_module    layer4_1_conv1                   0.00319433             4.83523
-    call_module    layer1_0_conv2                   0.00295234             4.46893
-    call_module    layer2_1_conv1                   0.0028708              4.3455
-    call_module    layer3_1_conv1                   0.00283694             4.29426
-    call_module    layer3_1_conv2                   0.00275111             4.16434
-    call_module    layer3_0_conv2                   0.00265932             4.02539
-    call_module    layer1_1_conv1                   0.00259066             3.92146
-    call_module    layer2_1_conv2                   0.0025208              3.81571
-    call_module    layer2_0_conv2                   0.00239658             3.62769
-    call_module    layer4_0_conv1                   0.00222993             3.37543
-    call_module    layer3_0_conv1                   0.00175953             2.66338
-    call_module    bn1                              0.00150132             2.27254
-    call_module    layer2_0_conv1                   0.00146842             2.22274
-    call_module    layer2_0_downsample_0            0.000971079            1.46991
-    call_module    layer3_0_downsample_0            0.000450134            0.681365
-    call_module    layer4_0_downsample_0            0.000447512            0.677395
-    call_function  add                              0.000422955            0.640223
-    call_function  add_1                            0.00041914             0.634449
-    call_module    relu                             0.000286579            0.433793
-    call_function  add_3                            0.000248194            0.375689
-    call_module    layer1_0_bn1                     0.000195265            0.295571
-    call_module    fc                               0.000192881            0.291962
-    call_module    layer1_1_bn2                     0.000168562            0.255151
-    call_module    layer1_0_bn2                     0.000168085            0.254429
-    call_module    avgpool                          0.000153065            0.231693
-    call_module    layer1_1_bn1                     0.000148773            0.225197
-    call_module    layer4_1_bn2                     0.000141859            0.214731
-    call_module    layer2_0_downsample_1            0.000134468            0.203543
-    call_module    layer2_1_bn1                     0.000132799            0.201017
-    call_module    layer2_0_bn2                     0.000128746            0.194882
-    call_module    layer1_1_relu_1                  0.000128508            0.194521
-    call_module    layer3_1_bn1                     0.000123501            0.186942
-    call_module    layer2_0_bn1                     0.000122309            0.185138
-    call_module    layer3_1_bn2                     0.000121117            0.183333
-    call_module    layer2_1_bn2                     0.000119686            0.181168
-    call_module    layer4_1_bn1                     0.000119448            0.180807
-    call_module    layer3_0_bn1                     0.000116348            0.176115
-    call_module    layer4_0_bn2                     0.000114679            0.173589
-    call_module    layer1_0_relu                    0.000113726            0.172146
-    call_module    layer3_0_bn2                     0.00011301             0.171063
-    call_module    layer4_0_bn1                     0.000112057            0.169619
-    call_module    layer3_0_downsample_1            0.00011158             0.168898
-    call_module    layer4_0_downsample_1            0.000108957            0.164928
-    call_module    layer1_1_relu                    9.84669e-05            0.149049
-    call_module    layer1_0_relu_1                  9.75132e-05            0.147605
-    call_module    layer4_1_relu                    8.63075e-05            0.130643
-    call_function  add_2                            8.05855e-05            0.121982
-    call_module    layer2_1_relu                    8.03471e-05            0.121621
-    call_module    layer4_0_relu                    8.01086e-05            0.12126
-    call_function  add_7                            8.01086e-05            0.12126
-    call_module    layer2_0_relu                    7.96318e-05            0.120538
-    call_module    layer2_1_relu_1                  7.82013e-05            0.118373
-    call_module    layer2_0_relu_1                  7.67708e-05            0.116207
-    call_module    layer3_1_relu                    7.39098e-05            0.111877
-    call_module    layer3_0_relu                    7.31945e-05            0.110794
-    call_function  add_4                            7.29561e-05            0.110433
-    call_module    layer4_1_relu_1                  7.27177e-05            0.110072
-    call_function  add_6                            7.15256e-05            0.108268
-    call_module    layer4_0_relu_1                  7.10487e-05            0.107546
-    call_module    layer3_1_relu_1                  6.98566e-05            0.105741
-    call_function  add_5                            6.96182e-05            0.105381
-    call_module    layer3_0_relu_1                  6.93798e-05            0.10502
-    call_function  flatten                          4.29153e-05            0.0649606
-    placeholder    x                                2.5034e-05             0.0378937
-    output         output                           1.85966e-05            0.0281496
+    call_module    maxpool                          0.00839758             8.8298
+    call_module    conv1                            0.00785089             8.25497
+    call_module    layer1_1_conv1                   0.00579381             6.09202
+    call_module    layer1_0_conv2                   0.00575066             6.04664
+    call_module    layer1_0_conv1                   0.00556517             5.85161
+    call_module    layer2_1_conv2                   0.00535631             5.632
+    call_module    layer2_1_conv1                   0.00509667             5.359
+    call_module    layer1_1_conv2                   0.00482035             5.06845
+    call_module    layer2_0_conv2                   0.00472021             4.96316
+    call_module    layer4_0_conv2                   0.00393105             4.13338
+    call_module    layer4_1_conv1                   0.00334334             3.51543
+    call_module    layer4_1_conv2                   0.00333142             3.50289
+    call_module    layer2_0_conv1                   0.00315976             3.32239
+    call_module    layer3_1_conv2                   0.00291038             3.06017
+    call_module    layer3_1_conv1                   0.00289321             3.04212
+    call_module    layer3_0_conv1                   0.00289011             3.03886
+    call_module    layer3_0_conv2                   0.00272608             2.86639
+    call_module    bn1                              0.0023737              2.49587
+    call_module    layer4_0_conv1                   0.00233245             2.4525
+    call_module    layer2_0_downsample_0            0.00140858             1.48108
+    call_function  add_1                            0.000641108            0.674105
+    call_module    relu                             0.000547171            0.575334
+    call_module    layer3_0_downsample_0            0.000541925            0.569818
+    call_module    layer4_0_downsample_0            0.000512362            0.538733
+    call_function  add                              0.000449419            0.472551
+    call_module    layer1_1_bn1                     0.000344038            0.361746
+    call_module    layer1_0_bn2                     0.00033474             0.351969
+    call_module    layer1_0_bn1                     0.00032258             0.339184
+    call_module    layer1_1_bn2                     0.000280857            0.295313
+    call_module    layer2_1_bn2                     0.000243187            0.255704
+    call_module    layer2_1_bn1                     0.000236273            0.248434
+    call_module    layer2_0_bn1                     0.000234127            0.246178
+    call_module    layer2_0_bn2                     0.000226974            0.238657
+    call_module    layer2_0_downsample_1            0.000204802            0.215343
+    call_module    fc                               0.000177145            0.186263
+    call_module    layer1_0_relu                    0.000175714            0.184759
+    call_module    layer4_0_bn2                     0.000174522            0.183505
+    call_module    layer1_1_relu                    0.000173569            0.182502
+    call_module    layer1_0_relu_1                  0.000165939            0.17448
+    call_function  add_3                            0.000164747            0.173227
+    call_module    layer3_0_bn1                     0.00016427             0.172725
+    call_module    layer1_1_relu_1                  0.000163555            0.171973
+    call_module    layer4_1_bn1                     0.000163078            0.171472
+    call_module    layer4_1_bn2                     0.000160694            0.168965
+    call_module    layer4_0_downsample_1            0.00015974             0.167962
+    call_module    avgpool                          0.000156164            0.164202
+    call_function  add_2                            0.000155926            0.163951
+    call_module    layer3_1_bn1                     0.000144958            0.15242
+    call_module    layer2_1_relu                    0.000142813            0.150163
+    call_module    layer3_1_bn2                     0.000141621            0.14891
+    call_module    layer2_1_relu_1                  0.000133753            0.140637
+    call_module    layer4_0_bn1                     0.000132799            0.139634
+    call_module    layer3_0_bn2                     0.000131845            0.138632
+    call_module    layer2_0_relu_1                  0.000128746            0.135373
+    call_module    layer2_0_relu                    0.000125408            0.131863
+    call_module    layer3_0_downsample_1            0.000123739            0.130108
+    call_module    layer4_1_relu                    0.000109196            0.114816
+    call_function  add_6                            0.000101089            0.106293
+    call_module    layer4_0_relu_1                  9.13143e-05            0.0960143
+    call_module    layer4_0_relu                    8.82149e-05            0.0927553
+    call_function  add_7                            8.79765e-05            0.0925046
+    call_function  add_5                            8.44002e-05            0.0887443
+    call_module    layer4_1_relu_1                  8.32081e-05            0.0874908
+    call_module    layer3_0_relu                    8.13007e-05            0.0854853
+    call_module    layer3_1_relu                    7.9155e-05             0.0832291
+    call_module    layer3_0_relu_1                  7.65324e-05            0.0804715
+    call_module    layer3_1_relu_1                  7.43866e-05            0.0782153
+    call_function  add_4                            7.27177e-05            0.0764605
+    call_function  flatten                          4.48227e-05            0.0471297
+    placeholder    x                                2.93255e-05            0.0308349
+    output         output                           1.90735e-05            0.0200552
 
 
 
@@ -580,7 +580,7 @@ you might have.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.418 seconds)
+   **Total running time of the script:** ( 0 minutes  0.493 seconds)
 
 
 .. _sphx_glr_download_intermediate_fx_profiling_tutorial.py:
diff --git a/_sources/intermediate/inductor_debug_cpu.rst.txt b/_sources/intermediate/inductor_debug_cpu.rst.txt
index cd880855c4..f748cd114e 100644
--- a/_sources/intermediate/inductor_debug_cpu.rst.txt
+++ b/_sources/intermediate/inductor_debug_cpu.rst.txt
@@ -76,12 +76,6 @@ Here is a simple example to run the ``torch.compile`` using Inductor and compare
     result = compiled_foo1(x1, x2)
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 57-58
 
 The correct implementation of ``neg`` in the ``cpp`` codegen is as follows:
@@ -95,12 +89,6 @@ The correct implementation of ``neg`` in the ``cpp`` codegen is as follows:
         return f"decltype({x})(-{x})"
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 63-102
 
 In order to demonstrate the debugging, we will modify the function to a wrong one later.
@@ -155,12 +143,6 @@ Here are the main parts of code extracted from the files and we correlate the C+
         return (clone,)
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 110-112
 
 C++ kernel in ``output_code``:
@@ -202,12 +184,6 @@ C++ kernel in ``output_code``:
 
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 144-176
 
 Determine component of error
@@ -252,12 +228,6 @@ For example, the ``neg`` function is modified like this:
         return f"-{x}"
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 181-229
 
 The logging gives the following compile error with a rather clear reason.
@@ -388,12 +358,6 @@ For instance, we modify the example like this:
     assert same(expected_result, actual_result) == True
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 302-303
 
 And also modify the ``neg`` function:
@@ -407,12 +371,6 @@ And also modify the ``neg`` function:
         return f"decltype({x})(2 * {x})"
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 308-339
 
 An accuracy problem would be raised as follows:
@@ -457,12 +415,6 @@ After running, we get the final minified graph with the target node ``neg``:
         return (neg,)
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 345-346
 
 For more usage details about Minifier, please refer to `Troubleshooting <https://pytorch.org/docs/stable/dynamo/troubleshooting.html>`_.
@@ -529,33 +481,6 @@ We set following environment variable as a best practice to benchmark on Intel(R
 
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning:
-
-    torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-
-    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning:
-
-    torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-
-
-    Downloading config.json:   0%|          | 0.00/765 [00:00<?, ?B/s]
-    Downloading config.json: 100%|##########| 765/765 [00:00<00:00, 4.69MB/s]
-
-    Downloading model.safetensors:   0%|          | 0.00/98.5M [00:00<?, ?B/s]
-    Downloading model.safetensors:  43%|####2     | 41.9M/98.5M [00:00<00:00, 373MB/s]
-    Downloading model.safetensors:  85%|########5 | 83.9M/98.5M [00:00<00:00, 297MB/s]
-    Downloading model.safetensors: 100%|##########| 98.5M/98.5M [00:00<00:00, 311MB/s]
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 403-417
 
 Output:
@@ -582,12 +507,6 @@ Inductor CPU backend has the support to report the time of the fusion kernels to
     config.cpp.enable_kernel_profile = True
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 422-424
 
 Following the steps in `Pytorch Profiler <https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html>`_
@@ -627,12 +546,6 @@ We are able to get the profiling table and trace files.
             p.step()
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 454-484
 
 We get the following performance profiling table for the eager-mode model (omitting some columns):
@@ -754,12 +667,6 @@ searching ``cpp_fused__mkl_linear_add_mul_relu_151`` in ``output_code.py``
     }''')
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 568-571
 
 From the generated code above, we can see this kernel has done a typical `Loop Fusion <https://en.wikipedia.org/wiki/Loop_fission_and_fusion>`_ on ``[add, add, mul, add]``.
@@ -809,12 +716,6 @@ we can infer the sizes and stride of the inputs and further benchmark this ``[ad
     # print(f"speed up ratio: {eager_t / inductor_t}")
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 610-621
 
 Output:
@@ -848,7 +749,7 @@ We show how to find the time-consuming hotspot with PyTorch Profiler and figure
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 9 minutes  30.097 seconds)
+   **Total running time of the script:** ( 0 minutes  0.000 seconds)
 
 
 .. _sphx_glr_download_intermediate_inductor_debug_cpu.py:
diff --git a/_sources/intermediate/jacobians_hessians.rst.txt b/_sources/intermediate/jacobians_hessians.rst.txt
index 00d4528be7..7d0b641a4b 100644
--- a/_sources/intermediate/jacobians_hessians.rst.txt
+++ b/_sources/intermediate/jacobians_hessians.rst.txt
@@ -261,13 +261,13 @@ And then run the performance comparison:
 
  .. code-block:: none
 
-    <torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba2eb5a20>
+    <torch.utils.benchmark.utils.common.Measurement object at 0x7fc877b78460>
     compute_jac(xp)
-      2.41 ms
+      2.40 ms
       1 measurement, 500 runs , 1 thread
-    <torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1bdca30>
+    <torch.utils.benchmark.utils.common.Measurement object at 0x7fc876894820>
     jacrev(predict, argnums=2)(weight, bias, x)
-      678.92 us
+      680.98 us
       1 measurement, 500 runs , 1 thread
 
 
@@ -292,7 +292,7 @@ Let's do a relative performance comparison of the above with our ``get_perf`` fu
 
  .. code-block:: none
 
-     Performance delta: 71.8454 percent improvement with vmap 
+     Performance delta: 71.6275 percent improvement with vmap 
 
 
 
@@ -394,13 +394,13 @@ First, let's benchmark with more inputs than outputs:
  .. code-block:: none
 
     torch.Size([2048, 32])
-    jacfwd time: <torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1a77fd0>
+    jacfwd time: <torch.utils.benchmark.utils.common.Measurement object at 0x7fc877b29c60>
     jacfwd(predict, argnums=2)(weight, bias, x)
-      1.23 ms
+      1.31 ms
       1 measurement, 500 runs , 1 thread
-    jacrev time: <torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1bdd030>
+    jacrev time: <torch.utils.benchmark.utils.common.Measurement object at 0x7fc876895990>
     jacrev(predict, argnums=2)(weight, bias, x)
-      13.41 ms
+      13.13 ms
       1 measurement, 500 runs , 1 thread
 
 
@@ -425,7 +425,7 @@ and then do a relative benchmark:
 
  .. code-block:: none
 
-     Performance delta: 985.8145 percent improvement with jacrev 
+     Performance delta: 904.8346 percent improvement with jacrev 
 
 
 
@@ -462,13 +462,13 @@ and now the reverse - more outputs (M) than inputs (N):
 
  .. code-block:: none
 
-    jacfwd time: <torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1a9a530>
+    jacfwd time: <torch.utils.benchmark.utils.common.Measurement object at 0x7fc8765927d0>
     jacfwd(predict, argnums=2)(weight, bias, x)
-      6.73 ms
+      8.65 ms
       1 measurement, 500 runs , 1 thread
-    jacrev time: <torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1a741c0>
+    jacrev time: <torch.utils.benchmark.utils.common.Measurement object at 0x7fc876573a90>
     jacrev(predict, argnums=2)(weight, bias, x)
-      783.92 us
+      822.61 us
       1 measurement, 500 runs , 1 thread
 
 
@@ -493,7 +493,7 @@ and a relative performance comparison:
 
  .. code-block:: none
 
-     Performance delta: 758.4283 percent improvement with jacfwd 
+     Performance delta: 951.6169 percent improvement with jacfwd 
 
 
 
@@ -749,7 +749,7 @@ instead compose reverse-mode AD with reverse-mode AD:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  13.847 seconds)
+   **Total running time of the script:** ( 0 minutes  14.813 seconds)
 
 
 .. _sphx_glr_download_intermediate_jacobians_hessians.py:
diff --git a/_sources/intermediate/mario_rl_tutorial.rst.txt b/_sources/intermediate/mario_rl_tutorial.rst.txt
index 017ca9286f..9246945efa 100644
--- a/_sources/intermediate/mario_rl_tutorial.rst.txt
+++ b/_sources/intermediate/mario_rl_tutorial.rst.txt
@@ -993,9 +993,9 @@ his world, we suggest running the loop for at least 40,000 episodes!
 
     Using CUDA: True
 
-    Episode 0 - Step 163 - Epsilon 0.9999592508251706 - Mean Reward 635.0 - Mean Length 163.0 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 1.995 - Time 2024-06-25T20:49:45
-    Episode 20 - Step 5007 - Epsilon 0.9987490329557962 - Mean Reward 667.429 - Mean Length 238.429 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 59.381 - Time 2024-06-25T20:50:45
-    Episode 39 - Step 8854 - Epsilon 0.9977889477081997 - Mean Reward 656.6 - Mean Length 221.35 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 47.646 - Time 2024-06-25T20:51:32
+    Episode 0 - Step 163 - Epsilon 0.9999592508251706 - Mean Reward 635.0 - Mean Length 163.0 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 2.018 - Time 2024-06-25T21:23:13
+    Episode 20 - Step 5007 - Epsilon 0.9987490329557962 - Mean Reward 667.429 - Mean Length 238.429 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 59.836 - Time 2024-06-25T21:24:12
+    Episode 39 - Step 8854 - Epsilon 0.9977889477081997 - Mean Reward 656.6 - Mean Length 221.35 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 48.305 - Time 2024-06-25T21:25:01
 
 
 
@@ -1012,7 +1012,7 @@ to train an AI to play any of the games at the `OpenAI gym <https://gym.openai.c
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  50.056 seconds)
+   **Total running time of the script:** ( 1 minutes  51.191 seconds)
 
 
 .. _sphx_glr_download_intermediate_mario_rl_tutorial.py:
diff --git a/_sources/intermediate/memory_format_tutorial.rst.txt b/_sources/intermediate/memory_format_tutorial.rst.txt
index 30d75122c3..985196f3a3 100644
--- a/_sources/intermediate/memory_format_tutorial.rst.txt
+++ b/_sources/intermediate/memory_format_tutorial.rst.txt
@@ -711,7 +711,7 @@ know by creating `an issue <https://github.com/pytorch/pytorch/issues>`_.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.064 seconds)
+   **Total running time of the script:** ( 0 minutes  0.062 seconds)
 
 
 .. _sphx_glr_download_intermediate_memory_format_tutorial.py:
diff --git a/_sources/intermediate/model_parallel_tutorial.rst.txt b/_sources/intermediate/model_parallel_tutorial.rst.txt
index 21996ae957..0c2e3777bf 100644
--- a/_sources/intermediate/model_parallel_tutorial.rst.txt
+++ b/_sources/intermediate/model_parallel_tutorial.rst.txt
@@ -485,7 +485,7 @@ inputs.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  42.800 seconds)
+   **Total running time of the script:** ( 2 minutes  42.102 seconds)
 
 
 .. _sphx_glr_download_intermediate_model_parallel_tutorial.py:
diff --git a/_sources/intermediate/neural_tangent_kernels.rst.txt b/_sources/intermediate/neural_tangent_kernels.rst.txt
index 5601466d7f..253e162bea 100644
--- a/_sources/intermediate/neural_tangent_kernels.rst.txt
+++ b/_sources/intermediate/neural_tangent_kernels.rst.txt
@@ -360,7 +360,7 @@ for details.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.538 seconds)
+   **Total running time of the script:** ( 0 minutes  0.528 seconds)
 
 
 .. _sphx_glr_download_intermediate_neural_tangent_kernels.py:
diff --git a/_sources/intermediate/optimizer_step_in_backward_tutorial.rst.txt b/_sources/intermediate/optimizer_step_in_backward_tutorial.rst.txt
index 9aba214f91..1a0cf03afe 100644
--- a/_sources/intermediate/optimizer_step_in_backward_tutorial.rst.txt
+++ b/_sources/intermediate/optimizer_step_in_backward_tutorial.rst.txt
@@ -68,127 +68,34 @@ but, again, feel free to substitute with your own optimizer.
     Downloading: "https://download.pytorch.org/models/vit_l_16-852ce7e3.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vit_l_16-852ce7e3.pth
 
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-     80%|########  | 934M/1.13G [00:17<00:04, 52.7MB/s]
-     82%|########1 | 950M/1.13G [00:17<00:03, 64.9MB/s]
-     83%|########3 | 965M/1.13G [00:17<00:02, 73.9MB/s]
-     84%|########3 | 972M/1.13G [00:17<00:02, 67.7MB/s]
-     85%|########4 | 983M/1.13G [00:17<00:02, 63.9MB/s]
-     85%|########5 | 989M/1.13G [00:17<00:03, 58.8MB/s]
-     86%|########5 | 998M/1.13G [00:18<00:02, 64.1MB/s]
-     86%|########6 | 0.98G/1.13G [00:18<00:03, 54.6MB/s]
-     87%|########7 | 0.99G/1.13G [00:18<00:03, 48.0MB/s]
-     88%|########7 | 1.00G/1.13G [00:18<00:04, 33.5MB/s]
-     89%|########8 | 1.01G/1.13G [00:19<00:03, 39.8MB/s]
-     89%|########9 | 1.01G/1.13G [00:19<00:03, 39.1MB/s]
-     90%|######### | 1.02G/1.13G [00:19<00:02, 42.1MB/s]
-     91%|######### | 1.03G/1.13G [00:19<00:02, 39.8MB/s]
-     92%|#########1| 1.04G/1.13G [00:19<00:01, 53.0MB/s]
-     92%|#########2| 1.04G/1.13G [00:19<00:02, 46.8MB/s]
-     93%|#########3| 1.05G/1.13G [00:20<00:01, 52.8MB/s]
-     93%|#########3| 1.06G/1.13G [00:20<00:01, 48.6MB/s]
-     94%|#########4| 1.07G/1.13G [00:20<00:01, 54.1MB/s]
-     95%|#########4| 1.08G/1.13G [00:20<00:01, 55.1MB/s]
-     96%|#########5| 1.09G/1.13G [00:20<00:00, 69.4MB/s]
-     97%|#########6| 1.10G/1.13G [00:20<00:00, 79.6MB/s]
-     98%|#########7| 1.11G/1.13G [00:20<00:00, 55.1MB/s]
-     99%|#########8| 1.12G/1.13G [00:21<00:00, 63.2MB/s]
-     99%|#########9| 1.13G/1.13G [00:21<00:00, 58.3MB/s]
-    100%|##########| 1.13G/1.13G [00:21<00:00, 57.1MB/s]
+      3%|2         | 31.5M/1.13G [00:00<00:03, 322MB/s]
+      6%|5         | 66.9M/1.13G [00:00<00:03, 350MB/s]
+      9%|9         | 105M/1.13G [00:00<00:02, 373MB/s] 
+     12%|#2        | 142M/1.13G [00:00<00:02, 377MB/s]
+     16%|#5        | 181M/1.13G [00:00<00:02, 389MB/s]
+     19%|#9        | 222M/1.13G [00:00<00:02, 402MB/s]
+     22%|##2       | 261M/1.13G [00:00<00:02, 402MB/s]
+     26%|##5       | 299M/1.13G [00:00<00:02, 402MB/s]
+     29%|##9       | 338M/1.13G [00:00<00:02, 402MB/s]
+     33%|###2      | 382M/1.13G [00:01<00:01, 422MB/s]
+     36%|###6      | 424M/1.13G [00:01<00:01, 425MB/s]
+     40%|###9      | 464M/1.13G [00:01<00:01, 413MB/s]
+     44%|####3     | 510M/1.13G [00:01<00:01, 431MB/s]
+     48%|####7     | 555M/1.13G [00:01<00:01, 443MB/s]
+     52%|#####1    | 598M/1.13G [00:01<00:01, 447MB/s]
+     55%|#####5    | 641M/1.13G [00:01<00:01, 449MB/s]
+     59%|#####8    | 684M/1.13G [00:01<00:01, 441MB/s]
+     63%|######2   | 726M/1.13G [00:01<00:01, 421MB/s]
+     66%|######6   | 770M/1.13G [00:01<00:00, 430MB/s]
+     70%|#######   | 814M/1.13G [00:02<00:00, 441MB/s]
+     74%|#######4  | 860M/1.13G [00:02<00:00, 450MB/s]
+     78%|#######7  | 904M/1.13G [00:02<00:00, 456MB/s]
+     82%|########1 | 950M/1.13G [00:02<00:00, 462MB/s]
+     86%|########5 | 994M/1.13G [00:02<00:00, 449MB/s]
+     89%|########9 | 1.01G/1.13G [00:02<00:00, 456MB/s]
+     93%|#########3| 1.06G/1.13G [00:02<00:00, 438MB/s]
+     97%|#########6| 1.10G/1.13G [00:02<00:00, 427MB/s]
+    100%|##########| 1.13G/1.13G [00:02<00:00, 425MB/s]
 
 
 
@@ -480,7 +387,7 @@ about memory snapshots, which are generally useful in memory optimization.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  28.177 seconds)
+   **Total running time of the script:** ( 0 minutes  8.820 seconds)
 
 
 .. _sphx_glr_download_intermediate_optimizer_step_in_backward_tutorial.py:
diff --git a/_sources/intermediate/parametrizations.rst.txt b/_sources/intermediate/parametrizations.rst.txt
index aad78c8e44..9061c29a87 100644
--- a/_sources/intermediate/parametrizations.rst.txt
+++ b/_sources/intermediate/parametrizations.rst.txt
@@ -939,7 +939,7 @@ the flag ``leave_parametrized=False``
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.057 seconds)
+   **Total running time of the script:** ( 0 minutes  0.052 seconds)
 
 
 .. _sphx_glr_download_intermediate_parametrizations.py:
diff --git a/_sources/intermediate/per_sample_grads.rst.txt b/_sources/intermediate/per_sample_grads.rst.txt
index d3420332ac..540b501a23 100644
--- a/_sources/intermediate/per_sample_grads.rst.txt
+++ b/_sources/intermediate/per_sample_grads.rst.txt
@@ -389,15 +389,15 @@ some results on our build machines:
 
  .. code-block:: none
 
-    Per-sample-grads without vmap <torch.utils.benchmark.utils.common.Measurement object at 0x7f0799583820>
+    Per-sample-grads without vmap <torch.utils.benchmark.utils.common.Measurement object at 0x7f3ba69d0c10>
     compute_sample_grads(data, targets)
-      57.70 ms
+      57.50 ms
       1 measurement, 100 runs , 1 thread
-    Per-sample-grads with vmap <torch.utils.benchmark.utils.common.Measurement object at 0x7f07995180d0>
+    Per-sample-grads with vmap <torch.utils.benchmark.utils.common.Measurement object at 0x7f3ba6980160>
     ft_compute_sample_grad(params, buffers, data, targets)
-      2.88 ms
+      2.84 ms
       1 measurement, 100 runs , 1 thread
-    Performance delta: 1904.7805 percent improvement with vmap 
+    Performance delta: 1921.5607 percent improvement with vmap 
 
 
 
@@ -419,7 +419,7 @@ at on GitHub.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  7.223 seconds)
+   **Total running time of the script:** ( 0 minutes  7.194 seconds)
 
 
 .. _sphx_glr_download_intermediate_per_sample_grads.py:
diff --git a/_sources/intermediate/pruning_tutorial.rst.txt b/_sources/intermediate/pruning_tutorial.rst.txt
index 64dc2ac572..ed511ba664 100644
--- a/_sources/intermediate/pruning_tutorial.rst.txt
+++ b/_sources/intermediate/pruning_tutorial.rst.txt
@@ -459,7 +459,7 @@ present.
 
  .. code-block:: none
 
-    OrderedDict([(3, <torch.nn.utils.prune.RandomUnstructured object at 0x7f31756338b0>)])
+    OrderedDict([(3, <torch.nn.utils.prune.RandomUnstructured object at 0x7ff77eb07400>)])
 
 
 
@@ -652,7 +652,7 @@ module attributes, and the module will now have two ``forward_pre_hooks``.
 
  .. code-block:: none
 
-    OrderedDict([(3, <torch.nn.utils.prune.RandomUnstructured object at 0x7f31756338b0>), (4, <torch.nn.utils.prune.L1Unstructured object at 0x7f31756338e0>)])
+    OrderedDict([(3, <torch.nn.utils.prune.RandomUnstructured object at 0x7ff77eb07400>), (4, <torch.nn.utils.prune.L1Unstructured object at 0x7ff77eb07eb0>)])
 
 
 
@@ -762,7 +762,7 @@ pruning applied to the ``weight`` parameter.
 
  .. code-block:: none
 
-    [<torch.nn.utils.prune.RandomUnstructured object at 0x7f31756338b0>, <torch.nn.utils.prune.LnStructured object at 0x7f3175633cd0>]
+    [<torch.nn.utils.prune.RandomUnstructured object at 0x7ff77eb07400>, <torch.nn.utils.prune.LnStructured object at 0x7ff77eb07c40>]
 
 
 
@@ -1359,7 +1359,7 @@ Let's try it out!
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.218 seconds)
+   **Total running time of the script:** ( 0 minutes  0.211 seconds)
 
 
 .. _sphx_glr_download_intermediate_pruning_tutorial.py:
diff --git a/_sources/intermediate/reinforcement_ppo.rst.txt b/_sources/intermediate/reinforcement_ppo.rst.txt
index cc55e6c1c4..db25f70932 100644
--- a/_sources/intermediate/reinforcement_ppo.rst.txt
+++ b/_sources/intermediate/reinforcement_ppo.rst.txt
@@ -1046,106 +1046,106 @@ The steps include:
 
 
       0%|          | 0/50000 [00:00<?, ?it/s]
-      2%|2         | 1000/50000 [00:04<04:03, 201.03it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.0852 (init= 9.0852), step count (max): 10, lr policy:  0.0003:   2%|2         | 1000/50000 [00:05<04:03, 201.03it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.0852 (init= 9.0852), step count (max): 10, lr policy:  0.0003:   4%|4         | 2000/50000 [00:09<03:58, 201.58it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1155 (init= 9.0852), step count (max): 14, lr policy:  0.0003:   4%|4         | 2000/50000 [00:09<03:58, 201.58it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1155 (init= 9.0852), step count (max): 14, lr policy:  0.0003:   6%|6         | 3000/50000 [00:14<03:51, 202.60it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1584 (init= 9.0852), step count (max): 18, lr policy:  0.0003:   6%|6         | 3000/50000 [00:14<03:51, 202.60it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1584 (init= 9.0852), step count (max): 18, lr policy:  0.0003:   8%|8         | 4000/50000 [00:19<03:46, 203.40it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1788 (init= 9.0852), step count (max): 23, lr policy:  0.0003:   8%|8         | 4000/50000 [00:19<03:46, 203.40it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1788 (init= 9.0852), step count (max): 23, lr policy:  0.0003:  10%|#         | 5000/50000 [00:24<03:43, 201.45it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1961 (init= 9.0852), step count (max): 23, lr policy:  0.0003:  10%|#         | 5000/50000 [00:24<03:43, 201.45it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1961 (init= 9.0852), step count (max): 23, lr policy:  0.0003:  12%|#2        | 6000/50000 [00:29<03:36, 202.94it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2111 (init= 9.0852), step count (max): 27, lr policy:  0.0003:  12%|#2        | 6000/50000 [00:29<03:36, 202.94it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2111 (init= 9.0852), step count (max): 27, lr policy:  0.0003:  14%|#4        | 7000/50000 [00:34<03:31, 203.68it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2304 (init= 9.0852), step count (max): 38, lr policy:  0.0003:  14%|#4        | 7000/50000 [00:34<03:31, 203.68it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2304 (init= 9.0852), step count (max): 38, lr policy:  0.0003:  16%|#6        | 8000/50000 [00:39<03:25, 204.42it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2350 (init= 9.0852), step count (max): 48, lr policy:  0.0003:  16%|#6        | 8000/50000 [00:39<03:25, 204.42it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2350 (init= 9.0852), step count (max): 48, lr policy:  0.0003:  18%|#8        | 9000/50000 [00:44<03:19, 205.22it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2473 (init= 9.0852), step count (max): 60, lr policy:  0.0003:  18%|#8        | 9000/50000 [00:44<03:19, 205.22it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2473 (init= 9.0852), step count (max): 60, lr policy:  0.0003:  20%|##        | 10000/50000 [00:49<03:14, 205.92it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2587 (init= 9.0852), step count (max): 47, lr policy:  0.0003:  20%|##        | 10000/50000 [00:49<03:14, 205.92it/s]
-    eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2587 (init= 9.0852), step count (max): 47, lr policy:  0.0003:  22%|##2       | 11000/50000 [00:53<03:08, 206.55it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2583 (init= 9.0852), step count (max): 61, lr policy:  0.0003:  22%|##2       | 11000/50000 [00:53<03:08, 206.55it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2583 (init= 9.0852), step count (max): 61, lr policy:  0.0003:  24%|##4       | 12000/50000 [00:58<03:06, 203.79it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2613 (init= 9.0852), step count (max): 57, lr policy:  0.0003:  24%|##4       | 12000/50000 [00:58<03:06, 203.79it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2613 (init= 9.0852), step count (max): 57, lr policy:  0.0003:  26%|##6       | 13000/50000 [01:03<03:00, 204.90it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2651 (init= 9.0852), step count (max): 65, lr policy:  0.0003:  26%|##6       | 13000/50000 [01:03<03:00, 204.90it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2651 (init= 9.0852), step count (max): 65, lr policy:  0.0003:  28%|##8       | 14000/50000 [01:08<02:54, 205.79it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2737 (init= 9.0852), step count (max): 73, lr policy:  0.0003:  28%|##8       | 14000/50000 [01:08<02:54, 205.79it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2737 (init= 9.0852), step count (max): 73, lr policy:  0.0003:  30%|###       | 15000/50000 [01:13<02:49, 206.24it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2758 (init= 9.0852), step count (max): 66, lr policy:  0.0002:  30%|###       | 15000/50000 [01:13<02:49, 206.24it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2758 (init= 9.0852), step count (max): 66, lr policy:  0.0002:  32%|###2      | 16000/50000 [01:18<02:44, 206.71it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2918 (init= 9.0852), step count (max): 110, lr policy:  0.0002:  32%|###2      | 16000/50000 [01:18<02:44, 206.71it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2918 (init= 9.0852), step count (max): 110, lr policy:  0.0002:  34%|###4      | 17000/50000 [01:22<02:39, 207.02it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2895 (init= 9.0852), step count (max): 79, lr policy:  0.0002:  34%|###4      | 17000/50000 [01:22<02:39, 207.02it/s] 
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2895 (init= 9.0852), step count (max): 79, lr policy:  0.0002:  36%|###6      | 18000/50000 [01:27<02:36, 204.79it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2775 (init= 9.0852), step count (max): 60, lr policy:  0.0002:  36%|###6      | 18000/50000 [01:27<02:36, 204.79it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2775 (init= 9.0852), step count (max): 60, lr policy:  0.0002:  38%|###8      | 19000/50000 [01:32<02:31, 205.13it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2841 (init= 9.0852), step count (max): 50, lr policy:  0.0002:  38%|###8      | 19000/50000 [01:32<02:31, 205.13it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2841 (init= 9.0852), step count (max): 50, lr policy:  0.0002:  40%|####      | 20000/50000 [01:37<02:25, 205.97it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2983 (init= 9.0852), step count (max): 92, lr policy:  0.0002:  40%|####      | 20000/50000 [01:37<02:25, 205.97it/s]
-    eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2983 (init= 9.0852), step count (max): 92, lr policy:  0.0002:  42%|####2     | 21000/50000 [01:42<02:20, 206.38it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2955 (init= 9.0852), step count (max): 74, lr policy:  0.0002:  42%|####2     | 21000/50000 [01:42<02:20, 206.38it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2955 (init= 9.0852), step count (max): 74, lr policy:  0.0002:  44%|####4     | 22000/50000 [01:47<02:16, 205.64it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2818 (init= 9.0852), step count (max): 54, lr policy:  0.0002:  44%|####4     | 22000/50000 [01:47<02:16, 205.64it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2818 (init= 9.0852), step count (max): 54, lr policy:  0.0002:  46%|####6     | 23000/50000 [01:52<02:10, 206.21it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2908 (init= 9.0852), step count (max): 64, lr policy:  0.0002:  46%|####6     | 23000/50000 [01:52<02:10, 206.21it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2908 (init= 9.0852), step count (max): 64, lr policy:  0.0002:  48%|####8     | 24000/50000 [01:56<02:05, 206.63it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.3003 (init= 9.0852), step count (max): 82, lr policy:  0.0002:  48%|####8     | 24000/50000 [01:56<02:05, 206.63it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.3003 (init= 9.0852), step count (max): 82, lr policy:  0.0002:  50%|#####     | 25000/50000 [02:01<02:02, 204.46it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2980 (init= 9.0852), step count (max): 80, lr policy:  0.0002:  50%|#####     | 25000/50000 [02:01<02:02, 204.46it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2980 (init= 9.0852), step count (max): 80, lr policy:  0.0002:  52%|#####2    | 26000/50000 [02:06<01:56, 205.34it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.3047 (init= 9.0852), step count (max): 109, lr policy:  0.0001:  52%|#####2    | 26000/50000 [02:06<01:56, 205.34it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.3047 (init= 9.0852), step count (max): 109, lr policy:  0.0001:  54%|#####4    | 27000/50000 [02:11<01:51, 205.62it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2958 (init= 9.0852), step count (max): 67, lr policy:  0.0001:  54%|#####4    | 27000/50000 [02:11<01:51, 205.62it/s] 
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2958 (init= 9.0852), step count (max): 67, lr policy:  0.0001:  56%|#####6    | 28000/50000 [02:16<01:47, 205.55it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2958 (init= 9.0852), step count (max): 76, lr policy:  0.0001:  56%|#####6    | 28000/50000 [02:16<01:47, 205.55it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2958 (init= 9.0852), step count (max): 76, lr policy:  0.0001:  58%|#####8    | 29000/50000 [02:21<01:42, 205.79it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2914 (init= 9.0852), step count (max): 68, lr policy:  0.0001:  58%|#####8    | 29000/50000 [02:21<01:42, 205.79it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2914 (init= 9.0852), step count (max): 68, lr policy:  0.0001:  60%|######    | 30000/50000 [02:26<01:37, 205.97it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2848 (init= 9.0852), step count (max): 57, lr policy:  0.0001:  60%|######    | 30000/50000 [02:26<01:37, 205.97it/s]
-    eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2848 (init= 9.0852), step count (max): 57, lr policy:  0.0001:  62%|######2   | 31000/50000 [02:31<01:32, 206.32it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2850 (init= 9.0852), step count (max): 58, lr policy:  0.0001:  62%|######2   | 31000/50000 [02:31<01:32, 206.32it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2850 (init= 9.0852), step count (max): 58, lr policy:  0.0001:  64%|######4   | 32000/50000 [02:36<01:28, 202.65it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2849 (init= 9.0852), step count (max): 59, lr policy:  0.0001:  64%|######4   | 32000/50000 [02:36<01:28, 202.65it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2849 (init= 9.0852), step count (max): 59, lr policy:  0.0001:  66%|######6   | 33000/50000 [02:40<01:23, 204.11it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2935 (init= 9.0852), step count (max): 63, lr policy:  0.0001:  66%|######6   | 33000/50000 [02:41<01:23, 204.11it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2935 (init= 9.0852), step count (max): 63, lr policy:  0.0001:  68%|######8   | 34000/50000 [02:45<01:18, 205.06it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2876 (init= 9.0852), step count (max): 63, lr policy:  0.0001:  68%|######8   | 34000/50000 [02:45<01:18, 205.06it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2876 (init= 9.0852), step count (max): 63, lr policy:  0.0001:  70%|#######   | 35000/50000 [02:50<01:12, 205.62it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2912 (init= 9.0852), step count (max): 83, lr policy:  0.0001:  70%|#######   | 35000/50000 [02:50<01:12, 205.62it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2912 (init= 9.0852), step count (max): 83, lr policy:  0.0001:  72%|#######2  | 36000/50000 [02:55<01:07, 206.24it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2952 (init= 9.0852), step count (max): 66, lr policy:  0.0001:  72%|#######2  | 36000/50000 [02:55<01:07, 206.24it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2952 (init= 9.0852), step count (max): 66, lr policy:  0.0001:  74%|#######4  | 37000/50000 [03:00<01:02, 206.51it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2947 (init= 9.0852), step count (max): 76, lr policy:  0.0001:  74%|#######4  | 37000/50000 [03:00<01:02, 206.51it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2947 (init= 9.0852), step count (max): 76, lr policy:  0.0001:  76%|#######6  | 38000/50000 [03:05<00:58, 206.78it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.3014 (init= 9.0852), step count (max): 87, lr policy:  0.0000:  76%|#######6  | 38000/50000 [03:05<00:58, 206.78it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.3014 (init= 9.0852), step count (max): 87, lr policy:  0.0000:  78%|#######8  | 39000/50000 [03:10<00:53, 204.68it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2982 (init= 9.0852), step count (max): 87, lr policy:  0.0000:  78%|#######8  | 39000/50000 [03:10<00:53, 204.68it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2982 (init= 9.0852), step count (max): 87, lr policy:  0.0000:  80%|########  | 40000/50000 [03:14<00:48, 205.46it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.3103 (init= 9.0852), step count (max): 109, lr policy:  0.0000:  80%|########  | 40000/50000 [03:14<00:48, 205.46it/s]
-    eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.3103 (init= 9.0852), step count (max): 109, lr policy:  0.0000:  82%|########2 | 41000/50000 [03:19<00:43, 206.00it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3071 (init= 9.0852), step count (max): 90, lr policy:  0.0000:  82%|########2 | 41000/50000 [03:19<00:43, 206.00it/s] 
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3071 (init= 9.0852), step count (max): 90, lr policy:  0.0000:  84%|########4 | 42000/50000 [03:24<00:39, 204.74it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3171 (init= 9.0852), step count (max): 107, lr policy:  0.0000:  84%|########4 | 42000/50000 [03:24<00:39, 204.74it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3171 (init= 9.0852), step count (max): 107, lr policy:  0.0000:  86%|########6 | 43000/50000 [03:29<00:34, 205.55it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3191 (init= 9.0852), step count (max): 109, lr policy:  0.0000:  86%|########6 | 43000/50000 [03:29<00:34, 205.55it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3191 (init= 9.0852), step count (max): 109, lr policy:  0.0000:  88%|########8 | 44000/50000 [03:34<00:29, 206.19it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3165 (init= 9.0852), step count (max): 102, lr policy:  0.0000:  88%|########8 | 44000/50000 [03:34<00:29, 206.19it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3165 (init= 9.0852), step count (max): 102, lr policy:  0.0000:  90%|######### | 45000/50000 [03:39<00:24, 206.50it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3110 (init= 9.0852), step count (max): 133, lr policy:  0.0000:  90%|######### | 45000/50000 [03:39<00:24, 206.50it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3110 (init= 9.0852), step count (max): 133, lr policy:  0.0000:  92%|#########2| 46000/50000 [03:44<00:19, 203.96it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3312 (init= 9.0852), step count (max): 120, lr policy:  0.0000:  92%|#########2| 46000/50000 [03:44<00:19, 203.96it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3312 (init= 9.0852), step count (max): 120, lr policy:  0.0000:  94%|#########3| 47000/50000 [03:49<00:14, 205.11it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3221 (init= 9.0852), step count (max): 148, lr policy:  0.0000:  94%|#########3| 47000/50000 [03:49<00:14, 205.11it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3221 (init= 9.0852), step count (max): 148, lr policy:  0.0000:  96%|#########6| 48000/50000 [03:53<00:09, 205.82it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3130 (init= 9.0852), step count (max): 125, lr policy:  0.0000:  96%|#########6| 48000/50000 [03:53<00:09, 205.82it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3130 (init= 9.0852), step count (max): 125, lr policy:  0.0000:  98%|#########8| 49000/50000 [03:58<00:04, 206.29it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3154 (init= 9.0852), step count (max): 107, lr policy:  0.0000:  98%|#########8| 49000/50000 [03:58<00:04, 206.29it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3154 (init= 9.0852), step count (max): 107, lr policy:  0.0000: 100%|##########| 50000/50000 [04:03<00:00, 206.57it/s]
-    eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3208 (init= 9.0852), step count (max): 154, lr policy:  0.0000: 100%|##########| 50000/50000 [04:03<00:00, 206.57it/s]
+      2%|2         | 1000/50000 [00:05<04:18, 189.23it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.0863 (init= 9.0863), step count (max): 11, lr policy:  0.0003:   2%|2         | 1000/50000 [00:05<04:18, 189.23it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.0863 (init= 9.0863), step count (max): 11, lr policy:  0.0003:   4%|4         | 2000/50000 [00:10<04:11, 191.10it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.1145 (init= 9.0863), step count (max): 12, lr policy:  0.0003:   4%|4         | 2000/50000 [00:10<04:11, 191.10it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.1145 (init= 9.0863), step count (max): 12, lr policy:  0.0003:   6%|6         | 3000/50000 [00:15<04:04, 192.60it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.1573 (init= 9.0863), step count (max): 17, lr policy:  0.0003:   6%|6         | 3000/50000 [00:15<04:04, 192.60it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.1573 (init= 9.0863), step count (max): 17, lr policy:  0.0003:   8%|8         | 4000/50000 [00:20<04:02, 189.96it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.1709 (init= 9.0863), step count (max): 15, lr policy:  0.0003:   8%|8         | 4000/50000 [00:21<04:02, 189.96it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.1709 (init= 9.0863), step count (max): 15, lr policy:  0.0003:  10%|#         | 5000/50000 [00:26<03:55, 191.26it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2157 (init= 9.0863), step count (max): 25, lr policy:  0.0003:  10%|#         | 5000/50000 [00:26<03:55, 191.26it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2157 (init= 9.0863), step count (max): 25, lr policy:  0.0003:  12%|#2        | 6000/50000 [00:31<03:48, 192.35it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2275 (init= 9.0863), step count (max): 32, lr policy:  0.0003:  12%|#2        | 6000/50000 [00:31<03:48, 192.35it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2275 (init= 9.0863), step count (max): 32, lr policy:  0.0003:  14%|#4        | 7000/50000 [00:36<03:42, 193.57it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2404 (init= 9.0863), step count (max): 29, lr policy:  0.0003:  14%|#4        | 7000/50000 [00:36<03:42, 193.57it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2404 (init= 9.0863), step count (max): 29, lr policy:  0.0003:  16%|#6        | 8000/50000 [00:41<03:36, 194.11it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2507 (init= 9.0863), step count (max): 41, lr policy:  0.0003:  16%|#6        | 8000/50000 [00:41<03:36, 194.11it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2507 (init= 9.0863), step count (max): 41, lr policy:  0.0003:  18%|#8        | 9000/50000 [00:46<03:30, 194.35it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2516 (init= 9.0863), step count (max): 44, lr policy:  0.0003:  18%|#8        | 9000/50000 [00:46<03:30, 194.35it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2516 (init= 9.0863), step count (max): 44, lr policy:  0.0003:  20%|##        | 10000/50000 [00:51<03:27, 192.74it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2501 (init= 9.0863), step count (max): 37, lr policy:  0.0003:  20%|##        | 10000/50000 [00:51<03:27, 192.74it/s]
+    eval cumulative reward:  92.0841 (init:  92.0841), eval step-count: 9, average reward= 9.2501 (init= 9.0863), step count (max): 37, lr policy:  0.0003:  22%|##2       | 11000/50000 [00:56<03:20, 194.32it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2563 (init= 9.0863), step count (max): 52, lr policy:  0.0003:  22%|##2       | 11000/50000 [00:57<03:20, 194.32it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2563 (init= 9.0863), step count (max): 52, lr policy:  0.0003:  24%|##4       | 12000/50000 [01:02<03:15, 194.30it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2680 (init= 9.0863), step count (max): 53, lr policy:  0.0003:  24%|##4       | 12000/50000 [01:02<03:15, 194.30it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2680 (init= 9.0863), step count (max): 53, lr policy:  0.0003:  26%|##6       | 13000/50000 [01:07<03:10, 194.43it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2547 (init= 9.0863), step count (max): 44, lr policy:  0.0003:  26%|##6       | 13000/50000 [01:07<03:10, 194.43it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2547 (init= 9.0863), step count (max): 44, lr policy:  0.0003:  28%|##8       | 14000/50000 [01:12<03:04, 194.81it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2742 (init= 9.0863), step count (max): 64, lr policy:  0.0003:  28%|##8       | 14000/50000 [01:12<03:04, 194.81it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2742 (init= 9.0863), step count (max): 64, lr policy:  0.0003:  30%|###       | 15000/50000 [01:17<02:58, 195.65it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2724 (init= 9.0863), step count (max): 54, lr policy:  0.0002:  30%|###       | 15000/50000 [01:17<02:58, 195.65it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2724 (init= 9.0863), step count (max): 54, lr policy:  0.0002:  32%|###2      | 16000/50000 [01:22<02:53, 196.31it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2681 (init= 9.0863), step count (max): 54, lr policy:  0.0002:  32%|###2      | 16000/50000 [01:22<02:53, 196.31it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2681 (init= 9.0863), step count (max): 54, lr policy:  0.0002:  34%|###4      | 17000/50000 [01:27<02:50, 193.93it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2653 (init= 9.0863), step count (max): 51, lr policy:  0.0002:  34%|###4      | 17000/50000 [01:27<02:50, 193.93it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2653 (init= 9.0863), step count (max): 51, lr policy:  0.0002:  36%|###6      | 18000/50000 [01:32<02:44, 194.56it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2845 (init= 9.0863), step count (max): 70, lr policy:  0.0002:  36%|###6      | 18000/50000 [01:32<02:44, 194.56it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2845 (init= 9.0863), step count (max): 70, lr policy:  0.0002:  38%|###8      | 19000/50000 [01:37<02:38, 195.57it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2825 (init= 9.0863), step count (max): 56, lr policy:  0.0002:  38%|###8      | 19000/50000 [01:37<02:38, 195.57it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2825 (init= 9.0863), step count (max): 56, lr policy:  0.0002:  40%|####      | 20000/50000 [01:43<02:32, 196.15it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2832 (init= 9.0863), step count (max): 56, lr policy:  0.0002:  40%|####      | 20000/50000 [01:43<02:32, 196.15it/s]
+    eval cumulative reward:  297.9816 (init:  92.0841), eval step-count: 31, average reward= 9.2832 (init= 9.0863), step count (max): 56, lr policy:  0.0002:  42%|####2     | 21000/50000 [01:48<02:27, 196.00it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.2875 (init= 9.0863), step count (max): 102, lr policy:  0.0002:  42%|####2     | 21000/50000 [01:48<02:27, 196.00it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.2875 (init= 9.0863), step count (max): 102, lr policy:  0.0002:  44%|####4     | 22000/50000 [01:53<02:23, 194.89it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.2951 (init= 9.0863), step count (max): 61, lr policy:  0.0002:  44%|####4     | 22000/50000 [01:53<02:23, 194.89it/s] 
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.2951 (init= 9.0863), step count (max): 61, lr policy:  0.0002:  46%|####6     | 23000/50000 [01:58<02:19, 193.78it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.2975 (init= 9.0863), step count (max): 92, lr policy:  0.0002:  46%|####6     | 23000/50000 [01:58<02:19, 193.78it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.2975 (init= 9.0863), step count (max): 92, lr policy:  0.0002:  48%|####8     | 24000/50000 [02:03<02:13, 195.05it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.2941 (init= 9.0863), step count (max): 91, lr policy:  0.0002:  48%|####8     | 24000/50000 [02:03<02:13, 195.05it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.2941 (init= 9.0863), step count (max): 91, lr policy:  0.0002:  50%|#####     | 25000/50000 [02:08<02:08, 194.94it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3060 (init= 9.0863), step count (max): 136, lr policy:  0.0002:  50%|#####     | 25000/50000 [02:08<02:08, 194.94it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3060 (init= 9.0863), step count (max): 136, lr policy:  0.0002:  52%|#####2    | 26000/50000 [02:13<02:02, 195.16it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3030 (init= 9.0863), step count (max): 115, lr policy:  0.0001:  52%|#####2    | 26000/50000 [02:13<02:02, 195.16it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3030 (init= 9.0863), step count (max): 115, lr policy:  0.0001:  54%|#####4    | 27000/50000 [02:18<01:57, 195.83it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3013 (init= 9.0863), step count (max): 90, lr policy:  0.0001:  54%|#####4    | 27000/50000 [02:18<01:57, 195.83it/s] 
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3013 (init= 9.0863), step count (max): 90, lr policy:  0.0001:  56%|#####6    | 28000/50000 [02:23<01:52, 196.32it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3049 (init= 9.0863), step count (max): 85, lr policy:  0.0001:  56%|#####6    | 28000/50000 [02:23<01:52, 196.32it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3049 (init= 9.0863), step count (max): 85, lr policy:  0.0001:  58%|#####8    | 29000/50000 [02:29<01:47, 196.26it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3137 (init= 9.0863), step count (max): 109, lr policy:  0.0001:  58%|#####8    | 29000/50000 [02:29<01:47, 196.26it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3137 (init= 9.0863), step count (max): 109, lr policy:  0.0001:  60%|######    | 30000/50000 [02:34<01:43, 193.69it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3097 (init= 9.0863), step count (max): 122, lr policy:  0.0001:  60%|######    | 30000/50000 [02:34<01:43, 193.69it/s]
+    eval cumulative reward:  381.7977 (init:  92.0841), eval step-count: 40, average reward= 9.3097 (init= 9.0863), step count (max): 122, lr policy:  0.0001:  62%|######2   | 31000/50000 [02:39<01:37, 194.75it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3122 (init= 9.0863), step count (max): 98, lr policy:  0.0001:  62%|######2   | 31000/50000 [02:39<01:37, 194.75it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3122 (init= 9.0863), step count (max): 98, lr policy:  0.0001:  64%|######4   | 32000/50000 [02:44<01:33, 192.23it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3091 (init= 9.0863), step count (max): 126, lr policy:  0.0001:  64%|######4   | 32000/50000 [02:44<01:33, 192.23it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3091 (init= 9.0863), step count (max): 126, lr policy:  0.0001:  66%|######6   | 33000/50000 [02:49<01:27, 193.79it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3136 (init= 9.0863), step count (max): 109, lr policy:  0.0001:  66%|######6   | 33000/50000 [02:49<01:27, 193.79it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3136 (init= 9.0863), step count (max): 109, lr policy:  0.0001:  68%|######8   | 34000/50000 [02:54<01:22, 194.48it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3128 (init= 9.0863), step count (max): 116, lr policy:  0.0001:  68%|######8   | 34000/50000 [02:54<01:22, 194.48it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3128 (init= 9.0863), step count (max): 116, lr policy:  0.0001:  70%|#######   | 35000/50000 [03:00<01:16, 194.86it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3108 (init= 9.0863), step count (max): 82, lr policy:  0.0001:  70%|#######   | 35000/50000 [03:00<01:16, 194.86it/s] 
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3108 (init= 9.0863), step count (max): 82, lr policy:  0.0001:  72%|#######2  | 36000/50000 [03:05<01:12, 193.62it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3166 (init= 9.0863), step count (max): 114, lr policy:  0.0001:  72%|#######2  | 36000/50000 [03:05<01:12, 193.62it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3166 (init= 9.0863), step count (max): 114, lr policy:  0.0001:  74%|#######4  | 37000/50000 [03:10<01:06, 194.81it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3136 (init= 9.0863), step count (max): 165, lr policy:  0.0001:  74%|#######4  | 37000/50000 [03:10<01:06, 194.81it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3136 (init= 9.0863), step count (max): 165, lr policy:  0.0001:  76%|#######6  | 38000/50000 [03:15<01:01, 195.26it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3138 (init= 9.0863), step count (max): 117, lr policy:  0.0000:  76%|#######6  | 38000/50000 [03:15<01:01, 195.26it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3138 (init= 9.0863), step count (max): 117, lr policy:  0.0000:  78%|#######8  | 39000/50000 [03:20<00:56, 195.74it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3094 (init= 9.0863), step count (max): 100, lr policy:  0.0000:  78%|#######8  | 39000/50000 [03:20<00:56, 195.74it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3094 (init= 9.0863), step count (max): 100, lr policy:  0.0000:  80%|########  | 40000/50000 [03:25<00:51, 196.03it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3165 (init= 9.0863), step count (max): 164, lr policy:  0.0000:  80%|########  | 40000/50000 [03:25<00:51, 196.03it/s]
+    eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3165 (init= 9.0863), step count (max): 164, lr policy:  0.0000:  82%|########2 | 41000/50000 [03:30<00:45, 196.18it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3179 (init= 9.0863), step count (max): 189, lr policy:  0.0000:  82%|########2 | 41000/50000 [03:30<00:45, 196.18it/s]  
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3179 (init= 9.0863), step count (max): 189, lr policy:  0.0000:  84%|########4 | 42000/50000 [03:36<00:41, 192.22it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3320 (init= 9.0863), step count (max): 128, lr policy:  0.0000:  84%|########4 | 42000/50000 [03:36<00:41, 192.22it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3320 (init= 9.0863), step count (max): 128, lr policy:  0.0000:  86%|########6 | 43000/50000 [03:41<00:36, 193.16it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3237 (init= 9.0863), step count (max): 206, lr policy:  0.0000:  86%|########6 | 43000/50000 [03:41<00:36, 193.16it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3237 (init= 9.0863), step count (max): 206, lr policy:  0.0000:  88%|########8 | 44000/50000 [03:46<00:30, 194.54it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3119 (init= 9.0863), step count (max): 117, lr policy:  0.0000:  88%|########8 | 44000/50000 [03:46<00:30, 194.54it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3119 (init= 9.0863), step count (max): 117, lr policy:  0.0000:  90%|######### | 45000/50000 [03:51<00:25, 195.59it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3173 (init= 9.0863), step count (max): 213, lr policy:  0.0000:  90%|######### | 45000/50000 [03:51<00:25, 195.59it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3173 (init= 9.0863), step count (max): 213, lr policy:  0.0000:  92%|#########2| 46000/50000 [03:56<00:20, 195.84it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3211 (init= 9.0863), step count (max): 275, lr policy:  0.0000:  92%|#########2| 46000/50000 [03:56<00:20, 195.84it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3211 (init= 9.0863), step count (max): 275, lr policy:  0.0000:  94%|#########3| 47000/50000 [04:01<00:15, 195.74it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3222 (init= 9.0863), step count (max): 347, lr policy:  0.0000:  94%|#########3| 47000/50000 [04:01<00:15, 195.74it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3222 (init= 9.0863), step count (max): 347, lr policy:  0.0000:  96%|#########6| 48000/50000 [04:06<00:10, 194.25it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3258 (init= 9.0863), step count (max): 165, lr policy:  0.0000:  96%|#########6| 48000/50000 [04:06<00:10, 194.25it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3258 (init= 9.0863), step count (max): 165, lr policy:  0.0000:  98%|#########8| 49000/50000 [04:11<00:05, 194.96it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3254 (init= 9.0863), step count (max): 169, lr policy:  0.0000:  98%|#########8| 49000/50000 [04:11<00:05, 194.96it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3254 (init= 9.0863), step count (max): 169, lr policy:  0.0000: 100%|##########| 50000/50000 [04:17<00:00, 195.42it/s]
+    eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3143 (init= 9.0863), step count (max): 130, lr policy:  0.0000: 100%|##########| 50000/50000 [04:17<00:00, 195.42it/s]
 
 
 
@@ -1216,7 +1216,7 @@ If you want to experiment with this tutorial a bit more, you can apply the follo
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 4 minutes  5.041 seconds)
+   **Total running time of the script:** ( 4 minutes  18.572 seconds)
 
 
 .. _sphx_glr_download_intermediate_reinforcement_ppo.py:
diff --git a/_sources/intermediate/reinforcement_q_learning.rst.txt b/_sources/intermediate/reinforcement_q_learning.rst.txt
index e36b207596..bb266bc731 100644
--- a/_sources/intermediate/reinforcement_q_learning.rst.txt
+++ b/_sources/intermediate/reinforcement_q_learning.rst.txt
@@ -574,7 +574,7 @@ expected Q values. A soft update of its weights are performed at every step.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 12 minutes  47.007 seconds)
+   **Total running time of the script:** ( 4 minutes  27.406 seconds)
 
 
 .. _sphx_glr_download_intermediate_reinforcement_q_learning.py:
diff --git a/_sources/intermediate/scaled_dot_product_attention_tutorial.rst.txt b/_sources/intermediate/scaled_dot_product_attention_tutorial.rst.txt
index 9cad15b4ab..0b8d8d9d4d 100644
--- a/_sources/intermediate/scaled_dot_product_attention_tutorial.rst.txt
+++ b/_sources/intermediate/scaled_dot_product_attention_tutorial.rst.txt
@@ -171,10 +171,10 @@ measuring performance.
 
  .. code-block:: none
 
-    The default implementation runs in 2303.418 microseconds
-    The math implementation runs in 19246.958 microseconds
-    The flash attention implementation runs in 2304.079 microseconds
-    The memory efficient implementation runs in 4235.871 microseconds
+    The default implementation runs in 2305.492 microseconds
+    The math implementation runs in 19256.061 microseconds
+    The flash attention implementation runs in 2304.993 microseconds
+    The memory efficient implementation runs in 4304.684 microseconds
 
 
 
@@ -354,8 +354,8 @@ without needing to pad each sequence to the maximum length in the batch. For mor
 
     The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
 
-    Random NT runs in 561.716 microseconds
-    Random Dense runs in 938.942 microseconds
+    Random NT runs in 560.097 microseconds
+    Random Dense runs in 935.449 microseconds
 
 
 
@@ -401,8 +401,8 @@ To demonstrate this, let's compile the ``CausalSelfAttention`` module using
 
  .. code-block:: none
 
-    The non compiled module runs in  408.355 microseconds
-    The compiled module runs in  518.208 microseconds
+    The non compiled module runs in  408.374 microseconds
+    The compiled module runs in  518.703 microseconds
 
 
 
@@ -460,36 +460,36 @@ inspect the performance characteristics of your code.
     -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
                                                        Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls  
     -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
-                             Non-Compilied Causal Attention        20.56%       2.233ms        77.56%       8.424ms       8.424ms       0.000us         0.00%      11.081ms      11.081ms             1  
-                             Non-Compilied Causal Attention         0.00%       0.000us         0.00%       0.000us       0.000us      10.325ms        50.44%      10.325ms      10.325ms             1  
-                                               aten::matmul         2.74%     298.000us        24.06%       2.613ms      52.260us       0.000us         0.00%       8.140ms     162.800us            50  
-                                                   aten::mm        14.94%       1.623ms        19.41%       2.108ms      42.160us       7.736ms        37.79%       8.140ms     162.800us            50  
-                                               aten::linear         2.87%     312.000us        27.88%       3.028ms      60.560us       0.000us         0.00%       7.522ms     150.440us            50  
-             ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_tn         0.00%       0.000us         0.00%       0.000us       0.000us       5.539ms        27.06%       5.539ms     221.560us            25  
-                         aten::scaled_dot_product_attention         2.28%     248.000us        19.66%       2.135ms      85.400us       0.000us         0.00%       2.941ms     117.640us            25  
-                  aten::_scaled_dot_product_flash_attention         3.40%     369.000us        17.37%       1.887ms      75.480us       0.000us         0.00%       2.941ms     117.640us            25  
-                             aten::_flash_attention_forward         5.05%     548.000us        12.86%       1.397ms      55.880us       2.410ms        11.77%       2.941ms     117.640us            25  
-    void pytorch_flash::flash_fwd_kernel<pytorch_flash::...         0.00%       0.000us         0.00%       0.000us       0.000us       2.410ms        11.77%       2.410ms      96.400us            25  
+                             Non-Compilied Causal Attention        21.06%       2.291ms        73.84%       8.032ms       8.032ms       0.000us         0.00%      11.101ms      11.101ms             1  
+                             Non-Compilied Causal Attention         0.00%       0.000us         0.00%       0.000us       0.000us      10.367ms        50.50%      10.367ms      10.367ms             1  
+                                               aten::linear         1.28%     139.000us        26.54%       2.887ms      57.740us       0.000us         0.00%       8.146ms     162.920us            50  
+                                               aten::matmul         2.62%     285.000us        22.84%       2.484ms      49.680us       0.000us         0.00%       8.146ms     162.920us            50  
+                                                   aten::mm        13.90%       1.512ms        18.27%       1.987ms      39.740us       7.738ms        37.69%       8.146ms     162.920us            50  
+             ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_tn         0.00%       0.000us         0.00%       0.000us       0.000us       5.543ms        27.00%       5.543ms     221.720us            25  
+                         aten::scaled_dot_product_attention         2.08%     226.000us        18.37%       1.998ms      79.920us       0.000us         0.00%       2.955ms     118.200us            25  
+                  aten::_scaled_dot_product_flash_attention         3.40%     370.000us        16.29%       1.772ms      70.880us       0.000us         0.00%       2.955ms     118.200us            25  
+                             aten::_flash_attention_forward         4.73%     514.000us        11.94%       1.299ms      51.960us       2.423ms        11.80%       2.955ms     118.200us            25  
+    void pytorch_flash::flash_fwd_kernel<pytorch_flash::...         0.00%       0.000us         0.00%       0.000us       0.000us       2.423ms        11.80%       2.423ms      96.920us            25  
     -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
-    Self CPU time total: 10.861ms
-    Self CUDA time total: 20.471ms
+    Self CPU time total: 10.878ms
+    Self CUDA time total: 20.528ms
 
     -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
                                                        Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls  
     -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
-                                  Compiled Causal Attention         6.72%     746.000us        93.75%      10.410ms      10.410ms       0.000us         0.00%      10.969ms      10.969ms             1  
-                                      Torch-Compiled Region        11.24%       1.248ms        85.71%       9.517ms     380.680us       0.000us         0.00%      10.969ms     438.760us            25  
-                                           CompiledFunction        42.37%       4.705ms        73.17%       8.125ms     325.000us       0.000us         0.00%      10.969ms     438.760us            25  
-                                  Compiled Causal Attention         0.00%       0.000us         0.00%       0.000us       0.000us      10.415ms        50.64%      10.415ms      10.415ms             1  
-                                                   aten::mm         8.88%     986.000us        13.86%       1.539ms      30.780us       7.742ms        37.65%       8.148ms     162.960us            50  
-             ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_tn         0.00%       0.000us         0.00%       0.000us       0.000us       5.542ms        26.95%       5.542ms     221.680us            25  
-                  aten::_scaled_dot_product_flash_attention         2.50%     278.000us        15.92%       1.768ms      70.720us       0.000us         0.00%       2.821ms     112.840us            25  
-                             aten::_flash_attention_forward         4.71%     523.000us        11.95%       1.327ms      53.080us       2.408ms        11.71%       2.821ms     112.840us            25  
-    void pytorch_flash::flash_fwd_kernel<pytorch_flash::...         0.00%       0.000us         0.00%       0.000us       0.000us       2.408ms        11.71%       2.408ms      96.320us            25  
-    ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_stages_3...         0.00%       0.000us         0.00%       0.000us       0.000us       2.200ms        10.70%       2.200ms      88.000us            25  
+                                  Compiled Causal Attention         6.78%     744.000us        89.35%       9.811ms       9.811ms       0.000us         0.00%      10.984ms      10.984ms             1  
+                                      Torch-Compiled Region        10.83%       1.189ms        81.32%       8.929ms     357.160us       0.000us         0.00%      10.984ms     439.360us            25  
+                                           CompiledFunction        39.49%       4.336ms        69.36%       7.616ms     304.640us       0.000us         0.00%      10.984ms     439.360us            25  
+                                  Compiled Causal Attention         0.00%       0.000us         0.00%       0.000us       0.000us      10.345ms        50.46%      10.345ms      10.345ms             1  
+                                                   aten::mm         8.70%     955.000us        13.47%       1.479ms      29.580us       7.746ms        37.78%       8.155ms     163.100us            50  
+             ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_tn         0.00%       0.000us         0.00%       0.000us       0.000us       5.548ms        27.06%       5.548ms     221.920us            25  
+                  aten::_scaled_dot_product_flash_attention         2.47%     271.000us        15.43%       1.694ms      67.760us       0.000us         0.00%       2.829ms     113.160us            25  
+                             aten::_flash_attention_forward         4.64%     510.000us        11.60%       1.274ms      50.960us       2.412ms        11.76%       2.829ms     113.160us            25  
+    void pytorch_flash::flash_fwd_kernel<pytorch_flash::...         0.00%       0.000us         0.00%       0.000us       0.000us       2.412ms        11.76%       2.412ms      96.480us            25  
+    ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_stages_3...         0.00%       0.000us         0.00%       0.000us       0.000us       2.198ms        10.72%       2.198ms      87.920us            25  
     -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  
-    Self CPU time total: 11.104ms
-    Self CUDA time total: 20.565ms
+    Self CPU time total: 10.980ms
+    Self CUDA time total: 20.503ms
 
 
 
@@ -624,7 +624,7 @@ module.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  6.228 seconds)
+   **Total running time of the script:** ( 0 minutes  7.838 seconds)
 
 
 .. _sphx_glr_download_intermediate_scaled_dot_product_attention_tutorial.py:
diff --git a/_sources/intermediate/seq2seq_translation_tutorial.rst.txt b/_sources/intermediate/seq2seq_translation_tutorial.rst.txt
index 7c9e2b718a..292b715273 100644
--- a/_sources/intermediate/seq2seq_translation_tutorial.rst.txt
+++ b/_sources/intermediate/seq2seq_translation_tutorial.rst.txt
@@ -1067,22 +1067,22 @@ reasonable results.
     Counted words:
     fra 4601
     eng 2991
-    0m 27s (- 6m 50s) (5 6%) 1.5304
-    0m 54s (- 6m 21s) (10 12%) 0.6776
-    1m 21s (- 5m 54s) (15 18%) 0.3528
-    1m 48s (- 5m 26s) (20 25%) 0.1946
-    2m 16s (- 4m 59s) (25 31%) 0.1205
-    2m 43s (- 4m 31s) (30 37%) 0.0841
-    3m 10s (- 4m 4s) (35 43%) 0.0639
-    3m 37s (- 3m 37s) (40 50%) 0.0521
-    4m 4s (- 3m 10s) (45 56%) 0.0452
-    4m 31s (- 2m 42s) (50 62%) 0.0395
-    4m 58s (- 2m 15s) (55 68%) 0.0377
-    5m 25s (- 1m 48s) (60 75%) 0.0349
-    5m 52s (- 1m 21s) (65 81%) 0.0324
-    6m 20s (- 0m 54s) (70 87%) 0.0316
-    6m 47s (- 0m 27s) (75 93%) 0.0298
-    7m 14s (- 0m 0s) (80 100%) 0.0291
+    0m 28s (- 7m 1s) (5 6%) 1.5304
+    0m 56s (- 6m 32s) (10 12%) 0.6776
+    1m 23s (- 6m 3s) (15 18%) 0.3528
+    1m 51s (- 5m 35s) (20 25%) 0.1946
+    2m 19s (- 5m 7s) (25 31%) 0.1205
+    2m 47s (- 4m 39s) (30 37%) 0.0841
+    3m 15s (- 4m 11s) (35 43%) 0.0639
+    3m 43s (- 3m 43s) (40 50%) 0.0521
+    4m 11s (- 3m 15s) (45 56%) 0.0452
+    4m 39s (- 2m 47s) (50 62%) 0.0395
+    5m 7s (- 2m 19s) (55 68%) 0.0377
+    5m 35s (- 1m 51s) (60 75%) 0.0349
+    6m 3s (- 1m 23s) (65 81%) 0.0324
+    6m 31s (- 0m 55s) (70 87%) 0.0316
+    6m 59s (- 0m 27s) (75 93%) 0.0298
+    7m 27s (- 0m 0s) (80 100%) 0.0291
 
 
 
@@ -1317,7 +1317,7 @@ Exercises
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 7 minutes  23.294 seconds)
+   **Total running time of the script:** ( 7 minutes  35.981 seconds)
 
 
 .. _sphx_glr_download_intermediate_seq2seq_translation_tutorial.py:
diff --git a/_sources/intermediate/spatial_transformer_tutorial.rst.txt b/_sources/intermediate/spatial_transformer_tutorial.rst.txt
index acfc6fc8ba..921d92e9a8 100644
--- a/_sources/intermediate/spatial_transformer_tutorial.rst.txt
+++ b/_sources/intermediate/spatial_transformer_tutorial.rst.txt
@@ -69,7 +69,7 @@ any existing CNN with very little modification.
  .. code-block:: none
 
 
-    <contextlib.ExitStack object at 0x7f8ba1bec700>
+    <contextlib.ExitStack object at 0x7fc87682f700>
 
 
 
@@ -124,7 +124,7 @@ network.
     Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz
 
       0%|          | 0/9912422 [00:00<?, ?it/s]
-    100%|##########| 9912422/9912422 [00:00<00:00, 134663407.95it/s]
+    100%|##########| 9912422/9912422 [00:00<00:00, 130659054.82it/s]
     Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw
 
     Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
@@ -135,7 +135,7 @@ network.
     Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz
 
       0%|          | 0/28881 [00:00<?, ?it/s]
-    100%|##########| 28881/28881 [00:00<00:00, 17903590.57it/s]
+    100%|##########| 28881/28881 [00:00<00:00, 17124073.20it/s]
     Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw
 
     Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
@@ -146,7 +146,7 @@ network.
     Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz
 
       0%|          | 0/1648877 [00:00<?, ?it/s]
-    100%|##########| 1648877/1648877 [00:00<00:00, 88552880.28it/s]
+    100%|##########| 1648877/1648877 [00:00<00:00, 198305129.65it/s]
     Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw
 
     Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
@@ -157,7 +157,7 @@ network.
     Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz
 
       0%|          | 0/4542 [00:00<?, ?it/s]
-    100%|##########| 4542/4542 [00:00<00:00, 6028648.34it/s]
+    100%|##########| 4542/4542 [00:00<00:00, 3616273.49it/s]
     Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw
 
 
@@ -398,104 +398,104 @@ transformations while training.
  .. code-block:: none
 
     Train Epoch: 1 [0/60000 (0%)]   Loss: 2.315648
-    Train Epoch: 1 [32000/60000 (53%)]      Loss: 1.056052
+    Train Epoch: 1 [32000/60000 (53%)]      Loss: 1.094919
 
-    Test set: Average loss: 0.2696, Accuracy: 9226/10000 (92%)
+    Test set: Average loss: 0.2729, Accuracy: 9232/10000 (92%)
 
-    Train Epoch: 2 [0/60000 (0%)]   Loss: 0.522132
-    Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.309089
+    Train Epoch: 2 [0/60000 (0%)]   Loss: 0.538398
+    Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.362289
 
-    Test set: Average loss: 0.1487, Accuracy: 9554/10000 (96%)
+    Test set: Average loss: 0.1911, Accuracy: 9415/10000 (94%)
 
-    Train Epoch: 3 [0/60000 (0%)]   Loss: 0.378176
-    Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.235657
+    Train Epoch: 3 [0/60000 (0%)]   Loss: 0.516258
+    Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.190172
 
-    Test set: Average loss: 0.1079, Accuracy: 9676/10000 (97%)
+    Test set: Average loss: 0.1147, Accuracy: 9647/10000 (96%)
 
-    Train Epoch: 4 [0/60000 (0%)]   Loss: 0.367314
-    Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.143735
+    Train Epoch: 4 [0/60000 (0%)]   Loss: 0.394187
+    Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.112920
 
-    Test set: Average loss: 0.1514, Accuracy: 9518/10000 (95%)
+    Test set: Average loss: 0.1002, Accuracy: 9703/10000 (97%)
 
-    Train Epoch: 5 [0/60000 (0%)]   Loss: 0.299169
-    Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.104072
+    Train Epoch: 5 [0/60000 (0%)]   Loss: 0.227524
+    Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.141361
 
-    Test set: Average loss: 0.0886, Accuracy: 9709/10000 (97%)
+    Test set: Average loss: 0.0825, Accuracy: 9732/10000 (97%)
 
-    Train Epoch: 6 [0/60000 (0%)]   Loss: 0.117600
-    Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.091880
+    Train Epoch: 6 [0/60000 (0%)]   Loss: 0.139464
+    Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.125213
 
-    Test set: Average loss: 0.0683, Accuracy: 9795/10000 (98%)
+    Test set: Average loss: 0.0734, Accuracy: 9779/10000 (98%)
 
-    Train Epoch: 7 [0/60000 (0%)]   Loss: 0.108650
-    Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.147451
+    Train Epoch: 7 [0/60000 (0%)]   Loss: 0.062508
+    Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.187347
 
-    Test set: Average loss: 0.0671, Accuracy: 9799/10000 (98%)
+    Test set: Average loss: 0.2350, Accuracy: 9267/10000 (93%)
 
-    Train Epoch: 8 [0/60000 (0%)]   Loss: 0.204512
-    Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.088205
+    Train Epoch: 8 [0/60000 (0%)]   Loss: 0.571213
+    Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.073750
 
-    Test set: Average loss: 0.0626, Accuracy: 9809/10000 (98%)
+    Test set: Average loss: 0.0892, Accuracy: 9732/10000 (97%)
 
-    Train Epoch: 9 [0/60000 (0%)]   Loss: 0.106027
-    Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.052493
+    Train Epoch: 9 [0/60000 (0%)]   Loss: 0.216066
+    Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.163309
 
-    Test set: Average loss: 0.0636, Accuracy: 9809/10000 (98%)
+    Test set: Average loss: 0.0666, Accuracy: 9813/10000 (98%)
 
-    Train Epoch: 10 [0/60000 (0%)]  Loss: 0.123162
-    Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.247202
+    Train Epoch: 10 [0/60000 (0%)]  Loss: 0.072257
+    Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.208973
 
-    Test set: Average loss: 0.0611, Accuracy: 9824/10000 (98%)
+    Test set: Average loss: 0.0573, Accuracy: 9830/10000 (98%)
 
-    Train Epoch: 11 [0/60000 (0%)]  Loss: 0.172087
-    Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.097818
+    Train Epoch: 11 [0/60000 (0%)]  Loss: 0.115926
+    Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.167152
 
-    Test set: Average loss: 0.0509, Accuracy: 9844/10000 (98%)
+    Test set: Average loss: 0.0731, Accuracy: 9784/10000 (98%)
 
-    Train Epoch: 12 [0/60000 (0%)]  Loss: 0.147289
-    Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.156528
+    Train Epoch: 12 [0/60000 (0%)]  Loss: 0.121299
+    Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.198820
 
-    Test set: Average loss: 0.0573, Accuracy: 9834/10000 (98%)
+    Test set: Average loss: 0.0576, Accuracy: 9832/10000 (98%)
 
-    Train Epoch: 13 [0/60000 (0%)]  Loss: 0.069093
-    Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.100166
+    Train Epoch: 13 [0/60000 (0%)]  Loss: 0.064778
+    Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.087009
 
-    Test set: Average loss: 0.0731, Accuracy: 9781/10000 (98%)
+    Test set: Average loss: 0.0509, Accuracy: 9842/10000 (98%)
 
-    Train Epoch: 14 [0/60000 (0%)]  Loss: 0.099262
-    Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.164480
+    Train Epoch: 14 [0/60000 (0%)]  Loss: 0.073423
+    Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.080678
 
-    Test set: Average loss: 0.0515, Accuracy: 9837/10000 (98%)
+    Test set: Average loss: 0.0574, Accuracy: 9826/10000 (98%)
 
-    Train Epoch: 15 [0/60000 (0%)]  Loss: 0.041433
-    Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.082513
+    Train Epoch: 15 [0/60000 (0%)]  Loss: 0.108524
+    Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.085530
 
-    Test set: Average loss: 0.0479, Accuracy: 9855/10000 (99%)
+    Test set: Average loss: 0.0560, Accuracy: 9834/10000 (98%)
 
-    Train Epoch: 16 [0/60000 (0%)]  Loss: 0.057918
-    Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.216040
+    Train Epoch: 16 [0/60000 (0%)]  Loss: 0.077510
+    Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.159949
 
-    Test set: Average loss: 0.0464, Accuracy: 9861/10000 (99%)
+    Test set: Average loss: 0.0489, Accuracy: 9860/10000 (99%)
 
-    Train Epoch: 17 [0/60000 (0%)]  Loss: 0.229464
-    Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.150280
+    Train Epoch: 17 [0/60000 (0%)]  Loss: 0.322598
+    Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.156583
 
-    Test set: Average loss: 0.0998, Accuracy: 9698/10000 (97%)
+    Test set: Average loss: 0.0610, Accuracy: 9816/10000 (98%)
 
-    Train Epoch: 18 [0/60000 (0%)]  Loss: 0.117676
-    Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.135358
+    Train Epoch: 18 [0/60000 (0%)]  Loss: 0.038340
+    Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.087965
 
-    Test set: Average loss: 0.0471, Accuracy: 9871/10000 (99%)
+    Test set: Average loss: 0.0597, Accuracy: 9813/10000 (98%)
 
-    Train Epoch: 19 [0/60000 (0%)]  Loss: 0.056745
-    Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.123519
+    Train Epoch: 19 [0/60000 (0%)]  Loss: 0.070837
+    Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.089139
 
-    Test set: Average loss: 0.0493, Accuracy: 9856/10000 (99%)
+    Test set: Average loss: 0.0424, Accuracy: 9876/10000 (99%)
 
-    Train Epoch: 20 [0/60000 (0%)]  Loss: 0.050441
-    Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.043098
+    Train Epoch: 20 [0/60000 (0%)]  Loss: 0.069807
+    Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.144610
 
-    Test set: Average loss: 0.0817, Accuracy: 9764/10000 (98%)
+    Test set: Average loss: 0.0543, Accuracy: 9844/10000 (98%)
 
 
 
@@ -504,7 +504,7 @@ transformations while training.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  0.273 seconds)
+   **Total running time of the script:** ( 2 minutes  12.932 seconds)
 
 
 .. _sphx_glr_download_intermediate_spatial_transformer_tutorial.py:
diff --git a/_sources/intermediate/torch_compile_tutorial.rst.txt b/_sources/intermediate/torch_compile_tutorial.rst.txt
index 026a8f8d30..b2d2e67372 100644
--- a/_sources/intermediate/torch_compile_tutorial.rst.txt
+++ b/_sources/intermediate/torch_compile_tutorial.rst.txt
@@ -331,12 +331,12 @@ Note that in the call to ``torch.compile``, we have the additional
 
  .. code-block:: none
 
-    eager: 0.19579493713378907
+    eager: 0.19602336120605468
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:124: UserWarning:
 
     TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
 
-    compile: 85.2317265625
+    compile: 84.86965625
 
 
 
@@ -390,29 +390,29 @@ see a significant improvement compared to eager.
 
  .. code-block:: none
 
-    eager eval time 0: 0.018774015426635742
-    eager eval time 1: 0.017271808624267578
-    eager eval time 2: 0.016611328125
-    eager eval time 3: 0.01659699249267578
-    eager eval time 4: 0.016461824417114256
-    eager eval time 5: 0.01663692855834961
-    eager eval time 6: 0.01643622398376465
-    eager eval time 7: 0.016538623809814454
-    eager eval time 8: 0.01648646354675293
-    eager eval time 9: 0.01640345573425293
+    eager eval time 0: 0.017921215057373048
+    eager eval time 1: 0.016335872650146483
+    eager eval time 2: 0.016227327346801757
+    eager eval time 3: 0.01622425651550293
+    eager eval time 4: 0.016085920333862306
+    eager eval time 5: 0.016227327346801757
+    eager eval time 6: 0.01606649589538574
+    eager eval time 7: 0.016202751159667968
+    eager eval time 8: 0.016100351333618163
+    eager eval time 9: 0.01623961639404297
     ~~~~~~~~~~
-    compile eval time 0: 0.6135510864257813
-    compile eval time 1: 0.008589311599731446
-    compile eval time 2: 0.008352767944335938
-    compile eval time 3: 0.008348671913146973
-    compile eval time 4: 0.0083374080657959
-    compile eval time 5: 0.008338432312011718
-    compile eval time 6: 0.008353792190551757
-    compile eval time 7: 0.008361984252929687
-    compile eval time 8: 0.008352767944335938
-    compile eval time 9: 0.008350720405578613
+    compile eval time 0: 0.6061260986328125
+    compile eval time 1: 0.008586239814758301
+    compile eval time 2: 0.008363007545471191
+    compile eval time 3: 0.008343551635742187
+    compile eval time 4: 0.008356863975524903
+    compile eval time 5: 0.008348671913146973
+    compile eval time 6: 0.008343551635742187
+    compile eval time 7: 0.008351743698120117
+    compile eval time 8: 0.008342528343200683
+    compile eval time 9: 0.008336319923400878
     ~~~~~~~~~~
-    (eval) eager median: 0.016567808151245117, compile median: 0.008352767944335938, speedup: 1.9835111260908245x
+    (eval) eager median: 0.016225791931152346, compile median: 0.008350207805633544, speedup: 1.9431602552699905x
     ~~~~~~~~~~
 
 
@@ -493,29 +493,29 @@ Now, let's consider comparing training.
 
  .. code-block:: none
 
-    eager train time 0: 0.30075186157226563
-    eager train time 1: 0.05288857650756836
-    eager train time 2: 0.05173759841918945
-    eager train time 3: 0.05079558563232422
-    eager train time 4: 0.05079244613647461
-    eager train time 5: 0.051041217803955076
-    eager train time 6: 0.050733055114746094
-    eager train time 7: 0.05195980834960937
-    eager train time 8: 0.05063167953491211
-    eager train time 9: 0.0510648307800293
+    eager train time 0: 0.29824200439453125
+    eager train time 1: 0.053937152862548826
+    eager train time 2: 0.05155632019042969
+    eager train time 3: 0.05148979187011719
+    eager train time 4: 0.05138227081298828
+    eager train time 5: 0.052450302124023435
+    eager train time 6: 0.05168025588989258
+    eager train time 7: 0.05139148712158203
+    eager train time 8: 0.05126758575439453
+    eager train time 9: 0.05125737762451172
     ~~~~~~~~~~
-    compile train time 0: 210.392171875
-    compile train time 1: 4.5687314453125
-    compile train time 2: 0.030623743057250977
-    compile train time 3: 0.02369024085998535
-    compile train time 4: 0.02368409538269043
-    compile train time 5: 0.023356416702270507
-    compile train time 6: 0.02330521583557129
-    compile train time 7: 0.024222719192504884
-    compile train time 8: 0.023751680374145507
-    compile train time 9: 0.023669759750366212
+    compile train time 0: 209.48853125
+    compile train time 1: 4.540728515625
+    compile train time 2: 0.02995199966430664
+    compile train time 3: 0.023593984603881835
+    compile train time 4: 0.023359487533569336
+    compile train time 5: 0.023391231536865235
+    compile train time 6: 0.023404544830322265
+    compile train time 7: 0.024204191207885743
+    compile train time 8: 0.02351411247253418
+    compile train time 9: 0.023315391540527343
     ~~~~~~~~~~
-    (train) eager median: 0.05105302429199218, compile median: 0.02372096061706543, speedup: 2.1522325809715905x
+    (train) eager median: 0.051523056030273434, compile median: 0.023554048538208007, speedup: 2.18743949460305x
     ~~~~~~~~~~
 
 
@@ -1007,432 +1007,432 @@ outputs the FX graph and simply returns the graph's unoptimized forward method.
     call_module    l__self___features_norm0                           L__self___features_norm0                                    (l__self___features_conv0,)                                                                                                                                                                                                                                                                                                                                                                                                                                         {}
     call_module    l__self___features_relu0                           L__self___features_relu0                                    (l__self___features_norm0,)                                                                                                                                                                                                                                                                                                                                                                                                                                         {}
     call_module    l__self___features_pool0                           L__self___features_pool0                                    (l__self___features_relu0,)                                                                                                                                                                                                                                                                                                                                                                                                                                         {}
-    call_function  concated_features                                  <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_pool0], 1)                                                                                                                                                                                                                                                                                                                                                                                                                                     {}
+    call_function  concated_features                                  <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_pool0], 1)                                                                                                                                                                                                                                                                                                                                                                                                                                     {}
     call_module    l__self___features_denseblock1_denselayer1_norm1   L__self___features_denseblock1_denselayer1_norm1            (concated_features,)                                                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock1_denselayer1_relu1   L__self___features_denseblock1_denselayer1_relu1            (l__self___features_denseblock1_denselayer1_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output                                  L__self___features_denseblock1_denselayer1_conv1            (l__self___features_denseblock1_denselayer1_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock1_denselayer1_norm2   L__self___features_denseblock1_denselayer1_norm2            (bottleneck_output,)                                                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock1_denselayer1_relu2   L__self___features_denseblock1_denselayer1_relu2            (l__self___features_denseblock1_denselayer1_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features                                       L__self___features_denseblock1_denselayer1_conv2            (l__self___features_denseblock1_denselayer1_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_1                                <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_pool0, new_features], 1)                                                                                                                                                                                                                                                                                                                                                                                                                       {}
+    call_function  concated_features_1                                <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_pool0, new_features], 1)                                                                                                                                                                                                                                                                                                                                                                                                                       {}
     call_module    l__self___features_denseblock1_denselayer2_norm1   L__self___features_denseblock1_denselayer2_norm1            (concated_features_1,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock1_denselayer2_relu1   L__self___features_denseblock1_denselayer2_relu1            (l__self___features_denseblock1_denselayer2_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_2                                L__self___features_denseblock1_denselayer2_conv1            (l__self___features_denseblock1_denselayer2_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock1_denselayer2_norm2   L__self___features_denseblock1_denselayer2_norm2            (bottleneck_output_2,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock1_denselayer2_relu2   L__self___features_denseblock1_denselayer2_relu2            (l__self___features_denseblock1_denselayer2_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_2                                     L__self___features_denseblock1_denselayer2_conv2            (l__self___features_denseblock1_denselayer2_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_2                                <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_pool0, new_features, new_features_2], 1)                                                                                                                                                                                                                                                                                                                                                                                                       {}
+    call_function  concated_features_2                                <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_pool0, new_features, new_features_2], 1)                                                                                                                                                                                                                                                                                                                                                                                                       {}
     call_module    l__self___features_denseblock1_denselayer3_norm1   L__self___features_denseblock1_denselayer3_norm1            (concated_features_2,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock1_denselayer3_relu1   L__self___features_denseblock1_denselayer3_relu1            (l__self___features_denseblock1_denselayer3_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_4                                L__self___features_denseblock1_denselayer3_conv1            (l__self___features_denseblock1_denselayer3_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock1_denselayer3_norm2   L__self___features_denseblock1_denselayer3_norm2            (bottleneck_output_4,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock1_denselayer3_relu2   L__self___features_denseblock1_denselayer3_relu2            (l__self___features_denseblock1_denselayer3_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_4                                     L__self___features_denseblock1_denselayer3_conv2            (l__self___features_denseblock1_denselayer3_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_3                                <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_pool0, new_features, new_features_2, new_features_4], 1)                                                                                                                                                                                                                                                                                                                                                                                       {}
+    call_function  concated_features_3                                <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_pool0, new_features, new_features_2, new_features_4], 1)                                                                                                                                                                                                                                                                                                                                                                                       {}
     call_module    l__self___features_denseblock1_denselayer4_norm1   L__self___features_denseblock1_denselayer4_norm1            (concated_features_3,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock1_denselayer4_relu1   L__self___features_denseblock1_denselayer4_relu1            (l__self___features_denseblock1_denselayer4_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_6                                L__self___features_denseblock1_denselayer4_conv1            (l__self___features_denseblock1_denselayer4_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock1_denselayer4_norm2   L__self___features_denseblock1_denselayer4_norm2            (bottleneck_output_6,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock1_denselayer4_relu2   L__self___features_denseblock1_denselayer4_relu2            (l__self___features_denseblock1_denselayer4_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_6                                     L__self___features_denseblock1_denselayer4_conv2            (l__self___features_denseblock1_denselayer4_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_4                                <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
+    call_function  concated_features_4                                <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
     call_module    l__self___features_denseblock1_denselayer5_norm1   L__self___features_denseblock1_denselayer5_norm1            (concated_features_4,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock1_denselayer5_relu1   L__self___features_denseblock1_denselayer5_relu1            (l__self___features_denseblock1_denselayer5_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_8                                L__self___features_denseblock1_denselayer5_conv1            (l__self___features_denseblock1_denselayer5_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock1_denselayer5_norm2   L__self___features_denseblock1_denselayer5_norm2            (bottleneck_output_8,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock1_denselayer5_relu2   L__self___features_denseblock1_denselayer5_relu2            (l__self___features_denseblock1_denselayer5_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_8                                     L__self___features_denseblock1_denselayer5_conv2            (l__self___features_denseblock1_denselayer5_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_5                                <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8], 1)                                                                                                                                                                                                                                                                                                                                                       {}
+    call_function  concated_features_5                                <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8], 1)                                                                                                                                                                                                                                                                                                                                                       {}
     call_module    l__self___features_denseblock1_denselayer6_norm1   L__self___features_denseblock1_denselayer6_norm1            (concated_features_5,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock1_denselayer6_relu1   L__self___features_denseblock1_denselayer6_relu1            (l__self___features_denseblock1_denselayer6_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_10                               L__self___features_denseblock1_denselayer6_conv1            (l__self___features_denseblock1_denselayer6_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock1_denselayer6_norm2   L__self___features_denseblock1_denselayer6_norm2            (bottleneck_output_10,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock1_denselayer6_relu2   L__self___features_denseblock1_denselayer6_relu2            (l__self___features_denseblock1_denselayer6_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_10                                    L__self___features_denseblock1_denselayer6_conv2            (l__self___features_denseblock1_denselayer6_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  cat_6                                              <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8, new_features_10], 1)                                                                                                                                                                                                                                                                                                                                      {}
+    call_function  cat_6                                              <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8, new_features_10], 1)                                                                                                                                                                                                                                                                                                                                      {}
     call_module    l__self___features_transition1_norm                L__self___features_transition1_norm                         (cat_6,)                                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     call_module    l__self___features_transition1_relu                L__self___features_transition1_relu                         (l__self___features_transition1_norm,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_transition1_conv                L__self___features_transition1_conv                         (l__self___features_transition1_relu,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_transition1_pool                L__self___features_transition1_pool                         (l__self___features_transition1_conv,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
-    call_function  concated_features_6                                <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
+    call_function  concated_features_6                                <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
     call_module    l__self___features_denseblock2_denselayer1_norm1   L__self___features_denseblock2_denselayer1_norm1            (concated_features_6,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock2_denselayer1_relu1   L__self___features_denseblock2_denselayer1_relu1            (l__self___features_denseblock2_denselayer1_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_12                               L__self___features_denseblock2_denselayer1_conv1            (l__self___features_denseblock2_denselayer1_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer1_norm2   L__self___features_denseblock2_denselayer1_norm2            (bottleneck_output_12,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer1_relu2   L__self___features_denseblock2_denselayer1_relu2            (l__self___features_denseblock2_denselayer1_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_12                                    L__self___features_denseblock2_denselayer1_conv2            (l__self___features_denseblock2_denselayer1_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_7                                <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
+    call_function  concated_features_7                                <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
     call_module    l__self___features_denseblock2_denselayer2_norm1   L__self___features_denseblock2_denselayer2_norm1            (concated_features_7,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock2_denselayer2_relu1   L__self___features_denseblock2_denselayer2_relu1            (l__self___features_denseblock2_denselayer2_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_14                               L__self___features_denseblock2_denselayer2_conv1            (l__self___features_denseblock2_denselayer2_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer2_norm2   L__self___features_denseblock2_denselayer2_norm2            (bottleneck_output_14,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer2_relu2   L__self___features_denseblock2_denselayer2_relu2            (l__self___features_denseblock2_denselayer2_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_14                                    L__self___features_denseblock2_denselayer2_conv2            (l__self___features_denseblock2_denselayer2_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_8                                <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
+    call_function  concated_features_8                                <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
     call_module    l__self___features_denseblock2_denselayer3_norm1   L__self___features_denseblock2_denselayer3_norm1            (concated_features_8,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock2_denselayer3_relu1   L__self___features_denseblock2_denselayer3_relu1            (l__self___features_denseblock2_denselayer3_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_16                               L__self___features_denseblock2_denselayer3_conv1            (l__self___features_denseblock2_denselayer3_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer3_norm2   L__self___features_denseblock2_denselayer3_norm2            (bottleneck_output_16,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer3_relu2   L__self___features_denseblock2_denselayer3_relu2            (l__self___features_denseblock2_denselayer3_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_16                                    L__self___features_denseblock2_denselayer3_conv2            (l__self___features_denseblock2_denselayer3_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_9                                <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
+    call_function  concated_features_9                                <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
     call_module    l__self___features_denseblock2_denselayer4_norm1   L__self___features_denseblock2_denselayer4_norm1            (concated_features_9,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock2_denselayer4_relu1   L__self___features_denseblock2_denselayer4_relu1            (l__self___features_denseblock2_denselayer4_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_18                               L__self___features_denseblock2_denselayer4_conv1            (l__self___features_denseblock2_denselayer4_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer4_norm2   L__self___features_denseblock2_denselayer4_norm2            (bottleneck_output_18,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer4_relu2   L__self___features_denseblock2_denselayer4_relu2            (l__self___features_denseblock2_denselayer4_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_18                                    L__self___features_denseblock2_denselayer4_conv2            (l__self___features_denseblock2_denselayer4_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_10                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18], 1)                                                                                                                                                                                                                                                                                                                                                      {}
+    call_function  concated_features_10                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18], 1)                                                                                                                                                                                                                                                                                                                                                      {}
     call_module    l__self___features_denseblock2_denselayer5_norm1   L__self___features_denseblock2_denselayer5_norm1            (concated_features_10,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer5_relu1   L__self___features_denseblock2_denselayer5_relu1            (l__self___features_denseblock2_denselayer5_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_20                               L__self___features_denseblock2_denselayer5_conv1            (l__self___features_denseblock2_denselayer5_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer5_norm2   L__self___features_denseblock2_denselayer5_norm2            (bottleneck_output_20,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer5_relu2   L__self___features_denseblock2_denselayer5_relu2            (l__self___features_denseblock2_denselayer5_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_20                                    L__self___features_denseblock2_denselayer5_conv2            (l__self___features_denseblock2_denselayer5_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_11                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20], 1)                                                                                                                                                                                                                                                                                                                                     {}
+    call_function  concated_features_11                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20], 1)                                                                                                                                                                                                                                                                                                                                     {}
     call_module    l__self___features_denseblock2_denselayer6_norm1   L__self___features_denseblock2_denselayer6_norm1            (concated_features_11,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer6_relu1   L__self___features_denseblock2_denselayer6_relu1            (l__self___features_denseblock2_denselayer6_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_22                               L__self___features_denseblock2_denselayer6_conv1            (l__self___features_denseblock2_denselayer6_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer6_norm2   L__self___features_denseblock2_denselayer6_norm2            (bottleneck_output_22,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer6_relu2   L__self___features_denseblock2_denselayer6_relu2            (l__self___features_denseblock2_denselayer6_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_22                                    L__self___features_denseblock2_denselayer6_conv2            (l__self___features_denseblock2_denselayer6_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_12                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22], 1)                                                                                                                                                                                                                                                                                                                    {}
+    call_function  concated_features_12                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22], 1)                                                                                                                                                                                                                                                                                                                    {}
     call_module    l__self___features_denseblock2_denselayer7_norm1   L__self___features_denseblock2_denselayer7_norm1            (concated_features_12,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer7_relu1   L__self___features_denseblock2_denselayer7_relu1            (l__self___features_denseblock2_denselayer7_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_24                               L__self___features_denseblock2_denselayer7_conv1            (l__self___features_denseblock2_denselayer7_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer7_norm2   L__self___features_denseblock2_denselayer7_norm2            (bottleneck_output_24,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer7_relu2   L__self___features_denseblock2_denselayer7_relu2            (l__self___features_denseblock2_denselayer7_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_24                                    L__self___features_denseblock2_denselayer7_conv2            (l__self___features_denseblock2_denselayer7_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_13                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24], 1)                                                                                                                                                                                                                                                                                                   {}
+    call_function  concated_features_13                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24], 1)                                                                                                                                                                                                                                                                                                   {}
     call_module    l__self___features_denseblock2_denselayer8_norm1   L__self___features_denseblock2_denselayer8_norm1            (concated_features_13,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer8_relu1   L__self___features_denseblock2_denselayer8_relu1            (l__self___features_denseblock2_denselayer8_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_26                               L__self___features_denseblock2_denselayer8_conv1            (l__self___features_denseblock2_denselayer8_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer8_norm2   L__self___features_denseblock2_denselayer8_norm2            (bottleneck_output_26,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer8_relu2   L__self___features_denseblock2_denselayer8_relu2            (l__self___features_denseblock2_denselayer8_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_26                                    L__self___features_denseblock2_denselayer8_conv2            (l__self___features_denseblock2_denselayer8_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_14                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26], 1)                                                                                                                                                                                                                                                                                  {}
+    call_function  concated_features_14                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26], 1)                                                                                                                                                                                                                                                                                  {}
     call_module    l__self___features_denseblock2_denselayer9_norm1   L__self___features_denseblock2_denselayer9_norm1            (concated_features_14,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer9_relu1   L__self___features_denseblock2_denselayer9_relu1            (l__self___features_denseblock2_denselayer9_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_28                               L__self___features_denseblock2_denselayer9_conv1            (l__self___features_denseblock2_denselayer9_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer9_norm2   L__self___features_denseblock2_denselayer9_norm2            (bottleneck_output_28,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer9_relu2   L__self___features_denseblock2_denselayer9_relu2            (l__self___features_denseblock2_denselayer9_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_28                                    L__self___features_denseblock2_denselayer9_conv2            (l__self___features_denseblock2_denselayer9_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_15                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28], 1)                                                                                                                                                                                                                                                                 {}
+    call_function  concated_features_15                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28], 1)                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock2_denselayer10_norm1  L__self___features_denseblock2_denselayer10_norm1           (concated_features_15,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer10_relu1  L__self___features_denseblock2_denselayer10_relu1           (l__self___features_denseblock2_denselayer10_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_30                               L__self___features_denseblock2_denselayer10_conv1           (l__self___features_denseblock2_denselayer10_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock2_denselayer10_norm2  L__self___features_denseblock2_denselayer10_norm2           (bottleneck_output_30,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer10_relu2  L__self___features_denseblock2_denselayer10_relu2           (l__self___features_denseblock2_denselayer10_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_30                                    L__self___features_denseblock2_denselayer10_conv2           (l__self___features_denseblock2_denselayer10_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_16                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30], 1)                                                                                                                                                                                                                                                {}
+    call_function  concated_features_16                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30], 1)                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock2_denselayer11_norm1  L__self___features_denseblock2_denselayer11_norm1           (concated_features_16,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer11_relu1  L__self___features_denseblock2_denselayer11_relu1           (l__self___features_denseblock2_denselayer11_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_32                               L__self___features_denseblock2_denselayer11_conv1           (l__self___features_denseblock2_denselayer11_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock2_denselayer11_norm2  L__self___features_denseblock2_denselayer11_norm2           (bottleneck_output_32,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer11_relu2  L__self___features_denseblock2_denselayer11_relu2           (l__self___features_denseblock2_denselayer11_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_32                                    L__self___features_denseblock2_denselayer11_conv2           (l__self___features_denseblock2_denselayer11_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_17                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32], 1)                                                                                                                                                                                                                               {}
+    call_function  concated_features_17                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32], 1)                                                                                                                                                                                                                               {}
     call_module    l__self___features_denseblock2_denselayer12_norm1  L__self___features_denseblock2_denselayer12_norm1           (concated_features_17,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer12_relu1  L__self___features_denseblock2_denselayer12_relu1           (l__self___features_denseblock2_denselayer12_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_34                               L__self___features_denseblock2_denselayer12_conv1           (l__self___features_denseblock2_denselayer12_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock2_denselayer12_norm2  L__self___features_denseblock2_denselayer12_norm2           (bottleneck_output_34,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock2_denselayer12_relu2  L__self___features_denseblock2_denselayer12_relu2           (l__self___features_denseblock2_denselayer12_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_34                                    L__self___features_denseblock2_denselayer12_conv2           (l__self___features_denseblock2_denselayer12_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  cat_19                                             <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32, new_features_34], 1)                                                                                                                                                                                                              {}
+    call_function  cat_19                                             <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32, new_features_34], 1)                                                                                                                                                                                                              {}
     call_module    l__self___features_transition2_norm                L__self___features_transition2_norm                         (cat_19,)                                                                                                                                                                                                                                                                                                                                                                                                                                                           {}
     call_module    l__self___features_transition2_relu                L__self___features_transition2_relu                         (l__self___features_transition2_norm,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_transition2_conv                L__self___features_transition2_conv                         (l__self___features_transition2_relu,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_transition2_pool                L__self___features_transition2_pool                         (l__self___features_transition2_conv,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
-    call_function  concated_features_18                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
+    call_function  concated_features_18                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
     call_module    l__self___features_denseblock3_denselayer1_norm1   L__self___features_denseblock3_denselayer1_norm1            (concated_features_18,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer1_relu1   L__self___features_denseblock3_denselayer1_relu1            (l__self___features_denseblock3_denselayer1_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_36                               L__self___features_denseblock3_denselayer1_conv1            (l__self___features_denseblock3_denselayer1_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer1_norm2   L__self___features_denseblock3_denselayer1_norm2            (bottleneck_output_36,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer1_relu2   L__self___features_denseblock3_denselayer1_relu2            (l__self___features_denseblock3_denselayer1_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_36                                    L__self___features_denseblock3_denselayer1_conv2            (l__self___features_denseblock3_denselayer1_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_19                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
+    call_function  concated_features_19                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
     call_module    l__self___features_denseblock3_denselayer2_norm1   L__self___features_denseblock3_denselayer2_norm1            (concated_features_19,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer2_relu1   L__self___features_denseblock3_denselayer2_relu1            (l__self___features_denseblock3_denselayer2_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_38                               L__self___features_denseblock3_denselayer2_conv1            (l__self___features_denseblock3_denselayer2_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer2_norm2   L__self___features_denseblock3_denselayer2_norm2            (bottleneck_output_38,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer2_relu2   L__self___features_denseblock3_denselayer2_relu2            (l__self___features_denseblock3_denselayer2_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_38                                    L__self___features_denseblock3_denselayer2_conv2            (l__self___features_denseblock3_denselayer2_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_20                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
+    call_function  concated_features_20                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
     call_module    l__self___features_denseblock3_denselayer3_norm1   L__self___features_denseblock3_denselayer3_norm1            (concated_features_20,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer3_relu1   L__self___features_denseblock3_denselayer3_relu1            (l__self___features_denseblock3_denselayer3_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_40                               L__self___features_denseblock3_denselayer3_conv1            (l__self___features_denseblock3_denselayer3_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer3_norm2   L__self___features_denseblock3_denselayer3_norm2            (bottleneck_output_40,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer3_relu2   L__self___features_denseblock3_denselayer3_relu2            (l__self___features_denseblock3_denselayer3_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_40                                    L__self___features_denseblock3_denselayer3_conv2            (l__self___features_denseblock3_denselayer3_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_21                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
+    call_function  concated_features_21                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
     call_module    l__self___features_denseblock3_denselayer4_norm1   L__self___features_denseblock3_denselayer4_norm1            (concated_features_21,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer4_relu1   L__self___features_denseblock3_denselayer4_relu1            (l__self___features_denseblock3_denselayer4_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_42                               L__self___features_denseblock3_denselayer4_conv1            (l__self___features_denseblock3_denselayer4_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer4_norm2   L__self___features_denseblock3_denselayer4_norm2            (bottleneck_output_42,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer4_relu2   L__self___features_denseblock3_denselayer4_relu2            (l__self___features_denseblock3_denselayer4_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_42                                    L__self___features_denseblock3_denselayer4_conv2            (l__self___features_denseblock3_denselayer4_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_22                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42], 1)                                                                                                                                                                                                                                                                                                                                                      {}
+    call_function  concated_features_22                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42], 1)                                                                                                                                                                                                                                                                                                                                                      {}
     call_module    l__self___features_denseblock3_denselayer5_norm1   L__self___features_denseblock3_denselayer5_norm1            (concated_features_22,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer5_relu1   L__self___features_denseblock3_denselayer5_relu1            (l__self___features_denseblock3_denselayer5_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_44                               L__self___features_denseblock3_denselayer5_conv1            (l__self___features_denseblock3_denselayer5_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer5_norm2   L__self___features_denseblock3_denselayer5_norm2            (bottleneck_output_44,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer5_relu2   L__self___features_denseblock3_denselayer5_relu2            (l__self___features_denseblock3_denselayer5_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_44                                    L__self___features_denseblock3_denselayer5_conv2            (l__self___features_denseblock3_denselayer5_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_23                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44], 1)                                                                                                                                                                                                                                                                                                                                     {}
+    call_function  concated_features_23                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44], 1)                                                                                                                                                                                                                                                                                                                                     {}
     call_module    l__self___features_denseblock3_denselayer6_norm1   L__self___features_denseblock3_denselayer6_norm1            (concated_features_23,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer6_relu1   L__self___features_denseblock3_denselayer6_relu1            (l__self___features_denseblock3_denselayer6_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_46                               L__self___features_denseblock3_denselayer6_conv1            (l__self___features_denseblock3_denselayer6_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer6_norm2   L__self___features_denseblock3_denselayer6_norm2            (bottleneck_output_46,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer6_relu2   L__self___features_denseblock3_denselayer6_relu2            (l__self___features_denseblock3_denselayer6_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_46                                    L__self___features_denseblock3_denselayer6_conv2            (l__self___features_denseblock3_denselayer6_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_24                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46], 1)                                                                                                                                                                                                                                                                                                                    {}
+    call_function  concated_features_24                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46], 1)                                                                                                                                                                                                                                                                                                                    {}
     call_module    l__self___features_denseblock3_denselayer7_norm1   L__self___features_denseblock3_denselayer7_norm1            (concated_features_24,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer7_relu1   L__self___features_denseblock3_denselayer7_relu1            (l__self___features_denseblock3_denselayer7_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_48                               L__self___features_denseblock3_denselayer7_conv1            (l__self___features_denseblock3_denselayer7_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer7_norm2   L__self___features_denseblock3_denselayer7_norm2            (bottleneck_output_48,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer7_relu2   L__self___features_denseblock3_denselayer7_relu2            (l__self___features_denseblock3_denselayer7_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_48                                    L__self___features_denseblock3_denselayer7_conv2            (l__self___features_denseblock3_denselayer7_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_25                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48], 1)                                                                                                                                                                                                                                                                                                   {}
+    call_function  concated_features_25                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48], 1)                                                                                                                                                                                                                                                                                                   {}
     call_module    l__self___features_denseblock3_denselayer8_norm1   L__self___features_denseblock3_denselayer8_norm1            (concated_features_25,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer8_relu1   L__self___features_denseblock3_denselayer8_relu1            (l__self___features_denseblock3_denselayer8_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_50                               L__self___features_denseblock3_denselayer8_conv1            (l__self___features_denseblock3_denselayer8_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer8_norm2   L__self___features_denseblock3_denselayer8_norm2            (bottleneck_output_50,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer8_relu2   L__self___features_denseblock3_denselayer8_relu2            (l__self___features_denseblock3_denselayer8_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_50                                    L__self___features_denseblock3_denselayer8_conv2            (l__self___features_denseblock3_denselayer8_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_26                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50], 1)                                                                                                                                                                                                                                                                                  {}
+    call_function  concated_features_26                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50], 1)                                                                                                                                                                                                                                                                                  {}
     call_module    l__self___features_denseblock3_denselayer9_norm1   L__self___features_denseblock3_denselayer9_norm1            (concated_features_26,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer9_relu1   L__self___features_denseblock3_denselayer9_relu1            (l__self___features_denseblock3_denselayer9_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_52                               L__self___features_denseblock3_denselayer9_conv1            (l__self___features_denseblock3_denselayer9_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer9_norm2   L__self___features_denseblock3_denselayer9_norm2            (bottleneck_output_52,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer9_relu2   L__self___features_denseblock3_denselayer9_relu2            (l__self___features_denseblock3_denselayer9_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_52                                    L__self___features_denseblock3_denselayer9_conv2            (l__self___features_denseblock3_denselayer9_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_27                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52], 1)                                                                                                                                                                                                                                                                 {}
+    call_function  concated_features_27                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52], 1)                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock3_denselayer10_norm1  L__self___features_denseblock3_denselayer10_norm1           (concated_features_27,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer10_relu1  L__self___features_denseblock3_denselayer10_relu1           (l__self___features_denseblock3_denselayer10_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_54                               L__self___features_denseblock3_denselayer10_conv1           (l__self___features_denseblock3_denselayer10_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer10_norm2  L__self___features_denseblock3_denselayer10_norm2           (bottleneck_output_54,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer10_relu2  L__self___features_denseblock3_denselayer10_relu2           (l__self___features_denseblock3_denselayer10_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_54                                    L__self___features_denseblock3_denselayer10_conv2           (l__self___features_denseblock3_denselayer10_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_28                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54], 1)                                                                                                                                                                                                                                                {}
+    call_function  concated_features_28                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54], 1)                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer11_norm1  L__self___features_denseblock3_denselayer11_norm1           (concated_features_28,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer11_relu1  L__self___features_denseblock3_denselayer11_relu1           (l__self___features_denseblock3_denselayer11_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_56                               L__self___features_denseblock3_denselayer11_conv1           (l__self___features_denseblock3_denselayer11_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer11_norm2  L__self___features_denseblock3_denselayer11_norm2           (bottleneck_output_56,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer11_relu2  L__self___features_denseblock3_denselayer11_relu2           (l__self___features_denseblock3_denselayer11_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_56                                    L__self___features_denseblock3_denselayer11_conv2           (l__self___features_denseblock3_denselayer11_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_29                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56], 1)                                                                                                                                                                                                                               {}
+    call_function  concated_features_29                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56], 1)                                                                                                                                                                                                                               {}
     call_module    l__self___features_denseblock3_denselayer12_norm1  L__self___features_denseblock3_denselayer12_norm1           (concated_features_29,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer12_relu1  L__self___features_denseblock3_denselayer12_relu1           (l__self___features_denseblock3_denselayer12_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_58                               L__self___features_denseblock3_denselayer12_conv1           (l__self___features_denseblock3_denselayer12_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer12_norm2  L__self___features_denseblock3_denselayer12_norm2           (bottleneck_output_58,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer12_relu2  L__self___features_denseblock3_denselayer12_relu2           (l__self___features_denseblock3_denselayer12_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_58                                    L__self___features_denseblock3_denselayer12_conv2           (l__self___features_denseblock3_denselayer12_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_30                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58], 1)                                                                                                                                                                                                              {}
+    call_function  concated_features_30                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58], 1)                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock3_denselayer13_norm1  L__self___features_denseblock3_denselayer13_norm1           (concated_features_30,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer13_relu1  L__self___features_denseblock3_denselayer13_relu1           (l__self___features_denseblock3_denselayer13_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_60                               L__self___features_denseblock3_denselayer13_conv1           (l__self___features_denseblock3_denselayer13_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer13_norm2  L__self___features_denseblock3_denselayer13_norm2           (bottleneck_output_60,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer13_relu2  L__self___features_denseblock3_denselayer13_relu2           (l__self___features_denseblock3_denselayer13_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_60                                    L__self___features_denseblock3_denselayer13_conv2           (l__self___features_denseblock3_denselayer13_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_31                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60], 1)                                                                                                                                                                                             {}
+    call_function  concated_features_31                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60], 1)                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer14_norm1  L__self___features_denseblock3_denselayer14_norm1           (concated_features_31,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer14_relu1  L__self___features_denseblock3_denselayer14_relu1           (l__self___features_denseblock3_denselayer14_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_62                               L__self___features_denseblock3_denselayer14_conv1           (l__self___features_denseblock3_denselayer14_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer14_norm2  L__self___features_denseblock3_denselayer14_norm2           (bottleneck_output_62,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer14_relu2  L__self___features_denseblock3_denselayer14_relu2           (l__self___features_denseblock3_denselayer14_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_62                                    L__self___features_denseblock3_denselayer14_conv2           (l__self___features_denseblock3_denselayer14_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_32                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62], 1)                                                                                                                                                                            {}
+    call_function  concated_features_32                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62], 1)                                                                                                                                                                            {}
     call_module    l__self___features_denseblock3_denselayer15_norm1  L__self___features_denseblock3_denselayer15_norm1           (concated_features_32,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer15_relu1  L__self___features_denseblock3_denselayer15_relu1           (l__self___features_denseblock3_denselayer15_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_64                               L__self___features_denseblock3_denselayer15_conv1           (l__self___features_denseblock3_denselayer15_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer15_norm2  L__self___features_denseblock3_denselayer15_norm2           (bottleneck_output_64,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer15_relu2  L__self___features_denseblock3_denselayer15_relu2           (l__self___features_denseblock3_denselayer15_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_64                                    L__self___features_denseblock3_denselayer15_conv2           (l__self___features_denseblock3_denselayer15_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_33                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64], 1)                                                                                                                                                           {}
+    call_function  concated_features_33                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64], 1)                                                                                                                                                           {}
     call_module    l__self___features_denseblock3_denselayer16_norm1  L__self___features_denseblock3_denselayer16_norm1           (concated_features_33,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer16_relu1  L__self___features_denseblock3_denselayer16_relu1           (l__self___features_denseblock3_denselayer16_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_66                               L__self___features_denseblock3_denselayer16_conv1           (l__self___features_denseblock3_denselayer16_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer16_norm2  L__self___features_denseblock3_denselayer16_norm2           (bottleneck_output_66,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer16_relu2  L__self___features_denseblock3_denselayer16_relu2           (l__self___features_denseblock3_denselayer16_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_66                                    L__self___features_denseblock3_denselayer16_conv2           (l__self___features_denseblock3_denselayer16_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_34                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66], 1)                                                                                                                                          {}
+    call_function  concated_features_34                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66], 1)                                                                                                                                          {}
     call_module    l__self___features_denseblock3_denselayer17_norm1  L__self___features_denseblock3_denselayer17_norm1           (concated_features_34,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer17_relu1  L__self___features_denseblock3_denselayer17_relu1           (l__self___features_denseblock3_denselayer17_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_68                               L__self___features_denseblock3_denselayer17_conv1           (l__self___features_denseblock3_denselayer17_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer17_norm2  L__self___features_denseblock3_denselayer17_norm2           (bottleneck_output_68,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer17_relu2  L__self___features_denseblock3_denselayer17_relu2           (l__self___features_denseblock3_denselayer17_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_68                                    L__self___features_denseblock3_denselayer17_conv2           (l__self___features_denseblock3_denselayer17_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_35                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68], 1)                                                                                                                         {}
+    call_function  concated_features_35                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68], 1)                                                                                                                         {}
     call_module    l__self___features_denseblock3_denselayer18_norm1  L__self___features_denseblock3_denselayer18_norm1           (concated_features_35,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer18_relu1  L__self___features_denseblock3_denselayer18_relu1           (l__self___features_denseblock3_denselayer18_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_70                               L__self___features_denseblock3_denselayer18_conv1           (l__self___features_denseblock3_denselayer18_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer18_norm2  L__self___features_denseblock3_denselayer18_norm2           (bottleneck_output_70,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer18_relu2  L__self___features_denseblock3_denselayer18_relu2           (l__self___features_denseblock3_denselayer18_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_70                                    L__self___features_denseblock3_denselayer18_conv2           (l__self___features_denseblock3_denselayer18_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_36                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70], 1)                                                                                                        {}
+    call_function  concated_features_36                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70], 1)                                                                                                        {}
     call_module    l__self___features_denseblock3_denselayer19_norm1  L__self___features_denseblock3_denselayer19_norm1           (concated_features_36,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer19_relu1  L__self___features_denseblock3_denselayer19_relu1           (l__self___features_denseblock3_denselayer19_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_72                               L__self___features_denseblock3_denselayer19_conv1           (l__self___features_denseblock3_denselayer19_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer19_norm2  L__self___features_denseblock3_denselayer19_norm2           (bottleneck_output_72,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer19_relu2  L__self___features_denseblock3_denselayer19_relu2           (l__self___features_denseblock3_denselayer19_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_72                                    L__self___features_denseblock3_denselayer19_conv2           (l__self___features_denseblock3_denselayer19_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_37                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72], 1)                                                                                       {}
+    call_function  concated_features_37                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72], 1)                                                                                       {}
     call_module    l__self___features_denseblock3_denselayer20_norm1  L__self___features_denseblock3_denselayer20_norm1           (concated_features_37,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer20_relu1  L__self___features_denseblock3_denselayer20_relu1           (l__self___features_denseblock3_denselayer20_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_74                               L__self___features_denseblock3_denselayer20_conv1           (l__self___features_denseblock3_denselayer20_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer20_norm2  L__self___features_denseblock3_denselayer20_norm2           (bottleneck_output_74,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer20_relu2  L__self___features_denseblock3_denselayer20_relu2           (l__self___features_denseblock3_denselayer20_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_74                                    L__self___features_denseblock3_denselayer20_conv2           (l__self___features_denseblock3_denselayer20_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_38                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74], 1)                                                                      {}
+    call_function  concated_features_38                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74], 1)                                                                      {}
     call_module    l__self___features_denseblock3_denselayer21_norm1  L__self___features_denseblock3_denselayer21_norm1           (concated_features_38,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer21_relu1  L__self___features_denseblock3_denselayer21_relu1           (l__self___features_denseblock3_denselayer21_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_76                               L__self___features_denseblock3_denselayer21_conv1           (l__self___features_denseblock3_denselayer21_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer21_norm2  L__self___features_denseblock3_denselayer21_norm2           (bottleneck_output_76,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer21_relu2  L__self___features_denseblock3_denselayer21_relu2           (l__self___features_denseblock3_denselayer21_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_76                                    L__self___features_denseblock3_denselayer21_conv2           (l__self___features_denseblock3_denselayer21_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_39                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76], 1)                                                     {}
+    call_function  concated_features_39                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76], 1)                                                     {}
     call_module    l__self___features_denseblock3_denselayer22_norm1  L__self___features_denseblock3_denselayer22_norm1           (concated_features_39,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer22_relu1  L__self___features_denseblock3_denselayer22_relu1           (l__self___features_denseblock3_denselayer22_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_78                               L__self___features_denseblock3_denselayer22_conv1           (l__self___features_denseblock3_denselayer22_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer22_norm2  L__self___features_denseblock3_denselayer22_norm2           (bottleneck_output_78,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer22_relu2  L__self___features_denseblock3_denselayer22_relu2           (l__self___features_denseblock3_denselayer22_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_78                                    L__self___features_denseblock3_denselayer22_conv2           (l__self___features_denseblock3_denselayer22_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_40                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78], 1)                                    {}
+    call_function  concated_features_40                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78], 1)                                    {}
     call_module    l__self___features_denseblock3_denselayer23_norm1  L__self___features_denseblock3_denselayer23_norm1           (concated_features_40,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer23_relu1  L__self___features_denseblock3_denselayer23_relu1           (l__self___features_denseblock3_denselayer23_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_80                               L__self___features_denseblock3_denselayer23_conv1           (l__self___features_denseblock3_denselayer23_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer23_norm2  L__self___features_denseblock3_denselayer23_norm2           (bottleneck_output_80,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer23_relu2  L__self___features_denseblock3_denselayer23_relu2           (l__self___features_denseblock3_denselayer23_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_80                                    L__self___features_denseblock3_denselayer23_conv2           (l__self___features_denseblock3_denselayer23_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_41                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80], 1)                   {}
+    call_function  concated_features_41                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80], 1)                   {}
     call_module    l__self___features_denseblock3_denselayer24_norm1  L__self___features_denseblock3_denselayer24_norm1           (concated_features_41,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer24_relu1  L__self___features_denseblock3_denselayer24_relu1           (l__self___features_denseblock3_denselayer24_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_82                               L__self___features_denseblock3_denselayer24_conv1           (l__self___features_denseblock3_denselayer24_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock3_denselayer24_norm2  L__self___features_denseblock3_denselayer24_norm2           (bottleneck_output_82,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock3_denselayer24_relu2  L__self___features_denseblock3_denselayer24_relu2           (l__self___features_denseblock3_denselayer24_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_82                                    L__self___features_denseblock3_denselayer24_conv2           (l__self___features_denseblock3_denselayer24_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  cat_44                                             <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80, new_features_82], 1)  {}
+    call_function  cat_44                                             <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80, new_features_82], 1)  {}
     call_module    l__self___features_transition3_norm                L__self___features_transition3_norm                         (cat_44,)                                                                                                                                                                                                                                                                                                                                                                                                                                                           {}
     call_module    l__self___features_transition3_relu                L__self___features_transition3_relu                         (l__self___features_transition3_norm,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_transition3_conv                L__self___features_transition3_conv                         (l__self___features_transition3_relu,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
     call_module    l__self___features_transition3_pool                L__self___features_transition3_pool                         (l__self___features_transition3_conv,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
-    call_function  concated_features_42                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
+    call_function  concated_features_42                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
     call_module    l__self___features_denseblock4_denselayer1_norm1   L__self___features_denseblock4_denselayer1_norm1            (concated_features_42,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer1_relu1   L__self___features_denseblock4_denselayer1_relu1            (l__self___features_denseblock4_denselayer1_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_84                               L__self___features_denseblock4_denselayer1_conv1            (l__self___features_denseblock4_denselayer1_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock4_denselayer1_norm2   L__self___features_denseblock4_denselayer1_norm2            (bottleneck_output_84,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer1_relu2   L__self___features_denseblock4_denselayer1_relu2            (l__self___features_denseblock4_denselayer1_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_84                                    L__self___features_denseblock4_denselayer1_conv2            (l__self___features_denseblock4_denselayer1_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_43                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
+    call_function  concated_features_43                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
     call_module    l__self___features_denseblock4_denselayer2_norm1   L__self___features_denseblock4_denselayer2_norm1            (concated_features_43,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer2_relu1   L__self___features_denseblock4_denselayer2_relu1            (l__self___features_denseblock4_denselayer2_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_86                               L__self___features_denseblock4_denselayer2_conv1            (l__self___features_denseblock4_denselayer2_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock4_denselayer2_norm2   L__self___features_denseblock4_denselayer2_norm2            (bottleneck_output_86,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer2_relu2   L__self___features_denseblock4_denselayer2_relu2            (l__self___features_denseblock4_denselayer2_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_86                                    L__self___features_denseblock4_denselayer2_conv2            (l__self___features_denseblock4_denselayer2_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_44                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
+    call_function  concated_features_44                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
     call_module    l__self___features_denseblock4_denselayer3_norm1   L__self___features_denseblock4_denselayer3_norm1            (concated_features_44,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer3_relu1   L__self___features_denseblock4_denselayer3_relu1            (l__self___features_denseblock4_denselayer3_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_88                               L__self___features_denseblock4_denselayer3_conv1            (l__self___features_denseblock4_denselayer3_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock4_denselayer3_norm2   L__self___features_denseblock4_denselayer3_norm2            (bottleneck_output_88,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer3_relu2   L__self___features_denseblock4_denselayer3_relu2            (l__self___features_denseblock4_denselayer3_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_88                                    L__self___features_denseblock4_denselayer3_conv2            (l__self___features_denseblock4_denselayer3_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_45                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
+    call_function  concated_features_45                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
     call_module    l__self___features_denseblock4_denselayer4_norm1   L__self___features_denseblock4_denselayer4_norm1            (concated_features_45,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer4_relu1   L__self___features_denseblock4_denselayer4_relu1            (l__self___features_denseblock4_denselayer4_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_90                               L__self___features_denseblock4_denselayer4_conv1            (l__self___features_denseblock4_denselayer4_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock4_denselayer4_norm2   L__self___features_denseblock4_denselayer4_norm2            (bottleneck_output_90,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer4_relu2   L__self___features_denseblock4_denselayer4_relu2            (l__self___features_denseblock4_denselayer4_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_90                                    L__self___features_denseblock4_denselayer4_conv2            (l__self___features_denseblock4_denselayer4_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_46                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90], 1)                                                                                                                                                                                                                                                                                                                                                      {}
+    call_function  concated_features_46                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90], 1)                                                                                                                                                                                                                                                                                                                                                      {}
     call_module    l__self___features_denseblock4_denselayer5_norm1   L__self___features_denseblock4_denselayer5_norm1            (concated_features_46,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer5_relu1   L__self___features_denseblock4_denselayer5_relu1            (l__self___features_denseblock4_denselayer5_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_92                               L__self___features_denseblock4_denselayer5_conv1            (l__self___features_denseblock4_denselayer5_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock4_denselayer5_norm2   L__self___features_denseblock4_denselayer5_norm2            (bottleneck_output_92,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer5_relu2   L__self___features_denseblock4_denselayer5_relu2            (l__self___features_denseblock4_denselayer5_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_92                                    L__self___features_denseblock4_denselayer5_conv2            (l__self___features_denseblock4_denselayer5_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_47                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92], 1)                                                                                                                                                                                                                                                                                                                                     {}
+    call_function  concated_features_47                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92], 1)                                                                                                                                                                                                                                                                                                                                     {}
     call_module    l__self___features_denseblock4_denselayer6_norm1   L__self___features_denseblock4_denselayer6_norm1            (concated_features_47,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer6_relu1   L__self___features_denseblock4_denselayer6_relu1            (l__self___features_denseblock4_denselayer6_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_94                               L__self___features_denseblock4_denselayer6_conv1            (l__self___features_denseblock4_denselayer6_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock4_denselayer6_norm2   L__self___features_denseblock4_denselayer6_norm2            (bottleneck_output_94,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer6_relu2   L__self___features_denseblock4_denselayer6_relu2            (l__self___features_denseblock4_denselayer6_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_94                                    L__self___features_denseblock4_denselayer6_conv2            (l__self___features_denseblock4_denselayer6_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_48                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94], 1)                                                                                                                                                                                                                                                                                                                    {}
+    call_function  concated_features_48                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94], 1)                                                                                                                                                                                                                                                                                                                    {}
     call_module    l__self___features_denseblock4_denselayer7_norm1   L__self___features_denseblock4_denselayer7_norm1            (concated_features_48,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer7_relu1   L__self___features_denseblock4_denselayer7_relu1            (l__self___features_denseblock4_denselayer7_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_96                               L__self___features_denseblock4_denselayer7_conv1            (l__self___features_denseblock4_denselayer7_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock4_denselayer7_norm2   L__self___features_denseblock4_denselayer7_norm2            (bottleneck_output_96,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer7_relu2   L__self___features_denseblock4_denselayer7_relu2            (l__self___features_denseblock4_denselayer7_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_96                                    L__self___features_denseblock4_denselayer7_conv2            (l__self___features_denseblock4_denselayer7_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_49                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96], 1)                                                                                                                                                                                                                                                                                                   {}
+    call_function  concated_features_49                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96], 1)                                                                                                                                                                                                                                                                                                   {}
     call_module    l__self___features_denseblock4_denselayer8_norm1   L__self___features_denseblock4_denselayer8_norm1            (concated_features_49,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer8_relu1   L__self___features_denseblock4_denselayer8_relu1            (l__self___features_denseblock4_denselayer8_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_98                               L__self___features_denseblock4_denselayer8_conv1            (l__self___features_denseblock4_denselayer8_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock4_denselayer8_norm2   L__self___features_denseblock4_denselayer8_norm2            (bottleneck_output_98,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer8_relu2   L__self___features_denseblock4_denselayer8_relu2            (l__self___features_denseblock4_denselayer8_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_98                                    L__self___features_denseblock4_denselayer8_conv2            (l__self___features_denseblock4_denselayer8_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_50                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98], 1)                                                                                                                                                                                                                                                                                  {}
+    call_function  concated_features_50                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98], 1)                                                                                                                                                                                                                                                                                  {}
     call_module    l__self___features_denseblock4_denselayer9_norm1   L__self___features_denseblock4_denselayer9_norm1            (concated_features_50,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer9_relu1   L__self___features_denseblock4_denselayer9_relu1            (l__self___features_denseblock4_denselayer9_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    bottleneck_output_100                              L__self___features_denseblock4_denselayer9_conv1            (l__self___features_denseblock4_denselayer9_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    l__self___features_denseblock4_denselayer9_norm2   L__self___features_denseblock4_denselayer9_norm2            (bottleneck_output_100,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     call_module    l__self___features_denseblock4_denselayer9_relu2   L__self___features_denseblock4_denselayer9_relu2            (l__self___features_denseblock4_denselayer9_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
     call_module    new_features_100                                   L__self___features_denseblock4_denselayer9_conv2            (l__self___features_denseblock4_denselayer9_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-    call_function  concated_features_51                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100], 1)                                                                                                                                                                                                                                                                {}
+    call_function  concated_features_51                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100], 1)                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock4_denselayer10_norm1  L__self___features_denseblock4_denselayer10_norm1           (concated_features_51,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer10_relu1  L__self___features_denseblock4_denselayer10_relu1           (l__self___features_denseblock4_denselayer10_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_102                              L__self___features_denseblock4_denselayer10_conv1           (l__self___features_denseblock4_denselayer10_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock4_denselayer10_norm2  L__self___features_denseblock4_denselayer10_norm2           (bottleneck_output_102,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     call_module    l__self___features_denseblock4_denselayer10_relu2  L__self___features_denseblock4_denselayer10_relu2           (l__self___features_denseblock4_denselayer10_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_102                                   L__self___features_denseblock4_denselayer10_conv2           (l__self___features_denseblock4_denselayer10_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_52                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102], 1)                                                                                                                                                                                                                                              {}
+    call_function  concated_features_52                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102], 1)                                                                                                                                                                                                                                              {}
     call_module    l__self___features_denseblock4_denselayer11_norm1  L__self___features_denseblock4_denselayer11_norm1           (concated_features_52,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer11_relu1  L__self___features_denseblock4_denselayer11_relu1           (l__self___features_denseblock4_denselayer11_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_104                              L__self___features_denseblock4_denselayer11_conv1           (l__self___features_denseblock4_denselayer11_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock4_denselayer11_norm2  L__self___features_denseblock4_denselayer11_norm2           (bottleneck_output_104,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     call_module    l__self___features_denseblock4_denselayer11_relu2  L__self___features_denseblock4_denselayer11_relu2           (l__self___features_denseblock4_denselayer11_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_104                                   L__self___features_denseblock4_denselayer11_conv2           (l__self___features_denseblock4_denselayer11_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_53                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104], 1)                                                                                                                                                                                                                            {}
+    call_function  concated_features_53                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104], 1)                                                                                                                                                                                                                            {}
     call_module    l__self___features_denseblock4_denselayer12_norm1  L__self___features_denseblock4_denselayer12_norm1           (concated_features_53,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer12_relu1  L__self___features_denseblock4_denselayer12_relu1           (l__self___features_denseblock4_denselayer12_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_106                              L__self___features_denseblock4_denselayer12_conv1           (l__self___features_denseblock4_denselayer12_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock4_denselayer12_norm2  L__self___features_denseblock4_denselayer12_norm2           (bottleneck_output_106,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     call_module    l__self___features_denseblock4_denselayer12_relu2  L__self___features_denseblock4_denselayer12_relu2           (l__self___features_denseblock4_denselayer12_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_106                                   L__self___features_denseblock4_denselayer12_conv2           (l__self___features_denseblock4_denselayer12_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_54                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106], 1)                                                                                                                                                                                                          {}
+    call_function  concated_features_54                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106], 1)                                                                                                                                                                                                          {}
     call_module    l__self___features_denseblock4_denselayer13_norm1  L__self___features_denseblock4_denselayer13_norm1           (concated_features_54,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer13_relu1  L__self___features_denseblock4_denselayer13_relu1           (l__self___features_denseblock4_denselayer13_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_108                              L__self___features_denseblock4_denselayer13_conv1           (l__self___features_denseblock4_denselayer13_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock4_denselayer13_norm2  L__self___features_denseblock4_denselayer13_norm2           (bottleneck_output_108,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     call_module    l__self___features_denseblock4_denselayer13_relu2  L__self___features_denseblock4_denselayer13_relu2           (l__self___features_denseblock4_denselayer13_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_108                                   L__self___features_denseblock4_denselayer13_conv2           (l__self___features_denseblock4_denselayer13_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_55                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108], 1)                                                                                                                                                                                        {}
+    call_function  concated_features_55                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108], 1)                                                                                                                                                                                        {}
     call_module    l__self___features_denseblock4_denselayer14_norm1  L__self___features_denseblock4_denselayer14_norm1           (concated_features_55,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer14_relu1  L__self___features_denseblock4_denselayer14_relu1           (l__self___features_denseblock4_denselayer14_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_110                              L__self___features_denseblock4_denselayer14_conv1           (l__self___features_denseblock4_denselayer14_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock4_denselayer14_norm2  L__self___features_denseblock4_denselayer14_norm2           (bottleneck_output_110,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     call_module    l__self___features_denseblock4_denselayer14_relu2  L__self___features_denseblock4_denselayer14_relu2           (l__self___features_denseblock4_denselayer14_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_110                                   L__self___features_denseblock4_denselayer14_conv2           (l__self___features_denseblock4_denselayer14_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_56                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110], 1)                                                                                                                                                                      {}
+    call_function  concated_features_56                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110], 1)                                                                                                                                                                      {}
     call_module    l__self___features_denseblock4_denselayer15_norm1  L__self___features_denseblock4_denselayer15_norm1           (concated_features_56,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer15_relu1  L__self___features_denseblock4_denselayer15_relu1           (l__self___features_denseblock4_denselayer15_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_112                              L__self___features_denseblock4_denselayer15_conv1           (l__self___features_denseblock4_denselayer15_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock4_denselayer15_norm2  L__self___features_denseblock4_denselayer15_norm2           (bottleneck_output_112,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     call_module    l__self___features_denseblock4_denselayer15_relu2  L__self___features_denseblock4_denselayer15_relu2           (l__self___features_denseblock4_denselayer15_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_112                                   L__self___features_denseblock4_denselayer15_conv2           (l__self___features_denseblock4_denselayer15_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  concated_features_57                               <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112], 1)                                                                                                                                                    {}
+    call_function  concated_features_57                               <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112], 1)                                                                                                                                                    {}
     call_module    l__self___features_denseblock4_denselayer16_norm1  L__self___features_denseblock4_denselayer16_norm1           (concated_features_57,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
     call_module    l__self___features_denseblock4_denselayer16_relu1  L__self___features_denseblock4_denselayer16_relu1           (l__self___features_denseblock4_denselayer16_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    bottleneck_output_114                              L__self___features_denseblock4_denselayer16_conv1           (l__self___features_denseblock4_denselayer16_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    l__self___features_denseblock4_denselayer16_norm2  L__self___features_denseblock4_denselayer16_norm2           (bottleneck_output_114,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     call_module    l__self___features_denseblock4_denselayer16_relu2  L__self___features_denseblock4_denselayer16_relu2           (l__self___features_denseblock4_denselayer16_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
     call_module    new_features_114                                   L__self___features_denseblock4_denselayer16_conv2           (l__self___features_denseblock4_denselayer16_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-    call_function  cat_61                                             <built-in method cat of type object at 0x7f0913c11760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112, new_features_114], 1)                                                                                                                                  {}
+    call_function  cat_61                                             <built-in method cat of type object at 0x7f3d04955760>      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112, new_features_114], 1)                                                                                                                                  {}
     call_module    features                                           L__self___features_norm5                                    (cat_61,)                                                                                                                                                                                                                                                                                                                                                                                                                                                           {}
-    call_function  out                                                <function relu at 0x7f083bd6d510>                           (features,)                                                                                                                                                                                                                                                                                                                                                                                                                                                         {'inplace': True}
-    call_function  out_1                                              <function adaptive_avg_pool2d at 0x7f083bd6d000>            (out, (1, 1))                                                                                                                                                                                                                                                                                                                                                                                                                                                       {}
-    call_function  out_2                                              <built-in method flatten of type object at 0x7f0913c11760>  (out_1, 1)                                                                                                                                                                                                                                                                                                                                                                                                                                                          {}
+    call_function  out                                                <function relu at 0x7f3c2cab1510>                           (features,)                                                                                                                                                                                                                                                                                                                                                                                                                                                         {'inplace': True}
+    call_function  out_1                                              <function adaptive_avg_pool2d at 0x7f3c2cab1000>            (out, (1, 1))                                                                                                                                                                                                                                                                                                                                                                                                                                                       {}
+    call_function  out_2                                              <built-in method flatten of type object at 0x7f3d04955760>  (out_1, 1)                                                                                                                                                                                                                                                                                                                                                                                                                                                          {}
     call_module    out_3                                              L__self___classifier                                        (out_2,)                                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
     output         output                                             output                                                      ((out_3,),)                                                                                                                                                                                                                                                                                                                                                                                                                                                         {}
 
@@ -1483,7 +1483,7 @@ data-dependent control flow. Consider the function below, where the line
     -------------  ------  ------------------------------------------------------  -----------  --------
     placeholder    l_a_    L_a_                                                    ()           {}
     placeholder    l_b_    L_b_                                                    ()           {}
-    call_function  abs_1   <built-in method abs of type object at 0x7f0913c11760>  (l_a_,)      {}
+    call_function  abs_1   <built-in method abs of type object at 0x7f3d04955760>  (l_a_,)      {}
     call_function  add     <built-in function add>                                 (abs_1, 1)   {}
     call_function  x       <built-in function truediv>                             (l_a_, add)  {}
     call_method    sum_1   sum                                                     (l_b_,)      {}
@@ -1565,7 +1565,7 @@ We can see where TorchDynamo breaks the graph by using ``torch._dynamo.explain``
           <FrameSummary file /var/lib/workspace/intermediate_source/torch_compile_tutorial.py, line 434 in bar>
     Ops per Graph:
       Ops 1:
-        <built-in method abs of type object at 0x7f0913c11760>
+        <built-in method abs of type object at 0x7f3d04955760>
         <built-in function add>
         <built-in function truediv>
         <built-in function lt>
@@ -1574,17 +1574,17 @@ We can see where TorchDynamo breaks the graph by using ``torch._dynamo.explain``
         <built-in function mul>
     Out Guards:
       Guard 1:
-        Name: "L['b']"
+        Name: "L['a']"
         Source: local
         Create Function: TENSOR_MATCH
         Guard Types: ['TENSOR_MATCH']
-        Code List: ["hasattr(L['b'], '_dynamo_dynamic_indices') == False"]
-        Object Weakref: <weakref at 0x7f072277a8e0; dead>
-        Guarded Class Weakref: <weakref at 0x7f083c3ffe20; to 'torch._C._TensorMeta' at 0x61ca680 (Tensor)>
+        Code List: ["hasattr(L['a'], '_dynamo_dynamic_indices') == False"]
+        Object Weakref: <weakref at 0x7f3b1385c450; dead>
+        Guarded Class Weakref: <weakref at 0x7f3c2d143e20; to 'torch._C._TensorMeta' at 0x5447680 (Tensor)>
       Guard 2:
         Name: ''
         Source: global
-        Create Function: DETERMINISTIC_ALGORITHMS
+        Create Function: TORCH_FUNCTION_STATE
         Guard Types: None
         Code List: None
         Object Weakref: None
@@ -1598,22 +1598,14 @@ We can see where TorchDynamo breaks the graph by using ``torch._dynamo.explain``
         Object Weakref: None
         Guarded Class Weakref: None
       Guard 4:
-        Name: ''
+        Name: "G['torch'].abs"
         Source: global
-        Create Function: TORCH_FUNCTION_STATE
+        Create Function: FUNCTION_MATCH
         Guard Types: None
         Code List: None
         Object Weakref: None
         Guarded Class Weakref: None
       Guard 5:
-        Name: ''
-        Source: shape_env
-        Create Function: SHAPE_ENV
-        Guard Types: None
-        Code List: None
-        Object Weakref: None
-        Guarded Class Weakref: None
-      Guard 6:
         Name: ''
         Source: global
         Create Function: DEFAULT_DEVICE
@@ -1621,31 +1613,23 @@ We can see where TorchDynamo breaks the graph by using ``torch._dynamo.explain``
         Code List: ['utils_device.CURRENT_DEVICE == None']
         Object Weakref: None
         Guarded Class Weakref: None
-      Guard 7:
-        Name: "G['torch'].abs"
-        Source: global
-        Create Function: FUNCTION_MATCH
+      Guard 6:
+        Name: ''
+        Source: shape_env
+        Create Function: SHAPE_ENV
         Guard Types: None
         Code List: None
         Object Weakref: None
         Guarded Class Weakref: None
-      Guard 8:
+      Guard 7:
         Name: ''
         Source: global
         Create Function: BACKEND_MATCH
         Guard Types: ['BACKEND_MATCH']
-        Code List: ['___check_current_backend(139670084077168)']
+        Code List: ['___check_current_backend(139891651057920)']
         Object Weakref: None
         Guarded Class Weakref: None
-      Guard 9:
-        Name: "L['a']"
-        Source: local
-        Create Function: TENSOR_MATCH
-        Guard Types: ['TENSOR_MATCH']
-        Code List: ["hasattr(L['a'], '_dynamo_dynamic_indices') == False"]
-        Object Weakref: <weakref at 0x7f078d95ee30; dead>
-        Guarded Class Weakref: <weakref at 0x7f083c3ffe20; to 'torch._C._TensorMeta' at 0x61ca680 (Tensor)>
-      Guard 10:
+      Guard 8:
         Name: ''
         Source: global
         Create Function: GRAD_MODE
@@ -1653,28 +1637,44 @@ We can see where TorchDynamo breaks the graph by using ``torch._dynamo.explain``
         Code List: None
         Object Weakref: None
         Guarded Class Weakref: None
-      Guard 11:
+      Guard 9:
+        Name: ''
+        Source: global
+        Create Function: DETERMINISTIC_ALGORITHMS
+        Guard Types: None
+        Code List: None
+        Object Weakref: None
+        Guarded Class Weakref: None
+      Guard 10:
         Name: "L['b']"
         Source: local
         Create Function: TENSOR_MATCH
         Guard Types: ['TENSOR_MATCH']
         Code List: ["hasattr(L['b'], '_dynamo_dynamic_indices') == False"]
-        Object Weakref: <weakref at 0x7f072277a8e0; dead>
-        Guarded Class Weakref: <weakref at 0x7f083c3ffe20; to 'torch._C._TensorMeta' at 0x61ca680 (Tensor)>
-      Guard 12:
+        Object Weakref: <weakref at 0x7f3b69ec48b0; dead>
+        Guarded Class Weakref: <weakref at 0x7f3c2d143e20; to 'torch._C._TensorMeta' at 0x5447680 (Tensor)>
+      Guard 11:
         Name: ''
         Source: global
-        Create Function: DETERMINISTIC_ALGORITHMS
+        Create Function: TORCH_FUNCTION_STATE
         Guard Types: None
         Code List: None
         Object Weakref: None
         Guarded Class Weakref: None
+      Guard 12:
+        Name: "L['x']"
+        Source: local
+        Create Function: TENSOR_MATCH
+        Guard Types: ['TENSOR_MATCH']
+        Code List: ["hasattr(L['x'], '_dynamo_dynamic_indices') == False"]
+        Object Weakref: <weakref at 0x7f3b109e8bd0; dead>
+        Guarded Class Weakref: <weakref at 0x7f3c2d143e20; to 'torch._C._TensorMeta' at 0x5447680 (Tensor)>
       Guard 13:
         Name: ''
         Source: global
-        Create Function: TORCH_FUNCTION_STATE
-        Guard Types: None
-        Code List: None
+        Create Function: DEFAULT_DEVICE
+        Guard Types: ['DEFAULT_DEVICE']
+        Code List: ['utils_device.CURRENT_DEVICE == None']
         Object Weakref: None
         Guarded Class Weakref: None
       Guard 14:
@@ -1686,30 +1686,22 @@ We can see where TorchDynamo breaks the graph by using ``torch._dynamo.explain``
         Object Weakref: None
         Guarded Class Weakref: None
       Guard 15:
-        Name: ''
-        Source: global
-        Create Function: DEFAULT_DEVICE
-        Guard Types: ['DEFAULT_DEVICE']
-        Code List: ['utils_device.CURRENT_DEVICE == None']
-        Object Weakref: None
-        Guarded Class Weakref: None
-      Guard 16:
         Name: ''
         Source: global
         Create Function: BACKEND_MATCH
         Guard Types: ['BACKEND_MATCH']
-        Code List: ['___check_current_backend(139670084077168)']
+        Code List: ['___check_current_backend(139891651057920)']
         Object Weakref: None
         Guarded Class Weakref: None
-      Guard 17:
-        Name: "L['x']"
+      Guard 16:
+        Name: "L['b']"
         Source: local
         Create Function: TENSOR_MATCH
         Guard Types: ['TENSOR_MATCH']
-        Code List: ["hasattr(L['x'], '_dynamo_dynamic_indices') == False"]
-        Object Weakref: <weakref at 0x7f084adb68e0; dead>
-        Guarded Class Weakref: <weakref at 0x7f083c3ffe20; to 'torch._C._TensorMeta' at 0x61ca680 (Tensor)>
-      Guard 18:
+        Code List: ["hasattr(L['b'], '_dynamo_dynamic_indices') == False"]
+        Object Weakref: <weakref at 0x7f3b69ec48b0; dead>
+        Guarded Class Weakref: <weakref at 0x7f3c2d143e20; to 'torch._C._TensorMeta' at 0x5447680 (Tensor)>
+      Guard 17:
         Name: ''
         Source: global
         Create Function: GRAD_MODE
@@ -1717,10 +1709,18 @@ We can see where TorchDynamo breaks the graph by using ``torch._dynamo.explain``
         Code List: None
         Object Weakref: None
         Guarded Class Weakref: None
+      Guard 18:
+        Name: ''
+        Source: global
+        Create Function: DETERMINISTIC_ALGORITHMS
+        Guard Types: None
+        Code List: None
+        Object Weakref: None
+        Guarded Class Weakref: None
     Compile Times: TorchDynamo compilation metrics:
     Function                         Runtimes (s)
     -------------------------------  --------------
-    _compile.<locals>.compile_inner  0.0111, 0.0067
+    _compile.<locals>.compile_inner  0.0109, 0.0067
     OutputGraph.call_user_compiler   0.0001, 0.0000
 
 
@@ -1821,13 +1821,13 @@ the model we used above for demonstrating speedups.
 
  .. code-block:: none
 
-    tensor([[ 0.1344,  0.1976, -0.2056,  ...,  0.1861, -0.1632, -0.1642],
-            [ 0.2680,  0.2349, -0.1903,  ...,  0.2170, -0.0055,  0.0309],
-            [ 0.0164,  0.2180, -0.1115,  ...,  0.1709, -0.1681, -0.0635],
+    tensor([[ 0.1343,  0.1977, -0.2055,  ...,  0.1862, -0.1632, -0.1640],
+            [ 0.2683,  0.2349, -0.1905,  ...,  0.2167, -0.0055,  0.0311],
+            [ 0.0162,  0.2181, -0.1115,  ...,  0.1708, -0.1679, -0.0636],
             ...,
-            [ 0.0807,  0.2680, -0.1889,  ...,  0.0381, -0.2073, -0.1444],
-            [-0.0209,  0.0858, -0.2457,  ...,  0.1862, -0.1280, -0.0282],
-            [-0.0386,  0.0730, -0.1961,  ...,  0.0864, -0.2199, -0.1485]],
+            [ 0.0805,  0.2680, -0.1888,  ...,  0.0382, -0.2072, -0.1445],
+            [-0.0210,  0.0859, -0.2458,  ...,  0.1863, -0.1280, -0.0282],
+            [-0.0387,  0.0729, -0.1960,  ...,  0.0863, -0.2200, -0.1486]],
            device='cuda:0', grad_fn=<CompiledFunctionBackward>)
 
 
@@ -1855,7 +1855,7 @@ with FX graphs. We hope that you will give ``torch.compile`` a try!
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 6 minutes  11.441 seconds)
+   **Total running time of the script:** ( 6 minutes  9.834 seconds)
 
 
 .. _sphx_glr_download_intermediate_torch_compile_tutorial.py:
diff --git a/_sources/intermediate/torch_export_tutorial.rst.txt b/_sources/intermediate/torch_export_tutorial.rst.txt
index e5dee147a5..b6f47bacb9 100644
--- a/_sources/intermediate/torch_export_tutorial.rst.txt
+++ b/_sources/intermediate/torch_export_tutorial.rst.txt
@@ -98,34 +98,6 @@ We will detail the ``dynamic_shapes`` argument later in the tutorial.
 
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    <class 'torch.export.exported_program.ExportedProgram'>
-    tensor([[0.8632, 0.8407, 0.0407, 0.0000, 0.4132, 0.0000, 0.0000, 0.1538, 0.6111,
-             0.0000],
-            [0.0000, 0.0000, 0.0273, 0.8057, 0.0000, 1.0162, 0.8042, 0.0000, 0.2660,
-             0.0000],
-            [0.9481, 0.1396, 1.0225, 0.9563, 0.5832, 0.2546, 0.4095, 0.4591, 0.0000,
-             2.0053],
-            [1.1300, 0.4873, 0.0000, 0.9663, 1.2275, 1.4015, 0.0000, 0.9444, 0.0000,
-             0.0000],
-            [0.0000, 0.8724, 1.1648, 0.6867, 0.0000, 0.2833, 0.3202, 0.5848, 0.0000,
-             0.0833],
-            [1.1311, 0.1324, 0.0000, 1.7842, 0.0000, 0.3474, 0.9916, 0.3571, 0.0000,
-             0.0000],
-            [1.4348, 1.0570, 0.1771, 0.0000, 0.9510, 0.0000, 0.0000, 0.0000, 0.2618,
-             0.0000],
-            [0.8853, 0.0000, 0.0000, 0.4486, 0.0000, 0.0000, 0.5841, 0.7604, 0.0000,
-             0.0000]], grad_fn=<ReluBackward0>)
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 79-90
 
 Let's review some attributes of ``ExportedProgram`` that are of interest.
@@ -149,42 +121,6 @@ so that it can be ran as a ``torch.nn.Module``.
     print(exported_mod.graph_module)
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    ExportedProgram:
-        class GraphModule(torch.nn.Module):
-            def forward(self, arg0_1: "f32[10, 100]", arg1_1: "f32[10]", arg2_1: "f32[8, 100]", arg3_1: "f32[8, 100]"):
-                # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:70 in forward, code: return torch.nn.functional.relu(self.lin(x + y), inplace=True)
-                add: "f32[8, 100]" = torch.ops.aten.add.Tensor(arg2_1, arg3_1);  arg2_1 = arg3_1 = None
-                t: "f32[100, 10]" = torch.ops.aten.t.default(arg0_1);  arg0_1 = None
-                addmm: "f32[8, 10]" = torch.ops.aten.addmm.default(arg1_1, add, t);  arg1_1 = add = t = None
-                relu: "f32[8, 10]" = torch.ops.aten.relu.default(addmm);  addmm = None
-                return (relu,)
-            
-    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg0_1'), target='lin.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg1_1'), target='lin.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg2_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg3_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='relu'), target=None)])
-    Range constraints: {}
-
-    GraphModule()
-
-
-
-    def forward(self, arg0_1, arg1_1, arg2_1, arg3_1):
-        add = torch.ops.aten.add.Tensor(arg2_1, arg3_1);  arg2_1 = arg3_1 = None
-        t = torch.ops.aten.t.default(arg0_1);  arg0_1 = None
-        addmm = torch.ops.aten.addmm.default(arg1_1, add, t);  arg1_1 = add = t = None
-        relu = torch.ops.aten.relu.default(addmm);  addmm = None
-        return (relu,)
-    
-    # To see more debug info, please use `graph_module.print_readable()`
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 95-105
 
 The printed code shows that FX graph only contains ATen-level ops (such as ``torch.ops.aten``)
@@ -206,18 +142,6 @@ Other attributes of interest in ``ExportedProgram`` include:
     print(exported_mod.graph_signature)
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg0_1'), target='lin.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg1_1'), target='lin.bias', persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg2_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg3_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='relu'), target=None)])
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 109-111
 
 See the ``torch.export`` `documentation <https://pytorch.org/docs/main/export.html#torch.export.export>`__
@@ -258,78 +182,6 @@ A graph break is necessary in cases such as:
         tb.print_exc()
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 136, in <module>
-        export(Bad1(), (torch.randn(3, 3),))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-        return _export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-        ep = fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-        gm_torch_level = _export_to_torch_ir(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-        gm_torch_level, _ = torch._dynamo.export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-        result_traced = opt_f(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-        return callback(frame, cache_entry, hooks, frame_state, skip=1)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-        return _compile(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-        return func(*args, **kwds)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-        guarded_code = compile_inner(code, one_graph, hooks, transform)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-        r = func(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
-        out_code = transform_code_object(code, transform)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
-        transformations(instructions, code_options)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 500, in transform
-        tracer.run()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2149, in run
-        super().run()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
-        and self.step()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
-        getattr(self, inst.opname)(inst)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 464, in inner
-        raise exc.UserError(
-    torch._dynamo.exc.UserError: Dynamic control flow is not supported at the moment. Please use functorch.experimental.control_flow.cond to explicitly capture the control flow. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#cond-operands
-
-    from user code:
-       File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 130, in forward
-        if x.sum() > 0:
-
-    Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 141-142
 
 - accessing tensor data with ``.data``
@@ -350,12 +202,6 @@ A graph break is necessary in cases such as:
         tb.print_exc()
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 154-155
 
 - calling unsupported functions (such as many built-in functions)
@@ -376,88 +222,6 @@ A graph break is necessary in cases such as:
         tb.print_exc()
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 162, in <module>
-        export(Bad3(), (torch.randn(3, 3),))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-        return _export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-        ep = fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-        gm_torch_level = _export_to_torch_ir(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-        gm_torch_level, _ = torch._dynamo.export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-        result_traced = opt_f(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-        return callback(frame, cache_entry, hooks, frame_state, skip=1)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-        return _compile(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-        return func(*args, **kwds)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-        guarded_code = compile_inner(code, one_graph, hooks, transform)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-        r = func(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
-        out_code = transform_code_object(code, transform)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
-        transformations(instructions, code_options)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 500, in transform
-        tracer.run()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2149, in run
-        super().run()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
-        and self.step()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
-        getattr(self, inst.opname)(inst)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
-        return inner_fn(self, inst)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1219, in CALL_FUNCTION
-        self.call_function(fn, args, {})
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
-        self.push(fn.call_function(self, args, kwargs))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 687, in call_function
-        result = handler(tx, *args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 1520, in call_id
-        unimplemented(f"call_id with args {args}")
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 190, in unimplemented
-        raise Unsupported(msg)
-    torch._dynamo.exc.Unsupported: call_id with args (TensorVariable(),)
-
-    from user code:
-       File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 159, in forward
-        return x + id(x)
-
-    Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 167-168
 
 - unsupported Python language features (e.g. throwing exceptions, match statements)
@@ -482,88 +246,6 @@ A graph break is necessary in cases such as:
         tb.print_exc()
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 179, in <module>
-        export(Bad4(), (torch.randn(3, 3),))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-        return _export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-        ep = fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-        gm_torch_level = _export_to_torch_ir(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-        gm_torch_level, _ = torch._dynamo.export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-        result_traced = opt_f(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-        return callback(frame, cache_entry, hooks, frame_state, skip=1)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-        return _compile(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-        return func(*args, **kwds)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-        guarded_code = compile_inner(code, one_graph, hooks, transform)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-        r = func(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
-        out_code = transform_code_object(code, transform)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
-        transformations(instructions, code_options)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 500, in transform
-        tracer.run()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2149, in run
-        super().run()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
-        and self.step()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
-        getattr(self, inst.opname)(inst)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
-        return inner_fn(self, inst)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1219, in CALL_FUNCTION
-        self.call_function(fn, args, {})
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
-        self.push(fn.call_function(self, args, kwargs))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 730, in call_function
-        return super().call_function(tx, args, kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 349, in call_function
-        unimplemented(f"call_function {self} {args} {kwargs}")
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 190, in unimplemented
-        raise Unsupported(msg)
-    torch._dynamo.exc.Unsupported: call_function BuiltinVariable(RuntimeError) [ConstantVariable()] {}
-
-    from user code:
-       File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 173, in forward
-        raise RuntimeError("bad")
-
-    Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 184-202
 
 Non-Strict Export
@@ -599,28 +281,6 @@ Looking at some of the previous examples which resulted in graph breaks:
     print(bad2_nonstrict.module()(torch.ones(3, 3)))
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py:58: UserWarning:
-
-    Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
-
-    /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph.py:1460: UserWarning:
-
-    Node _lifted_tensor_constant0_1 target _lifted_tensor_constant0 _lifted_tensor_constant0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
-
-    tensor([[3., 1., 1.],
-            [1., 1., 1.],
-            [1., 1., 1.]])
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 212-216
 
 - Calling unsupported functions (such as many built-in functions) traces
@@ -643,31 +303,6 @@ operation is not recorded in the graph.
     print(bad3_nonstrict.module()(torch.ones(3, 3)))
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    ExportedProgram:
-        class GraphModule(torch.nn.Module):
-            def forward(self, arg0_1: "f32[3, 3]"):
-                # No stacktrace found for following nodes
-                add: "f32[3, 3]" = torch.ops.aten.add.Tensor(arg0_1, 1);  arg0_1 = None
-                add_1: "f32[3, 3]" = torch.ops.aten.add.Tensor(add, 139668425840400);  add = None
-                return (add_1,)
-            
-    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_1'), target=None)])
-    Range constraints: {}
-
-    tensor([[1.3967e+14, 1.3967e+14, 1.3967e+14],
-            [1.3967e+14, 1.3967e+14, 1.3967e+14],
-            [1.3967e+14, 1.3967e+14, 1.3967e+14]])
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 227-229
 
 - Unsupported Python language features (such as throwing exceptions, match
@@ -692,20 +327,6 @@ statements) now also get traced through.
 
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    tensor([[4., 4., 4.],
-            [4., 4., 4.],
-            [4., 4., 4.]])
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 244-246
 
 However, there are still some features that require rewrites to the original
@@ -740,23 +361,6 @@ we can fix the control flow example above using the ``cond`` op, like so:
     print(exported_bad1_fixed.module()(-torch.ones(3, 3)))
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    tensor([[0.8415, 0.8415, 0.8415],
-            [0.8415, 0.8415, 0.8415],
-            [0.8415, 0.8415, 0.8415]])
-    tensor([[0.5403, 0.5403, 0.5403],
-            [0.5403, 0.5403, 0.5403],
-            [0.5403, 0.5403, 0.5403]])
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 270-281
 
 There are limitations to ``cond`` that one should be aware of:
@@ -826,39 +430,6 @@ with a different shape, we get an error:
         tb.print_exc()
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 325, in <module>
-        exported_mod2.module()(torch.randn(10, 100), torch.randn(10, 100))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-        return self._wrapped_call(self, *args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-        return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-        args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-        return _check_input_constraints_for_graph(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 129, in _check_input_constraints_for_graph
-        raise RuntimeError(
-    RuntimeError: Expected input at *args[0].shape[0] to be equal to 8, but got 10
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 330-351
 
 We can relax this constraint using the ``dynamic_shapes`` argument of
@@ -923,102 +494,6 @@ dimension must be in the interval [4, 18].
         tb.print_exc()
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    tensor([[[0.0000, 0.0000],
-             [0.8850, 1.2371],
-             [0.0000, 0.0000]],
-
-            [[0.0000, 0.0000],
-             [0.0000, 0.3487],
-             [0.2520, 1.2545]],
-
-            [[0.5863, 0.2831],
-             [0.0000, 0.4669],
-             [0.1059, 0.0000]],
-
-            [[0.7833, 0.0000],
-             [0.4480, 0.0523],
-             [0.0000, 0.0000]],
-
-            [[0.9306, 0.0000],
-             [0.0000, 0.7895],
-             [0.1160, 0.0000]]])
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 372, in <module>
-        exported_dynamic_shapes_example1.module()(torch.randn(8, 1, 2))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-        return self._wrapped_call(self, *args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-        return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-        args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-        return _check_input_constraints_for_graph(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 117, in _check_input_constraints_for_graph
-        raise RuntimeError(
-    RuntimeError: Expected input at *args[0].shape[1] to be >= 4, but got 1
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 377, in <module>
-        exported_dynamic_shapes_example1.module()(torch.randn(8, 20, 2))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-        return self._wrapped_call(self, *args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-        return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-        args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-        return _check_input_constraints_for_graph(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 123, in _check_input_constraints_for_graph
-        raise RuntimeError(
-    RuntimeError: Expected input at *args[0].shape[1] to be <= 18, but got 20
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 382, in <module>
-        exported_dynamic_shapes_example1.module()(torch.randn(8, 8, 3))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-        return self._wrapped_call(self, *args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-        return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-        args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-        return _check_input_constraints_for_graph(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 129, in _check_input_constraints_for_graph
-        raise RuntimeError(
-    RuntimeError: Expected input at *args[0].shape[2] to be equal to 2, but got 3
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 387-389
 
 Note that if our example inputs to ``torch.export`` do not satisfy the constraints
@@ -1040,132 +515,6 @@ given by ``dynamic_shapes``, then we get an error.
         tb.print_exc()
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    Traceback (most recent call last):
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-        gm_torch_level, _ = torch._dynamo.export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1354, in inner
-        raise constraint_violation_error
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-        result_traced = opt_f(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-        return callback(frame, cache_entry, hooks, frame_state, skip=1)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-        return _compile(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-        return func(*args, **kwds)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-        guarded_code = compile_inner(code, one_graph, hooks, transform)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-        r = func(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
-        out_code = transform_code_object(code, transform)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
-        transformations(instructions, code_options)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 482, in transform
-        tracer = InstructionTranslator(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2115, in __init__
-        self.symbolic_locals = VariableTracker.apply(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 217, in apply
-        result = {
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 218, in <dictcomp>
-        k: cls.apply(fn, v, cache, skip_fn) for k, v in list(value.items())
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 203, in apply
-        result = fn(update_object_dict(value))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2116, in <lambda>
-        lambda x: x.realize(), self.symbolic_locals
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/lazy.py", line 58, in realize
-        self._cache.realize(self.parents_tracker)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/lazy.py", line 24, in realize
-        self.vt = VariableBuilder(tx, self.source)(self.value)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 269, in __call__
-        vt = self._wrap(value)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 402, in _wrap
-        return type_dispatch(self, value)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1073, in wrap_tensor
-        tensor_variable = wrap_fx_proxy(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1330, in wrap_fx_proxy
-        return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1440, in wrap_fx_proxy_cls
-        example_value = wrap_to_fake_tensor_and_record(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1880, in wrap_to_fake_tensor_and_record
-        fake_e = wrap_fake_exception(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 1190, in wrap_fake_exception
-        return fn()
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1881, in <lambda>
-        lambda: tx.fake_mode.from_tensor(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 1666, in from_tensor
-        return self.fake_tensor_converter(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 349, in __call__
-        return self.from_real_tensor(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 306, in from_real_tensor
-        out = self.meta_converter(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py", line 967, in __call__
-        r = self.meta_tensor(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py", line 782, in meta_tensor
-        ) = sym_sizes_strides_storage_offset(t, source, symbolic_context)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py", line 269, in sym_sizes_strides_storage_offset
-        return shape_env.create_symbolic_sizes_strides_storage_offset(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 2339, in create_symbolic_sizes_strides_storage_offset
-        return self._create_symbolic_sizes_strides_storage_offset(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/recording.py", line 231, in wrapper
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 2395, in _create_symbolic_sizes_strides_storage_offset
-        size: List[sympy.Expr] = self._produce_dyn_sizes_from_int_tuple(ex_size, source, symbolic_context)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 2271, in _produce_dyn_sizes_from_int_tuple
-        size.append(self.create_symbol(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/recording.py", line 231, in wrapper
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 2709, in create_symbol
-        raise ConstraintViolationError(f"{val} not in range [{vr.lower}, {vr.upper}]")
-    torch.fx.experimental.symbolic_shapes.ConstraintViolationError: 10 not in range [11, 18]
-
-    Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-
-
-    During handling of the above exception, another exception occurred:
-
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 396, in <module>
-        export(DynamicShapesExample1(), (inp1,), dynamic_shapes=dynamic_shapes1_bad)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-        return _export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-        ep = fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-        gm_torch_level = _export_to_torch_ir(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 359, in _export_to_torch_ir
-        raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e))  # noqa: TRY200
-    torch._dynamo.exc.UserError: 10 not in range [11, 18]
-
-    Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 401-403
 
 We can enforce that equalities between dimensions of different tensors
@@ -1202,41 +551,6 @@ by using the same ``torch.export.Dim`` object, for example, in matrix multiplica
         tb.print_exc()
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    tensor([[-2.9354, -2.2066, -0.2080,  4.6121],
-            [-0.5658, -0.6108,  0.8887,  1.5908]])
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 425, in <module>
-        exported_dynamic_shapes_example2.module()(torch.randn(4, 8), torch.randn(4, 2))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-        return self._wrapped_call(self, *args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-        return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-        args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-        return _check_input_constraints_for_graph(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 85, in _check_input_constraints_for_graph
-        raise RuntimeError(
-    RuntimeError: Expected input at *args[1].shape[0] to be equal to 8, but got 4
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 430-433
 
 We can also describe one dimension in terms of other. There are some
@@ -1285,41 +599,6 @@ but generally, those in the form of ``A * Dim + B`` should work.
     print(derived_dim_example2.module()(torch.randn(7), torch.randn(19)))
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    tensor([ 0.3007, -1.7282, -0.0729,  0.1139])
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 450, in <module>
-        derived_dim_example1.module()(torch.randn(4), torch.randn(6))
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-        return self._wrapped_call(self, *args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-        return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-        args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-        return _check_input_constraints_for_graph(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 85, in _check_input_constraints_for_graph
-        raise RuntimeError(
-    RuntimeError: Expected input at *args[1].shape[0] to be equal to 5, but got 6
-    tensor([ 2.5416, -0.2760,  0.9003, -1.7479, -2.9716,  0.1013])
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 471-476
 
 We can actually use ``torch.export`` to guide us as to which ``dynamic_shapes`` constraints
@@ -1353,96 +632,6 @@ error out.
         tb.print_exc()
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    Traceback (most recent call last):
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-        gm_torch_level, _ = torch._dynamo.export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1354, in inner
-        raise constraint_violation_error
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-        result_traced = opt_f(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-        return self._call_impl(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-        return forward_call(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-        return callback(frame, cache_entry, hooks, frame_state, skip=1)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-        return _compile(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-        return func(*args, **kwds)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-        guarded_code = compile_inner(code, one_graph, hooks, transform)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-        r = func(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 634, in compile_inner
-        check_fn = CheckFunctionManager(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/guards.py", line 1048, in __init__
-        guard.create(builder)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_guards.py", line 249, in create
-        return self.create_fn(builder, self)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/guards.py", line 705, in SHAPE_ENV
-        guards = output_graph.shape_env.produce_guards(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 3308, in produce_guards
-        raise ConstraintViolationError(
-    torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated (inp4_dim0, inp5_dim0, inp5_dim1)! For more information, run with TORCH_LOGS="+dynamic".
-      - The values of inp5_dim0 = L['y'].size()[0] and inp4_dim1 = L['x'].size()[1] must always be equal.
-      - Not all values of inp5_dim1 = L['y'].size()[1] in the specified range satisfy the generated guard Ne(L['y'].size()[1], 16).
-      - Not all values of inp4_dim0 = L['x'].size()[0] in the specified range satisfy the generated guard 2 <= L['x'].size()[0] and L['x'].size()[0] <= 16
-      - Not all values of inp5_dim1 = L['y'].size()[1] in the specified range satisfy the generated guard 16 <= L['y'].size()[1] and L['y'].size()[1] <= 9223372036854775806
-
-    Suggested fixes:
-      inp4_dim0 = Dim('inp4_dim0', max=16)
-      inp4_dim1 = Dim('inp4_dim1')
-      inp4_dim1 = Dim('inp4_dim1', min=2, max=9223372036854775806)  # 2 <= inp4_dim1 <= 9223372036854775806
-      inp5_dim1 = Dim('inp5_dim1', min=16)
-      inp5_dim0 = inp4_dim1
-
-    During handling of the above exception, another exception occurred:
-
-    Traceback (most recent call last):
-      File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 492, in <module>
-        export(DynamicShapesExample3(), (inp4, inp5), dynamic_shapes=dynamic_shapes3)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-        return _export(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-        raise e
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-        ep = fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-        return fn(*args, **kwargs)
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-        gm_torch_level = _export_to_torch_ir(
-      File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 359, in _export_to_torch_ir
-        raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e))  # noqa: TRY200
-    torch._dynamo.exc.UserError: Constraints violated (inp4_dim0, inp5_dim0, inp5_dim1)! For more information, run with TORCH_LOGS="+dynamic".
-      - The values of inp5_dim0 = L['y'].size()[0] and inp4_dim1 = L['x'].size()[1] must always be equal.
-      - Not all values of inp5_dim1 = L['y'].size()[1] in the specified range satisfy the generated guard Ne(L['y'].size()[1], 16).
-      - Not all values of inp4_dim0 = L['x'].size()[0] in the specified range satisfy the generated guard 2 <= L['x'].size()[0] and L['x'].size()[0] <= 16
-      - Not all values of inp5_dim1 = L['y'].size()[1] in the specified range satisfy the generated guard 16 <= L['y'].size()[1] and L['y'].size()[1] <= 9223372036854775806
-
-    Suggested fixes:
-      inp4_dim0 = Dim('inp4_dim0', max=16)
-      inp4_dim1 = Dim('inp4_dim1')
-      inp4_dim1 = Dim('inp4_dim1', min=2, max=9223372036854775806)  # 2 <= inp4_dim1 <= 9223372036854775806
-      inp5_dim1 = Dim('inp5_dim1', min=16)
-      inp5_dim0 = inp4_dim1
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 497-500
 
 We can see that the error message gives us suggested fixes to our
@@ -1471,29 +660,6 @@ suggestions may differ slightly):
     print(exported_dynamic_shapes_example3.module()(torch.randn(4, 32), torch.randn(32, 64)))
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    tensor([[ 12.5915,  -0.7265,  -1.8981,  -8.0323,  -2.1447,  14.4020,  13.7854,
-               1.5568,   3.7933,   7.2591,  -2.2477,   1.6366,  -5.9276,   8.5279,
-               7.9349,  -1.1328],
-            [ -5.2210,   9.4576,  -0.2372,   9.0035,   6.2572,  -8.4716,   6.0191,
-               4.8424,  -0.4486,   0.1885,  -0.1749,   2.4314,   3.8271,   8.1822,
-               6.5064,   0.6512],
-            [ -3.6856,   7.5222,   4.8073,  13.1255,   3.6440,  -4.1587,   2.9806,
-               0.3689,   1.1133,  -1.7169,  -2.1537,   1.1841,   6.7619,   9.3401,
-              -1.1372,  -8.9628],
-            [ -4.3608,   5.1219,  -0.6240,  -6.8640,   2.3344,  -2.0273,   0.2769,
-              -0.9930,   2.0298, -10.3922,  -1.7186,  -5.0928,  11.7383,   6.4864,
-               8.0827,   0.5863]])
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 518-525
 
 Note that in the example above, because we constrained the value of ``x.shape[0]`` in
@@ -1517,51 +683,6 @@ or use ``torch._logging.set_logs``.
     torch._logging.set_logs(dynamic=logging.WARNING, dynamo=logging.WARNING)
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    I0625 21:38:32.787000 139677001331328 torch/_dynamo/logging.py:55] [16/0] Step 1: torchdynamo start tracing forward /var/lib/workspace/intermediate_source/torch_export_tutorial.py:481
-    I0625 21:38:32.790000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2724] [16/0] create_symbol s0 = 8 for L['x'].size()[0] [2, 16] (_dynamo/variables/builder.py:1881 in <lambda>)
-    I0625 21:38:32.791000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2724] [16/0] create_symbol s1 = 16 for L['x'].size()[1] [2, 9223372036854775806] (_dynamo/variables/builder.py:1881 in <lambda>)
-    I0625 21:38:32.793000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2724] [16/0] create_symbol s2 = 16 for L['y'].size()[0] [2, 9223372036854775806] (_dynamo/variables/builder.py:1881 in <lambda>)
-    I0625 21:38:32.794000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2724] [16/0] create_symbol s3 = 32 for L['y'].size()[1] [17, 9223372036854775806] (_dynamo/variables/builder.py:1881 in <lambda>)
-    I0625 21:38:32.802000 139677001331328 torch/fx/experimental/symbolic_shapes.py:3809] [16/0] set_replacement s2 = s1 (solve_backed) ValueRanges(lower=2, upper=9223372036854775806, is_bool=False)
-    I0625 21:38:32.802000 139677001331328 torch/fx/experimental/symbolic_shapes.py:4035] [16/0] eval Eq(s1, s2) [guard added] at ar/lib/workspace/intermediate_source/torch_export_tutorial.py:483 in forward (_meta_registrations.py:2014 in meta_mm)
-    I0625 21:38:32.803000 139677001331328 torch/_dynamo/logging.py:55] [16/0] Step 1: torchdynamo done tracing forward (RETURN_VALUE)
-    I0625 21:38:32.805000 139677001331328 torch/fx/experimental/symbolic_shapes.py:3809] [16/0] set_replacement s2 = s1 (find) ValueRanges(lower=2, upper=9223372036854775806, is_bool=False)
-    I0625 21:38:32.805000 139677001331328 torch/_dynamo/logging.py:55] [16/0] Step 2: calling compiler function dynamo_normalization_capturing_compiler
-    I0625 21:38:32.805000 139677001331328 torch/_dynamo/logging.py:55] [16/0] Step 2: done compiler function dynamo_normalization_capturing_compiler
-    I0625 21:38:32.807000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2806] [16/0] produce_guards
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339] Summary of dimension constraints:
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339] Suggested fixes:
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339]   inp4_dim0 = Dim('inp4_dim0', max=16)
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339]   inp5_dim1 = Dim('inp5_dim1', min=17)
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339]   shared_dim = Dim('shared_dim')
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363] Dynamo captured graph:
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363] 
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363] class GraphModule(torch.nn.Module):
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]     def forward(self, L_x_ : torch.Tensor, L_y_ : torch.Tensor):
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         l_x_ = L_x_
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         l_y_ = L_y_
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:482 in forward, code: if x.shape[0] <= 16:
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         size = l_x_.size()
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         getitem = size[0];  size = None
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         le = getitem <= 16;  getitem = None
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:483 in forward, code: return x @ y[:, :16]
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         getitem_2 = l_y_[(slice(None, None, None), slice(None, 16, None))];  l_y_ = None
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         matmul = l_x_ @ getitem_2;  l_x_ = getitem_2 = None
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         return (matmul,)
-    I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 534-536
 
 We can view an ``ExportedProgram``'s symbolic shape ranges using the
@@ -1575,18 +696,6 @@ We can view an ``ExportedProgram``'s symbolic shape ranges using the
     print(exported_dynamic_shapes_example3.range_constraints)
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    {s0: ValueRanges(lower=2, upper=16, is_bool=False), s1: ValueRanges(lower=2, upper=oo, is_bool=False), s3: ValueRanges(lower=17, upper=oo, is_bool=False)}
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 540-549
 
 Custom Ops
@@ -1616,12 +725,6 @@ Currently, the steps to register a custom op for use by ``torch.export`` are:
         return torch.relu(x)
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 562-564
 
 - Define a ``"Meta"`` implementation of the custom op that returns an empty
@@ -1637,12 +740,6 @@ Currently, the steps to register a custom op for use by ``torch.export`` are:
         return torch.empty_like(x)
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 570-571
 
 - Call the custom op from the code you want to export using ``torch.ops``
@@ -1660,12 +757,6 @@ Currently, the steps to register a custom op for use by ``torch.export`` are:
             return x
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 580-581
 
 - Export the code as before
@@ -1680,33 +771,6 @@ Currently, the steps to register a custom op for use by ``torch.export`` are:
     print(exported_custom_op_example.module()(torch.randn(3, 3)))
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    class GraphModule(torch.nn.Module):
-        def forward(self, arg0_1: "f32[3, 3]"):
-            # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:574 in forward, code: x = torch.sin(x)
-            sin: "f32[3, 3]" = torch.ops.aten.sin.default(arg0_1);  arg0_1 = None
-        
-            # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:575 in forward, code: x = torch.ops.my_custom_library.custom_op(x)
-            custom_op: "f32[3, 3]" = torch.ops.my_custom_library.custom_op.default(sin);  sin = None
-        
-            # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:576 in forward, code: x = torch.cos(x)
-            cos: "f32[3, 3]" = torch.ops.aten.cos.default(custom_op);  custom_op = None
-            return (cos,)
-        
-    custom_op called!
-    tensor([[0.6387, 0.5722, 1.0000],
-            [0.8776, 0.9846, 0.8223],
-            [0.9983, 1.0000, 0.9979]])
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 587-594
 
 Note in the above outputs that the custom op is included in the exported graph.
@@ -1739,19 +803,6 @@ and you can inspect if an operator is functional by checking
     print(torch.ops.aten.add_.Tensor._schema.is_mutable)
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    False
-    True
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 611-633
 
 By default, the environment in which you want to run the exported graph
@@ -1797,31 +848,6 @@ which consists of only ~180 operators.
     print(core_ir_ep.graph)
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    graph():
-        %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
-        %arg1_1 : [num_users=1] = placeholder[target=arg1_1]
-        %arg2_1 : [num_users=1] = placeholder[target=arg2_1]
-        %t : [num_users=1] = call_function[target=torch.ops.aten.t.default](args = (%arg0_1,), kwargs = {})
-        %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%arg1_1, %arg2_1, %t), kwargs = {})
-        return (addmm,)
-    graph():
-        %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
-        %arg1_1 : [num_users=1] = placeholder[target=arg1_1]
-        %arg2_1 : [num_users=1] = placeholder[target=arg2_1]
-        %permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%arg0_1, [1, 0]), kwargs = {})
-        %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%arg1_1, %arg2_1, %permute), kwargs = {})
-        return (addmm,)
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 649-661
 
 Notice that after running ``run_decompositions`` the
@@ -1859,31 +885,6 @@ decomposition implementations.
     print(core_ir_ep.graph)
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    graph():
-        %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
-        %arg1_1 : [num_users=1] = placeholder[target=arg1_1]
-        %arg2_1 : [num_users=1] = placeholder[target=arg2_1]
-        %t : [num_users=1] = call_function[target=torch.ops.aten.t.default](args = (%arg0_1,), kwargs = {})
-        %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%arg1_1, %arg2_1, %t), kwargs = {})
-        return (addmm,)
-    graph():
-        %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
-        %arg1_1 : [num_users=1] = placeholder[target=arg1_1]
-        %arg2_1 : [num_users=1] = placeholder[target=arg2_1]
-        %permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%arg0_1, [1, 0]), kwargs = {})
-        %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%arg1_1, %arg2_1, %permute), kwargs = {})
-        return (addmm,)
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 679-682
 
 If there is no existing decomposition function for an ATen operator that you would
@@ -1924,12 +925,6 @@ We can look at the example called ``cond_predicate``, which has a ``torch.cond``
         return cond(pred, lambda x: x.cos(), lambda y: y.sin(), [x])
 
 
-
-
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 711-719
 
 More generally, ExportDB can be used as a reference when one of the following occurs:
@@ -1980,23 +975,6 @@ or `TensorRT <https://pytorch.org/TensorRT/dynamo/dynamo_export.html>`__.
     print(res)
 
 
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
-    tensor([[ 1.3676, -0.4303, -0.2113],
-            [-0.5053, -0.0877,  0.5134]], device='cuda:0',
-           grad_fn=<AddmmBackward0>)
-    tensor([[ 1.3676, -0.4303, -0.2113],
-            [-0.5053, -0.0877,  0.5134]], device='cuda:0',
-           grad_fn=<CompiledFunctionBackward>)
-
-
-
-
 .. GENERATED FROM PYTHON SOURCE LINES 753-767
 
 .. code-block:: python
@@ -2026,7 +1004,7 @@ and considerations (control flow ops, constraints, etc.) that need to be made in
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.776 seconds)
+   **Total running time of the script:** ( 0 minutes  0.000 seconds)
 
 
 .. _sphx_glr_download_intermediate_torch_export_tutorial.py:
diff --git a/_sources/intermediate/torchvision_tutorial.rst.txt b/_sources/intermediate/torchvision_tutorial.rst.txt
index b387bda675..9293743f1d 100644
--- a/_sources/intermediate/torchvision_tutorial.rst.txt
+++ b/_sources/intermediate/torchvision_tutorial.rst.txt
@@ -157,7 +157,7 @@ Here is one example of a pair of images and segmentation masks
  .. code-block:: none
 
 
-    <matplotlib.image.AxesImage object at 0x7f071c580730>
+    <matplotlib.image.AxesImage object at 0x7f3b07a53af0>
 
 
 
@@ -321,10 +321,10 @@ way of doing it:
     Downloading: "https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
 
       0%|          | 0.00/160M [00:00<?, ?B/s]
-     26%|##5       | 41.1M/160M [00:00<00:00, 429MB/s]
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-     76%|#######6  | 122M/160M [00:00<00:00, 395MB/s] 
-    100%|##########| 160M/160M [00:00<00:00, 397MB/s]
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+     83%|########2 | 132M/160M [00:00<00:00, 463MB/s] 
+    100%|##########| 160M/160M [00:00<00:00, 416MB/s]
 
 
 
@@ -394,8 +394,7 @@ way of doing it:
     Downloading: "https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/mobilenet_v2-7ebf99e0.pth
 
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+    100%|##########| 13.6M/13.6M [00:00<00:00, 351MB/s]
 
 
 
@@ -661,108 +660,107 @@ validation:
     Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
 
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+     47%|####6     | 79.2M/170M [00:00<00:00, 346MB/s]
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+     88%|########7 | 149M/170M [00:00<00:00, 352MB/s]
+    100%|##########| 170M/170M [00:00<00:00, 338MB/s]
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning:
 
     Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)
 
-    Epoch: [0]  [ 0/60]  eta: 0:00:27  lr: 0.000090  loss: 4.9131 (4.9131)  loss_classifier: 0.4438 (0.4438)  loss_box_reg: 0.1060 (0.1060)  loss_mask: 4.3589 (4.3589)  loss_objectness: 0.0021 (0.0021)  loss_rpn_box_reg: 0.0023 (0.0023)  time: 0.4561  data: 0.0143  max mem: 2421
+    Epoch: [0]  [ 0/60]  eta: 0:00:27  lr: 0.000090  loss: 4.9131 (4.9131)  loss_classifier: 0.4438 (0.4438)  loss_box_reg: 0.1060 (0.1060)  loss_mask: 4.3589 (4.3589)  loss_objectness: 0.0021 (0.0021)  loss_rpn_box_reg: 0.0023 (0.0023)  time: 0.4656  data: 0.0142  max mem: 2421
     /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning:
 
     Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)
 
-    Epoch: [0]  [10/60]  eta: 0:00:15  lr: 0.000936  loss: 1.8000 (2.7745)  loss_classifier: 0.4127 (0.3557)  loss_box_reg: 0.3088 (0.2540)  loss_mask: 0.9491 (2.1313)  loss_objectness: 0.0227 (0.0266)  loss_rpn_box_reg: 0.0056 (0.0069)  time: 0.3010  data: 0.0155  max mem: 2918
-    Epoch: [0]  [20/60]  eta: 0:00:10  lr: 0.001783  loss: 0.7878 (1.7889)  loss_classifier: 0.2150 (0.2676)  loss_box_reg: 0.2016 (0.2331)  loss_mask: 0.3993 (1.2588)  loss_objectness: 0.0161 (0.0214)  loss_rpn_box_reg: 0.0076 (0.0079)  time: 0.2647  data: 0.0156  max mem: 2920
-    Epoch: [0]  [30/60]  eta: 0:00:07  lr: 0.002629  loss: 0.6785 (1.4253)  loss_classifier: 0.1440 (0.2254)  loss_box_reg: 0.2312 (0.2433)  loss_mask: 0.2576 (0.9274)  loss_objectness: 0.0119 (0.0194)  loss_rpn_box_reg: 0.0101 (0.0098)  time: 0.2444  data: 0.0165  max mem: 2921
-    Epoch: [0]  [40/60]  eta: 0:00:05  lr: 0.003476  loss: 0.5640 (1.2068)  loss_classifier: 0.0918 (0.1907)  loss_box_reg: 0.2431 (0.2362)  loss_mask: 0.2276 (0.7535)  loss_objectness: 0.0093 (0.0167)  loss_rpn_box_reg: 0.0117 (0.0097)  time: 0.2421  data: 0.0170  max mem: 2921
-    Epoch: [0]  [50/60]  eta: 0:00:02  lr: 0.004323  loss: 0.3651 (1.0410)  loss_classifier: 0.0546 (0.1627)  loss_box_reg: 0.1496 (0.2179)  loss_mask: 0.1592 (0.6374)  loss_objectness: 0.0020 (0.0139)  loss_rpn_box_reg: 0.0072 (0.0092)  time: 0.2288  data: 0.0163  max mem: 2921
-    Epoch: [0]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.3469 (0.9417)  loss_classifier: 0.0410 (0.1447)  loss_box_reg: 0.1223 (0.2055)  loss_mask: 0.1592 (0.5706)  loss_objectness: 0.0013 (0.0121)  loss_rpn_box_reg: 0.0063 (0.0088)  time: 0.2229  data: 0.0155  max mem: 2921
-    Epoch: [0] Total time: 0:00:14 (0.2476 s / it)
+    Epoch: [0]  [10/60]  eta: 0:00:15  lr: 0.000936  loss: 1.7988 (2.7740)  loss_classifier: 0.4161 (0.3552)  loss_box_reg: 0.3087 (0.2540)  loss_mask: 0.9491 (2.1314)  loss_objectness: 0.0227 (0.0266)  loss_rpn_box_reg: 0.0056 (0.0069)  time: 0.3027  data: 0.0155  max mem: 2918
+    Epoch: [0]  [20/60]  eta: 0:00:10  lr: 0.001783  loss: 0.7890 (1.7882)  loss_classifier: 0.2149 (0.2672)  loss_box_reg: 0.2016 (0.2329)  loss_mask: 0.3993 (1.2588)  loss_objectness: 0.0162 (0.0214)  loss_rpn_box_reg: 0.0076 (0.0079)  time: 0.2652  data: 0.0155  max mem: 2920
+    Epoch: [0]  [30/60]  eta: 0:00:07  lr: 0.002629  loss: 0.6767 (1.4244)  loss_classifier: 0.1435 (0.2252)  loss_box_reg: 0.2288 (0.2425)  loss_mask: 0.2597 (0.9274)  loss_objectness: 0.0123 (0.0195)  loss_rpn_box_reg: 0.0101 (0.0098)  time: 0.2443  data: 0.0164  max mem: 2921
+    Epoch: [0]  [40/60]  eta: 0:00:05  lr: 0.003476  loss: 0.5594 (1.2069)  loss_classifier: 0.0942 (0.1909)  loss_box_reg: 0.2408 (0.2357)  loss_mask: 0.2277 (0.7536)  loss_objectness: 0.0071 (0.0169)  loss_rpn_box_reg: 0.0118 (0.0097)  time: 0.2398  data: 0.0168  max mem: 2921
+    Epoch: [0]  [50/60]  eta: 0:00:02  lr: 0.004323  loss: 0.3676 (1.0410)  loss_classifier: 0.0578 (0.1629)  loss_box_reg: 0.1529 (0.2171)  loss_mask: 0.1593 (0.6378)  loss_objectness: 0.0030 (0.0140)  loss_rpn_box_reg: 0.0073 (0.0092)  time: 0.2282  data: 0.0161  max mem: 2921
+    Epoch: [0]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.3425 (0.9415)  loss_classifier: 0.0407 (0.1449)  loss_box_reg: 0.1228 (0.2045)  loss_mask: 0.1593 (0.5710)  loss_objectness: 0.0017 (0.0123)  loss_rpn_box_reg: 0.0063 (0.0088)  time: 0.2230  data: 0.0153  max mem: 2921
+    Epoch: [0] Total time: 0:00:14 (0.2475 s / it)
     creating index...
     index created!
-    Test:  [ 0/50]  eta: 0:00:07  model_time: 0.1291 (0.1291)  evaluator_time: 0.0057 (0.0057)  time: 0.1485  data: 0.0132  max mem: 2921
-    Test:  [49/50]  eta: 0:00:00  model_time: 0.0422 (0.0768)  evaluator_time: 0.0047 (0.0070)  time: 0.0743  data: 0.0104  max mem: 2921
-    Test: Total time: 0:00:04 (0.0958 s / it)
-    Averaged stats: model_time: 0.0422 (0.0768)  evaluator_time: 0.0047 (0.0070)
+    Test:  [ 0/50]  eta: 0:00:07  model_time: 0.1294 (0.1294)  evaluator_time: 0.0071 (0.0071)  time: 0.1499  data: 0.0130  max mem: 2921
+    Test:  [49/50]  eta: 0:00:00  model_time: 0.0424 (0.0770)  evaluator_time: 0.0048 (0.0074)  time: 0.0747  data: 0.0103  max mem: 2921
+    Test: Total time: 0:00:04 (0.0962 s / it)
+    Averaged stats: model_time: 0.0424 (0.0770)  evaluator_time: 0.0048 (0.0074)
     Accumulating evaluation results...
     DONE (t=0.01s).
     Accumulating evaluation results...
     DONE (t=0.01s).
     IoU metric: bbox
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.643
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.666
      Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.984
-     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.858
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.288
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.593
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.654
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.284
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.696
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.696
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.683
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.706
+     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.889
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.622
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.678
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.289
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.717
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.717
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.708
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.726
     IoU metric: segm
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.668
-     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.975
-     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.776
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.682
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.294
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.721
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.723
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.676
+     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.973
+     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.805
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.446
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.690
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.299
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.731
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.732
      Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.633
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.658
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.732
-    Epoch: [1]  [ 0/60]  eta: 0:00:10  lr: 0.005000  loss: 0.2554 (0.2554)  loss_classifier: 0.0176 (0.0176)  loss_box_reg: 0.0572 (0.0572)  loss_mask: 0.1772 (0.1772)  loss_objectness: 0.0003 (0.0003)  loss_rpn_box_reg: 0.0031 (0.0031)  time: 0.1796  data: 0.0147  max mem: 2921
-    Epoch: [1]  [10/60]  eta: 0:00:12  lr: 0.005000  loss: 0.3557 (0.3762)  loss_classifier: 0.0444 (0.0532)  loss_box_reg: 0.1343 (0.1466)  loss_mask: 0.1590 (0.1656)  loss_objectness: 0.0009 (0.0026)  loss_rpn_box_reg: 0.0086 (0.0083)  time: 0.2410  data: 0.0171  max mem: 2921
-    Epoch: [1]  [20/60]  eta: 0:00:08  lr: 0.005000  loss: 0.3557 (0.3526)  loss_classifier: 0.0421 (0.0456)  loss_box_reg: 0.1241 (0.1217)  loss_mask: 0.1695 (0.1761)  loss_objectness: 0.0009 (0.0019)  loss_rpn_box_reg: 0.0069 (0.0072)  time: 0.2242  data: 0.0160  max mem: 2921
-    Epoch: [1]  [30/60]  eta: 0:00:06  lr: 0.005000  loss: 0.3102 (0.3336)  loss_classifier: 0.0421 (0.0450)  loss_box_reg: 0.0922 (0.1158)  loss_mask: 0.1501 (0.1638)  loss_objectness: 0.0010 (0.0020)  loss_rpn_box_reg: 0.0052 (0.0071)  time: 0.2109  data: 0.0157  max mem: 2921
-    Epoch: [1]  [40/60]  eta: 0:00:04  lr: 0.005000  loss: 0.2814 (0.3281)  loss_classifier: 0.0470 (0.0444)  loss_box_reg: 0.0922 (0.1093)  loss_mask: 0.1441 (0.1652)  loss_objectness: 0.0014 (0.0018)  loss_rpn_box_reg: 0.0052 (0.0073)  time: 0.2218  data: 0.0163  max mem: 2921
-    Epoch: [1]  [50/60]  eta: 0:00:02  lr: 0.005000  loss: 0.2674 (0.3154)  loss_classifier: 0.0286 (0.0422)  loss_box_reg: 0.0561 (0.1018)  loss_mask: 0.1601 (0.1628)  loss_objectness: 0.0011 (0.0018)  loss_rpn_box_reg: 0.0047 (0.0068)  time: 0.2277  data: 0.0153  max mem: 2921
-    Epoch: [1]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.2249 (0.3010)  loss_classifier: 0.0283 (0.0414)  loss_box_reg: 0.0512 (0.0950)  loss_mask: 0.1284 (0.1564)  loss_objectness: 0.0009 (0.0018)  loss_rpn_box_reg: 0.0037 (0.0065)  time: 0.2242  data: 0.0160  max mem: 2921
-    Epoch: [1] Total time: 0:00:13 (0.2219 s / it)
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.692
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.739
+    Epoch: [1]  [ 0/60]  eta: 0:00:10  lr: 0.005000  loss: 0.2538 (0.2538)  loss_classifier: 0.0204 (0.0204)  loss_box_reg: 0.0604 (0.0604)  loss_mask: 0.1699 (0.1699)  loss_objectness: 0.0001 (0.0001)  loss_rpn_box_reg: 0.0031 (0.0031)  time: 0.1800  data: 0.0149  max mem: 2921
+    Epoch: [1]  [10/60]  eta: 0:00:11  lr: 0.005000  loss: 0.3333 (0.3693)  loss_classifier: 0.0447 (0.0525)  loss_box_reg: 0.1257 (0.1422)  loss_mask: 0.1603 (0.1642)  loss_objectness: 0.0016 (0.0022)  loss_rpn_box_reg: 0.0084 (0.0082)  time: 0.2395  data: 0.0172  max mem: 2921
+    Epoch: [1]  [20/60]  eta: 0:00:08  lr: 0.005000  loss: 0.3333 (0.3463)  loss_classifier: 0.0365 (0.0448)  loss_box_reg: 0.1092 (0.1181)  loss_mask: 0.1679 (0.1743)  loss_objectness: 0.0016 (0.0020)  loss_rpn_box_reg: 0.0069 (0.0071)  time: 0.2214  data: 0.0159  max mem: 2921
+    Epoch: [1]  [30/60]  eta: 0:00:06  lr: 0.005000  loss: 0.3062 (0.3281)  loss_classifier: 0.0355 (0.0450)  loss_box_reg: 0.0809 (0.1122)  loss_mask: 0.1473 (0.1624)  loss_objectness: 0.0010 (0.0017)  loss_rpn_box_reg: 0.0048 (0.0068)  time: 0.2082  data: 0.0155  max mem: 2921
+    Epoch: [1]  [40/60]  eta: 0:00:04  lr: 0.005000  loss: 0.2747 (0.3229)  loss_classifier: 0.0461 (0.0440)  loss_box_reg: 0.0809 (0.1069)  loss_mask: 0.1425 (0.1633)  loss_objectness: 0.0009 (0.0017)  loss_rpn_box_reg: 0.0051 (0.0070)  time: 0.2188  data: 0.0162  max mem: 2921
+    Epoch: [1]  [50/60]  eta: 0:00:02  lr: 0.005000  loss: 0.2658 (0.3112)  loss_classifier: 0.0307 (0.0420)  loss_box_reg: 0.0674 (0.0999)  loss_mask: 0.1544 (0.1610)  loss_objectness: 0.0009 (0.0018)  loss_rpn_box_reg: 0.0043 (0.0065)  time: 0.2254  data: 0.0152  max mem: 2921
+    Epoch: [1]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.2286 (0.2973)  loss_classifier: 0.0351 (0.0407)  loss_box_reg: 0.0544 (0.0933)  loss_mask: 0.1298 (0.1551)  loss_objectness: 0.0009 (0.0020)  loss_rpn_box_reg: 0.0032 (0.0062)  time: 0.2254  data: 0.0158  max mem: 2921
+    Epoch: [1] Total time: 0:00:13 (0.2204 s / it)
     creating index...
     index created!
-    Test:  [ 0/50]  eta: 0:00:02  model_time: 0.0406 (0.0406)  evaluator_time: 0.0033 (0.0033)  time: 0.0575  data: 0.0131  max mem: 2921
-    Test:  [49/50]  eta: 0:00:00  model_time: 0.0398 (0.0415)  evaluator_time: 0.0029 (0.0042)  time: 0.0560  data: 0.0104  max mem: 2921
-    Test: Total time: 0:00:02 (0.0576 s / it)
-    Averaged stats: model_time: 0.0398 (0.0415)  evaluator_time: 0.0029 (0.0042)
+    Test:  [ 0/50]  eta: 0:00:02  model_time: 0.0411 (0.0411)  evaluator_time: 0.0039 (0.0039)  time: 0.0584  data: 0.0130  max mem: 2921
+    Test:  [49/50]  eta: 0:00:00  model_time: 0.0395 (0.0410)  evaluator_time: 0.0029 (0.0041)  time: 0.0554  data: 0.0103  max mem: 2921
+    Test: Total time: 0:00:02 (0.0570 s / it)
+    Averaged stats: model_time: 0.0395 (0.0410)  evaluator_time: 0.0029 (0.0041)
     Accumulating evaluation results...
     DONE (t=0.01s).
     Accumulating evaluation results...
     DONE (t=0.01s).
     IoU metric: bbox
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.755
-     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.993
-     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.933
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.420
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.691
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.323
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.801
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.801
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.500
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.767
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812
-    IoU metric: segm
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.729
-     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.990
-     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.892
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.401
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.585
-     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.747
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.732
+     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.985
+     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.937
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.433
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.676
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.746
      Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.312
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.773
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.773
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.778
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.778
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.433
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.758
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.789
+    IoU metric: segm
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.715
+     Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.993
+     Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.869
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.368
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.553
+     Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.733
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.313
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.760
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.760
      Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.533
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700
-     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.787
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.683
+     Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.774
     That's it!
 
 
@@ -825,7 +823,7 @@ dataset and verify
  .. code-block:: none
 
 
-    <matplotlib.image.AxesImage object at 0x7f078d82a260>
+    <matplotlib.image.AxesImage object at 0x7f3b6a7e80d0>
 
 
 
@@ -851,7 +849,7 @@ the torchvision repository.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  49.792 seconds)
+   **Total running time of the script:** ( 0 minutes  49.338 seconds)
 
 
 .. _sphx_glr_download_intermediate_torchvision_tutorial.py:
diff --git a/_sources/prototype/sg_execution_times.rst.txt b/_sources/prototype/sg_execution_times.rst.txt
index 26e91b8e4b..8f90959997 100644
--- a/_sources/prototype/sg_execution_times.rst.txt
+++ b/_sources/prototype/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:00.029** total execution time for **prototype** files:
+**00:00.028** total execution time for **prototype** files:
 
 +-----------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_prototype_maskedtensor_advanced_semantics.py` (``maskedtensor_advanced_semantics.py``)     | 00:00.025 | 0.0 MB |
@@ -14,10 +14,10 @@ Computation times
 +-----------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_prototype_maskedtensor_overview.py` (``maskedtensor_overview.py``)                         | 00:00.001 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_prototype_numeric_suite_tutorial.py` (``numeric_suite_tutorial.py``)                       | 00:00.001 | 0.0 MB |
-+-----------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_prototype_maskedtensor_adagrad.py` (``maskedtensor_adagrad.py``)                           | 00:00.001 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_prototype_numeric_suite_tutorial.py` (``numeric_suite_tutorial.py``)                       | 00:00.001 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_prototype_maskedtensor_sparsity.py` (``maskedtensor_sparsity.py``)                         | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_prototype_fx_graph_mode_ptq_dynamic.py` (``fx_graph_mode_ptq_dynamic.py``)                 | 00:00.000 | 0.0 MB |
diff --git a/_sources/recipes/compiling_optimizer_lr_scheduler.rst.txt b/_sources/recipes/compiling_optimizer_lr_scheduler.rst.txt
index e95d8cb824..3dded62004 100644
--- a/_sources/recipes/compiling_optimizer_lr_scheduler.rst.txt
+++ b/_sources/recipes/compiling_optimizer_lr_scheduler.rst.txt
@@ -167,31 +167,31 @@ LR in a tensor.
 
  .. code-block:: none
 
-    V0625 22:03:41.119000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-    V0625 22:03:41.119000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-    V0625 22:03:41.119000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - ___key_to_id(L['self'].state) == [139670086608880,139670086608960,139670086601920,139670086603360,139670086600320,139670086599760,139670086606880,139670086609760,139670086612080,139670086602480]
-    V0625 22:03:44.430000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-    V0625 22:03:44.430000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-    V0625 22:03:44.430000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.003333333333333333     
-    V0625 22:03:44.430000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - G['__optimizer_139668517417792_139670031288176_c79']() is not None
-    V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-    V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-    V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.004666666666666667     
-    V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.003333333333333333     
-    V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - G['__optimizer_139668517417792_139670031288176_c79']() is not None
-    V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-    V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-    V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.006000000000000001     
-    V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.004666666666666667     
-    V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.003333333333333333     
-    V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - G['__optimizer_139668517417792_139670031288176_c79']() is not None
-    V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-    V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-    V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.007333333333333335     
-    V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.006000000000000001     
-    V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.004666666666666667     
-    V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.003333333333333333     
-    V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - G['__optimizer_139668517417792_139670031288176_c79']() is not None
+    V0625 22:40:10.944000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+    V0625 22:40:10.944000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+    V0625 22:40:10.944000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - ___key_to_id(L['self'].state) == [139893161731440,139893161724240,139893161722720,139893161727120,139893161732960,139893161726480,139893161722640,139893161724800,139893161731680,139893161724960]
+    V0625 22:40:14.236000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+    V0625 22:40:14.236000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+    V0625 22:40:14.236000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.003333333333333333     
+    V0625 22:40:14.236000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - G['__optimizer_139893146295488_139893161731840_c59']() is not None
+    V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+    V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+    V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.004666666666666667     
+    V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.003333333333333333     
+    V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - G['__optimizer_139893146295488_139893161731840_c59']() is not None
+    V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+    V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+    V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.006000000000000001     
+    V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.004666666666666667     
+    V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.003333333333333333     
+    V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - G['__optimizer_139893146295488_139893161731840_c59']() is not None
+    V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+    V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+    V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.007333333333333335     
+    V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.006000000000000001     
+    V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.004666666666666667     
+    V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L['self'].param_groups[0]['lr'] == 0.003333333333333333     
+    V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - G['__optimizer_139893146295488_139893161731840_c59']() is not None
 
 
 
@@ -219,7 +219,7 @@ See also:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  16.936 seconds)
+   **Total running time of the script:** ( 0 minutes  16.831 seconds)
 
 
 .. _sphx_glr_download_recipes_compiling_optimizer_lr_scheduler.py:
diff --git a/_sources/recipes/recipes/changing_default_device.rst.txt b/_sources/recipes/recipes/changing_default_device.rst.txt
index 19d81b73b7..f34b71539e 100644
--- a/_sources/recipes/recipes/changing_default_device.rst.txt
+++ b/_sources/recipes/recipes/changing_default_device.rst.txt
@@ -128,7 +128,7 @@ is causing problems for you, please comment on
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.099 seconds)
+   **Total running time of the script:** ( 0 minutes  0.101 seconds)
 
 
 .. _sphx_glr_download_recipes_recipes_changing_default_device.py:
diff --git a/_sources/recipes/recipes/module_load_state_dict_tips.rst.txt b/_sources/recipes/recipes/module_load_state_dict_tips.rst.txt
index 9b6abf59a9..9905a0ebc3 100644
--- a/_sources/recipes/recipes/module_load_state_dict_tips.rst.txt
+++ b/_sources/recipes/recipes/module_load_state_dict_tips.rst.txt
@@ -157,7 +157,7 @@ loaded into CPU RAM, which can be undesirable when:
 
  .. code-block:: none
 
-    loading time without mmap=0.021975040435791016
+    loading time without mmap=0.022372722625732422
 
 
 
@@ -189,7 +189,7 @@ storages will be memory-mapped.
 
  .. code-block:: none
 
-    loading time with mmap=0.0016951560974121094
+    loading time with mmap=0.0015723705291748047
 
 
 
@@ -352,7 +352,7 @@ be used to aid when loading a model from a checkpoint.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.387 seconds)
+   **Total running time of the script:** ( 0 minutes  0.391 seconds)
 
 
 .. _sphx_glr_download_recipes_recipes_module_load_state_dict_tips.py:
diff --git a/_sources/recipes/recipes/reasoning_about_shapes.rst.txt b/_sources/recipes/recipes/reasoning_about_shapes.rst.txt
index 5633207423..09a3c9df77 100644
--- a/_sources/recipes/recipes/reasoning_about_shapes.rst.txt
+++ b/_sources/recipes/recipes/reasoning_about_shapes.rst.txt
@@ -60,7 +60,7 @@ of a layer without materializing any data.
  .. code-block:: none
 
     tensor(..., device='meta', size=(2, 5, 9, 9), grad_fn=<ConvolutionBackward0>)
-    Time taken: 0.00018522299978940282
+    Time taken: 0.00019496499953675084
 
 
 
@@ -94,7 +94,7 @@ inputs will not significantly alter the time taken for shape computation.
 
     tensor(..., device='meta', size=(1024, 5, 65535, 65535),
            grad_fn=<ConvolutionBackward0>)
-    Time taken: 0.00011191299927304499
+    Time taken: 0.00011925200124096591
 
 
 
@@ -193,7 +193,7 @@ forward hook to each layer that prints the shape of the output.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.015 seconds)
+   **Total running time of the script:** ( 0 minutes  0.016 seconds)
 
 
 .. _sphx_glr_download_recipes_recipes_reasoning_about_shapes.py:
diff --git a/_sources/recipes/recipes/swap_tensors.rst.txt b/_sources/recipes/recipes/swap_tensors.rst.txt
index 47c47fbf60..07494e3f10 100644
--- a/_sources/recipes/recipes/swap_tensors.rst.txt
+++ b/_sources/recipes/recipes/swap_tensors.rst.txt
@@ -217,8 +217,8 @@ of the subclass' payload (``elem``) does not change.
 
  .. code-block:: none
 
-    Before: id(m.weight)=139960653111168, id(m.bias)=139960653111328
-    After: id(m.weight)=139960653111168, id(m.bias)=139960653111328
+    Before: id(m.weight)=139895005243936, id(m.bias)=139895005241616
+    After: id(m.weight)=139895005243936, id(m.bias)=139895005241616
     m.weight.dtype: torch.bfloat16
     m.weight.elem.dtype: torch.float32
     m.bias.dtype: torch.bfloat16
@@ -259,8 +259,8 @@ the ``dtype`` of the payload is properly converted.
 
  .. code-block:: none
 
-    Before: id(m.weight)=139960653106848, id(m.bias)=139960652981136
-    After: id(m.weight)=139960653106848, id(m.bias)=139960652981136
+    Before: id(m.weight)=139895005239936, id(m.bias)=139896021365792
+    After: id(m.weight)=139895005239936, id(m.bias)=139896021365792
     m.weight.dtype: torch.bfloat16
     m.weight.elem.dtype: torch.bfloat16
     m.bias.dtype: torch.bfloat16
@@ -392,7 +392,7 @@ for biases, we want to preserve the properties of the tensor in the ``state_dict
 
  .. code-block:: none
 
-    Before: id(weight)=139960653108048, id(bias)=139960653117168
+    Before: id(weight)=139895005242736, id(bias)=139895005237136
     m.state_dict() before load_state_dict():
      OrderedDict([('weight', MyQuantizedLinearWeight(tensor(..., device='meta', size=(5, 3)), scale=0.5)), ('bias', tensor(..., device='meta', size=(5,)))])
     state_dict:
@@ -401,7 +401,7 @@ for biases, we want to preserve the properties of the tensor in the ``state_dict
             [ 0.2932, -0.3519, -0.5715],
             [-0.2231, -0.4428,  0.4737],
             [ 0.1663,  0.2391,  0.1826]])), ('bias', tensor([-0.0100,  0.4518, -0.4102,  0.0364, -0.3941]))])
-    After: id(weight)=139960653108048, id(bias)=139960653117168
+    After: id(weight)=139895005242736, id(bias)=139895005237136
     m.state_dict() after load_state_dict():
      OrderedDict([('weight', MyQuantizedLinearWeight(tensor([[ 0.2430,  0.5155,  0.3337],
             [-0.2524,  0.3333,  0.1033],
@@ -431,7 +431,7 @@ use the two new extension points that are gated by
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.018 seconds)
+   **Total running time of the script:** ( 0 minutes  0.017 seconds)
 
 
 .. _sphx_glr_download_recipes_recipes_swap_tensors.py:
diff --git a/_sources/recipes/torch_compile_user_defined_triton_kernel_tutorial.rst.txt b/_sources/recipes/torch_compile_user_defined_triton_kernel_tutorial.rst.txt
index 87577ee551..08be9ee54d 100644
--- a/_sources/recipes/torch_compile_user_defined_triton_kernel_tutorial.rst.txt
+++ b/_sources/recipes/torch_compile_user_defined_triton_kernel_tutorial.rst.txt
@@ -249,7 +249,7 @@ See Also
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.471 seconds)
+   **Total running time of the script:** ( 0 minutes  1.478 seconds)
 
 
 .. _sphx_glr_download_recipes_torch_compile_user_defined_triton_kernel_tutorial.py:
diff --git a/_sources/recipes/torch_logs.rst.txt b/_sources/recipes/torch_logs.rst.txt
index 471482909f..ef69bc630a 100644
--- a/_sources/recipes/torch_logs.rst.txt
+++ b/_sources/recipes/torch_logs.rst.txt
@@ -147,7 +147,7 @@ For more information on torch.compile, see the `torch.compile tutorial`_.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.002 seconds)
+   **Total running time of the script:** ( 0 minutes  0.003 seconds)
 
 
 .. _sphx_glr_download_recipes_torch_logs.py:
diff --git a/advanced/coding_ddpg.html b/advanced/coding_ddpg.html
index a08919fbaf..1b9d69b0f4 100644
--- a/advanced/coding_ddpg.html
+++ b/advanced/coding_ddpg.html
@@ -1559,26 +1559,26 @@ <h2>Time to train the policy<a class="headerlink" href="#time-to-train-the-polic
 </pre></div>
 </div>
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-reward: -0.10 (r0 = -2.20), reward eval: reward: -0.01, reward normalized=-2.70/6.05, grad norm= 81.33, loss_value= 343.52, loss_actor= 14.75, target value: -16.15:  72%|#######2  | 7200/10000 [00:12&lt;00:05, 486.42it/s]
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+reward: -4.90 (r0 = -2.76), reward eval: reward:  0.00, reward normalized=-2.51/4.70, grad norm= 34.93, loss_value= 161.79, loss_actor= 15.98, target value: -16.57:  88%|########8 | 8800/10000 [00:16&lt;00:03, 388.29it/s]
+reward: -4.41 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-2.43/5.13, grad norm= 72.99, loss_value= 241.28, loss_actor= 13.02, target value: -16.27:  88%|########8 | 8800/10000 [00:19&lt;00:03, 388.29it/s]
+reward: -4.41 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-2.43/5.13, grad norm= 72.99, loss_value= 241.28, loss_actor= 13.02, target value: -16.27:  96%|#########6| 9600/10000 [00:21&lt;00:01, 288.47it/s]
+reward: -4.65 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-2.92/5.14, grad norm= 147.22, loss_value= 251.85, loss_actor= 11.03, target value: -20.73:  96%|#########6| 9600/10000 [00:22&lt;00:01, 288.47it/s]
+reward: -4.65 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-2.92/5.14, grad norm= 147.22, loss_value= 251.85, loss_actor= 11.03, target value: -20.73: : 10400it [00:24, 268.73it/s]
+reward: -4.72 (r0 = -2.76), reward eval: reward: -5.39, reward normalized=-3.49/4.19, grad norm= 133.24, loss_value= 169.25, loss_actor= 14.39, target value: -24.45: : 10400it [00:25, 268.73it/s]
 </pre></div>
 </div>
 </div>
@@ -1622,7 +1622,7 @@ <h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this
 <li><p>Using <cite>@dispatch</cite> (see <a class="reference external" href="https://github.com/pytorch/rl/pull/1230">[Feature] Distpatch IQL loss module</a>.)</p></li>
 <li><p>Allowing flexible TensorDict keys.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  41.549 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  40.906 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-advanced-coding-ddpg-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/592c87306513ba042d759e142b59fea4/coding_ddpg.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">coding_ddpg.py</span></code></a></p>
diff --git a/advanced/dynamic_quantization_tutorial.html b/advanced/dynamic_quantization_tutorial.html
index 7d95145a09..4f88a7c43a 100644
--- a/advanced/dynamic_quantization_tutorial.html
+++ b/advanced/dynamic_quantization_tutorial.html
@@ -838,9 +838,9 @@ <h2>4. Test dynamic quantization<a class="headerlink" href="#test-dynamic-quanti
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loss: 5.167
-elapsed time (seconds): 206.3
+elapsed time (seconds): 205.3
 loss: 5.168
-elapsed time (seconds): 116.9
+elapsed time (seconds): 117.6
 </pre></div>
 </div>
 <p>Running this locally on a MacBook Pro, without quantization, inference takes about 200 seconds,
@@ -852,7 +852,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 having a limited effect on accuracy.</p>
 <p>Thanks for reading! As always, we welcome any feedback, so please create an issue
 <a class="reference external" href="https://github.com/pytorch/pytorch/issues">here</a> if you have any.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  32.168 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  31.684 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-advanced-dynamic-quantization-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/256861ec2cab5f0dc50c523f520dfefd/dynamic_quantization_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">dynamic_quantization_tutorial.py</span></code></a></p>
diff --git a/advanced/neural_style_tutorial.html b/advanced/neural_style_tutorial.html
index 13d1b1acd8..685aa3283b 100644
--- a/advanced/neural_style_tutorial.html
+++ b/advanced/neural_style_tutorial.html
@@ -729,41 +729,39 @@ <h2>Importing the Model<a class="headerlink" href="#importing-the-model" title="
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth
 
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 </div>
 <p>Additionally, VGG networks are trained on images with each channel
@@ -975,25 +973,25 @@ <h2>Gradient Descent<a class="headerlink" href="#gradient-descent" title="Permal
 
 Optimizing..
 run [50]:
-Style Loss : 4.037464 Content Loss: 4.144108
+Style Loss : 4.108783 Content Loss: 4.076407
 
 run [100]:
-Style Loss : 1.150719 Content Loss: 3.040204
+Style Loss : 1.131557 Content Loss: 3.022170
 
 run [150]:
-Style Loss : 0.710417 Content Loss: 2.655678
+Style Loss : 0.701424 Content Loss: 2.643672
 
 run [200]:
-Style Loss : 0.479242 Content Loss: 2.487361
+Style Loss : 0.470905 Content Loss: 2.488043
 
 run [250]:
-Style Loss : 0.344841 Content Loss: 2.403615
+Style Loss : 0.342489 Content Loss: 2.402129
 
 run [300]:
-Style Loss : 0.262127 Content Loss: 2.350151
+Style Loss : 0.262571 Content Loss: 2.348458
 </pre></div>
 </div>
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 <p><a class="reference download internal" download="" href="../_downloads/b3bcd49c4dd9be3a44c6c5d2e2a8b0cd/neural_style_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">neural_style_tutorial.py</span></code></a></p>
diff --git a/advanced/numpy_extensions_tutorial.html b/advanced/numpy_extensions_tutorial.html
index f67f77fc4c..03910126be 100644
--- a/advanced/numpy_extensions_tutorial.html
+++ b/advanced/numpy_extensions_tutorial.html
@@ -674,7 +674,7 @@ <h2>Parametrized example<a class="headerlink" href="#parametrized-example" title
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Are the gradients correct:  True
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.560 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.619 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-advanced-numpy-extensions-tutorial-py">
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 <p><a class="reference download internal" download="" href="../_downloads/ee55f2537c08fa13e041e479675a6c2c/numpy_extensions_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">numpy_extensions_tutorial.py</span></code></a></p>
diff --git a/advanced/pendulum.html b/advanced/pendulum.html
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+++ b/advanced/pendulum.html
@@ -1645,1256 +1645,1256 @@ <h3>Training loop<a class="headerlink" href="#training-loop" title="Permalink to
 </div>
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+reward: -2.1253, last reward: -0.0001, gradient norm:  0.6622: 100%|#########9| 624/625 [01:38&lt;00:00,  6.38it/s]
+reward: -2.1253, last reward: -0.0001, gradient norm:  0.6622: 100%|##########| 625/625 [01:38&lt;00:00,  6.39it/s]
+reward: -2.1253, last reward: -0.0001, gradient norm:  0.6622: 100%|##########| 625/625 [01:38&lt;00:00,  6.35it/s]
 </pre></div>
 </div>
 </div>
@@ -2914,7 +2914,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 to write custom transformations;</p></li>
 <li><p>How to train a policy on a fully differentiable simulator.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  39.914 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  38.707 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-advanced-pendulum-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/0c4dc681209d8c964aae9de5e477d280/pendulum.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">pendulum.py</span></code></a></p>
diff --git a/advanced/semi_structured_sparse.html b/advanced/semi_structured_sparse.html
index 43e0446d40..d7c29201ff 100644
--- a/advanced/semi_structured_sparse.html
+++ b/advanced/semi_structured_sparse.html
@@ -550,7 +550,7 @@ <h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to t
     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Dense: </span><span class="si">{</span><span class="n">dense_t</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">ms Sparse: </span><span class="si">{</span><span class="n">sparse_t</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">ms | Speedup: </span><span class="si">{</span><span class="p">(</span><span class="n">dense_t</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">sparse_t</span><span class="p">)</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">x"</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Dense: 2.921ms Sparse: 1.660ms | Speedup: 1.760x
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Dense: 2.922ms Sparse: 1.649ms | Speedup: 1.772x
 </pre></div>
 </div>
 </div>
@@ -705,7 +705,7 @@ <h2>Intro &amp; Setup<a class="headerlink" href="#intro-setup" title="Permalink
 <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span></a><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;torch._C.Generator object at 0x7f083c737510&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;torch._C.Generator object at 0x7f3c2d47b510&gt;
 </pre></div>
 </div>
 <p>We’ll also need to define some helper functions that are specific to the
@@ -903,199 +903,167 @@ <h2>Intro &amp; Setup<a class="headerlink" href="#intro-setup" title="Permalink
 </pre></div>
 </div>
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 </pre></div>
 </div>
 </div>
@@ -1161,46 +1129,46 @@ <h1>Establishing a baseline<a class="headerlink" href="#establishing-a-baseline"
 <img alt="Loss vs. # steps" class="sphx-glr-single-img" src="../_images/sphx_glr_semi_structured_sparse_001.png" srcset="../_images/sphx_glr_semi_structured_sparse_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Detected kernel version 4.14.336, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
 
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+  4%|4         | 20/500 [00:11&lt;04:41,  1.71it/s]
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+  6%|6         | 32/500 [00:18&lt;04:34,  1.70it/s]
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+  7%|6         | 34/500 [00:19&lt;04:34,  1.70it/s]
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+  7%|7         | 36/500 [00:21&lt;04:32,  1.70it/s]
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-  8%|7         | 38/500 [00:22&lt;04:32,  1.70it/s]
+  8%|7         | 38/500 [00:22&lt;04:31,  1.70it/s]
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-  8%|8         | 40/500 [00:23&lt;04:31,  1.70it/s]
+  8%|8         | 40/500 [00:23&lt;04:30,  1.70it/s]
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@@ -1223,43 +1191,43 @@ <h1>Establishing a baseline<a class="headerlink" href="#establishing-a-baseline"
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- 18%|#7        | 89/500 [00:52&lt;04:03,  1.69it/s]
+ 18%|#7        | 89/500 [00:52&lt;04:02,  1.69it/s]
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@@ -1271,7 +1239,7 @@ <h1>Establishing a baseline<a class="headerlink" href="#establishing-a-baseline"
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+ 21%|##        | 103/500 [01:00&lt;03:55,  1.69it/s]
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@@ -1283,9 +1251,9 @@ <h1>Establishing a baseline<a class="headerlink" href="#establishing-a-baseline"
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- 23%|##3       | 115/500 [01:07&lt;03:47,  1.69it/s]
+ 23%|##3       | 115/500 [01:07&lt;03:48,  1.69it/s]
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- 23%|##3       | 117/500 [01:09&lt;03:46,  1.69it/s]
+ 23%|##3       | 117/500 [01:08&lt;03:46,  1.69it/s]
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  24%|##4       | 120/500 [01:10&lt;03:45,  1.69it/s]
@@ -1312,7 +1280,7 @@ <h1>Establishing a baseline<a class="headerlink" href="#establishing-a-baseline"
  28%|##8       | 141/500 [01:23&lt;03:32,  1.69it/s]
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- 29%|##8       | 144/500 [01:25&lt;03:30,  1.69it/s]
+ 29%|##8       | 144/500 [01:24&lt;03:31,  1.69it/s]
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@@ -1328,173 +1296,173 @@ <h1>Establishing a baseline<a class="headerlink" href="#establishing-a-baseline"
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- 31%|###1      | 156/500 [01:32&lt;03:23,  1.69it/s]
+ 31%|###1      | 156/500 [01:32&lt;03:24,  1.69it/s]
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- 32%|###2      | 161/500 [01:35&lt;03:20,  1.69it/s]
- 32%|###2      | 162/500 [01:35&lt;03:20,  1.69it/s]
+ 32%|###2      | 161/500 [01:35&lt;03:21,  1.69it/s]
+ 32%|###2      | 162/500 [01:35&lt;03:20,  1.68it/s]
  33%|###2      | 163/500 [01:36&lt;03:19,  1.69it/s]
- 33%|###2      | 164/500 [01:36&lt;03:19,  1.69it/s]
- 33%|###3      | 165/500 [01:37&lt;03:18,  1.69it/s]
- 33%|###3      | 166/500 [01:38&lt;03:17,  1.69it/s]
- 33%|###3      | 167/500 [01:38&lt;03:17,  1.69it/s]
- 34%|###3      | 168/500 [01:39&lt;03:16,  1.69it/s]
- 34%|###3      | 169/500 [01:39&lt;03:16,  1.69it/s]
- 34%|###4      | 170/500 [01:40&lt;03:15,  1.69it/s]
- 34%|###4      | 171/500 [01:41&lt;03:15,  1.69it/s]
- 34%|###4      | 172/500 [01:41&lt;03:14,  1.69it/s]
- 35%|###4      | 173/500 [01:42&lt;03:13,  1.69it/s]
- 35%|###4      | 174/500 [01:42&lt;03:13,  1.69it/s]
- 35%|###5      | 175/500 [01:43&lt;03:12,  1.69it/s]
- 35%|###5      | 176/500 [01:44&lt;03:12,  1.69it/s]
- 35%|###5      | 177/500 [01:44&lt;03:11,  1.69it/s]
- 36%|###5      | 178/500 [01:45&lt;03:10,  1.69it/s]
- 36%|###5      | 179/500 [01:45&lt;03:10,  1.69it/s]
- 36%|###6      | 180/500 [01:46&lt;03:09,  1.69it/s]
- 36%|###6      | 181/500 [01:46&lt;03:09,  1.69it/s]
- 36%|###6      | 182/500 [01:47&lt;03:08,  1.69it/s]
- 37%|###6      | 183/500 [01:48&lt;03:08,  1.69it/s]
+ 33%|###2      | 164/500 [01:36&lt;03:19,  1.68it/s]
+ 33%|###3      | 165/500 [01:37&lt;03:18,  1.68it/s]
+ 33%|###3      | 166/500 [01:38&lt;03:18,  1.69it/s]
+ 33%|###3      | 167/500 [01:38&lt;03:17,  1.68it/s]
+ 34%|###3      | 168/500 [01:39&lt;03:17,  1.68it/s]
+ 34%|###3      | 169/500 [01:39&lt;03:16,  1.68it/s]
+ 34%|###4      | 170/500 [01:40&lt;03:15,  1.68it/s]
+ 34%|###4      | 171/500 [01:41&lt;03:15,  1.68it/s]
+ 34%|###4      | 172/500 [01:41&lt;03:14,  1.68it/s]
+ 35%|###4      | 173/500 [01:42&lt;03:14,  1.68it/s]
+ 35%|###4      | 174/500 [01:42&lt;03:13,  1.68it/s]
+ 35%|###5      | 175/500 [01:43&lt;03:12,  1.68it/s]
+ 35%|###5      | 176/500 [01:43&lt;03:12,  1.68it/s]
+ 35%|###5      | 177/500 [01:44&lt;03:11,  1.68it/s]
+ 36%|###5      | 178/500 [01:45&lt;03:11,  1.68it/s]
+ 36%|###5      | 179/500 [01:45&lt;03:10,  1.68it/s]
+ 36%|###6      | 180/500 [01:46&lt;03:09,  1.68it/s]
+ 36%|###6      | 181/500 [01:46&lt;03:09,  1.68it/s]
+ 36%|###6      | 182/500 [01:47&lt;03:08,  1.68it/s]
+ 37%|###6      | 183/500 [01:48&lt;03:08,  1.68it/s]
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- 37%|###7      | 186/500 [01:49&lt;03:06,  1.69it/s]
+ 37%|###7      | 186/500 [01:49&lt;03:06,  1.68it/s]
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- 38%|###8      | 190/500 [01:52&lt;03:03,  1.68it/s]
+ 38%|###8      | 190/500 [01:52&lt;03:04,  1.68it/s]
  38%|###8      | 191/500 [01:52&lt;03:03,  1.68it/s]
- 38%|###8      | 192/500 [01:53&lt;03:02,  1.68it/s]
- 39%|###8      | 193/500 [01:54&lt;03:02,  1.69it/s]
- 39%|###8      | 194/500 [01:54&lt;03:01,  1.69it/s]
- 39%|###9      | 195/500 [01:55&lt;03:00,  1.69it/s]
- 39%|###9      | 196/500 [01:55&lt;03:00,  1.69it/s]
- 39%|###9      | 197/500 [01:56&lt;02:59,  1.69it/s]
- 40%|###9      | 198/500 [01:57&lt;02:59,  1.69it/s]
- 40%|###9      | 199/500 [01:57&lt;02:58,  1.69it/s]
+ 38%|###8      | 192/500 [01:53&lt;03:03,  1.68it/s]
+ 39%|###8      | 193/500 [01:54&lt;03:02,  1.68it/s]
+ 39%|###8      | 194/500 [01:54&lt;03:01,  1.68it/s]
+ 39%|###9      | 195/500 [01:55&lt;03:01,  1.68it/s]
+ 39%|###9      | 196/500 [01:55&lt;03:00,  1.68it/s]
+ 39%|###9      | 197/500 [01:56&lt;03:00,  1.68it/s]
+ 40%|###9      | 198/500 [01:57&lt;02:59,  1.68it/s]
+ 40%|###9      | 199/500 [01:57&lt;02:59,  1.68it/s]
  40%|####      | 200/500 [01:58&lt;02:58,  1.68it/s]
 
 {'loss': 1.6519, 'learning_rate': 5e-05, 'epoch': 0.07}
 
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+ 50%|#####     | 250/500 [02:28&lt;02:28,  1.68it/s]
 
 {'loss': 1.5305, 'learning_rate': 5e-05, 'epoch': 0.09}
 
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- 60%|######    | 300/500 [02:57&lt;01:58,  1.68it/s]
+ 60%|######    | 300/500 [02:57&lt;01:59,  1.68it/s]
 
 {'loss': 1.5315, 'learning_rate': 5e-05, 'epoch': 0.11}
 
- 60%|######    | 300/500 [02:57&lt;01:58,  1.68it/s]
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@@ -1503,35 +1471,35 @@ <h1>Establishing a baseline<a class="headerlink" href="#establishing-a-baseline"
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@@ -1544,30 +1512,30 @@ <h1>Establishing a baseline<a class="headerlink" href="#establishing-a-baseline"
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 {'loss': 1.3616, 'learning_rate': 5e-05, 'epoch': 0.16}
 
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@@ -1702,44 +1670,44 @@ <h1>Establishing a baseline<a class="headerlink" href="#establishing-a-baseline"
 
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-{'train_runtime': 297.6, 'train_samples_per_second': 53.763, 'train_steps_per_second': 1.68, 'train_loss': 1.7749846496582031, 'epoch': 0.18}
+{'train_runtime': 297.7951, 'train_samples_per_second': 53.728, 'train_steps_per_second': 1.679, 'train_loss': 1.7749846496582031, 'epoch': 0.18}
 
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 fp16 {'exact_match': 71.77861873226112, 'f1': 81.35189141414303}
-cuda_fp16 time {4: 14.114074000099208, '4_compile': 9.624771002563648, 16: 51.17472790007014, '16_compile': 36.57838400067703, 64: 201.03624100011075, '64_compile': 123.16050250046828, 256: 785.2311540009396, '256_compile': 474.52999999950407}
+cuda_fp16 time {4: 14.086745750137197, '4_compile': 9.647232000133954, 16: 51.1473719998321, '16_compile': 36.34630699889385, 64: 200.96267699773307, '64_compile': 123.04909100021177, 256: 785.1076100014325, '256_compile': 474.5083670022723}
 
 &lt;Axes: title={'center': 'Loss vs. # steps'}, xlabel='step', ylabel='loss'&gt;
 </pre></div>
@@ -1839,49 +1807,49 @@ <h2>Pruning BERT to be 2:4 sparse<a class="headerlink" href="#pruning-bert-to-be
 </pre></div>
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-100%|##########| 43/43 [00:38&lt;00:00,  1.11it/s]
+100%|##########| 43/43 [00:38&lt;00:00,  1.48it/s]
+100%|##########| 43/43 [00:38&lt;00:00,  1.12it/s]
 pruned eval metrics: {'exact_match': 31.069063386944183, 'f1': 43.162708225870595}
 </pre></div>
 </div>
@@ -1904,49 +1872,49 @@ <h2>Pruning BERT to be 2:4 sparse<a class="headerlink" href="#pruning-bert-to-be
   0%|          | 2/500 [00:01&lt;05:00,  1.66it/s]
   1%|          | 3/500 [00:01&lt;04:57,  1.67it/s]
   1%|          | 4/500 [00:02&lt;04:55,  1.68it/s]
-  1%|1         | 5/500 [00:02&lt;04:54,  1.68it/s]
-  1%|1         | 6/500 [00:03&lt;04:53,  1.69it/s]
-  1%|1         | 7/500 [00:04&lt;04:52,  1.69it/s]
+  1%|1         | 5/500 [00:02&lt;04:53,  1.69it/s]
+  1%|1         | 6/500 [00:03&lt;04:52,  1.69it/s]
+  1%|1         | 7/500 [00:04&lt;04:51,  1.69it/s]
   2%|1         | 8/500 [00:04&lt;04:51,  1.69it/s]
-  2%|1         | 9/500 [00:05&lt;04:51,  1.69it/s]
-  2%|2         | 10/500 [00:05&lt;04:50,  1.69it/s]
+  2%|1         | 9/500 [00:05&lt;04:50,  1.69it/s]
+  2%|2         | 10/500 [00:05&lt;04:49,  1.69it/s]
   2%|2         | 11/500 [00:06&lt;04:49,  1.69it/s]
-  2%|2         | 12/500 [00:07&lt;04:49,  1.69it/s]
-  3%|2         | 13/500 [00:07&lt;04:48,  1.69it/s]
-  3%|2         | 14/500 [00:08&lt;04:48,  1.69it/s]
-  3%|3         | 15/500 [00:08&lt;04:47,  1.69it/s]
-  3%|3         | 16/500 [00:09&lt;04:47,  1.69it/s]
-  3%|3         | 17/500 [00:10&lt;04:46,  1.69it/s]
+  2%|2         | 12/500 [00:07&lt;04:48,  1.69it/s]
+  3%|2         | 13/500 [00:07&lt;04:47,  1.69it/s]
+  3%|2         | 14/500 [00:08&lt;04:47,  1.69it/s]
+  3%|3         | 15/500 [00:08&lt;04:46,  1.69it/s]
+  3%|3         | 16/500 [00:09&lt;04:46,  1.69it/s]
+  3%|3         | 17/500 [00:10&lt;04:45,  1.69it/s]
   4%|3         | 18/500 [00:10&lt;04:45,  1.69it/s]
-  4%|3         | 19/500 [00:11&lt;04:45,  1.69it/s]
+  4%|3         | 19/500 [00:11&lt;04:44,  1.69it/s]
   4%|4         | 20/500 [00:11&lt;04:44,  1.69it/s]
-  4%|4         | 21/500 [00:12&lt;04:44,  1.69it/s]
-  4%|4         | 22/500 [00:13&lt;04:43,  1.68it/s]
-  5%|4         | 23/500 [00:13&lt;04:43,  1.69it/s]
+  4%|4         | 21/500 [00:12&lt;04:43,  1.69it/s]
+  4%|4         | 22/500 [00:13&lt;04:43,  1.69it/s]
+  5%|4         | 23/500 [00:13&lt;04:42,  1.69it/s]
   5%|4         | 24/500 [00:14&lt;04:42,  1.69it/s]
   5%|5         | 25/500 [00:14&lt;04:41,  1.69it/s]
-  5%|5         | 26/500 [00:15&lt;04:41,  1.69it/s]
+  5%|5         | 26/500 [00:15&lt;04:40,  1.69it/s]
   5%|5         | 27/500 [00:16&lt;04:40,  1.69it/s]
   6%|5         | 28/500 [00:16&lt;04:39,  1.69it/s]
   6%|5         | 29/500 [00:17&lt;04:39,  1.69it/s]
   6%|6         | 30/500 [00:17&lt;04:38,  1.69it/s]
   6%|6         | 31/500 [00:18&lt;04:38,  1.69it/s]
-  6%|6         | 32/500 [00:19&lt;04:37,  1.69it/s]
-  7%|6         | 33/500 [00:19&lt;04:37,  1.68it/s]
-  7%|6         | 34/500 [00:20&lt;04:36,  1.68it/s]
-  7%|7         | 35/500 [00:20&lt;04:36,  1.68it/s]
-  7%|7         | 36/500 [00:21&lt;04:35,  1.68it/s]
-  7%|7         | 37/500 [00:21&lt;04:34,  1.68it/s]
-  8%|7         | 38/500 [00:22&lt;04:34,  1.68it/s]
-  8%|7         | 39/500 [00:23&lt;04:33,  1.68it/s]
-  8%|8         | 40/500 [00:23&lt;04:33,  1.68it/s]
-  8%|8         | 41/500 [00:24&lt;04:32,  1.68it/s]
-  8%|8         | 42/500 [00:24&lt;04:31,  1.68it/s]
-  9%|8         | 43/500 [00:25&lt;04:31,  1.68it/s]
-  9%|8         | 44/500 [00:26&lt;04:30,  1.68it/s]
+  6%|6         | 32/500 [00:18&lt;04:37,  1.69it/s]
+  7%|6         | 33/500 [00:19&lt;04:36,  1.69it/s]
+  7%|6         | 34/500 [00:20&lt;04:36,  1.69it/s]
+  7%|7         | 35/500 [00:20&lt;04:35,  1.69it/s]
+  7%|7         | 36/500 [00:21&lt;04:35,  1.69it/s]
+  7%|7         | 37/500 [00:21&lt;04:34,  1.69it/s]
+  8%|7         | 38/500 [00:22&lt;04:33,  1.69it/s]
+  8%|7         | 39/500 [00:23&lt;04:33,  1.69it/s]
+  8%|8         | 40/500 [00:23&lt;04:32,  1.69it/s]
+  8%|8         | 41/500 [00:24&lt;04:32,  1.69it/s]
+  8%|8         | 42/500 [00:24&lt;04:31,  1.69it/s]
+  9%|8         | 43/500 [00:25&lt;04:31,  1.69it/s]
+  9%|8         | 44/500 [00:26&lt;04:30,  1.69it/s]
   9%|9         | 45/500 [00:26&lt;04:30,  1.68it/s]
   9%|9         | 46/500 [00:27&lt;04:29,  1.68it/s]
-  9%|9         | 47/500 [00:27&lt;04:29,  1.68it/s]
+  9%|9         | 47/500 [00:27&lt;04:28,  1.68it/s]
  10%|9         | 48/500 [00:28&lt;04:28,  1.68it/s]
  10%|9         | 49/500 [00:29&lt;04:27,  1.68it/s]
  10%|#         | 50/500 [00:29&lt;04:27,  1.68it/s]
@@ -1957,7 +1925,7 @@ <h2>Pruning BERT to be 2:4 sparse<a class="headerlink" href="#pruning-bert-to-be
  10%|#         | 51/500 [00:30&lt;04:26,  1.68it/s]
  10%|#         | 52/500 [00:30&lt;04:26,  1.68it/s]
  11%|#         | 53/500 [00:31&lt;04:25,  1.68it/s]
- 11%|#         | 54/500 [00:32&lt;04:25,  1.68it/s]
+ 11%|#         | 54/500 [00:32&lt;04:24,  1.68it/s]
  11%|#1        | 55/500 [00:32&lt;04:24,  1.68it/s]
  11%|#1        | 56/500 [00:33&lt;04:23,  1.68it/s]
  11%|#1        | 57/500 [00:33&lt;04:23,  1.68it/s]
@@ -1967,50 +1935,50 @@ <h2>Pruning BERT to be 2:4 sparse<a class="headerlink" href="#pruning-bert-to-be
  12%|#2        | 61/500 [00:36&lt;04:20,  1.68it/s]
  12%|#2        | 62/500 [00:36&lt;04:20,  1.68it/s]
  13%|#2        | 63/500 [00:37&lt;04:19,  1.68it/s]
- 13%|#2        | 64/500 [00:38&lt;04:19,  1.68it/s]
+ 13%|#2        | 64/500 [00:37&lt;04:18,  1.68it/s]
  13%|#3        | 65/500 [00:38&lt;04:18,  1.68it/s]
- 13%|#3        | 66/500 [00:39&lt;04:18,  1.68it/s]
+ 13%|#3        | 66/500 [00:39&lt;04:17,  1.68it/s]
  13%|#3        | 67/500 [00:39&lt;04:17,  1.68it/s]
- 14%|#3        | 68/500 [00:40&lt;04:17,  1.68it/s]
+ 14%|#3        | 68/500 [00:40&lt;04:16,  1.68it/s]
  14%|#3        | 69/500 [00:40&lt;04:16,  1.68it/s]
- 14%|#4        | 70/500 [00:41&lt;04:16,  1.68it/s]
+ 14%|#4        | 70/500 [00:41&lt;04:15,  1.68it/s]
  14%|#4        | 71/500 [00:42&lt;04:15,  1.68it/s]
- 14%|#4        | 72/500 [00:42&lt;04:15,  1.68it/s]
+ 14%|#4        | 72/500 [00:42&lt;04:14,  1.68it/s]
  15%|#4        | 73/500 [00:43&lt;04:14,  1.68it/s]
  15%|#4        | 74/500 [00:43&lt;04:13,  1.68it/s]
  15%|#5        | 75/500 [00:44&lt;04:13,  1.68it/s]
  15%|#5        | 76/500 [00:45&lt;04:12,  1.68it/s]
- 15%|#5        | 77/500 [00:45&lt;04:12,  1.68it/s]
+ 15%|#5        | 77/500 [00:45&lt;04:11,  1.68it/s]
  16%|#5        | 78/500 [00:46&lt;04:11,  1.68it/s]
- 16%|#5        | 79/500 [00:46&lt;04:11,  1.68it/s]
+ 16%|#5        | 79/500 [00:46&lt;04:10,  1.68it/s]
  16%|#6        | 80/500 [00:47&lt;04:10,  1.68it/s]
- 16%|#6        | 81/500 [00:48&lt;04:10,  1.68it/s]
+ 16%|#6        | 81/500 [00:48&lt;04:09,  1.68it/s]
  16%|#6        | 82/500 [00:48&lt;04:09,  1.68it/s]
  17%|#6        | 83/500 [00:49&lt;04:08,  1.68it/s]
- 17%|#6        | 84/500 [00:49&lt;04:08,  1.68it/s]
+ 17%|#6        | 84/500 [00:49&lt;04:07,  1.68it/s]
  17%|#7        | 85/500 [00:50&lt;04:07,  1.68it/s]
- 17%|#7        | 86/500 [00:51&lt;04:07,  1.68it/s]
+ 17%|#7        | 86/500 [00:51&lt;04:06,  1.68it/s]
  17%|#7        | 87/500 [00:51&lt;04:06,  1.68it/s]
  18%|#7        | 88/500 [00:52&lt;04:05,  1.68it/s]
- 18%|#7        | 89/500 [00:52&lt;04:05,  1.68it/s]
+ 18%|#7        | 89/500 [00:52&lt;04:04,  1.68it/s]
  18%|#8        | 90/500 [00:53&lt;04:04,  1.68it/s]
- 18%|#8        | 91/500 [00:54&lt;04:04,  1.68it/s]
+ 18%|#8        | 91/500 [00:54&lt;04:03,  1.68it/s]
  18%|#8        | 92/500 [00:54&lt;04:03,  1.68it/s]
  19%|#8        | 93/500 [00:55&lt;04:02,  1.68it/s]
  19%|#8        | 94/500 [00:55&lt;04:02,  1.68it/s]
- 19%|#9        | 95/500 [00:56&lt;04:01,  1.67it/s]
- 19%|#9        | 96/500 [00:57&lt;04:01,  1.68it/s]
- 19%|#9        | 97/500 [00:57&lt;04:00,  1.67it/s]
+ 19%|#9        | 95/500 [00:56&lt;04:01,  1.68it/s]
+ 19%|#9        | 96/500 [00:57&lt;04:00,  1.68it/s]
+ 19%|#9        | 97/500 [00:57&lt;04:00,  1.68it/s]
  20%|#9        | 98/500 [00:58&lt;03:59,  1.68it/s]
  20%|#9        | 99/500 [00:58&lt;03:59,  1.68it/s]
- 20%|##        | 100/500 [00:59&lt;03:58,  1.67it/s]
+ 20%|##        | 100/500 [00:59&lt;03:58,  1.68it/s]
 
 {'loss': 1.4599, 'learning_rate': 5e-05, 'epoch': 0.04}
 
- 20%|##        | 100/500 [00:59&lt;03:58,  1.67it/s]
- 20%|##        | 101/500 [01:00&lt;03:58,  1.67it/s]
- 20%|##        | 102/500 [01:00&lt;03:57,  1.67it/s]
- 21%|##        | 103/500 [01:01&lt;03:57,  1.68it/s]
+ 20%|##        | 100/500 [00:59&lt;03:58,  1.68it/s]
+ 20%|##        | 101/500 [01:00&lt;03:58,  1.68it/s]
+ 20%|##        | 102/500 [01:00&lt;03:57,  1.68it/s]
+ 21%|##        | 103/500 [01:01&lt;03:56,  1.68it/s]
  21%|##        | 104/500 [01:01&lt;03:56,  1.68it/s]
  21%|##1       | 105/500 [01:02&lt;03:55,  1.68it/s]
  21%|##1       | 106/500 [01:03&lt;03:55,  1.68it/s]
@@ -2018,31 +1986,31 @@ <h2>Pruning BERT to be 2:4 sparse<a class="headerlink" href="#pruning-bert-to-be
  22%|##1       | 108/500 [01:04&lt;03:53,  1.68it/s]
  22%|##1       | 109/500 [01:04&lt;03:53,  1.68it/s]
  22%|##2       | 110/500 [01:05&lt;03:52,  1.68it/s]
- 22%|##2       | 111/500 [01:06&lt;03:52,  1.68it/s]
+ 22%|##2       | 111/500 [01:05&lt;03:52,  1.68it/s]
  22%|##2       | 112/500 [01:06&lt;03:51,  1.68it/s]
  23%|##2       | 113/500 [01:07&lt;03:50,  1.68it/s]
  23%|##2       | 114/500 [01:07&lt;03:50,  1.68it/s]
- 23%|##3       | 115/500 [01:08&lt;03:49,  1.67it/s]
- 23%|##3       | 116/500 [01:09&lt;03:49,  1.67it/s]
- 23%|##3       | 117/500 [01:09&lt;03:48,  1.67it/s]
- 24%|##3       | 118/500 [01:10&lt;03:48,  1.67it/s]
- 24%|##3       | 119/500 [01:10&lt;03:47,  1.67it/s]
- 24%|##4       | 120/500 [01:11&lt;03:47,  1.67it/s]
- 24%|##4       | 121/500 [01:12&lt;03:46,  1.67it/s]
- 24%|##4       | 122/500 [01:12&lt;03:45,  1.67it/s]
- 25%|##4       | 123/500 [01:13&lt;03:45,  1.67it/s]
- 25%|##4       | 124/500 [01:13&lt;03:44,  1.67it/s]
- 25%|##5       | 125/500 [01:14&lt;03:44,  1.67it/s]
- 25%|##5       | 126/500 [01:15&lt;03:43,  1.67it/s]
+ 23%|##3       | 115/500 [01:08&lt;03:49,  1.68it/s]
+ 23%|##3       | 116/500 [01:08&lt;03:49,  1.68it/s]
+ 23%|##3       | 117/500 [01:09&lt;03:48,  1.68it/s]
+ 24%|##3       | 118/500 [01:10&lt;03:47,  1.68it/s]
+ 24%|##3       | 119/500 [01:10&lt;03:47,  1.68it/s]
+ 24%|##4       | 120/500 [01:11&lt;03:46,  1.68it/s]
+ 24%|##4       | 121/500 [01:11&lt;03:46,  1.68it/s]
+ 24%|##4       | 122/500 [01:12&lt;03:45,  1.68it/s]
+ 25%|##4       | 123/500 [01:13&lt;03:44,  1.68it/s]
+ 25%|##4       | 124/500 [01:13&lt;03:44,  1.68it/s]
+ 25%|##5       | 125/500 [01:14&lt;03:43,  1.68it/s]
+ 25%|##5       | 126/500 [01:14&lt;03:43,  1.67it/s]
  25%|##5       | 127/500 [01:15&lt;03:42,  1.67it/s]
  26%|##5       | 128/500 [01:16&lt;03:42,  1.67it/s]
  26%|##5       | 129/500 [01:16&lt;03:41,  1.67it/s]
- 26%|##6       | 130/500 [01:17&lt;03:40,  1.67it/s]
- 26%|##6       | 131/500 [01:18&lt;03:40,  1.67it/s]
+ 26%|##6       | 130/500 [01:17&lt;03:41,  1.67it/s]
+ 26%|##6       | 131/500 [01:17&lt;03:40,  1.67it/s]
  26%|##6       | 132/500 [01:18&lt;03:39,  1.67it/s]
  27%|##6       | 133/500 [01:19&lt;03:39,  1.67it/s]
  27%|##6       | 134/500 [01:19&lt;03:38,  1.67it/s]
- 27%|##7       | 135/500 [01:20&lt;03:37,  1.67it/s]
+ 27%|##7       | 135/500 [01:20&lt;03:38,  1.67it/s]
  27%|##7       | 136/500 [01:20&lt;03:37,  1.67it/s]
  27%|##7       | 137/500 [01:21&lt;03:36,  1.67it/s]
  28%|##7       | 138/500 [01:22&lt;03:36,  1.67it/s]
@@ -2063,29 +2031,29 @@ <h2>Pruning BERT to be 2:4 sparse<a class="headerlink" href="#pruning-bert-to-be
 
  30%|###       | 150/500 [01:29&lt;03:29,  1.67it/s]
  30%|###       | 151/500 [01:29&lt;03:28,  1.67it/s]
- 30%|###       | 152/500 [01:30&lt;03:28,  1.67it/s]
+ 30%|###       | 152/500 [01:30&lt;03:27,  1.67it/s]
  31%|###       | 153/500 [01:31&lt;03:27,  1.67it/s]
  31%|###       | 154/500 [01:31&lt;03:26,  1.67it/s]
  31%|###1      | 155/500 [01:32&lt;03:26,  1.67it/s]
  31%|###1      | 156/500 [01:32&lt;03:25,  1.67it/s]
  31%|###1      | 157/500 [01:33&lt;03:25,  1.67it/s]
  32%|###1      | 158/500 [01:34&lt;03:24,  1.67it/s]
- 32%|###1      | 159/500 [01:34&lt;03:23,  1.67it/s]
+ 32%|###1      | 159/500 [01:34&lt;03:24,  1.67it/s]
  32%|###2      | 160/500 [01:35&lt;03:23,  1.67it/s]
  32%|###2      | 161/500 [01:35&lt;03:22,  1.67it/s]
  32%|###2      | 162/500 [01:36&lt;03:22,  1.67it/s]
  33%|###2      | 163/500 [01:37&lt;03:21,  1.67it/s]
- 33%|###2      | 164/500 [01:37&lt;03:20,  1.67it/s]
+ 33%|###2      | 164/500 [01:37&lt;03:21,  1.67it/s]
  33%|###3      | 165/500 [01:38&lt;03:20,  1.67it/s]
  33%|###3      | 166/500 [01:38&lt;03:19,  1.67it/s]
  33%|###3      | 167/500 [01:39&lt;03:19,  1.67it/s]
  34%|###3      | 168/500 [01:40&lt;03:18,  1.67it/s]
- 34%|###3      | 169/500 [01:40&lt;03:17,  1.67it/s]
+ 34%|###3      | 169/500 [01:40&lt;03:18,  1.67it/s]
  34%|###4      | 170/500 [01:41&lt;03:17,  1.67it/s]
  34%|###4      | 171/500 [01:41&lt;03:16,  1.67it/s]
  34%|###4      | 172/500 [01:42&lt;03:16,  1.67it/s]
  35%|###4      | 173/500 [01:43&lt;03:15,  1.67it/s]
- 35%|###4      | 174/500 [01:43&lt;03:14,  1.67it/s]
+ 35%|###4      | 174/500 [01:43&lt;03:15,  1.67it/s]
  35%|###5      | 175/500 [01:44&lt;03:14,  1.67it/s]
  35%|###5      | 176/500 [01:44&lt;03:13,  1.67it/s]
  35%|###5      | 177/500 [01:45&lt;03:13,  1.67it/s]
@@ -2095,7 +2063,7 @@ <h2>Pruning BERT to be 2:4 sparse<a class="headerlink" href="#pruning-bert-to-be
  36%|###6      | 181/500 [01:47&lt;03:10,  1.67it/s]
  36%|###6      | 182/500 [01:48&lt;03:10,  1.67it/s]
  37%|###6      | 183/500 [01:49&lt;03:09,  1.67it/s]
- 37%|###6      | 184/500 [01:49&lt;03:08,  1.67it/s]
+ 37%|###6      | 184/500 [01:49&lt;03:09,  1.67it/s]
  37%|###7      | 185/500 [01:50&lt;03:08,  1.67it/s]
  37%|###7      | 186/500 [01:50&lt;03:07,  1.67it/s]
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@@ -2107,7 +2075,7 @@ <h2>Pruning BERT to be 2:4 sparse<a class="headerlink" href="#pruning-bert-to-be
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-{'train_runtime': 308.189, 'train_samples_per_second': 51.916, 'train_steps_per_second': 1.622, 'train_loss': 1.402785385131836, 'epoch': 0.18}
+{'train_runtime': 308.1732, 'train_samples_per_second': 51.919, 'train_steps_per_second': 1.622, 'train_loss': 1.402785385131836, 'epoch': 0.18}
 
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@@ -2488,50 +2456,50 @@ <h2>Accelerating 2:4 sparse models for inference<a class="headerlink" href="#acc
 </pre></div>
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 sparse eval metrics:  {'exact_match': 71.69347209082308, 'f1': 81.04133166079365}
-sparse perf metrics:  {4: 15.225212249970355, '4_compile': 7.838782999897376, 16: 33.27060020019417, '16_compile': 25.207834400134743, 64: 129.30342100116832, '64_compile': 98.90185570002359, 256: 751.4368839983945, '256_compile': 412.98149100111914}
+sparse perf metrics:  {4: 15.75757575010357, '4_compile': 7.653417000256013, 16: 33.264404800138436, '16_compile': 25.207538699760335, 64: 128.67821350118902, '64_compile': 98.73939639983291, 256: 748.8537140015978, '256_compile': 410.3211130022828}
 </pre></div>
 </div>
 <p>Retraining our model after magnitude pruning has recovered nearly all of
@@ -2633,7 +2601,7 @@ <h1>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 1.3x speedup over the fp16 baseline, and up to 2x with
 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. We also demonstrated the benefits of 2:4 sparsity by
 fine-tuning BERT to recover any lost F1 (86.92 dense vs 86.48 sparse).</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 21 minutes  7.733 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 17 minutes  23.687 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-advanced-semi-structured-sparse-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/5508d56677e1ebd9b3560596e7e41f57/semi_structured_sparse.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">semi_structured_sparse.py</span></code></a></p>
diff --git a/beginner/Intro_to_TorchScript_tutorial.html b/beginner/Intro_to_TorchScript_tutorial.html
index 1bf6987403..b4733aee3a 100644
--- a/beginner/Intro_to_TorchScript_tutorial.html
+++ b/beginner/Intro_to_TorchScript_tutorial.html
@@ -520,7 +520,7 @@
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>2.3.0+cu121
 
-&lt;torch._C.Generator object at 0x7f4b55b2f510&gt;
+&lt;torch._C.Generator object at 0x7f3bed1d7510&gt;
 </pre></div>
 </div>
 <div class="section" id="basics-of-pytorch-model-authoring">
@@ -975,7 +975,7 @@ <h3>Further Reading<a class="headerlink" href="#further-reading" title="Permalin
 out the NeurIPS demo for converting machine translation models using
 TorchScript:
 <a class="reference external" href="https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ">https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ</a></p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.219 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.224 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-intro-to-torchscript-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/07d05907b3ff859aeed5f76f1acc5df4/Intro_to_TorchScript_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">Intro_to_TorchScript_tutorial.py</span></code></a></p>
diff --git a/beginner/basics/autogradqs_tutorial.html b/beginner/basics/autogradqs_tutorial.html
index ffbfc636b0..f047cb455b 100644
--- a/beginner/basics/autogradqs_tutorial.html
+++ b/beginner/basics/autogradqs_tutorial.html
@@ -541,8 +541,8 @@ <h2>Tensors, Functions and Computational graph<a class="headerlink" href="#tenso
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Gradient function for loss = </span><span class="si">{</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">loss</span></a><span class="o">.</span><span class="n">grad_fn</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Gradient function for z = &lt;AddBackward0 object at 0x7f9ebc3e3970&gt;
-Gradient function for loss = &lt;BinaryCrossEntropyWithLogitsBackward0 object at 0x7f9ebebc32e0&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Gradient function for z = &lt;AddBackward0 object at 0x7fedc54b9510&gt;
+Gradient function for loss = &lt;BinaryCrossEntropyWithLogitsBackward0 object at 0x7fedc54b9bd0&gt;
 </pre></div>
 </div>
 </div>
diff --git a/beginner/basics/buildmodel_tutorial.html b/beginner/basics/buildmodel_tutorial.html
index d10b53987c..4e226e6e1e 100644
--- a/beginner/basics/buildmodel_tutorial.html
+++ b/beginner/basics/buildmodel_tutorial.html
@@ -738,7 +738,7 @@ <h2>Further Reading<a class="headerlink" href="#further-reading" title="Permalin
 <ul class="simple">
 <li><p><a class="reference external" href="https://pytorch.org/docs/stable/nn.html">torch.nn API</a></p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.322 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.309 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-basics-buildmodel-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/ac800c8c4c9c372154788058b1e89246/buildmodel_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">buildmodel_tutorial.py</span></code></a></p>
diff --git a/beginner/basics/data_tutorial.html b/beginner/basics/data_tutorial.html
index 621e2d704f..386f54e69e 100644
--- a/beginner/basics/data_tutorial.html
+++ b/beginner/basics/data_tutorial.html
@@ -547,43 +547,62 @@ <h2>Loading a Dataset<a class="headerlink" href="#loading-a-dataset" title="Perm
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
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 Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
 
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
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 </pre></div>
 </div>
@@ -740,7 +759,7 @@ <h2>Further Reading<a class="headerlink" href="#further-reading" title="Permalin
 <ul class="simple">
 <li><p><a class="reference external" href="https://pytorch.org/docs/stable/data.html">torch.utils.data API</a></p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  5.375 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  8.455 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-basics-data-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/56e3f440fc204e02856f8889c226d2d1/data_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">data_tutorial.py</span></code></a></p>
diff --git a/beginner/basics/optimization_tutorial.html b/beginner/basics/optimization_tutorial.html
index 20804299b9..eba3f7eef8 100644
--- a/beginner/basics/optimization_tutorial.html
+++ b/beginner/basics/optimization_tutorial.html
@@ -551,49 +551,92 @@ <h2>Prerequisite Code<a class="headerlink" href="#prerequisite-code" title="Perm
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
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 </pre></div>
 </div>
@@ -883,7 +926,7 @@ <h2>Further Reading<a class="headerlink" href="#further-reading" title="Permalin
 <li><p><a class="reference external" href="https://pytorch.org/docs/stable/optim.html">torch.optim</a></p></li>
 <li><p><a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html">Warmstart Training a Model</a></p></li>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  12.018 seconds)</p>
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index 16649e6aef..6847e63a2e 100644
--- a/beginner/basics/quickstart_tutorial.html
+++ b/beginner/basics/quickstart_tutorial.html
@@ -537,57 +537,42 @@ <h2>Working with data<a class="headerlink" href="#working-with-data" title="Perm
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
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 </pre></div>
 </div>
@@ -855,7 +840,7 @@ <h2>Loading Models<a class="headerlink" href="#loading-models" title="Permalink
 </pre></div>
 </div>
 <p>Read more about <a class="reference external" href="saveloadrun_tutorial.html">Saving &amp; Loading your model</a>.</p>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  6.083 seconds)</p>
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 <p><a class="reference download internal" download="" href="../../_downloads/51f1e1167acc0fda8f9d8fd8597ee626/quickstart_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">quickstart_tutorial.py</span></code></a></p>
diff --git a/beginner/basics/saveloadrun_tutorial.html b/beginner/basics/saveloadrun_tutorial.html
index 589896e1a2..62b90a4ecd 100644
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 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vgg16-397923af.pth
 
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 <p>To load model weights, you need to create an instance of the same model first, and then load the parameters
@@ -628,7 +627,7 @@ <h2>Related Tutorials<a class="headerlink" href="#related-tutorials" title="Perm
 <li><p><a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_a_general_checkpoint.html">Saving and Loading a General Checkpoint in PyTorch</a></p></li>
 <li><p><a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/module_load_state_dict_tips.html?highlight=loading%20nn%20module%20from%20checkpoint">Tips for loading an nn.Module from a checkpoint</a></p></li>
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
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 </pre></div>
 </div>
@@ -595,7 +582,7 @@ <h3>Further Reading<a class="headerlink" href="#further-reading" title="Permalin
 <ul class="simple">
 <li><p><a class="reference external" href="https://pytorch.org/vision/stable/transforms.html">torchvision.transforms API</a></p></li>
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diff --git a/beginner/blitz/autograd_tutorial.html b/beginner/blitz/autograd_tutorial.html
index 3644c01110..026b8b3af4 100644
--- a/beginner/blitz/autograd_tutorial.html
+++ b/beginner/blitz/autograd_tutorial.html
@@ -529,9 +529,9 @@ <h2>Usage in PyTorch<a class="headerlink" href="#usage-in-pytorch" title="Permal
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 </pre></div>
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 <p>Next, we run the input data through the model through each of its layers to make a prediction.
@@ -760,7 +760,7 @@ <h2>Further readings:<a class="headerlink" href="#further-readings" title="Perma
 <li><p><a class="reference external" href="https://colab.research.google.com/drive/1VpeE6UvEPRz9HmsHh1KS0XxXjYu533EC">Example implementation of reverse-mode autodiff</a></p></li>
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diff --git a/beginner/blitz/cifar10_tutorial.html b/beginner/blitz/cifar10_tutorial.html
index 61e6476ff1..3d20e86131 100644
--- a/beginner/blitz/cifar10_tutorial.html
+++ b/beginner/blitz/cifar10_tutorial.html
@@ -564,22 +564,24 @@ <h3>1. Load and normalize CIFAR10<a class="headerlink" href="#load-and-normalize
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
 
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 Files already downloaded and verified
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@@ -875,7 +877,7 @@ <h2>Where do I go next?<a class="headerlink" href="#where-do-i-go-next" title="P
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">del</span> <span class="n">dataiter</span>
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diff --git a/beginner/blitz/data_parallel_tutorial.html b/beginner/blitz/data_parallel_tutorial.html
index dad2c1a6fb..840b506e15 100644
--- a/beginner/blitz/data_parallel_tutorial.html
+++ b/beginner/blitz/data_parallel_tutorial.html
@@ -728,7 +728,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headin
 collects and merges the results before returning it to you.</p>
 <p>For more information, please check out
 <a class="reference external" href="https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html">https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html</a>.</p>
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diff --git a/beginner/blitz/neural_networks_tutorial.html b/beginner/blitz/neural_networks_tutorial.html
index 2b93fac32b..50add11c0c 100644
--- a/beginner/blitz/neural_networks_tutorial.html
+++ b/beginner/blitz/neural_networks_tutorial.html
@@ -687,9 +687,9 @@ <h2>Loss Function<a class="headerlink" href="#loss-function" title="Permalink to
 <span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">loss</span></a><span class="o">.</span><span class="n">grad_fn</span><span class="o">.</span><span class="n">next_functions</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">next_functions</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>  <span class="c1"># ReLU</span>
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-&lt;AccumulateGrad object at 0x7f05db0b2f20&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;MseLossBackward0 object at 0x7f67bc851000&gt;
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 <code class="docutils literal notranslate"><span class="pre">optimizer.zero_grad()</span></code>. This is because gradients are accumulated
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diff --git a/beginner/blitz/tensor_tutorial.html b/beginner/blitz/tensor_tutorial.html
index c0801e2b4b..1686ff6cac 100644
--- a/beginner/blitz/tensor_tutorial.html
+++ b/beginner/blitz/tensor_tutorial.html
@@ -727,7 +727,7 @@ <h3>NumPy array to Tensor<a class="headerlink" href="#numpy-array-to-tensor" tit
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index ba889c5a2f..2269ea56a8 100644
--- a/beginner/chatbot_tutorial.html
+++ b/beginner/chatbot_tutorial.html
@@ -5833,7 +5833,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 the model on.</p>
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 in PyTorch!</p>
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diff --git a/beginner/data_loading_tutorial.html b/beginner/data_loading_tutorial.html
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--- a/beginner/data_loading_tutorial.html
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 </pre></div>
 </div>
 <p>The dataset we are going to deal with is that of facial pose.
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-<button aria-label="Play" onclick="anim5c4a5c884f6a4b178d66187683458b26.play_animation()" title="Play">
+<button aria-label="Play" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.play_animation()" title="Play">
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+<button aria-label="Next frame" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.next_frame()" title="Next frame">
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-<button aria-label="Last frame" onclick="anim5c4a5c884f6a4b178d66187683458b26.last_frame()" title="Last frame">
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-<button aria-label="Increase speed" onclick="anim5c4a5c884f6a4b178d66187683458b26.faster()" title="Increase speed">
+<button aria-label="Increase speed" onclick="anim50f3b675e3ef4e27b2eaabe76970a12c.faster()" title="Increase speed">
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-<label for="_anim_radio3_5c4a5c884f6a4b178d66187683458b26">Reflect</label>
+<form action="#n" aria-label="Repetition mode" class="anim-state" name="_anim_loop_select50f3b675e3ef4e27b2eaabe76970a12c" title="Repetition mode">
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-    var loop_select_id = "_anim_loop_select5c4a5c884f6a4b178d66187683458b26";
+    var img_id = "_anim_img50f3b675e3ef4e27b2eaabe76970a12c";
+    var slider_id = "_anim_slider50f3b675e3ef4e27b2eaabe76970a12c";
+    var loop_select_id = "_anim_loop_select50f3b675e3ef4e27b2eaabe76970a12c";
     var frames = new Array(17);
 
   frames[0] = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAMgCAYAAADbcAZoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\
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     /* set a timeout to make sure all the above elements are created before
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     setTimeout(function() {
-        anim5c4a5c884f6a4b178d66187683458b26 = new Animation(frames, img_id, slider_id, 1000.0,
+        anim50f3b675e3ef4e27b2eaabe76970a12c = new Animation(frames, img_id, slider_id, 1000.0,
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@@ -233919,7 +233919,7 @@ <h2>Where to Go Next<a class="headerlink" href="#where-to-go-next" title="Permal
 <li><p>Create GANs that generate
 <a class="reference external" href="https://www.deepmind.com/blog/wavenet-a-generative-model-for-raw-audio/">music</a></p></li>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes  1.022 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-dcgan-faces-tutorial-py">
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 <p><a class="reference download internal" download="" href="../_downloads/176a0eb17083f2a53b7e388098a15704/dcgan_faces_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">dcgan_faces_tutorial.py</span></code></a></p>
diff --git a/beginner/deploy_seq2seq_hybrid_frontend_tutorial.html b/beginner/deploy_seq2seq_hybrid_frontend_tutorial.html
index 2fd108af02..097e5d6d14 100644
--- a/beginner/deploy_seq2seq_hybrid_frontend_tutorial.html
+++ b/beginner/deploy_seq2seq_hybrid_frontend_tutorial.html
@@ -1339,7 +1339,7 @@ <h2>Save Model<a class="headerlink" href="#save-model" title="Permalink to this
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a class="sphx-glr-backref-module-torch-jit sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule.save" title="torch.jit.ScriptModule.save"><span class="n">scripted_searcher</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span><span class="s2">"scripted_chatbot.pth"</span><span class="p">)</span>
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diff --git a/beginner/examples_nn/polynomial_nn.html b/beginner/examples_nn/polynomial_nn.html
index 2b132adb02..0ace567b6e 100644
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@@ -592,7 +592,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Result: y = </span><span class="si">{</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">linear_layer</span></a><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s1"> + </span><span class="si">{</span><a class="sphx-glr-backref-module-torch-nn-parameter sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter" title="torch.nn.parameter.Parameter"><span class="n">linear_layer</span><span class="o">.</span><span class="n">weight</span></a><span class="p">[:,</span><span class="w"> </span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s1"> x + </span><span class="si">{</span><a class="sphx-glr-backref-module-torch-nn-parameter sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter" title="torch.nn.parameter.Parameter"><span class="n">linear_layer</span><span class="o">.</span><span class="n">weight</span></a><span class="p">[:,</span><span class="w"> </span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s1"> x^2 + </span><span class="si">{</span><a class="sphx-glr-backref-module-torch-nn-parameter sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter" title="torch.nn.parameter.Parameter"><span class="n">linear_layer</span><span class="o">.</span><span class="n">weight</span></a><span class="p">[:,</span><span class="w"> </span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s1"> x^3'</span><span class="p">)</span>
 </pre></div>
 </div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.883 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-examples-nn-polynomial-nn-py">
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 <p><a class="reference download internal" download="" href="../../_downloads/cae15c367856daa07db2b142cddfe23f/polynomial_nn.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">polynomial_nn.py</span></code></a></p>
diff --git a/beginner/examples_tensor/polynomial_numpy.html b/beginner/examples_tensor/polynomial_numpy.html
index e4df9bec3c..e0f2333cdf 100644
--- a/beginner/examples_tensor/polynomial_numpy.html
+++ b/beginner/examples_tensor/polynomial_numpy.html
@@ -560,7 +560,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Result: y = </span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s1"> + </span><span class="si">{</span><span class="n">b</span><span class="si">}</span><span class="s1"> x + </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s1"> x^2 + </span><span class="si">{</span><span class="n">d</span><span class="si">}</span><span class="s1"> x^3'</span><span class="p">)</span>
 </pre></div>
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-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.353 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.350 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-examples-tensor-polynomial-numpy-py">
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diff --git a/beginner/fgsm_tutorial.html b/beginner/fgsm_tutorial.html
index 2a97ff4e53..c6355ee103 100644
--- a/beginner/fgsm_tutorial.html
+++ b/beginner/fgsm_tutorial.html
@@ -599,7 +599,7 @@ <h3>Inputs<a class="headerlink" href="#inputs" title="Permalink to this heading"
 <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span></a><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
 </pre></div>
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-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;torch._C.Generator object at 0x7f4b55b2f510&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;torch._C.Generator object at 0x7f3bed1d7510&gt;
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 </div>
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@@ -668,7 +668,7 @@ <h3>Model Under Attack<a class="headerlink" href="#model-under-attack" title="Pe
 Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz
 
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 Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw
 
 Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
@@ -679,7 +679,7 @@ <h3>Model Under Attack<a class="headerlink" href="#model-under-attack" title="Pe
 Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz
 
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+100%|##########| 28881/28881 [00:00&lt;00:00, 14763643.37it/s]
 Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw
 
 Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
@@ -690,7 +690,7 @@ <h3>Model Under Attack<a class="headerlink" href="#model-under-attack" title="Pe
 Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz
 
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+100%|##########| 1648877/1648877 [00:00&lt;00:00, 68442322.85it/s]
 Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw
 
 Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
@@ -701,7 +701,7 @@ <h3>Model Under Attack<a class="headerlink" href="#model-under-attack" title="Pe
 Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz
 
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 Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw
 
 CUDA Available:  True
@@ -962,7 +962,7 @@ <h2>Where to go next?<a class="headerlink" href="#where-to-go-next" title="Perma
 <p>A further direction to go, depending on available resources, is to modify
 the code to support processing work in batch, in parallel, and or distributed
 vs working on one attack at a time in the above for each <code class="docutils literal notranslate"><span class="pre">epsilon</span> <span class="pre">test()</span></code> loop.</p>
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diff --git a/beginner/flava_finetuning_tutorial.html b/beginner/flava_finetuning_tutorial.html
index faf495ddbd..ff2c1547a3 100644
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@@ -555,2063 +555,1956 @@ <h2>Steps<a class="headerlink" href="#steps" title="Permalink to this heading">
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+Downloading data:  81%|########1 | 790M/970M [00:09&lt;00:02, 85.9MB/s]
 
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+Downloading data:  82%|########2 | 799M/970M [00:09&lt;00:01, 86.0MB/s]
 
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+Downloading data:  84%|########4 | 816M/970M [00:09&lt;00:01, 86.2MB/s]
 
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+Downloading data:  85%|########5 | 825M/970M [00:09&lt;00:01, 86.4MB/s]
 
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+Downloading data:  86%|########5 | 834M/970M [00:09&lt;00:01, 86.5MB/s]
 
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+Downloading data:  87%|########6 | 842M/970M [00:09&lt;00:01, 86.2MB/s]
 
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+Downloading data:  88%|########7 | 851M/970M [00:09&lt;00:01, 86.1MB/s]
 
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+Downloading data:  89%|########8 | 860M/970M [00:10&lt;00:01, 86.1MB/s]
 
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+Downloading data:  89%|########9 | 868M/970M [00:10&lt;00:01, 86.2MB/s]
 
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+Downloading data:  90%|######### | 877M/970M [00:10&lt;00:01, 86.3MB/s]
 
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+Downloading data:  91%|#########1| 886M/970M [00:10&lt;00:00, 86.5MB/s]
 
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+Downloading data:  92%|#########2| 894M/970M [00:10&lt;00:00, 86.4MB/s]
 
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+Downloading data:  93%|#########3| 903M/970M [00:10&lt;00:00, 86.3MB/s]
 
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+Downloading data:  98%|#########8| 955M/970M [00:11&lt;00:00, 86.2MB/s]
 
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-Downloading data: 100%|##########| 970M/970M [00:11&lt;00:00, 83.8MB/s]
+Downloading data:  99%|#########9| 963M/970M [00:11&lt;00:00, 86.1MB/s]
+Downloading data: 100%|##########| 970M/970M [00:11&lt;00:00, 85.7MB/s]
 
-Downloading data files: 100%|##########| 5/5 [01:39&lt;00:00, 26.23s/it]
-Downloading data files: 100%|##########| 5/5 [01:39&lt;00:00, 19.86s/it]
+Downloading data files: 100%|##########| 5/5 [01:33&lt;00:00, 24.74s/it]
+Downloading data files: 100%|##########| 5/5 [01:33&lt;00:00, 18.70s/it]
 
 Extracting data files:   0%|          | 0/5 [00:00&lt;?, ?it/s]
-Extracting data files:  80%|########  | 4/5 [00:27&lt;00:06,  6.82s/it]
+Extracting data files:  80%|########  | 4/5 [00:27&lt;00:06,  6.81s/it]
 Extracting data files: 100%|##########| 5/5 [00:31&lt;00:00,  6.03s/it]
 Extracting data files: 100%|##########| 5/5 [00:31&lt;00:00,  6.22s/it]
 
 Generating train split:   0%|          | 0/34602 [00:00&lt;?, ? examples/s]
-Generating train split:   0%|          | 1/34602 [00:00&lt;4:08:17,  2.32 examples/s]
-Generating train split:   1%|1         | 495/34602 [00:00&lt;00:27, 1231.51 examples/s]
-Generating train split:   3%|2         | 1000/34602 [00:00&lt;00:24, 1348.44 examples/s]
-Generating train split:   4%|4         | 1529/34602 [00:00&lt;00:15, 2074.86 examples/s]
-Generating train split:   6%|5         | 2000/34602 [00:01&lt;00:12, 2609.49 examples/s]
-Generating train split:   7%|7         | 2528/34602 [00:01&lt;00:09, 3218.51 examples/s]
-Generating train split:   9%|8         | 3000/34602 [00:01&lt;00:08, 3552.12 examples/s]
-Generating train split:  10%|#         | 3524/34602 [00:01&lt;00:07, 3987.18 examples/s]
-Generating train split:  12%|#1        | 4000/34602 [00:01&lt;00:07, 4142.53 examples/s]
-Generating train split:  13%|#3        | 4527/34602 [00:01&lt;00:06, 4451.44 examples/s]
-Generating train split:  15%|#5        | 5262/34602 [00:01&lt;00:06, 4581.83 examples/s]
-Generating train split:  17%|#6        | 5786/34602 [00:01&lt;00:06, 4750.48 examples/s]
-Generating train split:  19%|#8        | 6526/34602 [00:02&lt;00:05, 4755.44 examples/s]
-Generating train split:  21%|##        | 7263/34602 [00:02&lt;00:05, 4758.07 examples/s]
-Generating train split:  23%|##2       | 7800/34602 [00:02&lt;00:05, 4903.16 examples/s]
-Generating train split:  25%|##4       | 8527/34602 [00:02&lt;00:05, 4857.17 examples/s]
-Generating train split:  27%|##6       | 9267/34602 [00:02&lt;00:05, 4871.62 examples/s]
-Generating train split:  28%|##8       | 9797/34602 [00:02&lt;00:04, 4969.11 examples/s]
-Generating train split:  30%|###       | 10523/34602 [00:02&lt;00:04, 4894.48 examples/s]
-Generating train split:  33%|###2      | 11265/34602 [00:02&lt;00:04, 4831.47 examples/s]
-Generating train split:  34%|###4      | 11785/34602 [00:03&lt;00:04, 4914.18 examples/s]
-Generating train split:  36%|###6      | 12502/34602 [00:03&lt;00:04, 4778.45 examples/s]
-Generating train split:  38%|###7      | 13000/34602 [00:03&lt;00:04, 4731.65 examples/s]
-Generating train split:  39%|###9      | 13537/34602 [00:03&lt;00:04, 4888.32 examples/s]
-Generating train split:  41%|####1     | 14266/34602 [00:03&lt;00:04, 4871.24 examples/s]
-Generating train split:  43%|####2     | 14783/34602 [00:03&lt;00:04, 4943.37 examples/s]
-Generating train split:  45%|####4     | 15533/34602 [00:03&lt;00:03, 4884.26 examples/s]
-Generating train split:  47%|####7     | 16270/34602 [00:03&lt;00:03, 4876.30 examples/s]
-Generating train split:  49%|####8     | 16806/34602 [00:04&lt;00:03, 4990.52 examples/s]
-Generating train split:  51%|#####     | 17540/34602 [00:04&lt;00:03, 4947.84 examples/s]
-Generating train split:  53%|#####2    | 18261/34602 [00:04&lt;00:03, 4847.24 examples/s]
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-Generating train split:  56%|#####6    | 19504/34602 [00:04&lt;00:03, 4795.80 examples/s]
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-Generating train split:  59%|#####9    | 20504/34602 [00:04&lt;00:02, 4781.14 examples/s]
-Generating train split:  61%|######    | 21000/34602 [00:04&lt;00:02, 4638.84 examples/s]
-Generating train split:  62%|######2   | 21493/34602 [00:05&lt;00:02, 4715.28 examples/s]
-Generating train split:  64%|######3   | 21985/34602 [00:05&lt;00:02, 4770.30 examples/s]
-Generating train split:  65%|######5   | 22647/34602 [00:05&lt;00:02, 4634.14 examples/s]
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-Generating train split:  69%|######8   | 23808/34602 [00:05&lt;00:02, 4652.17 examples/s]
-Generating train split:  71%|#######   | 24497/34602 [00:05&lt;00:02, 4579.80 examples/s]
-Generating train split:  72%|#######2  | 25000/34602 [00:05&lt;00:02, 4536.86 examples/s]
-Generating train split:  74%|#######3  | 25498/34602 [00:05&lt;00:01, 4648.44 examples/s]
-Generating train split:  75%|#######5  | 26000/34602 [00:06&lt;00:01, 4598.69 examples/s]
-Generating train split:  77%|#######6  | 26527/34602 [00:06&lt;00:01, 4777.58 examples/s]
-Generating train split:  79%|#######8  | 27264/34602 [00:06&lt;00:01, 4775.69 examples/s]
-Generating train split:  80%|########  | 27798/34602 [00:06&lt;00:01, 4918.30 examples/s]
-Generating train split:  82%|########2 | 28523/34602 [00:06&lt;00:01, 4836.89 examples/s]
-Generating train split:  85%|########4 | 29259/34602 [00:06&lt;00:01, 4775.76 examples/s]
-Generating train split:  86%|########6 | 29774/34602 [00:06&lt;00:00, 4862.96 examples/s]
-Generating train split:  88%|########8 | 30526/34602 [00:06&lt;00:00, 4816.20 examples/s]
-Generating train split:  90%|######### | 31265/34602 [00:07&lt;00:00, 4777.57 examples/s]
-Generating train split:  92%|#########1| 31788/34602 [00:07&lt;00:00, 4880.58 examples/s]
-Generating train split:  94%|#########3| 32521/34602 [00:07&lt;00:00, 4812.52 examples/s]
-Generating train split:  96%|#########6| 33247/34602 [00:07&lt;00:00, 4742.85 examples/s]
-Generating train split:  98%|#########7| 33765/34602 [00:07&lt;00:00, 4841.02 examples/s]
-Generating train split:  99%|#########9| 34262/34602 [00:07&lt;00:00, 4750.15 examples/s]
-Generating train split: 100%|##########| 34602/34602 [00:07&lt;00:00, 4382.63 examples/s]
+Generating train split:   0%|          | 1/34602 [00:00&lt;4:13:27,  2.28 examples/s]
+Generating train split:   1%|1         | 493/34602 [00:00&lt;00:28, 1208.24 examples/s]
+Generating train split:   3%|2         | 1000/34602 [00:00&lt;00:25, 1330.67 examples/s]
+Generating train split:   4%|4         | 1529/34602 [00:00&lt;00:16, 2052.12 examples/s]
+Generating train split:   6%|5         | 2000/34602 [00:01&lt;00:12, 2582.20 examples/s]
+Generating train split:   7%|7         | 2525/34602 [00:01&lt;00:10, 3185.70 examples/s]
+Generating train split:   9%|8         | 3000/34602 [00:01&lt;00:08, 3514.51 examples/s]
+Generating train split:  10%|#         | 3520/34602 [00:01&lt;00:07, 3944.71 examples/s]
+Generating train split:  12%|#1        | 4000/34602 [00:01&lt;00:07, 4093.78 examples/s]
+Generating train split:  13%|#3        | 4522/34602 [00:01&lt;00:06, 4397.07 examples/s]
+Generating train split:  14%|#4        | 5000/34602 [00:01&lt;00:06, 4428.33 examples/s]
+Generating train split:  16%|#5        | 5521/34602 [00:01&lt;00:06, 4644.49 examples/s]
+Generating train split:  18%|#8        | 6261/34602 [00:01&lt;00:06, 4658.61 examples/s]
+Generating train split:  20%|#9        | 6781/34602 [00:02&lt;00:05, 4798.13 examples/s]
+Generating train split:  22%|##1       | 7529/34602 [00:02&lt;00:05, 4772.35 examples/s]
+Generating train split:  24%|##3       | 8260/34602 [00:02&lt;00:05, 4760.33 examples/s]
+Generating train split:  25%|##5       | 8794/34602 [00:02&lt;00:05, 4896.27 examples/s]
+Generating train split:  28%|##7       | 9529/34602 [00:02&lt;00:05, 4851.71 examples/s]
+Generating train split:  30%|##9       | 10263/34602 [00:02&lt;00:05, 4811.39 examples/s]
+Generating train split:  31%|###1      | 10769/34602 [00:02&lt;00:04, 4867.91 examples/s]
+Generating train split:  33%|###2      | 11263/34602 [00:03&lt;00:04, 4754.12 examples/s]
+Generating train split:  34%|###4      | 11781/34602 [00:03&lt;00:04, 4863.47 examples/s]
+Generating train split:  36%|###6      | 12501/34602 [00:03&lt;00:04, 4727.19 examples/s]
+Generating train split:  38%|###7      | 13000/34602 [00:03&lt;00:04, 4681.84 examples/s]
+Generating train split:  39%|###9      | 13535/34602 [00:03&lt;00:04, 4853.13 examples/s]
+Generating train split:  41%|####1     | 14264/34602 [00:03&lt;00:04, 4837.58 examples/s]
+Generating train split:  43%|####2     | 14779/34602 [00:03&lt;00:04, 4915.29 examples/s]
+Generating train split:  45%|####4     | 15531/34602 [00:03&lt;00:03, 4848.57 examples/s]
+Generating train split:  47%|####7     | 16268/34602 [00:04&lt;00:03, 4836.04 examples/s]
+Generating train split:  49%|####8     | 16802/34602 [00:04&lt;00:03, 4952.40 examples/s]
+Generating train split:  51%|#####     | 17539/34602 [00:04&lt;00:03, 4906.93 examples/s]
+Generating train split:  53%|#####2    | 18260/34602 [00:04&lt;00:03, 4807.76 examples/s]
+Generating train split:  54%|#####4    | 18783/34602 [00:04&lt;00:03, 4904.21 examples/s]
+Generating train split:  56%|#####6    | 19504/34602 [00:04&lt;00:03, 4765.84 examples/s]
+Generating train split:  58%|#####7    | 20000/34602 [00:04&lt;00:03, 4651.14 examples/s]
+Generating train split:  59%|#####9    | 20503/34602 [00:04&lt;00:02, 4743.36 examples/s]
+Generating train split:  61%|######    | 21000/34602 [00:05&lt;00:02, 4610.75 examples/s]
+Generating train split:  62%|######2   | 21491/34602 [00:05&lt;00:02, 4688.21 examples/s]
+Generating train split:  64%|######3   | 21984/34602 [00:05&lt;00:02, 4751.63 examples/s]
+Generating train split:  65%|######5   | 22643/34602 [00:05&lt;00:02, 4617.39 examples/s]
+Generating train split:  67%|######7   | 23306/34602 [00:05&lt;00:02, 4544.78 examples/s]
+Generating train split:  69%|######8   | 23792/34602 [00:05&lt;00:02, 4621.10 examples/s]
+Generating train split:  71%|#######   | 24493/34602 [00:05&lt;00:02, 4546.11 examples/s]
+Generating train split:  72%|#######2  | 25000/34602 [00:05&lt;00:02, 4506.91 examples/s]
+Generating train split:  74%|#######3  | 25496/34602 [00:06&lt;00:01, 4618.73 examples/s]
+Generating train split:  75%|#######5  | 26000/34602 [00:06&lt;00:01, 4568.86 examples/s]
+Generating train split:  77%|#######6  | 26527/34602 [00:06&lt;00:01, 4754.23 examples/s]
+Generating train split:  79%|#######8  | 27262/34602 [00:06&lt;00:01, 4749.49 examples/s]
+Generating train split:  80%|########  | 27796/34602 [00:06&lt;00:01, 4897.95 examples/s]
+Generating train split:  82%|########2 | 28522/34602 [00:06&lt;00:01, 4819.74 examples/s]
+Generating train split:  85%|########4 | 29256/34602 [00:06&lt;00:01, 4760.06 examples/s]
+Generating train split:  86%|########6 | 29768/34602 [00:06&lt;00:00, 4843.95 examples/s]
+Generating train split:  87%|########7 | 30264/34602 [00:07&lt;00:00, 4745.09 examples/s]
+Generating train split:  89%|########8 | 30783/34602 [00:07&lt;00:00, 4858.98 examples/s]
+Generating train split:  91%|#########1| 31530/34602 [00:07&lt;00:00, 4808.35 examples/s]
+Generating train split:  93%|#########3| 32260/34602 [00:07&lt;00:00, 4759.76 examples/s]
+Generating train split:  95%|#########4| 32782/34602 [00:07&lt;00:00, 4867.71 examples/s]
+Generating train split:  97%|#########6| 33507/34602 [00:07&lt;00:00, 4767.92 examples/s]
+Generating train split:  98%|#########8| 34000/34602 [00:07&lt;00:00, 4693.30 examples/s]
+Generating train split: 100%|#########9| 34512/34602 [00:07&lt;00:00, 4796.54 examples/s]
+Generating train split: 100%|##########| 34602/34602 [00:07&lt;00:00, 4346.72 examples/s]
 
 Generating validation split:   0%|          | 0/5000 [00:00&lt;?, ? examples/s]
-Generating validation split:   7%|7         | 356/5000 [00:00&lt;00:01, 3547.00 examples/s]
-Generating validation split:  17%|#7        | 870/5000 [00:00&lt;00:00, 4481.34 examples/s]
-Generating validation split:  31%|###1      | 1561/5000 [00:00&lt;00:00, 4544.64 examples/s]
-Generating validation split:  45%|####5     | 2264/5000 [00:00&lt;00:00, 4604.09 examples/s]
-Generating validation split:  56%|#####5    | 2784/5000 [00:00&lt;00:00, 4778.24 examples/s]
-Generating validation split:  70%|#######   | 3520/5000 [00:00&lt;00:00, 4745.14 examples/s]
-Generating validation split:  80%|########  | 4000/5000 [00:00&lt;00:00, 4671.98 examples/s]
-Generating validation split:  90%|######### | 4519/5000 [00:00&lt;00:00, 4809.53 examples/s]
-Generating validation split: 100%|##########| 5000/5000 [00:01&lt;00:00, 4637.87 examples/s]
+Generating validation split:   7%|7         | 351/5000 [00:00&lt;00:01, 3496.00 examples/s]
+Generating validation split:  17%|#7        | 863/5000 [00:00&lt;00:00, 4441.53 examples/s]
+Generating validation split:  31%|###       | 1544/5000 [00:00&lt;00:00, 4494.08 examples/s]
+Generating validation split:  40%|####      | 2000/5000 [00:00&lt;00:00, 4456.37 examples/s]
+Generating validation split:  50%|#####     | 2519/5000 [00:00&lt;00:00, 4693.05 examples/s]
+Generating validation split:  60%|######    | 3000/5000 [00:00&lt;00:00, 4603.84 examples/s]
+Generating validation split:  70%|#######   | 3518/5000 [00:00&lt;00:00, 4778.69 examples/s]
+Generating validation split:  80%|########  | 4000/5000 [00:00&lt;00:00, 4675.86 examples/s]
+Generating validation split:  90%|######### | 4517/5000 [00:00&lt;00:00, 4819.74 examples/s]
+Generating validation split: 100%|##########| 5000/5000 [00:01&lt;00:00, 4601.83 examples/s]
 
 Generating test split:   0%|          | 0/5734 [00:00&lt;?, ? examples/s]
-Generating test split:   7%|7         | 422/5734 [00:00&lt;00:01, 4204.80 examples/s]
-Generating test split:  17%|#6        | 954/5734 [00:00&lt;00:00, 4853.00 examples/s]
-Generating test split:  29%|##9       | 1679/5734 [00:00&lt;00:00, 4836.17 examples/s]
-Generating test split:  42%|####2     | 2414/5734 [00:00&lt;00:00, 4861.85 examples/s]
-Generating test split:  52%|#####1    | 2963/5734 [00:00&lt;00:00, 5044.84 examples/s]
-Generating test split:  65%|######4   | 3700/5734 [00:00&lt;00:00, 4989.13 examples/s]
-Generating test split:  77%|#######7  | 4435/5734 [00:00&lt;00:00, 4952.99 examples/s]
-Generating test split:  87%|########6 | 4982/5734 [00:01&lt;00:00, 5082.98 examples/s]
-Generating test split: 100%|#########9| 5717/5734 [00:01&lt;00:00, 5015.22 examples/s]
-Generating test split: 100%|##########| 5734/5734 [00:01&lt;00:00, 4898.82 examples/s]
+Generating test split:   7%|7         | 413/5734 [00:00&lt;00:01, 4114.88 examples/s]
+Generating test split:  16%|#6        | 939/5734 [00:00&lt;00:01, 4783.10 examples/s]
+Generating test split:  29%|##8       | 1652/5734 [00:00&lt;00:00, 4761.12 examples/s]
+Generating test split:  41%|####1     | 2379/5734 [00:00&lt;00:00, 4797.20 examples/s]
+Generating test split:  51%|#####1    | 2925/5734 [00:00&lt;00:00, 4991.43 examples/s]
+Generating test split:  64%|######3   | 3656/5734 [00:00&lt;00:00, 4942.84 examples/s]
+Generating test split:  76%|#######6  | 4380/5734 [00:00&lt;00:00, 4897.69 examples/s]
+Generating test split:  86%|########5 | 4923/5734 [00:01&lt;00:00, 5029.84 examples/s]
+Generating test split:  99%|#########8| 5653/5734 [00:01&lt;00:00, 4968.23 examples/s]
+Generating test split: 100%|##########| 5734/5734 [00:01&lt;00:00, 4845.81 examples/s]
 </pre></div>
 </div>
 <p>Lets display a sample entry from the dataset:</p>
@@ -2665,16 +2558,16 @@ <h2>Steps<a class="headerlink" href="#steps" title="Permalink to this heading">
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading tokenizer_config.json:   0%|          | 0.00/48.0 [00:00&lt;?, ?B/s]
-Downloading tokenizer_config.json: 100%|##########| 48.0/48.0 [00:00&lt;00:00, 284kB/s]
+Downloading tokenizer_config.json: 100%|##########| 48.0/48.0 [00:00&lt;00:00, 278kB/s]
 
 Downloading vocab.txt:   0%|          | 0.00/232k [00:00&lt;?, ?B/s]
-Downloading vocab.txt: 100%|##########| 232k/232k [00:00&lt;00:00, 67.6MB/s]
+Downloading vocab.txt: 100%|##########| 232k/232k [00:00&lt;00:00, 78.7MB/s]
 
 Downloading tokenizer.json:   0%|          | 0.00/466k [00:00&lt;?, ?B/s]
-Downloading tokenizer.json: 100%|##########| 466k/466k [00:00&lt;00:00, 23.2MB/s]
+Downloading tokenizer.json: 100%|##########| 466k/466k [00:00&lt;00:00, 76.1MB/s]
 
 Downloading config.json:   0%|          | 0.00/570 [00:00&lt;?, ?B/s]
-Downloading config.json: 100%|##########| 570/570 [00:00&lt;00:00, 3.07MB/s]
+Downloading config.json: 100%|##########| 570/570 [00:00&lt;00:00, 3.76MB/s]
 </pre></div>
 </div>
 <p>4. Finally, we import the <code class="docutils literal notranslate"><span class="pre">flava_model_for_classification</span></code> from
@@ -2691,67 +2584,58 @@ <h2>Steps<a class="headerlink" href="#steps" title="Permalink to this heading">
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>flava_for_pretraining_unified_text_encoder.pt: 0.00B [00:00, ?B/s]
-flava_for_pretraining_unified_text_encoder.pt:   1%|          | 10.0M/1.43G [00:00&lt;00:14, 100MB/s]
-flava_for_pretraining_unified_text_encoder.pt:   2%|2         | 30.3M/1.43G [00:00&lt;00:08, 160MB/s]
-flava_for_pretraining_unified_text_encoder.pt:   4%|3         | 53.3M/1.43G [00:00&lt;00:07, 192MB/s]
-flava_for_pretraining_unified_text_encoder.pt:   5%|5         | 78.2M/1.43G [00:00&lt;00:06, 215MB/s]
-flava_for_pretraining_unified_text_encoder.pt:   7%|7         | 101M/1.43G [00:00&lt;00:06, 221MB/s]
-flava_for_pretraining_unified_text_encoder.pt:   9%|8         | 126M/1.43G [00:00&lt;00:05, 228MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  10%|#         | 150M/1.43G [00:00&lt;00:05, 233MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  12%|#2        | 174M/1.43G [00:00&lt;00:05, 235MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  14%|#3        | 199M/1.43G [00:00&lt;00:05, 239MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  16%|#5        | 223M/1.43G [00:01&lt;00:05, 240MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  17%|#7        | 244M/1.43G [00:01&lt;00:05, 228MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  19%|#8        | 266M/1.43G [00:01&lt;00:05, 226MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  20%|##        | 290M/1.43G [00:01&lt;00:04, 230MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  22%|##1       | 314M/1.43G [00:01&lt;00:04, 234MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  24%|##3       | 338M/1.43G [00:01&lt;00:04, 235MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  25%|##5       | 361M/1.43G [00:01&lt;00:04, 234MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  27%|##6       | 384M/1.43G [00:01&lt;00:04, 234MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  29%|##8       | 409M/1.43G [00:01&lt;00:04, 237MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  30%|###       | 433M/1.43G [00:01&lt;00:04, 239MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  32%|###1      | 458M/1.43G [00:02&lt;00:04, 241MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  34%|###3      | 482M/1.43G [00:02&lt;00:03, 242MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  35%|###5      | 506M/1.43G [00:02&lt;00:03, 241MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  37%|###7      | 530M/1.43G [00:02&lt;00:03, 240MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  39%|###8      | 554M/1.43G [00:02&lt;00:03, 241MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  40%|####      | 579M/1.43G [00:02&lt;00:03, 242MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  42%|####2     | 603M/1.43G [00:02&lt;00:03, 243MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  44%|####3     | 627M/1.43G [00:02&lt;00:03, 243MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  46%|####5     | 654M/1.43G [00:02&lt;00:03, 251MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  47%|####7     | 678M/1.43G [00:02&lt;00:03, 248MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  49%|####9     | 703M/1.43G [00:03&lt;00:02, 247MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  51%|#####     | 727M/1.43G [00:03&lt;00:02, 244MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  52%|#####2    | 751M/1.43G [00:03&lt;00:02, 243MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  54%|#####4    | 775M/1.43G [00:03&lt;00:02, 243MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  56%|#####5    | 799M/1.43G [00:03&lt;00:02, 243MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  58%|#####7    | 823M/1.43G [00:03&lt;00:02, 243MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  59%|#####9    | 847M/1.43G [00:03&lt;00:02, 242MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  61%|######    | 872M/1.43G [00:03&lt;00:02, 243MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  63%|######2   | 896M/1.43G [00:03&lt;00:02, 243MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  64%|######4   | 920M/1.43G [00:03&lt;00:02, 242MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  66%|######5   | 943M/1.43G [00:04&lt;00:02, 238MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  67%|######7   | 965M/1.43G [00:04&lt;00:02, 232MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  69%|######8   | 987M/1.43G [00:04&lt;00:01, 228MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  70%|#######   | 1.01G/1.43G [00:04&lt;00:01, 225MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  72%|#######2  | 1.03G/1.43G [00:04&lt;00:01, 228MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  74%|#######3  | 1.06G/1.43G [00:04&lt;00:01, 233MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  75%|#######5  | 1.08G/1.43G [00:04&lt;00:01, 231MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  77%|#######7  | 1.10G/1.43G [00:04&lt;00:01, 234MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  79%|#######8  | 1.13G/1.43G [00:04&lt;00:01, 237MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  81%|########1 | 1.16G/1.43G [00:04&lt;00:01, 262MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  83%|########3 | 1.19G/1.43G [00:05&lt;00:00, 281MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  85%|########4 | 1.21G/1.43G [00:05&lt;00:00, 261MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  87%|########6 | 1.24G/1.43G [00:05&lt;00:00, 255MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  88%|########8 | 1.26G/1.43G [00:05&lt;00:00, 251MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  90%|########9 | 1.29G/1.43G [00:05&lt;00:00, 247MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  92%|#########1| 1.31G/1.43G [00:05&lt;00:00, 246MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  93%|#########3| 1.33G/1.43G [00:05&lt;00:00, 243MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  95%|#########4| 1.36G/1.43G [00:05&lt;00:00, 242MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  97%|#########6| 1.38G/1.43G [00:05&lt;00:00, 240MB/s]
-flava_for_pretraining_unified_text_encoder.pt:  98%|#########8| 1.41G/1.43G [00:05&lt;00:00, 240MB/s]
-flava_for_pretraining_unified_text_encoder.pt: 100%|#########9| 1.43G/1.43G [00:06&lt;00:00, 240MB/s]
-flava_for_pretraining_unified_text_encoder.pt: 1.43GB [00:06, 238MB/s]
+flava_for_pretraining_unified_text_encoder.pt:   1%|          | 11.3M/1.43G [00:00&lt;00:12, 113MB/s]
+flava_for_pretraining_unified_text_encoder.pt:   3%|2         | 40.2M/1.43G [00:00&lt;00:06, 216MB/s]
+flava_for_pretraining_unified_text_encoder.pt:   5%|4         | 69.4M/1.43G [00:00&lt;00:05, 251MB/s]
+flava_for_pretraining_unified_text_encoder.pt:   7%|6         | 98.6M/1.43G [00:00&lt;00:04, 267MB/s]
+flava_for_pretraining_unified_text_encoder.pt:   9%|8         | 128M/1.43G [00:00&lt;00:04, 275MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  11%|#         | 156M/1.43G [00:00&lt;00:04, 277MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  13%|#2        | 183M/1.43G [00:00&lt;00:04, 277MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  14%|#4        | 207M/1.43G [00:00&lt;00:04, 263MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  16%|#6        | 234M/1.43G [00:00&lt;00:04, 267MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  18%|#8        | 263M/1.43G [00:01&lt;00:04, 274MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  20%|##        | 292M/1.43G [00:01&lt;00:04, 278MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  22%|##2       | 321M/1.43G [00:01&lt;00:03, 281MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  24%|##4       | 349M/1.43G [00:01&lt;00:03, 283MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  26%|##6       | 378M/1.43G [00:01&lt;00:03, 285MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  28%|##8       | 407M/1.43G [00:01&lt;00:03, 286MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  31%|###       | 437M/1.43G [00:01&lt;00:03, 288MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  33%|###2      | 465M/1.43G [00:01&lt;00:03, 288MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  35%|###4      | 494M/1.43G [00:01&lt;00:03, 288MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  37%|###6      | 523M/1.43G [00:01&lt;00:03, 288MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  39%|###8      | 552M/1.43G [00:02&lt;00:03, 288MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  41%|####      | 581M/1.43G [00:02&lt;00:02, 288MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  43%|####2     | 609M/1.43G [00:02&lt;00:02, 288MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  45%|####4     | 638M/1.43G [00:02&lt;00:02, 288MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  47%|####6     | 667M/1.43G [00:02&lt;00:02, 288MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  49%|####8     | 696M/1.43G [00:02&lt;00:02, 289MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  51%|#####     | 725M/1.43G [00:02&lt;00:02, 289MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  53%|#####2    | 754M/1.43G [00:02&lt;00:02, 289MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  54%|#####3    | 772M/1.43G [00:02&lt;00:02, 257MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  56%|#####5    | 794M/1.43G [00:02&lt;00:02, 247MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  58%|#####7    | 823M/1.43G [00:03&lt;00:02, 259MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  60%|#####9    | 852M/1.43G [00:03&lt;00:02, 268MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  62%|######1   | 881M/1.43G [00:03&lt;00:02, 274MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  64%|######3   | 910M/1.43G [00:03&lt;00:01, 278MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  66%|######5   | 938M/1.43G [00:03&lt;00:01, 281MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  68%|######7   | 967M/1.43G [00:03&lt;00:01, 284MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  70%|######9   | 996M/1.43G [00:03&lt;00:01, 285MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  72%|#######1  | 1.03G/1.43G [00:03&lt;00:01, 286MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  74%|#######3  | 1.05G/1.43G [00:03&lt;00:01, 287MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  76%|#######5  | 1.08G/1.43G [00:03&lt;00:01, 287MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  78%|#######7  | 1.11G/1.43G [00:04&lt;00:01, 287MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  80%|#######9  | 1.14G/1.43G [00:04&lt;00:01, 286MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  82%|########1 | 1.17G/1.43G [00:04&lt;00:00, 286MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  84%|########3 | 1.20G/1.43G [00:04&lt;00:00, 286MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  86%|########5 | 1.23G/1.43G [00:04&lt;00:00, 285MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  88%|########7 | 1.25G/1.43G [00:04&lt;00:00, 284MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  90%|########9 | 1.28G/1.43G [00:04&lt;00:00, 285MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  91%|######### | 1.30G/1.43G [00:04&lt;00:00, 259MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  93%|#########2| 1.33G/1.43G [00:04&lt;00:00, 260MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  95%|#########4| 1.36G/1.43G [00:04&lt;00:00, 267MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  97%|#########6| 1.38G/1.43G [00:05&lt;00:00, 271MB/s]
+flava_for_pretraining_unified_text_encoder.pt:  99%|#########8| 1.41G/1.43G [00:05&lt;00:00, 273MB/s]
+flava_for_pretraining_unified_text_encoder.pt: 1.43GB [00:05, 277MB/s]
 </pre></div>
 </div>
 <p>5. We put together the dataset and model in a toy training loop to
@@ -2793,7 +2677,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 which is a multimodal model for object detection and
 <a class="reference external" href="https://github.com/facebookresearch/multimodal/blob/main/torchmultimodal/models/omnivore.py">Omnivore</a>
 which is multitask model spanning image, video and 3d classification.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  42.433 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  35.105 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-flava-finetuning-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/03d2b0c71eeabf3687d88081641d7a1c/flava_finetuning_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">flava_finetuning_tutorial.py</span></code></a></p>
diff --git a/beginner/former_torchies/nnft_tutorial.html b/beginner/former_torchies/nnft_tutorial.html
index 512a5dcb6b..935950fce9 100644
--- a/beginner/former_torchies/nnft_tutorial.html
+++ b/beginner/former_torchies/nnft_tutorial.html
@@ -780,7 +780,7 @@ <h2>Example 2: Recurrent Net<a class="headerlink" href="#example-2-recurrent-net
 <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/generated/torch.Tensor.backward.html#torch.Tensor.backward" title="torch.Tensor.backward"><span class="n">loss</span><span class="o">.</span><span class="n">backward</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.027 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.031 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-former-torchies-nnft-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/9184ab4adcdfb4d0dbcf2e566b2705d3/nnft_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">nnft_tutorial.py</span></code></a></p>
diff --git a/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html b/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html
index c8cd566aea..e8d542653b 100644
--- a/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html
+++ b/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html
@@ -778,7 +778,7 @@ <h3>Tracing the Top-Level Model<a class="headerlink" href="#tracing-the-top-leve
 scripting model <em>is</em> designed to work with both traced and non-traced
 code which means you can call non-traced code from traced functions.
 However, such a model may not be exported to ONNX.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.106 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.114 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-hybrid-frontend-learning-hybrid-frontend-through-example-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/751cefdd3d9e8bae811354ef83e87cba/learning_hybrid_frontend_through_example_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">learning_hybrid_frontend_through_example_tutorial.py</span></code></a></p>
diff --git a/beginner/hyperparameter_tuning_tutorial.html b/beginner/hyperparameter_tuning_tutorial.html
index 070c23c471..2f64ce013b 100644
--- a/beginner/hyperparameter_tuning_tutorial.html
+++ b/beginner/hyperparameter_tuning_tutorial.html
@@ -917,72 +917,83 @@ <h2>Configuring the search space<a class="headerlink" href="#configuring-the-sea
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /var/lib/workspace/beginner_source/data/cifar-10-python.tar.gz
 
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 Extracting /var/lib/workspace/beginner_source/data/cifar-10-python.tar.gz to /var/lib/workspace/beginner_source/data
 Files already downloaded and verified
-2024-06-25 20:32:09,767 WARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 2147479552 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=10.24gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.
-2024-06-25 20:32:09,901 INFO worker.py:1642 -- Started a local Ray instance.
-2024-06-25 20:32:11,013 INFO tune.py:228 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `tune.run(...)`.
-2024-06-25 20:32:11,015 INFO tune.py:654 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see https://github.com/ray-project/ray/issues/36949
+2024-06-25 21:07:32,044 WARNING services.py:1889 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 2147479552 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=10.24gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.
+2024-06-25 21:07:33,182 INFO worker.py:1642 -- Started a local Ray instance.
+2024-06-25 21:07:34,259 INFO tune.py:228 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `tune.run(...)`.
+2024-06-25 21:07:34,261 INFO tune.py:654 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see https://github.com/ray-project/ray/issues/36949
 +--------------------------------------------------------------------+
-| Configuration for experiment     train_cifar_2024-06-25_20-32-11   |
+| Configuration for experiment     train_cifar_2024-06-25_21-07-34   |
 +--------------------------------------------------------------------+
 | Search algorithm                 BasicVariantGenerator             |
 | Scheduler                        AsyncHyperBandScheduler           |
 | Number of trials                 10                                |
 +--------------------------------------------------------------------+
 
-View detailed results here: /var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11
-To visualize your results with TensorBoard, run: `tensorboard --logdir /var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11`
+View detailed results here: /var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34
+To visualize your results with TensorBoard, run: `tensorboard --logdir /var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34`
 
 Trial status: 10 PENDING
-Current time: 2024-06-25 20:32:11. Total running time: 0s
+Current time: 2024-06-25 21:07:34. Total running time: 0s
 Logical resource usage: 0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +-------------------------------------------------------------------------------+
 | Trial name                status       l1     l2            lr     batch_size |
 +-------------------------------------------------------------------------------+
-| train_cifar_ffd52_00000   PENDING      16      1   0.00213327               2 |
-| train_cifar_ffd52_00001   PENDING       1      2   0.013416                 4 |
-| train_cifar_ffd52_00002   PENDING     256     64   0.0113784                2 |
-| train_cifar_ffd52_00003   PENDING      64    256   0.0274071                8 |
-| train_cifar_ffd52_00004   PENDING      16      2   0.056666                 4 |
-| train_cifar_ffd52_00005   PENDING       8     64   0.000353097              4 |
-| train_cifar_ffd52_00006   PENDING      16      4   0.000147684              8 |
-| train_cifar_ffd52_00007   PENDING     256    256   0.00477469               8 |
-| train_cifar_ffd52_00008   PENDING     128    256   0.0306227                8 |
-| train_cifar_ffd52_00009   PENDING       2     16   0.0286986                2 |
+| train_cifar_f162b_00000   PENDING      16      1   0.00213327               2 |
+| train_cifar_f162b_00001   PENDING       1      2   0.013416                 4 |
+| train_cifar_f162b_00002   PENDING     256     64   0.0113784                2 |
+| train_cifar_f162b_00003   PENDING      64    256   0.0274071                8 |
+| train_cifar_f162b_00004   PENDING      16      2   0.056666                 4 |
+| train_cifar_f162b_00005   PENDING       8     64   0.000353097              4 |
+| train_cifar_f162b_00006   PENDING      16      4   0.000147684              8 |
+| train_cifar_f162b_00007   PENDING     256    256   0.00477469               8 |
+| train_cifar_f162b_00008   PENDING     128    256   0.0306227                8 |
+| train_cifar_f162b_00009   PENDING       2     16   0.0286986                2 |
 +-------------------------------------------------------------------------------+
-(pid=5288) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-(pid=5288)   _torch_pytree._register_pytree_node(
+(pid=5286) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+(pid=5286)   _torch_pytree._register_pytree_node(
 
-Trial train_cifar_ffd52_00005 started with configuration:
+Trial train_cifar_f162b_00000 started with configuration:
 +--------------------------------------------------+
-| Trial train_cifar_ffd52_00005 config             |
+| Trial train_cifar_f162b_00000 config             |
++--------------------------------------------------+
+| batch_size                                     2 |
+| l1                                            16 |
+| l2                                             1 |
+| lr                                       0.00213 |
++--------------------------------------------------+
+
+Trial train_cifar_f162b_00001 started with configuration:
++--------------------------------------------------+
+| Trial train_cifar_f162b_00001 config             |
 +--------------------------------------------------+
 | batch_size                                     4 |
-| l1                                             8 |
-| l2                                            64 |
-| lr                                       0.00035 |
+| l1                                             1 |
+| l2                                             2 |
+| lr                                       0.01342 |
 +--------------------------------------------------+
 
-Trial train_cifar_ffd52_00006 started with configuration:
+Trial train_cifar_f162b_00006 started with configuration:
 +--------------------------------------------------+
-| Trial train_cifar_ffd52_00006 config             |
+| Trial train_cifar_f162b_00006 config             |
 +--------------------------------------------------+
 | batch_size                                     8 |
 | l1                                            16 |
@@ -990,9 +1001,9 @@ <h2>Configuring the search space<a class="headerlink" href="#configuring-the-sea
 | lr                                       0.00015 |
 +--------------------------------------------------+
 
-Trial train_cifar_ffd52_00004 started with configuration:
+Trial train_cifar_f162b_00004 started with configuration:
 +--------------------------------------------------+
-| Trial train_cifar_ffd52_00004 config             |
+| Trial train_cifar_f162b_00004 config             |
 +--------------------------------------------------+
 | batch_size                                     4 |
 | l1                                            16 |
@@ -1000,19 +1011,19 @@ <h2>Configuring the search space<a class="headerlink" href="#configuring-the-sea
 | lr                                       0.05667 |
 +--------------------------------------------------+
 
-Trial train_cifar_ffd52_00001 started with configuration:
+Trial train_cifar_f162b_00002 started with configuration:
 +--------------------------------------------------+
-| Trial train_cifar_ffd52_00001 config             |
+| Trial train_cifar_f162b_00002 config             |
 +--------------------------------------------------+
-| batch_size                                     4 |
-| l1                                             1 |
-| l2                                             2 |
-| lr                                       0.01342 |
+| batch_size                                     2 |
+| l1                                           256 |
+| l2                                            64 |
+| lr                                       0.01138 |
 +--------------------------------------------------+
 
-Trial train_cifar_ffd52_00007 started with configuration:
+Trial train_cifar_f162b_00007 started with configuration:
 +--------------------------------------------------+
-| Trial train_cifar_ffd52_00007 config             |
+| Trial train_cifar_f162b_00007 config             |
 +--------------------------------------------------+
 | batch_size                                     8 |
 | l1                                           256 |
@@ -1020,125 +1031,116 @@ <h2>Configuring the search space<a class="headerlink" href="#configuring-the-sea
 | lr                                       0.00477 |
 +--------------------------------------------------+
 
-Trial train_cifar_ffd52_00000 started with configuration:
+Trial train_cifar_f162b_00005 started with configuration:
 +--------------------------------------------------+
-| Trial train_cifar_ffd52_00000 config             |
+| Trial train_cifar_f162b_00005 config             |
 +--------------------------------------------------+
-| batch_size                                     2 |
-| l1                                            16 |
-| l2                                             1 |
-| lr                                       0.00213 |
+| batch_size                                     4 |
+| l1                                             8 |
+| l2                                            64 |
+| lr                                       0.00035 |
 +--------------------------------------------------+
 
-Trial train_cifar_ffd52_00003 started with configuration:
+Trial train_cifar_f162b_00003 started with configuration:
 +--------------------------------------------------+
-| Trial train_cifar_ffd52_00003 config             |
+| Trial train_cifar_f162b_00003 config             |
 +--------------------------------------------------+
 | batch_size                                     8 |
 | l1                                            64 |
 | l2                                           256 |
 | lr                                       0.02741 |
 +--------------------------------------------------+
-
-Trial train_cifar_ffd52_00002 started with configuration:
-+--------------------------------------------------+
-| Trial train_cifar_ffd52_00002 config             |
-+--------------------------------------------------+
-| batch_size                                     2 |
-| l1                                           256 |
-| l2                                            64 |
-| lr                                       0.01138 |
-+--------------------------------------------------+
-(func pid=5287) Files already downloaded and verified
-(func pid=5276) [1,  2000] loss: 2.328
-(func pid=5284) Files already downloaded and verified [repeated 15x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)
+(func pid=5288) Files already downloaded and verified
+(func pid=5274) [1,  2000] loss: 2.328
+(func pid=5285) Files already downloaded and verified [repeated 15x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)
 
 Trial status: 8 RUNNING | 2 PENDING
-Current time: 2024-06-25 20:32:41. Total running time: 30s
+Current time: 2024-06-25 21:08:04. Total running time: 30s
 Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +-------------------------------------------------------------------------------+
 | Trial name                status       l1     l2            lr     batch_size |
 +-------------------------------------------------------------------------------+
-| train_cifar_ffd52_00000   RUNNING      16      1   0.00213327               2 |
-| train_cifar_ffd52_00001   RUNNING       1      2   0.013416                 4 |
-| train_cifar_ffd52_00002   RUNNING     256     64   0.0113784                2 |
-| train_cifar_ffd52_00003   RUNNING      64    256   0.0274071                8 |
-| train_cifar_ffd52_00004   RUNNING      16      2   0.056666                 4 |
-| train_cifar_ffd52_00005   RUNNING       8     64   0.000353097              4 |
-| train_cifar_ffd52_00006   RUNNING      16      4   0.000147684              8 |
-| train_cifar_ffd52_00007   RUNNING     256    256   0.00477469               8 |
-| train_cifar_ffd52_00008   PENDING     128    256   0.0306227                8 |
-| train_cifar_ffd52_00009   PENDING       2     16   0.0286986                2 |
-+-------------------------------------------------------------------------------+
-(func pid=5276) [1,  4000] loss: 1.152 [repeated 8x across cluster]
-(func pid=5288) [1,  4000] loss: 1.157 [repeated 5x across cluster]
-(func pid=5276) [1,  6000] loss: 0.768 [repeated 3x across cluster]
-(func pid=5284) [1,  6000] loss: 0.738 [repeated 4x across cluster]
-Trial status: 8 RUNNING | 2 PENDING
-Current time: 2024-06-25 20:33:11. Total running time: 1min 0s
-Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-+-------------------------------------------------------------------------------+
-| Trial name                status       l1     l2            lr     batch_size |
-+-------------------------------------------------------------------------------+
-| train_cifar_ffd52_00000   RUNNING      16      1   0.00213327               2 |
-| train_cifar_ffd52_00001   RUNNING       1      2   0.013416                 4 |
-| train_cifar_ffd52_00002   RUNNING     256     64   0.0113784                2 |
-| train_cifar_ffd52_00003   RUNNING      64    256   0.0274071                8 |
-| train_cifar_ffd52_00004   RUNNING      16      2   0.056666                 4 |
-| train_cifar_ffd52_00005   RUNNING       8     64   0.000353097              4 |
-| train_cifar_ffd52_00006   RUNNING      16      4   0.000147684              8 |
-| train_cifar_ffd52_00007   RUNNING     256    256   0.00477469               8 |
-| train_cifar_ffd52_00008   PENDING     128    256   0.0306227                8 |
-| train_cifar_ffd52_00009   PENDING       2     16   0.0286986                2 |
+| train_cifar_f162b_00000   RUNNING      16      1   0.00213327               2 |
+| train_cifar_f162b_00001   RUNNING       1      2   0.013416                 4 |
+| train_cifar_f162b_00002   RUNNING     256     64   0.0113784                2 |
+| train_cifar_f162b_00003   RUNNING      64    256   0.0274071                8 |
+| train_cifar_f162b_00004   RUNNING      16      2   0.056666                 4 |
+| train_cifar_f162b_00005   RUNNING       8     64   0.000353097              4 |
+| train_cifar_f162b_00006   RUNNING      16      4   0.000147684              8 |
+| train_cifar_f162b_00007   RUNNING     256    256   0.00477469               8 |
+| train_cifar_f162b_00008   PENDING     128    256   0.0306227                8 |
+| train_cifar_f162b_00009   PENDING       2     16   0.0286986                2 |
 +-------------------------------------------------------------------------------+
+(func pid=5274) [1,  4000] loss: 1.113 [repeated 8x across cluster]
+(func pid=5289) [1,  4000] loss: 0.791 [repeated 6x across cluster]
+(func pid=5274) [1,  6000] loss: 0.699 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00006 finished iteration 1 at 2024-06-25 20:33:12. Total running time: 1min 1s
+Trial train_cifar_f162b_00006 finished iteration 1 at 2024-06-25 21:08:34. Total running time: 1min 0s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                  55.87247 |
-| time_total_s                                      55.87247 |
+| time_this_iter_s                                  54.28015 |
+| time_total_s                                      54.28015 |
 | training_iteration                                       1 |
-| accuracy                                            0.1452 |
-| loss                                                2.2988 |
+| accuracy                                            0.1018 |
+| loss                                               2.30517 |
 +------------------------------------------------------------+
-(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000000)
-(pid=5284) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead. [repeated 7x across cluster]
-(pid=5284)   _torch_pytree._register_pytree_node( [repeated 7x across cluster]
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000000
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000000
+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000000)
+(pid=5275) /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead. [repeated 7x across cluster]
+(pid=5275)   _torch_pytree._register_pytree_node( [repeated 7x across cluster]
 
-Trial train_cifar_ffd52_00007 finished iteration 1 at 2024-06-25 20:33:14. Total running time: 1min 3s
-+------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
+Trial status: 8 RUNNING | 2 PENDING
+Current time: 2024-06-25 21:08:34. Total running time: 1min 0s
+Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
++----------------------------------------------------------------------------------------------------------------------------------+
+| Trial name                status       l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
++----------------------------------------------------------------------------------------------------------------------------------+
+| train_cifar_f162b_00000   RUNNING      16      1   0.00213327               2                                                    |
+| train_cifar_f162b_00001   RUNNING       1      2   0.013416                 4                                                    |
+| train_cifar_f162b_00002   RUNNING     256     64   0.0113784                2                                                    |
+| train_cifar_f162b_00003   RUNNING      64    256   0.0274071                8                                                    |
+| train_cifar_f162b_00004   RUNNING      16      2   0.056666                 4                                                    |
+| train_cifar_f162b_00005   RUNNING       8     64   0.000353097              4                                                    |
+| train_cifar_f162b_00006   RUNNING      16      4   0.000147684              8        1            54.2801   2.30517       0.1018 |
+| train_cifar_f162b_00007   RUNNING     256    256   0.00477469               8                                                    |
+| train_cifar_f162b_00008   PENDING     128    256   0.0306227                8                                                    |
+| train_cifar_f162b_00009   PENDING       2     16   0.0286986                2                                                    |
++----------------------------------------------------------------------------------------------------------------------------------+
+(func pid=5284) [1,  6000] loss: 0.732 [repeated 4x across cluster]
+
+Trial train_cifar_f162b_00007 finished iteration 1 at 2024-06-25 21:08:36. Total running time: 1min 1s
++------------------------------------------------------------+
+| Trial train_cifar_f162b_00007 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                   57.7987 |
-| time_total_s                                       57.7987 |
+| time_this_iter_s                                  55.98108 |
+| time_total_s                                      55.98108 |
 | training_iteration                                       1 |
-| accuracy                                            0.4914 |
-| loss                                               1.41195 |
+| accuracy                                            0.4231 |
+| loss                                               1.54688 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000000
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000000
 
-Trial train_cifar_ffd52_00003 finished iteration 1 at 2024-06-25 20:33:15. Total running time: 1min 4s
+Trial train_cifar_f162b_00003 finished iteration 1 at 2024-06-25 21:08:37. Total running time: 1min 3s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00003 result                       |
+| Trial train_cifar_f162b_00003 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                  58.11498 |
-| time_total_s                                      58.11498 |
+| time_this_iter_s                                  57.11161 |
+| time_total_s                                      57.11161 |
 | training_iteration                                       1 |
-| accuracy                                            0.2241 |
-| loss                                               2.05448 |
+| accuracy                                            0.2199 |
+| loss                                               2.10497 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00003 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00003_3_batch_size=8,l1=64,l2=256,lr=0.0274_2024-06-25_20-32-11/checkpoint_000000
+Trial train_cifar_f162b_00003 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00003_3_batch_size=8,l1=64,l2=256,lr=0.0274_2024-06-25_21-07-34/checkpoint_000000
 
-Trial train_cifar_ffd52_00003 completed after 1 iterations at 2024-06-25 20:33:15. Total running time: 1min 4s
+Trial train_cifar_f162b_00003 completed after 1 iterations at 2024-06-25 21:08:37. Total running time: 1min 3s
 
-Trial train_cifar_ffd52_00008 started with configuration:
+Trial train_cifar_f162b_00008 started with configuration:
 +--------------------------------------------------+
-| Trial train_cifar_ffd52_00008 config             |
+| Trial train_cifar_f162b_00008 config             |
 +--------------------------------------------------+
 | batch_size                                     8 |
 | l1                                           128 |
@@ -1147,935 +1149,934 @@ <h2>Configuring the search space<a class="headerlink" href="#configuring-the-sea
 +--------------------------------------------------+
 (func pid=5285) Files already downloaded and verified
 (func pid=5285) Files already downloaded and verified
-(func pid=5276) [1,  8000] loss: 0.576
-(func pid=5284) [1,  8000] loss: 0.547 [repeated 4x across cluster]
-(func pid=5285) [1,  2000] loss: 2.121 [repeated 3x across cluster]
+(func pid=5274) [1,  8000] loss: 0.497
+(func pid=5275) [1,  8000] loss: 0.577
+(func pid=5284) [1,  8000] loss: 0.600 [repeated 3x across cluster]
+(func pid=5285) [1,  2000] loss: 2.116 [repeated 3x across cluster]
 
 Trial status: 8 RUNNING | 1 TERMINATED | 1 PENDING
-Current time: 2024-06-25 20:33:41. Total running time: 1min 30s
+Current time: 2024-06-25 21:09:04. Total running time: 1min 30s
 Logical resource usage: 16.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00000   RUNNING        16      1   0.00213327               2                                                    |
-| train_cifar_ffd52_00001   RUNNING         1      2   0.013416                 4                                                    |
-| train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2                                                    |
-| train_cifar_ffd52_00004   RUNNING        16      2   0.056666                 4                                                    |
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4                                                    |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        1            55.8725   2.2988        0.1452 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        1            57.7987   1.41195       0.4914 |
-| train_cifar_ffd52_00008   RUNNING       128    256   0.0306227                8                                                    |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00009   PENDING         2     16   0.0286986                2                                                    |
+| train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2                                                    |
+| train_cifar_f162b_00001   RUNNING         1      2   0.013416                 4                                                    |
+| train_cifar_f162b_00002   RUNNING       256     64   0.0113784                2                                                    |
+| train_cifar_f162b_00004   RUNNING        16      2   0.056666                 4                                                    |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4                                                    |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        1            54.2801   2.30517       0.1018 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        1            55.9811   1.54688       0.4231 |
+| train_cifar_f162b_00008   RUNNING       128    256   0.0306227                8                                                    |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00009   PENDING         2     16   0.0286986                2                                                    |
 +------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5283) [1, 10000] loss: 0.462 [repeated 4x across cluster]
-(func pid=5284) [1, 10000] loss: 0.452
-(func pid=5288) [2,  4000] loss: 1.122
+(func pid=5286) [1, 10000] loss: 0.467 [repeated 4x across cluster]
+(func pid=5284) [1, 10000] loss: 0.463 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00004 finished iteration 1 at 2024-06-25 20:33:55. Total running time: 1min 43s
+Trial train_cifar_f162b_00001 finished iteration 1 at 2024-06-25 21:09:16. Total running time: 1min 41s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00004 result                       |
+| Trial train_cifar_f162b_00001 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                  97.86952 |
-| time_total_s                                      97.86952 |
+| time_this_iter_s                                  95.96261 |
+| time_total_s                                      95.96261 |
 | training_iteration                                       1 |
-| accuracy                                            0.1023 |
-| loss                                               2.34534 |
+| accuracy                                            0.0997 |
+| loss                                               2.30698 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00004 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00004_4_batch_size=4,l1=16,l2=2,lr=0.0567_2024-06-25_20-32-11/checkpoint_000000
+Trial train_cifar_f162b_00001 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00001_1_batch_size=4,l1=1,l2=2,lr=0.0134_2024-06-25_21-07-34/checkpoint_000000
+
+Trial train_cifar_f162b_00001 completed after 1 iterations at 2024-06-25 21:09:16. Total running time: 1min 41s
 
-Trial train_cifar_ffd52_00004 completed after 1 iterations at 2024-06-25 20:33:55. Total running time: 1min 43s
+Trial train_cifar_f162b_00005 finished iteration 1 at 2024-06-25 21:09:16. Total running time: 1min 41s
++------------------------------------------------------------+
+| Trial train_cifar_f162b_00005 result                       |
++------------------------------------------------------------+
+| checkpoint_dir_name                      checkpoint_000000 |
+| time_this_iter_s                                  95.87316 |
+| time_total_s                                      95.87316 |
+| training_iteration                                       1 |
+| accuracy                                            0.3733 |
+| loss                                               1.65649 |
++------------------------------------------------------------+
 
-Trial train_cifar_ffd52_00009 started with configuration:
+Trial train_cifar_f162b_00009 started with configuration:
 +-------------------------------------------------+
-| Trial train_cifar_ffd52_00009 config            |
+| Trial train_cifar_f162b_00009 config            |
 +-------------------------------------------------+
 | batch_size                                    2 |
 | l1                                            2 |
 | l2                                           16 |
 | lr                                       0.0287 |
 +-------------------------------------------------+
-(func pid=5286) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00004_4_batch_size=4,l1=16,l2=2,lr=0.0567_2024-06-25_20-32-11/checkpoint_000000) [repeated 3x across cluster]
 
-Trial train_cifar_ffd52_00005 finished iteration 1 at 2024-06-25 20:33:55. Total running time: 1min 44s
-+------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
-+------------------------------------------------------------+
-| checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                  98.30064 |
-| time_total_s                                      98.30064 |
-| training_iteration                                       1 |
-| accuracy                                            0.3493 |
-| loss                                               1.76885 |
-+------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000000
-(func pid=5286) Files already downloaded and verified
-(func pid=5276) [1, 12000] loss: 0.384 [repeated 2x across cluster]
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000000
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000000) [repeated 3x across cluster]
+(func pid=5275) Files already downloaded and verified
+(func pid=5274) [1, 12000] loss: 0.327 [repeated 2x across cluster]
+(func pid=5275) Files already downloaded and verified
 
-Trial train_cifar_ffd52_00001 finished iteration 1 at 2024-06-25 20:33:56. Total running time: 1min 45s
+Trial train_cifar_f162b_00004 finished iteration 1 at 2024-06-25 21:09:20. Total running time: 1min 45s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00001 result                       |
+| Trial train_cifar_f162b_00004 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                  99.25883 |
-| time_total_s                                      99.25883 |
+| time_this_iter_s                                  99.76729 |
+| time_total_s                                      99.76729 |
 | training_iteration                                       1 |
-| accuracy                                            0.1022 |
-| loss                                               2.31335 |
+| accuracy                                            0.0939 |
+| loss                                               2.36081 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00001 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00001_1_batch_size=4,l1=1,l2=2,lr=0.0134_2024-06-25_20-32-11/checkpoint_000000
+Trial train_cifar_f162b_00004 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00004_4_batch_size=4,l1=16,l2=2,lr=0.0567_2024-06-25_21-07-34/checkpoint_000000
 
-Trial train_cifar_ffd52_00001 completed after 1 iterations at 2024-06-25 20:33:56. Total running time: 1min 45s
-(func pid=5286) Files already downloaded and verified
+Trial train_cifar_f162b_00004 completed after 1 iterations at 2024-06-25 21:09:20. Total running time: 1min 45s
 
-Trial train_cifar_ffd52_00006 finished iteration 2 at 2024-06-25 20:34:05. Total running time: 1min 54s
+Trial train_cifar_f162b_00006 finished iteration 2 at 2024-06-25 21:09:26. Total running time: 1min 51s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000001 |
-| time_this_iter_s                                  52.81679 |
-| time_total_s                                     108.68926 |
+| time_this_iter_s                                  51.49323 |
+| time_total_s                                     105.77338 |
 | training_iteration                                       2 |
-| accuracy                                            0.1768 |
-| loss                                               2.13897 |
+| accuracy                                            0.1361 |
+| loss                                               2.29136 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000001
-(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000001) [repeated 3x across cluster]
-(func pid=5284) [1, 12000] loss: 0.368 [repeated 2x across cluster]
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000001
+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000001) [repeated 3x across cluster]
+(func pid=5274) [1, 14000] loss: 0.277 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00007 finished iteration 2 at 2024-06-25 20:34:09. Total running time: 1min 58s
+Trial train_cifar_f162b_00007 finished iteration 2 at 2024-06-25 21:09:29. Total running time: 1min 54s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
+| Trial train_cifar_f162b_00007 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000001 |
-| time_this_iter_s                                  54.29292 |
-| time_total_s                                     112.09162 |
+| time_this_iter_s                                  52.92892 |
+| time_total_s                                        108.91 |
 | training_iteration                                       2 |
-| accuracy                                            0.5402 |
-| loss                                               1.30084 |
+| accuracy                                            0.4883 |
+| loss                                               1.45272 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000001
-(func pid=5286) [1,  2000] loss: 2.334 [repeated 3x across cluster]
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000001
 
-Trial status: 7 RUNNING | 3 TERMINATED
-Current time: 2024-06-25 20:34:11. Total running time: 2min 0s
-Logical resource usage: 14.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-+------------------------------------------------------------------------------------------------------------------------------------+
-| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
-+------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00000   RUNNING        16      1   0.00213327               2                                                    |
-| train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2                                                    |
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        1            98.3006   1.76885       0.3493 |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        2           108.689    2.13897       0.1768 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        2           112.092    1.30084       0.5402 |
-| train_cifar_ffd52_00008   RUNNING       128    256   0.0306227                8                                                    |
-| train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-+------------------------------------------------------------------------------------------------------------------------------------+
-
-Trial train_cifar_ffd52_00008 finished iteration 1 at 2024-06-25 20:34:12. Total running time: 2min 1s
+Trial train_cifar_f162b_00008 finished iteration 1 at 2024-06-25 21:09:33. Total running time: 1min 59s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00008 result                       |
+| Trial train_cifar_f162b_00008 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                  57.37668 |
-| time_total_s                                      57.37668 |
+| time_this_iter_s                                  56.15682 |
+| time_total_s                                      56.15682 |
 | training_iteration                                       1 |
-| accuracy                                            0.1941 |
-| loss                                               2.14956 |
+| accuracy                                            0.2268 |
+| loss                                               2.01125 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00008 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_20-32-11/checkpoint_000000
-(func pid=5285) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_20-32-11/checkpoint_000000) [repeated 2x across cluster]
-(func pid=5276) [1, 16000] loss: 0.288
-(func pid=5288) [3,  2000] loss: 2.079
-(func pid=5285) [2,  2000] loss: 2.102 [repeated 5x across cluster]
-(func pid=5288) [3,  4000] loss: 0.988 [repeated 2x across cluster]
+Trial train_cifar_f162b_00008 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_21-07-34/checkpoint_000000
+(func pid=5285) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_21-07-34/checkpoint_000000) [repeated 2x across cluster]
 
 Trial status: 7 RUNNING | 3 TERMINATED
-Current time: 2024-06-25 20:34:41. Total running time: 2min 30s
+Current time: 2024-06-25 21:09:34. Total running time: 2min 0s
 Logical resource usage: 14.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00000   RUNNING        16      1   0.00213327               2                                                    |
-| train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2                                                    |
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        1            98.3006   1.76885       0.3493 |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        2           108.689    2.13897       0.1768 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        2           112.092    1.30084       0.5402 |
-| train_cifar_ffd52_00008   RUNNING       128    256   0.0306227                8        1            57.3767   2.14956       0.1941 |
-| train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
+| train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2                                                    |
+| train_cifar_f162b_00002   RUNNING       256     64   0.0113784                2                                                    |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        1            95.8732   1.65649       0.3733 |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        2           105.773    2.29136       0.1361 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        2           108.91     1.45272       0.4883 |
+| train_cifar_f162b_00008   RUNNING       128    256   0.0306227                8        1            56.1568   2.01125       0.2268 |
+| train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
++------------------------------------------------------------------------------------------------------------------------------------+
+(func pid=5274) [1, 16000] loss: 0.242 [repeated 4x across cluster]
+(func pid=5285) [2,  2000] loss: 2.071 [repeated 6x across cluster]
+(func pid=5288) [3,  4000] loss: 1.131 [repeated 2x across cluster]
+Trial status: 7 RUNNING | 3 TERMINATED
+Current time: 2024-06-25 21:10:04. Total running time: 2min 30s
+Logical resource usage: 14.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
++------------------------------------------------------------------------------------------------------------------------------------+
+| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5276) [1, 20000] loss: 0.230 [repeated 5x across cluster]
+| train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2                                                    |
+| train_cifar_f162b_00002   RUNNING       256     64   0.0113784                2                                                    |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        1            95.8732   1.65649       0.3733 |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        2           105.773    2.29136       0.1361 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        2           108.91     1.45272       0.4883 |
+| train_cifar_f162b_00008   RUNNING       128    256   0.0306227                8        1            56.1568   2.01125       0.2268 |
+| train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
++------------------------------------------------------------------------------------------------------------------------------------+
+(func pid=5274) [1, 20000] loss: 0.196 [repeated 5x across cluster]
 
-Trial train_cifar_ffd52_00006 finished iteration 3 at 2024-06-25 20:34:52. Total running time: 2min 41s
+Trial train_cifar_f162b_00006 finished iteration 3 at 2024-06-25 21:10:11. Total running time: 2min 37s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000002 |
-| time_this_iter_s                                   46.6738 |
-| time_total_s                                     155.36306 |
+| time_this_iter_s                                   45.3082 |
+| time_total_s                                     151.08158 |
 | training_iteration                                       3 |
-| accuracy                                            0.2232 |
-| loss                                               1.92145 |
+| accuracy                                             0.174 |
+| loss                                               2.24346 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000002
-(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000002)
-(func pid=5284) [1, 18000] loss: 0.241 [repeated 4x across cluster]
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000002
+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000002)
+(func pid=5275) [1,  8000] loss: 0.583 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00007 finished iteration 3 at 2024-06-25 20:34:56. Total running time: 2min 45s
+Trial train_cifar_f162b_00007 finished iteration 3 at 2024-06-25 21:10:15. Total running time: 2min 41s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
+| Trial train_cifar_f162b_00007 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000002 |
-| time_this_iter_s                                  47.31363 |
-| time_total_s                                     159.40525 |
+| time_this_iter_s                                  46.53087 |
+| time_total_s                                     155.44087 |
 | training_iteration                                       3 |
-| accuracy                                            0.5642 |
-| loss                                               1.22132 |
+| accuracy                                            0.5147 |
+| loss                                               1.42976 |
 +------------------------------------------------------------+
-(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000002)
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000002
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000002
+(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000002)
 
-Trial train_cifar_ffd52_00008 finished iteration 2 at 2024-06-25 20:35:04. Total running time: 2min 53s
+Trial train_cifar_f162b_00008 finished iteration 2 at 2024-06-25 21:10:25. Total running time: 2min 50s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00008 result                       |
+| Trial train_cifar_f162b_00008 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000001 |
-| time_this_iter_s                                  52.03223 |
-| time_total_s                                     109.40892 |
+| time_this_iter_s                                  51.44847 |
+| time_total_s                                     107.60529 |
 | training_iteration                                       2 |
-| accuracy                                            0.2231 |
-| loss                                               2.07936 |
+| accuracy                                            0.1834 |
+| loss                                               2.09518 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00008 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_20-32-11/checkpoint_000001
+Trial train_cifar_f162b_00008 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_21-07-34/checkpoint_000001
 
-Trial train_cifar_ffd52_00008 completed after 2 iterations at 2024-06-25 20:35:04. Total running time: 2min 53s
-(func pid=5285) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_20-32-11/checkpoint_000001)
-(func pid=5286) [1, 10000] loss: 0.467
-(func pid=5287) [2, 10000] loss: 0.301
+Trial train_cifar_f162b_00008 completed after 2 iterations at 2024-06-25 21:10:25. Total running time: 2min 50s
+(func pid=5285) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00008_8_batch_size=8,l1=128,l2=256,lr=0.0306_2024-06-25_21-07-34/checkpoint_000001)
+(func pid=5275) [1, 10000] loss: 0.466 [repeated 3x across cluster]
 
-Trial train_cifar_ffd52_00000 finished iteration 1 at 2024-06-25 20:35:09. Total running time: 2min 58s
+Trial train_cifar_f162b_00000 finished iteration 1 at 2024-06-25 21:10:28. Total running time: 2min 53s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00000 result                       |
+| Trial train_cifar_f162b_00000 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                 172.11847 |
-| time_total_s                                     172.11847 |
+| time_this_iter_s                                 168.05027 |
+| time_total_s                                     168.05027 |
 | training_iteration                                       1 |
-| accuracy                                            0.1011 |
-| loss                                                2.3056 |
+| accuracy                                            0.1994 |
+| loss                                               1.93221 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00000 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_20-32-11/checkpoint_000000
-
-(func pid=5276) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_20-32-11/checkpoint_000000)
-Trial train_cifar_ffd52_00000 completed after 1 iterations at 2024-06-25 20:35:09. Total running time: 2min 58s
+Trial train_cifar_f162b_00000 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_21-07-34/checkpoint_000000
+(func pid=5274) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_21-07-34/checkpoint_000000)
+(func pid=5289) [4,  2000] loss: 1.194 [repeated 3x across cluster]
 
-Trial status: 5 TERMINATED | 5 RUNNING
-Current time: 2024-06-25 20:35:11. Total running time: 3min 0s
-Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+Trial status: 6 RUNNING | 4 TERMINATED
+Current time: 2024-06-25 21:10:34. Total running time: 3min 0s
+Logical resource usage: 12.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2                                                    |
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        1            98.3006   1.76885       0.3493 |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        3           155.363    1.92145       0.2232 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        3           159.405    1.22132       0.5642 |
-| train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
+| train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2        1           168.05     1.93221       0.1994 |
+| train_cifar_f162b_00002   RUNNING       256     64   0.0113784                2                                                    |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        1            95.8732   1.65649       0.3733 |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        3           151.082    2.24346       0.174  |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        3           155.441    1.42976       0.5147 |
+| train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
 +------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5289) [4,  2000] loss: 1.122 [repeated 3x across cluster]
 
-Trial train_cifar_ffd52_00005 finished iteration 2 at 2024-06-25 20:35:15. Total running time: 3min 4s
+Trial train_cifar_f162b_00005 finished iteration 2 at 2024-06-25 21:10:36. Total running time: 3min 2s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
+| Trial train_cifar_f162b_00005 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000001 |
-| time_this_iter_s                                  80.62054 |
-| time_total_s                                     178.92117 |
+| time_this_iter_s                                  80.48369 |
+| time_total_s                                     176.35684 |
 | training_iteration                                       2 |
-| accuracy                                             0.438 |
-| loss                                                1.5187 |
+| accuracy                                            0.4429 |
+| loss                                               1.49254 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000001
-(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000001)
-(func pid=5288) [4,  4000] loss: 0.936 [repeated 2x across cluster]
-(func pid=5286) [1, 14000] loss: 0.334 [repeated 2x across cluster]
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000001
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000001)
+(func pid=5275) [1, 12000] loss: 0.389 [repeated 2x across cluster]
+(func pid=5289) [4,  4000] loss: 0.609 [repeated 3x across cluster]
 
-Trial train_cifar_ffd52_00002 finished iteration 1 at 2024-06-25 20:35:27. Total running time: 3min 16s
+Trial train_cifar_f162b_00002 finished iteration 1 at 2024-06-25 21:10:50. Total running time: 3min 16s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00002 result                       |
+| Trial train_cifar_f162b_00002 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                 189.89642 |
-| time_total_s                                     189.89642 |
+| time_this_iter_s                                 190.49445 |
+| time_total_s                                     190.49445 |
 | training_iteration                                       1 |
-| accuracy                                            0.1695 |
-| loss                                               2.14381 |
+| accuracy                                            0.1002 |
+| loss                                               2.31482 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00002 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_20-32-11/checkpoint_000000
-(func pid=5284) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_20-32-11/checkpoint_000000)
+Trial train_cifar_f162b_00002 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_21-07-34/checkpoint_000000
 
-Trial train_cifar_ffd52_00006 finished iteration 4 at 2024-06-25 20:35:31. Total running time: 3min 20s
+Trial train_cifar_f162b_00002 completed after 1 iterations at 2024-06-25 21:10:50. Total running time: 3min 16s
+(func pid=5284) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_21-07-34/checkpoint_000000)
+(func pid=5274) [2,  4000] loss: 0.962 [repeated 3x across cluster]
+
+Trial train_cifar_f162b_00006 finished iteration 4 at 2024-06-25 21:10:52. Total running time: 3min 17s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000003 |
-| time_this_iter_s                                  39.09685 |
-| time_total_s                                     194.45991 |
+| time_this_iter_s                                  40.77518 |
+| time_total_s                                     191.85676 |
 | training_iteration                                       4 |
-| accuracy                                            0.2643 |
-| loss                                               1.84947 |
+| accuracy                                            0.2204 |
+| loss                                                 2.133 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000003
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000003
 
-Trial train_cifar_ffd52_00007 finished iteration 4 at 2024-06-25 20:35:36. Total running time: 3min 25s
+Trial train_cifar_f162b_00007 finished iteration 4 at 2024-06-25 21:10:56. Total running time: 3min 22s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
+| Trial train_cifar_f162b_00007 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000003 |
-| time_this_iter_s                                  40.05575 |
-| time_total_s                                     199.46101 |
+| time_this_iter_s                                  41.20969 |
+| time_total_s                                     196.65056 |
 | training_iteration                                       4 |
-| accuracy                                            0.5966 |
-| loss                                               1.17434 |
+| accuracy                                            0.5547 |
+| loss                                               1.26787 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000003
-(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000003) [repeated 2x across cluster]
-(func pid=5286) [1, 16000] loss: 0.292 [repeated 2x across cluster]
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000003
+(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000003) [repeated 2x across cluster]
+(func pid=5275) [1, 16000] loss: 0.292
+(func pid=5287) [3,  4000] loss: 0.723
 
-Trial status: 5 TERMINATED | 5 RUNNING
-Current time: 2024-06-25 20:35:41. Total running time: 3min 30s
+Trial status: 5 RUNNING | 5 TERMINATED
+Current time: 2024-06-25 21:11:04. Total running time: 3min 30s
 Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        2           178.921    1.5187        0.438  |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        4           194.46     1.84947       0.2643 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        4           199.461    1.17434       0.5966 |
-| train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
+| train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2        1           168.05     1.93221       0.1994 |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        2           176.357    1.49254       0.4429 |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        4           191.857    2.133         0.2204 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        4           196.651    1.26787       0.5547 |
+| train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
 +------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5288) [5,  2000] loss: 1.829 [repeated 3x across cluster]
-(func pid=5287) [3,  6000] loss: 0.485 [repeated 2x across cluster]
-(func pid=5288) [5,  4000] loss: 0.898 [repeated 3x across cluster]
-(func pid=5289) [5,  4000] loss: 0.542 [repeated 3x across cluster]
+(func pid=5275) [1, 18000] loss: 0.260 [repeated 3x across cluster]
+(func pid=5288) [5,  4000] loss: 1.037 [repeated 4x across cluster]
+(func pid=5289) [5,  4000] loss: 0.581 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00006 finished iteration 5 at 2024-06-25 20:36:06. Total running time: 3min 55s
+Trial train_cifar_f162b_00006 finished iteration 5 at 2024-06-25 21:11:26. Total running time: 3min 51s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000004 |
-| time_this_iter_s                                  34.93373 |
-| time_total_s                                     229.39364 |
+| time_this_iter_s                                  34.14598 |
+| time_total_s                                     226.00273 |
 | training_iteration                                       5 |
-| accuracy                                            0.3267 |
-| loss                                               1.77951 |
+| accuracy                                            0.2423 |
+| loss                                               2.04993 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000004
-(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000004)
-(func pid=5287) [3, 10000] loss: 0.277 [repeated 2x across cluster]
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000004
+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000004)
+(func pid=5274) [2, 12000] loss: 0.320 [repeated 3x across cluster]
+
+Trial train_cifar_f162b_00007 finished iteration 5 at 2024-06-25 21:11:33. Total running time: 3min 59s
++------------------------------------------------------------+
+| Trial train_cifar_f162b_00007 result                       |
++------------------------------------------------------------+
+| checkpoint_dir_name                      checkpoint_000004 |
+| time_this_iter_s                                  36.37413 |
+| time_total_s                                     233.02469 |
+| training_iteration                                       5 |
+| accuracy                                            0.5505 |
+| loss                                               1.31606 |
++------------------------------------------------------------+(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000004)
 
-Trial status: 5 TERMINATED | 5 RUNNING
-Current time: 2024-06-25 20:36:11. Total running time: 4min 0s
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000004
+
+Trial status: 5 RUNNING | 5 TERMINATED
+Current time: 2024-06-25 21:11:34. Total running time: 4min 0s
 Logical resource usage: 10.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        2           178.921    1.5187        0.438  |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        5           229.394    1.77951       0.3267 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        4           199.461    1.17434       0.5966 |
-| train_cifar_ffd52_00009   RUNNING         2     16   0.0286986                2                                                    |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
+| train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2        1           168.05     1.93221       0.1994 |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        2           176.357    1.49254       0.4429 |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        5           226.003    2.04993       0.2423 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        5           233.025    1.31606       0.5505 |
+| train_cifar_f162b_00009   RUNNING         2     16   0.0286986                2                                                    |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
 +------------------------------------------------------------------------------------------------------------------------------------+
 
-Trial train_cifar_ffd52_00007 finished iteration 5 at 2024-06-25 20:36:13. Total running time: 4min 2s
-+------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
-+------------------------------------------------------------+
-| checkpoint_dir_name                      checkpoint_000004 |
-| time_this_iter_s                                  37.25813 |
-| time_total_s                                     236.71913 |
-| training_iteration                                       5 |
-| accuracy                                            0.5748 |
-| loss                                               1.25535 |
-+------------------------------------------------------------+
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000004
-(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000004)
-
-Trial train_cifar_ffd52_00009 finished iteration 1 at 2024-06-25 20:36:14. Total running time: 4min 3s
+Trial train_cifar_f162b_00009 finished iteration 1 at 2024-06-25 21:11:35. Total running time: 4min 1s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00009 result                       |
+| Trial train_cifar_f162b_00009 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000000 |
-| time_this_iter_s                                 139.19683 |
-| time_total_s                                     139.19683 |
+| time_this_iter_s                                 139.18021 |
+| time_total_s                                     139.18021 |
 | training_iteration                                       1 |
-| accuracy                                            0.1016 |
-| loss                                               2.33735 |
+| accuracy                                            0.0962 |
+| loss                                               2.36893 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00009 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00009_9_batch_size=2,l1=2,l2=16,lr=0.0287_2024-06-25_20-32-11/checkpoint_000000
+Trial train_cifar_f162b_00009 saved a checkpoint for iteration 1 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00009_9_batch_size=2,l1=2,l2=16,lr=0.0287_2024-06-25_21-07-34/checkpoint_000000
 
-Trial train_cifar_ffd52_00009 completed after 1 iterations at 2024-06-25 20:36:14. Total running time: 4min 3s
-(func pid=5288) [6,  2000] loss: 1.758
+Trial train_cifar_f162b_00009 completed after 1 iterations at 2024-06-25 21:11:35. Total running time: 4min 1s
+(func pid=5288) [6,  2000] loss: 2.023 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00005 finished iteration 3 at 2024-06-25 20:36:19. Total running time: 4min 8s
+Trial train_cifar_f162b_00005 finished iteration 3 at 2024-06-25 21:11:41. Total running time: 4min 6s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
+| Trial train_cifar_f162b_00005 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000002 |
-| time_this_iter_s                                  63.42164 |
-| time_total_s                                     242.34282 |
+| time_this_iter_s                                  64.30477 |
+| time_total_s                                     240.66161 |
 | training_iteration                                       3 |
-| accuracy                                            0.5075 |
-| loss                                               1.34967 |
+| accuracy                                            0.4872 |
+| loss                                               1.39914 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000002
-(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000002) [repeated 2x across cluster]
-(func pid=5284) [2,  8000] loss: 0.559
-(func pid=5289) [6,  2000] loss: 0.994
-(func pid=5287) [4,  2000] loss: 1.390
-(func pid=5289) [6,  4000] loss: 0.520 [repeated 3x across cluster]
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 3 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000002
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000002) [repeated 2x across cluster]
+(func pid=5289) [6,  2000] loss: 1.059 [repeated 2x across cluster]
+(func pid=5287) [4,  2000] loss: 1.362 [repeated 2x across cluster]
+(func pid=5289) [6,  4000] loss: 0.564 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00006 finished iteration 6 at 2024-06-25 20:36:39. Total running time: 4min 28s
+Trial train_cifar_f162b_00006 finished iteration 6 at 2024-06-25 21:11:59. Total running time: 4min 24s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000005 |
-| time_this_iter_s                                  33.11123 |
-| time_total_s                                     262.50487 |
+| time_this_iter_s                                  32.75475 |
+| time_total_s                                     258.75748 |
 | training_iteration                                       6 |
-| accuracy                                            0.3527 |
-| loss                                               1.72881 |
+| accuracy                                            0.2521 |
+| loss                                               1.97434 |
 +------------------------------------------------------------+
-(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000005)
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000005
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000005
+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000005)
 
-Trial status: 6 TERMINATED | 4 RUNNING
-Current time: 2024-06-25 20:36:41. Total running time: 4min 30s
+Trial status: 4 RUNNING | 6 TERMINATED
+Current time: 2024-06-25 21:12:04. Total running time: 4min 30s
 Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        3           242.343    1.34967       0.5075 |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        6           262.505    1.72881       0.3527 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        5           236.719    1.25535       0.5748 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+| train_cifar_f162b_00000   RUNNING        16      1   0.00213327               2        1           168.05     1.93221       0.1994 |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        3           240.662    1.39914       0.4872 |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        6           258.757    1.97434       0.2521 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        5           233.025    1.31606       0.5505 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
 +------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5284) [2, 12000] loss: 0.370 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00007 finished iteration 6 at 2024-06-25 20:36:48. Total running time: 4min 37s
+Trial train_cifar_f162b_00007 finished iteration 6 at 2024-06-25 21:12:07. Total running time: 4min 32s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
+| Trial train_cifar_f162b_00007 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000005 |
-| time_this_iter_s                                  34.28092 |
-| time_total_s                                     271.00006 |
+| time_this_iter_s                                  33.75116 |
+| time_total_s                                     266.77585 |
 | training_iteration                                       6 |
-| accuracy                                            0.5616 |
-| loss                                               1.31087 |
+| accuracy                                             0.566 |
+| loss                                               1.25734 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000005
-(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000005)
-(func pid=5287) [4,  6000] loss: 0.449
-(func pid=5288) [7,  2000] loss: 1.710
-(func pid=5284) [2, 14000] loss: 0.315
-(func pid=5287) [4,  8000] loss: 0.335
-(func pid=5284) [2, 16000] loss: 0.276 [repeated 3x across cluster]
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000005
+(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000005)
+(func pid=5288) [7,  2000] loss: 1.963 [repeated 3x across cluster]
+(func pid=5289) [7,  2000] loss: 1.027 [repeated 3x across cluster]
 
-Trial train_cifar_ffd52_00006 finished iteration 7 at 2024-06-25 20:37:11. Total running time: 5min 0s
+Trial train_cifar_f162b_00000 finished iteration 2 at 2024-06-25 21:12:24. Total running time: 4min 50s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00000 result                       |
 +------------------------------------------------------------+
-| checkpoint_dir_name                      checkpoint_000006 |
-| time_this_iter_s                                  32.06512 |
-| time_total_s                                        294.57 |
-| training_iteration                                       7 |
-| accuracy                                            0.3767 |
-| loss                                                1.6804 |
+| checkpoint_dir_name                      checkpoint_000001 |
+| time_this_iter_s                                 116.14539 |
+| time_total_s                                     284.19566 |
+| training_iteration                                       2 |
+| accuracy                                            0.2374 |
+| loss                                               1.89564 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000006
-(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000006)
-
-Trial status: 6 TERMINATED | 4 RUNNING
-Current time: 2024-06-25 20:37:11. Total running time: 5min 0s
-Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-+------------------------------------------------------------------------------------------------------------------------------------+
-| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
-+------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        3           242.343    1.34967       0.5075 |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        7           294.57     1.6804        0.3767 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        6           271        1.31087       0.5616 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
-+------------------------------------------------------------------------------------------------------------------------------------+
+Trial train_cifar_f162b_00000 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_21-07-34/checkpoint_000001
 
-Trial train_cifar_ffd52_00005 finished iteration 4 at 2024-06-25 20:37:17. Total running time: 5min 6s
-+------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
-+------------------------------------------------------------+
-| checkpoint_dir_name                      checkpoint_000003 |
-| time_this_iter_s                                  58.04506 |
-| time_total_s                                     300.38788 |
-| training_iteration                                       4 |
-| accuracy                                            0.5308 |
-| loss                                               1.29584 |
-+------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000003
-(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000003)
-(func pid=5284) [2, 18000] loss: 0.244 [repeated 3x across cluster]
+Trial train_cifar_f162b_00000 completed after 2 iterations at 2024-06-25 21:12:24. Total running time: 4min 50s
+(func pid=5274) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00000_0_batch_size=2,l1=16,l2=1,lr=0.0021_2024-06-25_21-07-34/checkpoint_000001)
 
-Trial train_cifar_ffd52_00007 finished iteration 7 at 2024-06-25 20:37:22. Total running time: 5min 11s
+Trial train_cifar_f162b_00006 finished iteration 7 at 2024-06-25 21:12:30. Total running time: 4min 56s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000006 |
-| time_this_iter_s                                  34.36209 |
-| time_total_s                                     305.36215 |
+| time_this_iter_s                                  31.28651 |
+| time_total_s                                       290.044 |
 | training_iteration                                       7 |
-| accuracy                                            0.5518 |
-| loss                                               1.35099 |
-+------------------------------------------------------------+
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000006
-(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000006)
-(func pid=5287) [5,  2000] loss: 1.305 [repeated 2x across cluster]
-(func pid=5284) [2, 20000] loss: 0.223
-(func pid=5288) [8,  4000] loss: 0.829
+| accuracy                                            0.2675 |
+| loss                                               1.89649 |
++------------------------------------------------------------+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000006)
 
-Trial status: 6 TERMINATED | 4 RUNNING
-Current time: 2024-06-25 20:37:41. Total running time: 5min 30s
-Logical resource usage: 8.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000006
+(func pid=5287) [4, 10000] loss: 0.269 [repeated 3x across cluster]
+
+Trial status: 7 TERMINATED | 3 RUNNING
+Current time: 2024-06-25 21:12:35. Total running time: 5min 0s
+Logical resource usage: 6.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00002   RUNNING       256     64   0.0113784                2        1           189.896    2.14381       0.1695 |
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        4           300.388    1.29584       0.5308 |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        7           294.57     1.6804        0.3767 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        7           305.362    1.35099       0.5518 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        3           240.662    1.39914       0.4872 |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        7           290.044    1.89649       0.2675 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        6           266.776    1.25734       0.566  |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
 +------------------------------------------------------------------------------------------------------------------------------------+
 
-Trial train_cifar_ffd52_00006 finished iteration 8 at 2024-06-25 20:37:44. Total running time: 5min 32s
+Trial train_cifar_f162b_00005 finished iteration 4 at 2024-06-25 21:12:37. Total running time: 5min 3s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00005 result                       |
 +------------------------------------------------------------+
-| checkpoint_dir_name                      checkpoint_000007 |
-| time_this_iter_s                                  32.38367 |
-| time_total_s                                     326.95366 |
-| training_iteration                                       8 |
-| accuracy                                            0.3772 |
-| loss                                               1.66349 |
+| checkpoint_dir_name                      checkpoint_000003 |
+| time_this_iter_s                                  56.43809 |
+| time_total_s                                      297.0997 |
+| training_iteration                                       4 |
+| accuracy                                            0.4976 |
+| loss                                               1.35867 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000007
-(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000007)
-(func pid=5289) [8,  4000] loss: 0.490 [repeated 3x across cluster]
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 4 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000003
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000003)
 
-Trial train_cifar_ffd52_00002 finished iteration 2 at 2024-06-25 20:37:48. Total running time: 5min 37s
+Trial train_cifar_f162b_00007 finished iteration 7 at 2024-06-25 21:12:40. Total running time: 5min 5s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00002 result                       |
+| Trial train_cifar_f162b_00007 result                       |
 +------------------------------------------------------------+
-| checkpoint_dir_name                      checkpoint_000001 |
-| time_this_iter_s                                 141.05097 |
-| time_total_s                                     330.94739 |
-| training_iteration                                       2 |
-| accuracy                                            0.1713 |
-| loss                                               2.18945 |
+| checkpoint_dir_name                      checkpoint_000006 |
+| time_this_iter_s                                  32.88982 |
+| time_total_s                                     299.66567 |
+| training_iteration                                       7 |
+| accuracy                                            0.5503 |
+| loss                                               1.32877 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00002 saved a checkpoint for iteration 2 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00002_2_batch_size=2,l1=256,l2=64,lr=0.0114_2024-06-25_20-32-11/checkpoint_000001
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000006
+(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000006)
+(func pid=5288) [8,  2000] loss: 1.885 [repeated 2x across cluster]
+(func pid=5287) [5,  2000] loss: 1.301
+(func pid=5288) [8,  4000] loss: 0.932
+(func pid=5287) [5,  4000] loss: 0.651 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00002 completed after 2 iterations at 2024-06-25 20:37:48. Total running time: 5min 37s
-(func pid=5288) [9,  2000] loss: 1.630 [repeated 2x across cluster]
-
-Trial train_cifar_ffd52_00007 finished iteration 8 at 2024-06-25 20:37:56. Total running time: 5min 45s
+Trial train_cifar_f162b_00006 finished iteration 8 at 2024-06-25 21:12:59. Total running time: 5min 25s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000007 |
-| time_this_iter_s                                  33.90971 |
-| time_total_s                                     339.27186 |
+| time_this_iter_s                                   29.4743 |
+| time_total_s                                      319.5183 |
 | training_iteration                                       8 |
-| accuracy                                            0.5812 |
-| loss                                               1.26995 |
+| accuracy                                            0.2794 |
+| loss                                               1.83365 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000007
-(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000007) [repeated 2x across cluster]
-(func pid=5288) [9,  4000] loss: 0.805 [repeated 2x across cluster]
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000007
+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000007)
+(func pid=5289) [8,  4000] loss: 0.528
 
 Trial status: 7 TERMINATED | 3 RUNNING
-Current time: 2024-06-25 20:38:11. Total running time: 6min 0s
+Current time: 2024-06-25 21:13:05. Total running time: 5min 30s
 Logical resource usage: 6.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        4           300.388    1.29584       0.5308 |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        8           326.954    1.66349       0.3772 |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        8           339.272    1.26995       0.5812 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        4           297.1      1.35867       0.4976 |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        8           319.518    1.83365       0.2794 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        7           299.666    1.32877       0.5503 |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
 +------------------------------------------------------------------------------------------------------------------------------------+
+(func pid=5287) [5,  6000] loss: 0.445
+(func pid=5288) [9,  2000] loss: 1.825
 
-Trial train_cifar_ffd52_00005 finished iteration 5 at 2024-06-25 20:38:13. Total running time: 6min 2s
+Trial train_cifar_f162b_00007 finished iteration 8 at 2024-06-25 21:13:10. Total running time: 5min 36s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
+| Trial train_cifar_f162b_00007 result                       |
 +------------------------------------------------------------+
-| checkpoint_dir_name                      checkpoint_000004 |
-| time_this_iter_s                                  56.50784 |
-| time_total_s                                     356.89571 |
-| training_iteration                                       5 |
-| accuracy                                            0.5478 |
-| loss                                               1.25522 |
+| checkpoint_dir_name                      checkpoint_000007 |
+| time_this_iter_s                                  30.88169 |
+| time_total_s                                     330.54736 |
+| training_iteration                                       8 |
+| accuracy                                             0.554 |
+| loss                                               1.35308 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000004
-(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000004)
+(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000007)
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000007
+(func pid=5287) [5,  8000] loss: 0.324
+(func pid=5288) [9,  4000] loss: 0.902
+(func pid=5289) [9,  2000] loss: 0.963
 
-Trial train_cifar_ffd52_00006 finished iteration 9 at 2024-06-25 20:38:14. Total running time: 6min 3s
+Trial train_cifar_f162b_00006 finished iteration 9 at 2024-06-25 21:13:29. Total running time: 5min 55s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000008 |
-| time_this_iter_s                                  30.33792 |
-| time_total_s                                     357.29159 |
+| time_this_iter_s                                  29.63863 |
+| time_total_s                                     349.15693 |
 | training_iteration                                       9 |
-| accuracy                                               0.4 |
-| loss                                               1.61052 |
+| accuracy                                            0.3127 |
+| loss                                               1.77556 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000008
-(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000008)
-(func pid=5289) [9,  4000] loss: 0.484 [repeated 3x across cluster]
-(func pid=5288) [10,  2000] loss: 1.586 [repeated 2x across cluster]
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000008
+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000008)
 
-Trial train_cifar_ffd52_00007 finished iteration 9 at 2024-06-25 20:38:28. Total running time: 6min 17s
+Trial train_cifar_f162b_00005 finished iteration 5 at 2024-06-25 21:13:30. Total running time: 5min 56s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
+| Trial train_cifar_f162b_00005 result                       |
 +------------------------------------------------------------+
-| checkpoint_dir_name                      checkpoint_000008 |
-| time_this_iter_s                                  32.01444 |
-| time_total_s                                     371.28629 |
-| training_iteration                                       9 |
-| accuracy                                             0.594 |
-| loss                                               1.25797 |
+| checkpoint_dir_name                      checkpoint_000004 |
+| time_this_iter_s                                  53.22804 |
+| time_total_s                                     350.32774 |
+| training_iteration                                       5 |
+| accuracy                                            0.5108 |
+| loss                                               1.33224 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000008
-(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000008)
-(func pid=5287) [6,  4000] loss: 0.617
-(func pid=5288) [10,  4000] loss: 0.792
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 5 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000004
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000004)
+(func pid=5289) [9,  4000] loss: 0.518 [repeated 2x across cluster]
 
 Trial status: 7 TERMINATED | 3 RUNNING
-Current time: 2024-06-25 20:38:42. Total running time: 6min 30s
+Current time: 2024-06-25 21:13:35. Total running time: 6min 0s
 Logical resource usage: 6.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        5           356.896    1.25522       0.5478 |
-| train_cifar_ffd52_00006   RUNNING        16      4   0.000147684              8        9           357.292    1.61052       0.4    |
-| train_cifar_ffd52_00007   RUNNING       256    256   0.00477469               8        9           371.286    1.25797       0.594  |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        5           350.328    1.33224       0.5108 |
+| train_cifar_f162b_00006   RUNNING        16      4   0.000147684              8        9           349.157    1.77556       0.3127 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        8           330.547    1.35308       0.554  |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
 +------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5287) [6,  6000] loss: 0.418 [repeated 2x across cluster]
+(func pid=5288) [10,  2000] loss: 1.772
+(func pid=5287) [6,  2000] loss: 1.257
+
+Trial train_cifar_f162b_00007 finished iteration 9 at 2024-06-25 21:13:42. Total running time: 6min 8s
++------------------------------------------------------------+
+| Trial train_cifar_f162b_00007 result                       |
++------------------------------------------------------------+
+| checkpoint_dir_name                      checkpoint_000008 |
+| time_this_iter_s                                  31.42566 |
+| time_total_s                                     361.97302 |
+| training_iteration                                       9 |
+| accuracy                                            0.5507 |
+| loss                                               1.38221 |
++------------------------------------------------------------+
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000008
+(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000008)
+(func pid=5287) [6,  4000] loss: 0.644
+(func pid=5288) [10,  4000] loss: 0.880
+(func pid=5287) [6,  6000] loss: 0.423 [repeated 2x across cluster]
 
-Trial train_cifar_ffd52_00006 finished iteration 10 at 2024-06-25 20:38:43. Total running time: 6min 32s
+Trial train_cifar_f162b_00006 finished iteration 10 at 2024-06-25 21:13:58. Total running time: 6min 24s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00006 result                       |
+| Trial train_cifar_f162b_00006 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000009 |
-| time_this_iter_s                                  29.54813 |
-| time_total_s                                     386.83972 |
+| time_this_iter_s                                   29.2616 |
+| time_total_s                                     378.41852 |
 | training_iteration                                      10 |
-| accuracy                                            0.4213 |
-| loss                                               1.57777 |
+| accuracy                                            0.3241 |
+| loss                                               1.73982 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00006 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000009
+Trial train_cifar_f162b_00006 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000009
 
-Trial train_cifar_ffd52_00006 completed after 10 iterations at 2024-06-25 20:38:43. Total running time: 6min 32s
-(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_20-32-11/checkpoint_000009)
-(func pid=5289) [10,  4000] loss: 0.473
-(func pid=5287) [6,  8000] loss: 0.307
+Trial train_cifar_f162b_00006 completed after 10 iterations at 2024-06-25 21:13:58. Total running time: 6min 24s
+(func pid=5288) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00006_6_batch_size=8,l1=16,l2=4,lr=0.0001_2024-06-25_21-07-34/checkpoint_000009)
+(func pid=5289) [10,  4000] loss: 0.502
 
-Trial train_cifar_ffd52_00007 finished iteration 10 at 2024-06-25 20:38:58. Total running time: 6min 47s
+Trial status: 8 TERMINATED | 2 RUNNING
+Current time: 2024-06-25 21:14:05. Total running time: 6min 30s
+Logical resource usage: 4.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
++------------------------------------------------------------------------------------------------------------------------------------+
+| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
++------------------------------------------------------------------------------------------------------------------------------------+
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        5           350.328    1.33224       0.5108 |
+| train_cifar_f162b_00007   RUNNING       256    256   0.00477469               8        9           361.973    1.38221       0.5507 |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
++------------------------------------------------------------------------------------------------------------------------------------+
+(func pid=5287) [6,  8000] loss: 0.321
+
+Trial train_cifar_f162b_00007 finished iteration 10 at 2024-06-25 21:14:11. Total running time: 6min 37s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00007 result                       |
+| Trial train_cifar_f162b_00007 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000009 |
-| time_this_iter_s                                  29.95987 |
-| time_total_s                                     401.24617 |
+| time_this_iter_s                                  29.31768 |
+| time_total_s                                      391.2907 |
 | training_iteration                                      10 |
-| accuracy                                            0.5766 |
-| loss                                               1.36315 |
+| accuracy                                             0.562 |
+| loss                                               1.42098 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00007 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000009
+Trial train_cifar_f162b_00007 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000009
 
-Trial train_cifar_ffd52_00007 completed after 10 iterations at 2024-06-25 20:38:58. Total running time: 6min 47s
-(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_20-32-11/checkpoint_000009)
-(func pid=5287) [6, 10000] loss: 0.245
+Trial train_cifar_f162b_00007 completed after 10 iterations at 2024-06-25 21:14:11. Total running time: 6min 37s
+(func pid=5289) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00007_7_batch_size=8,l1=256,l2=256,lr=0.0048_2024-06-25_21-07-34/checkpoint_000009)
+(func pid=5287) [6, 10000] loss: 0.252
 
-Trial train_cifar_ffd52_00005 finished iteration 6 at 2024-06-25 20:39:04. Total running time: 6min 53s
+Trial train_cifar_f162b_00005 finished iteration 6 at 2024-06-25 21:14:20. Total running time: 6min 45s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
+| Trial train_cifar_f162b_00005 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000005 |
-| time_this_iter_s                                  50.71725 |
-| time_total_s                                     407.61296 |
+| time_this_iter_s                                  49.44468 |
+| time_total_s                                     399.77242 |
 | training_iteration                                       6 |
-| accuracy                                            0.5493 |
-| loss                                                1.2737 |
+| accuracy                                            0.5258 |
+| loss                                               1.32112 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000005
-(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000005)
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 6 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000005
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000005)
+(func pid=5287) [7,  2000] loss: 1.244
+(func pid=5287) [7,  4000] loss: 0.634
 
 Trial status: 9 TERMINATED | 1 RUNNING
-Current time: 2024-06-25 20:39:12. Total running time: 7min 0s
+Current time: 2024-06-25 21:14:35. Total running time: 7min 0s
 Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        6           407.613    1.2737        0.5493 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-| train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
-+------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5287) [7,  2000] loss: 1.199
-(func pid=5287) [7,  4000] loss: 0.609
-(func pid=5287) [7,  6000] loss: 0.398
-(func pid=5287) [7,  8000] loss: 0.299
-Trial status: 9 TERMINATED | 1 RUNNING
-Current time: 2024-06-25 20:39:42. Total running time: 7min 30s
-Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-+------------------------------------------------------------------------------------------------------------------------------------+
-| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        6           399.772    1.32112       0.5258 |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+| train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        6           407.613    1.2737        0.5493 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-| train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
-+------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5287) [7, 10000] loss: 0.239
+(func pid=5287) [7,  6000] loss: 0.415
+(func pid=5287) [7,  8000] loss: 0.306
+(func pid=5287) [7, 10000] loss: 0.246
 
-Trial train_cifar_ffd52_00005 finished iteration 7 at 2024-06-25 20:39:49. Total running time: 7min 38s
+Trial train_cifar_f162b_00005 finished iteration 7 at 2024-06-25 21:15:03. Total running time: 7min 28s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
+| Trial train_cifar_f162b_00005 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000006 |
-| time_this_iter_s                                  44.67267 |
-| time_total_s                                     452.28563 |
+| time_this_iter_s                                  43.15288 |
+| time_total_s                                      442.9253 |
 | training_iteration                                       7 |
-| accuracy                                            0.5668 |
-| loss                                               1.20639 |
+| accuracy                                            0.5389 |
+| loss                                               1.27488 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000006
-(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000006)
-(func pid=5287) [8,  2000] loss: 1.179
-(func pid=5287) [8,  4000] loss: 0.575
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 7 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000006
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000006)
 
 Trial status: 9 TERMINATED | 1 RUNNING
-Current time: 2024-06-25 20:40:12. Total running time: 8min 0s
+Current time: 2024-06-25 21:15:05. Total running time: 7min 31s
+Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
++------------------------------------------------------------------------------------------------------------------------------------+
+| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
++------------------------------------------------------------------------------------------------------------------------------------+
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        7           442.925    1.27488       0.5389 |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+| train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
++------------------------------------------------------------------------------------------------------------------------------------+
+(func pid=5287) [8,  2000] loss: 1.195
+(func pid=5287) [8,  4000] loss: 0.606
+(func pid=5287) [8,  6000] loss: 0.412
+(func pid=5287) [8,  8000] loss: 0.307
+Trial status: 9 TERMINATED | 1 RUNNING
+Current time: 2024-06-25 21:15:35. Total running time: 8min 1s
 Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        7           452.286    1.20639       0.5668 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-| train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        7           442.925    1.27488       0.5389 |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+| train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
 +------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5287) [8,  6000] loss: 0.393
-(func pid=5287) [8,  8000] loss: 0.293
-(func pid=5287) [8, 10000] loss: 0.236
+(func pid=5287) [8, 10000] loss: 0.243
 
-Trial train_cifar_ffd52_00005 finished iteration 8 at 2024-06-25 20:40:33. Total running time: 8min 22s
+Trial train_cifar_f162b_00005 finished iteration 8 at 2024-06-25 21:15:46. Total running time: 8min 12s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
+| Trial train_cifar_f162b_00005 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000007 |
-| time_this_iter_s                                   44.3303 |
-| time_total_s                                     496.61593 |
+| time_this_iter_s                                  43.62678 |
+| time_total_s                                     486.55208 |
 | training_iteration                                       8 |
-| accuracy                                            0.5922 |
-| loss                                               1.16386 |
+| accuracy                                             0.558 |
+| loss                                               1.23456 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000007
-(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000007)
-(func pid=5287) [9,  2000] loss: 1.136
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 8 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000007
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000007)
+(func pid=5287) [9,  2000] loss: 1.185
+(func pid=5287) [9,  4000] loss: 0.599
 
 Trial status: 9 TERMINATED | 1 RUNNING
-Current time: 2024-06-25 20:40:42. Total running time: 8min 31s
+Current time: 2024-06-25 21:16:05. Total running time: 8min 31s
 Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        8           496.616    1.16386       0.5922 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-| train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
-+------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5287) [9,  4000] loss: 0.578
-(func pid=5287) [9,  6000] loss: 0.377
-(func pid=5287) [9,  8000] loss: 0.290
-(func pid=5287) [9, 10000] loss: 0.231
-Trial status: 9 TERMINATED | 1 RUNNING
-Current time: 2024-06-25 20:41:12. Total running time: 9min 1s
-Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
-+------------------------------------------------------------------------------------------------------------------------------------+
-| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
-+------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        8           496.616    1.16386       0.5922 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-| train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        8           486.552    1.23456       0.558  |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+| train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
 +------------------------------------------------------------------------------------------------------------------------------------+
+(func pid=5287) [9,  6000] loss: 0.398
+(func pid=5287) [9,  8000] loss: 0.300
+(func pid=5287) [9, 10000] loss: 0.239
 
-Trial train_cifar_ffd52_00005 finished iteration 9 at 2024-06-25 20:41:18. Total running time: 9min 6s
+Trial train_cifar_f162b_00005 finished iteration 9 at 2024-06-25 21:16:30. Total running time: 8min 56s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
+| Trial train_cifar_f162b_00005 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000008 |
-| time_this_iter_s                                  44.30344 |
-| time_total_s                                     540.91937 |
+| time_this_iter_s                                  44.00693 |
+| time_total_s                                     530.55901 |
 | training_iteration                                       9 |
-| accuracy                                            0.5751 |
-| loss                                               1.18888 |
+| accuracy                                            0.5576 |
+| loss                                               1.24916 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000008
-(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000008)
-(func pid=5287) [10,  2000] loss: 1.123
-(func pid=5287) [10,  4000] loss: 0.573
-(func pid=5287) [10,  6000] loss: 0.379
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 9 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000008
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000008)
 
 Trial status: 9 TERMINATED | 1 RUNNING
-Current time: 2024-06-25 20:41:42. Total running time: 9min 31s
+Current time: 2024-06-25 21:16:35. Total running time: 9min 1s
+Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
++------------------------------------------------------------------------------------------------------------------------------------+
+| Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
++------------------------------------------------------------------------------------------------------------------------------------+
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        9           530.559    1.24916       0.5576 |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+| train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
++------------------------------------------------------------------------------------------------------------------------------------+
+(func pid=5287) [10,  2000] loss: 1.186
+(func pid=5287) [10,  4000] loss: 0.595
+(func pid=5287) [10,  6000] loss: 0.397
+(func pid=5287) [10,  8000] loss: 0.296
+Trial status: 9 TERMINATED | 1 RUNNING
+Current time: 2024-06-25 21:17:05. Total running time: 9min 31s
 Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00005   RUNNING         8     64   0.000353097              4        9           540.919    1.18888       0.5751 |
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-| train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+| train_cifar_f162b_00005   RUNNING         8     64   0.000353097              4        9           530.559    1.24916       0.5576 |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+| train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
 +------------------------------------------------------------------------------------------------------------------------------------+
-(func pid=5287) [10,  8000] loss: 0.283
-(func pid=5287) [10, 10000] loss: 0.225
+(func pid=5287) [10, 10000] loss: 0.230
 
-Trial train_cifar_ffd52_00005 finished iteration 10 at 2024-06-25 20:42:02. Total running time: 9min 51s
+Trial train_cifar_f162b_00005 finished iteration 10 at 2024-06-25 21:17:14. Total running time: 9min 39s
 +------------------------------------------------------------+
-| Trial train_cifar_ffd52_00005 result                       |
+| Trial train_cifar_f162b_00005 result                       |
 +------------------------------------------------------------+
 | checkpoint_dir_name                      checkpoint_000009 |
-| time_this_iter_s                                  44.58802 |
-| time_total_s                                     585.50738 |
+| time_this_iter_s                                  43.34379 |
+| time_total_s                                      573.9028 |
 | training_iteration                                      10 |
-| accuracy                                            0.5827 |
-| loss                                               1.19666 |
+| accuracy                                            0.5445 |
+| loss                                                1.3022 |
 +------------------------------------------------------------+
-Trial train_cifar_ffd52_00005 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000009
+Trial train_cifar_f162b_00005 saved a checkpoint for iteration 10 at: (local)/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000009
 
-Trial train_cifar_ffd52_00005 completed after 10 iterations at 2024-06-25 20:42:02. Total running time: 9min 51s
+Trial train_cifar_f162b_00005 completed after 10 iterations at 2024-06-25 21:17:14. Total running time: 9min 39s
 
 Trial status: 10 TERMINATED
-Current time: 2024-06-25 20:42:02. Total running time: 9min 51s
+Current time: 2024-06-25 21:17:14. Total running time: 9min 39s
 Logical resource usage: 2.0/16 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:M60)
 +------------------------------------------------------------------------------------------------------------------------------------+
 | Trial name                status         l1     l2            lr     batch_size     iter     total time (s)      loss     accuracy |
 +------------------------------------------------------------------------------------------------------------------------------------+
-| train_cifar_ffd52_00000   TERMINATED     16      1   0.00213327               2        1           172.118    2.3056        0.1011 |
-| train_cifar_ffd52_00001   TERMINATED      1      2   0.013416                 4        1            99.2588   2.31335       0.1022 |
-| train_cifar_ffd52_00002   TERMINATED    256     64   0.0113784                2        2           330.947    2.18945       0.1713 |
-| train_cifar_ffd52_00003   TERMINATED     64    256   0.0274071                8        1            58.115    2.05448       0.2241 |
-| train_cifar_ffd52_00004   TERMINATED     16      2   0.056666                 4        1            97.8695   2.34534       0.1023 |
-| train_cifar_ffd52_00005   TERMINATED      8     64   0.000353097              4       10           585.507    1.19666       0.5827 |
-| train_cifar_ffd52_00006   TERMINATED     16      4   0.000147684              8       10           386.84     1.57777       0.4213 |
-| train_cifar_ffd52_00007   TERMINATED    256    256   0.00477469               8       10           401.246    1.36315       0.5766 |
-| train_cifar_ffd52_00008   TERMINATED    128    256   0.0306227                8        2           109.409    2.07936       0.2231 |
-| train_cifar_ffd52_00009   TERMINATED      2     16   0.0286986                2        1           139.197    2.33735       0.1016 |
+| train_cifar_f162b_00000   TERMINATED     16      1   0.00213327               2        2           284.196    1.89564       0.2374 |
+| train_cifar_f162b_00001   TERMINATED      1      2   0.013416                 4        1            95.9626   2.30698       0.0997 |
+| train_cifar_f162b_00002   TERMINATED    256     64   0.0113784                2        1           190.494    2.31482       0.1002 |
+| train_cifar_f162b_00003   TERMINATED     64    256   0.0274071                8        1            57.1116   2.10497       0.2199 |
+| train_cifar_f162b_00004   TERMINATED     16      2   0.056666                 4        1            99.7673   2.36081       0.0939 |
+| train_cifar_f162b_00005   TERMINATED      8     64   0.000353097              4       10           573.903    1.3022        0.5445 |
+| train_cifar_f162b_00006   TERMINATED     16      4   0.000147684              8       10           378.419    1.73982       0.3241 |
+| train_cifar_f162b_00007   TERMINATED    256    256   0.00477469               8       10           391.291    1.42098       0.562  |
+| train_cifar_f162b_00008   TERMINATED    128    256   0.0306227                8        2           107.605    2.09518       0.1834 |
+| train_cifar_f162b_00009   TERMINATED      2     16   0.0286986                2        1           139.18     2.36893       0.0962 |
 +------------------------------------------------------------------------------------------------------------------------------------+
 
+(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_21-07-34/train_cifar_f162b_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_21-07-34/checkpoint_000009)
 Best trial config: {'l1': 8, 'l2': 64, 'lr': 0.0003530972286268149, 'batch_size': 4}
-Best trial final validation loss: 1.196658158802986
-Best trial final validation accuracy: 0.5827
-(func pid=5287) Checkpoint successfully created at: Checkpoint(filesystem=local, path=/var/lib/ci-user/ray_results/train_cifar_2024-06-25_20-32-11/train_cifar_ffd52_00005_5_batch_size=4,l1=8,l2=64,lr=0.0004_2024-06-25_20-32-11/checkpoint_000009)
+Best trial final validation loss: 1.302202902083099
+Best trial final validation accuracy: 0.5445
 Files already downloaded and verified
 Files already downloaded and verified
-Best trial test set accuracy: 0.5918
+Best trial test set accuracy: 0.5611
 </pre></div>
 </div>
 <p>If you run the code, an example output could look like this:</p>
@@ -2105,7 +2106,7 @@ <h2>Configuring the search space<a class="headerlink" href="#configuring-the-sea
 The best performing trial achieved a validation accuracy of about 47%, which could
 be confirmed on the test set.</p>
 <p>So that’s it! You can now tune the parameters of your PyTorch models.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 10 minutes  7.553 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 9 minutes  56.863 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-hyperparameter-tuning-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/b2e3bdbf14ea1e9b3a80770f0a498037/hyperparameter_tuning_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">hyperparameter_tuning_tutorial.py</span></code></a></p>
diff --git a/beginner/introyt/autogradyt_tutorial.html b/beginner/introyt/autogradyt_tutorial.html
index 5b853031b0..de019ad70d 100644
--- a/beginner/introyt/autogradyt_tutorial.html
+++ b/beginner/introyt/autogradyt_tutorial.html
@@ -603,7 +603,7 @@ <h2>A Simple Example<a class="headerlink" href="#a-simple-example" title="Permal
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">a</span></a><span class="o">.</span><span class="n">detach</span><span class="p">(),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">b</span></a><span class="o">.</span><span class="n">detach</span><span class="p">())</span>
 </pre></div>
 </div>
-<img alt="autogradyt tutorial" class="sphx-glr-single-img" src="../../_images/sphx_glr_autogradyt_tutorial_001.png" srcset="../../_images/sphx_glr_autogradyt_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D object at 0x7f4695b2c040&gt;]
+<img alt="autogradyt tutorial" class="sphx-glr-single-img" src="../../_images/sphx_glr_autogradyt_tutorial_001.png" srcset="../../_images/sphx_glr_autogradyt_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D object at 0x7f2100c80880&gt;]
 </pre></div>
 </div>
 <p>Let’s have a closer look at the tensor <code class="docutils literal notranslate"><span class="pre">b</span></code>. When we print it, we see
@@ -676,17 +676,17 @@ <h2>A Simple Example<a class="headerlink" href="#a-simple-example" title="Permal
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>d:
-&lt;AddBackward0 object at 0x7f467c1db9d0&gt;
-((&lt;MulBackward0 object at 0x7f467c1db880&gt;, 0), (None, 0))
-((&lt;SinBackward0 object at 0x7f467c1db880&gt;, 0), (None, 0))
-((&lt;AccumulateGrad object at 0x7f467c1db9d0&gt;, 0),)
+&lt;AddBackward0 object at 0x7f21004c76a0&gt;
+((&lt;MulBackward0 object at 0x7f21004c7550&gt;, 0), (None, 0))
+((&lt;SinBackward0 object at 0x7f21004c7550&gt;, 0), (None, 0))
+((&lt;AccumulateGrad object at 0x7f21004c76a0&gt;, 0),)
 ()
 
 c:
-&lt;MulBackward0 object at 0x7f467c1db880&gt;
+&lt;MulBackward0 object at 0x7f21004c7550&gt;
 
 b:
-&lt;SinBackward0 object at 0x7f467c1db880&gt;
+&lt;SinBackward0 object at 0x7f21004c7550&gt;
 
 a:
 None
@@ -706,7 +706,7 @@ <h2>A Simple Example<a class="headerlink" href="#a-simple-example" title="Permal
         -1.4142e+00, -1.0000e+00, -5.1764e-01,  2.3850e-08,  5.1764e-01,
          1.0000e+00,  1.4142e+00,  1.7321e+00,  1.9319e+00,  2.0000e+00])
 
-[&lt;matplotlib.lines.Line2D object at 0x7f467c20cb80&gt;]
+[&lt;matplotlib.lines.Line2D object at 0x7f21004f5630&gt;]
 </pre></div>
 </div>
 <p>Recall the computation steps we took to get here:</p>
@@ -999,17 +999,17 @@ <h2>Autograd Profiler<a class="headerlink" href="#autograd-profiler" title="Perm
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
                                 Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls
 ------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
-                     cudaEventRecord        45.07%       9.215ms        45.07%       9.215ms       2.304us       0.000us         0.00%       0.000us       0.000us          4000
-                           aten::div        27.27%       5.575ms        27.27%       5.575ms       5.575us      16.286ms        50.14%      16.286ms      16.286us          1000
-                           aten::mul        26.58%       5.433ms        26.58%       5.433ms       5.433us      16.193ms        49.86%      16.193ms      16.193us          1000
-          cudaGetDeviceProperties_v2         0.96%     196.000us         0.96%     196.000us     196.000us       0.000us         0.00%       0.000us       0.000us             1
+                     cudaEventRecord        46.67%       9.570ms        46.67%       9.570ms       2.393us       0.000us         0.00%       0.000us       0.000us          4000
+                           aten::div        26.28%       5.389ms        26.28%       5.389ms       5.389us      16.622ms        50.62%      16.622ms      16.622us          1000
+                           aten::mul        26.07%       5.346ms        26.07%       5.346ms       5.346us      16.216ms        49.38%      16.216ms      16.216us          1000
+          cudaGetDeviceProperties_v2         0.85%     174.000us         0.85%     174.000us     174.000us       0.000us         0.00%       0.000us       0.000us             1
                cudaDeviceSynchronize         0.06%      12.000us         0.06%      12.000us      12.000us       0.000us         0.00%       0.000us       0.000us             1
-               cudaStreamIsCapturing         0.04%       8.000us         0.04%       8.000us       2.000us       0.000us         0.00%       0.000us       0.000us             4
+               cudaStreamIsCapturing         0.04%       9.000us         0.04%       9.000us       2.250us       0.000us         0.00%       0.000us       0.000us             4
     cudaDeviceGetStreamPriorityRange         0.01%       3.000us         0.01%       3.000us       3.000us       0.000us         0.00%       0.000us       0.000us             1
                   cudaGetDeviceCount         0.01%       2.000us         0.01%       2.000us       1.000us       0.000us         0.00%       0.000us       0.000us             2
 ------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
-Self CPU time total: 20.444ms
-Self CUDA time total: 32.479ms
+Self CPU time total: 20.505ms
+Self CUDA time total: 32.838ms
 </pre></div>
 </div>
 <p>The profiler can also label individual sub-blocks of code, break out the
@@ -1164,7 +1164,7 @@ <h3>The High-Level API<a class="headerlink" href="#the-high-level-api" title="Pe
 <p>For more information, including performance notes on the <a class="reference external" href="https://pytorch.org/docs/stable/autograd.html#functional-higher-level-api">docs for the
 functional
 API</a></p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.803 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.778 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-introyt-autogradyt-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/1a94e27be9b0e79da5acafc1f68a7143/autogradyt_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">autogradyt_tutorial.py</span></code></a></p>
diff --git a/beginner/introyt/introyt1_tutorial.html b/beginner/introyt/introyt1_tutorial.html
index 81962f9267..fa51db2c40 100644
--- a/beginner/introyt/introyt1_tutorial.html
+++ b/beginner/introyt/introyt1_tutorial.html
@@ -853,22 +853,22 @@ <h2>Datasets and Dataloaders<a class="headerlink" href="#datasets-and-dataloader
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
 
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 Extracting ./data/cifar-10-python.tar.gz to ./data
 </pre></div>
 </div>
@@ -1153,7 +1153,7 @@ <h2>Training Your PyTorch Model<a class="headerlink" href="#training-your-pytorc
 accurate at this point. That’s not exactly state-of-the-art, but it’s
 far better than the 10% accuracy we’d expect from a random output. This
 demonstrates that some general learning did happen in the model.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  58.852 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  59.366 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-introyt-introyt1-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/0e4c2becda3dfc54e1816634d49f8e73/introyt1_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">introyt1_tutorial.py</span></code></a></p>
diff --git a/beginner/introyt/tensorboardyt_tutorial.html b/beginner/introyt/tensorboardyt_tutorial.html
index c39b3d3695..440ada13db 100644
--- a/beginner/introyt/tensorboardyt_tutorial.html
+++ b/beginner/introyt/tensorboardyt_tutorial.html
@@ -602,75 +602,40 @@ <h2>Showing Images in TensorBoard<a class="headerlink" href="#showing-images-in-
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
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 Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
 
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
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 </pre></div>
 </div>
@@ -864,7 +829,7 @@ <h2>Other Resources<a class="headerlink" href="#other-resources" title="Permalin
 <li><p>For more information about TensorBoard, see the <a class="reference external" href="https://www.tensorflow.org/tensorboard">TensorBoard
 documentation</a></p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  42.301 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  40.903 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-introyt-tensorboardyt-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/ba6d64f1f8bd0d6b3c21839705dc840a/tensorboardyt_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tensorboardyt_tutorial.py</span></code></a></p>
diff --git a/beginner/introyt/tensors_deeper_tutorial.html b/beginner/introyt/tensors_deeper_tutorial.html
index e167af1755..f47d6e4e44 100644
--- a/beginner/introyt/tensors_deeper_tutorial.html
+++ b/beginner/introyt/tensors_deeper_tutorial.html
@@ -514,9 +514,9 @@ <h2>Creating Tensors<a class="headerlink" href="#creating-tensors" title="Permal
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;class 'torch.Tensor'&gt;
-tensor([[-9.6404e-31,  4.5629e-41,  2.4354e-05,  0.0000e+00],
-        [ 2.0735e-37,  0.0000e+00,  0.0000e+00,  0.0000e+00],
-        [ 5.5904e+07,  0.0000e+00, -6.3661e-37,  4.5629e-41]])
+tensor([[ 0.0000e+00,  0.0000e+00,  1.4013e-45,  1.4013e-45],
+        [ 1.7331e-37,  0.0000e+00,  0.0000e+00,  0.0000e+00],
+        [ 6.6902e-29,  0.0000e+00, -2.4643e-31,  4.5907e-41]])
 </pre></div>
 </div>
 <p>Let’s unpack what we just did:</p>
@@ -637,17 +637,17 @@ <h3>Tensor Shapes<a class="headerlink" href="#tensor-shapes" title="Permalink to
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>torch.Size([2, 2, 3])
-tensor([[[ 9.5868e-10,  0.0000e+00,  0.0000e+00],
-         [ 0.0000e+00,  8.9683e-44,  0.0000e+00]],
+tensor([[[-3.6793e-25,  4.5907e-41,  7.1389e-14],
+         [ 0.0000e+00,  1.7331e-37,  0.0000e+00]],
 
-        [[ 1.5695e-43,  0.0000e+00,  2.0005e+03],
-         [ 0.0000e+00, -6.3661e-37,  4.5629e-41]]])
+        [[ 0.0000e+00,  0.0000e+00,  7.1043e-28],
+         [ 0.0000e+00, -2.4643e-31,  4.5907e-41]]])
 torch.Size([2, 2, 3])
-tensor([[[ 1.5769e-31,  0.0000e+00,  7.4182e-13],
-         [ 0.0000e+00,  4.4842e-44,  0.0000e+00]],
+tensor([[[ 2.0829e-07,  0.0000e+00,  4.5751e-29],
+         [ 0.0000e+00,  8.9683e-44,  0.0000e+00]],
 
-        [[ 1.5695e-43,  0.0000e+00,  1.3849e-31],
-         [ 0.0000e+00, -6.3661e-37,  4.5629e-41]]])
+        [[ 1.5695e-43,  0.0000e+00,  2.4362e-27],
+         [ 0.0000e+00, -2.4643e-31,  4.5907e-41]]])
 torch.Size([2, 2, 3])
 tensor([[[0., 0., 0.],
          [0., 0., 0.]],
@@ -1595,7 +1595,7 @@ <h2>NumPy Bridge<a class="headerlink" href="#numpy-bridge" title="Permalink to t
  [ 0.2853077  17.          0.5695162 ]]
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.275 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.299 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-introyt-tensors-deeper-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/be017e7b39198fdf668c138fd8d57abe/tensors_deeper_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tensors_deeper_tutorial.py</span></code></a></p>
diff --git a/beginner/introyt/trainingyt.html b/beginner/introyt/trainingyt.html
index 88bc71bb26..cf83c24f29 100644
--- a/beginner/introyt/trainingyt.html
+++ b/beginner/introyt/trainingyt.html
@@ -566,88 +566,78 @@ <h2>Dataset and DataLoader<a class="headerlink" href="#dataset-and-dataloader" t
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz
 
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 Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw
 
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
 
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 Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
 
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
 
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 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
 Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
 
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 Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
 
 Training set has 60000 instances
@@ -1008,7 +998,7 @@ <h2>Other Resources<a class="headerlink" href="#other-resources" title="Permalin
 different domains, generative adversarial networks, reinforcement
 learning, and more</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  16.953 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  26.753 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-introyt-trainingyt-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/9f7a57e14d8a2ebf975344f34d6ef247/trainingyt.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">trainingyt.py</span></code></a></p>
diff --git a/beginner/knowledge_distillation_tutorial.html b/beginner/knowledge_distillation_tutorial.html
index ee93219b00..893f88545e 100644
--- a/beginner/knowledge_distillation_tutorial.html
+++ b/beginner/knowledge_distillation_tutorial.html
@@ -710,17 +710,17 @@ <h3>Cross-entropy runs<a class="headerlink" href="#cross-entropy-runs" title="Pe
 <span class="n">nn_light</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">LightNN</span></a><span class="p">(</span><span class="n">num_classes</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 1.3250710057175679
-Epoch 2/10, Loss: 0.8651102480985929
-Epoch 3/10, Loss: 0.6783160822623221
-Epoch 4/10, Loss: 0.5429468773819907
-Epoch 5/10, Loss: 0.4233940201418479
-Epoch 6/10, Loss: 0.3186772309453286
-Epoch 7/10, Loss: 0.23109134231382014
-Epoch 8/10, Loss: 0.1697552327514457
-Epoch 9/10, Loss: 0.14379946459704043
-Epoch 10/10, Loss: 0.12531204655995148
-Test Accuracy: 75.62%
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 1.3316447629648096
+Epoch 2/10, Loss: 0.8751732992089313
+Epoch 3/10, Loss: 0.6836497868265947
+Epoch 4/10, Loss: 0.5458234034078505
+Epoch 5/10, Loss: 0.4359907577257327
+Epoch 6/10, Loss: 0.3299281927554504
+Epoch 7/10, Loss: 0.24488897051881342
+Epoch 8/10, Loss: 0.18661382771513957
+Epoch 9/10, Loss: 0.15769847224244987
+Epoch 10/10, Loss: 0.12847331230578674
+Test Accuracy: 74.97%
 </pre></div>
 </div>
 <p>We instantiate one more lightweight network model to compare their performances.
@@ -758,17 +758,17 @@ <h3>Cross-entropy runs<a class="headerlink" href="#cross-entropy-runs" title="Pe
 <span class="n">test_accuracy_light_ce</span> <span class="o">=</span> <span class="n">test</span><span class="p">(</span><span class="n">nn_light</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><span class="n">test_loader</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 1.4690716739200875
-Epoch 2/10, Loss: 1.1584134421994925
-Epoch 3/10, Loss: 1.0249660551700446
-Epoch 4/10, Loss: 0.9242221932581929
-Epoch 5/10, Loss: 0.8470086699251629
-Epoch 6/10, Loss: 0.7781770118057271
-Epoch 7/10, Loss: 0.7141712661594382
-Epoch 8/10, Loss: 0.6576351702518171
-Epoch 9/10, Loss: 0.6013266375607542
-Epoch 10/10, Loss: 0.5511761150701576
-Test Accuracy: 70.30%
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 1.467257319508916
+Epoch 2/10, Loss: 1.1540557117108494
+Epoch 3/10, Loss: 1.0237963839869975
+Epoch 4/10, Loss: 0.9230599127462148
+Epoch 5/10, Loss: 0.8483210123713364
+Epoch 6/10, Loss: 0.781327926289395
+Epoch 7/10, Loss: 0.7165115344554872
+Epoch 8/10, Loss: 0.6570775745927221
+Epoch 9/10, Loss: 0.6063216873599441
+Epoch 10/10, Loss: 0.5517354763834678
+Test Accuracy: 71.13%
 </pre></div>
 </div>
 <p>As we can see, based on test accuracy, we can now compare the deeper network that is to be used as a teacher with the lightweight network that is our supposed student. So far, our student has not intervened with the teacher, therefore this performance is achieved by the student itself.
@@ -777,8 +777,8 @@ <h3>Cross-entropy runs<a class="headerlink" href="#cross-entropy-runs" title="Pe
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Student accuracy: </span><span class="si">{</span><span class="n">test_accuracy_light_ce</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%"</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Teacher accuracy: 75.62%
-Student accuracy: 70.30%
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Teacher accuracy: 74.97%
+Student accuracy: 71.13%
 </pre></div>
 </div>
 </div>
@@ -859,20 +859,20 @@ <h3>Knowledge distillation run<a class="headerlink" href="#knowledge-distillatio
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Student accuracy with CE + KD: </span><span class="si">{</span><span class="n">test_accuracy_light_ce_and_kd</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%"</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 2.3669486673896576
-Epoch 2/10, Loss: 1.8551302818995912
-Epoch 3/10, Loss: 1.6341343560182224
-Epoch 4/10, Loss: 1.4716220581927872
-Epoch 5/10, Loss: 1.3448759957652567
-Epoch 6/10, Loss: 1.230848588754454
-Epoch 7/10, Loss: 1.137130338212718
-Epoch 8/10, Loss: 1.0464744631896543
-Epoch 9/10, Loss: 0.9703757310157541
-Epoch 10/10, Loss: 0.9071777760220305
-Test Accuracy: 71.17%
-Teacher accuracy: 75.62%
-Student accuracy without teacher: 70.30%
-Student accuracy with CE + KD: 71.17%
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 2.363848817927758
+Epoch 2/10, Loss: 1.846368370458598
+Epoch 3/10, Loss: 1.6295966614237831
+Epoch 4/10, Loss: 1.470853830847289
+Epoch 5/10, Loss: 1.3469416916827717
+Epoch 6/10, Loss: 1.23279900700235
+Epoch 7/10, Loss: 1.1376631096805758
+Epoch 8/10, Loss: 1.050427379053267
+Epoch 9/10, Loss: 0.9787890670244651
+Epoch 10/10, Loss: 0.9075392502957903
+Test Accuracy: 70.41%
+Teacher accuracy: 74.97%
+Student accuracy without teacher: 71.13%
+Student accuracy with CE + KD: 70.41%
 </pre></div>
 </div>
 </div>
@@ -972,8 +972,8 @@ <h3>Cosine loss minimization run<a class="headerlink" href="#cosine-loss-minimiz
 <span class="nb">print</span><span class="p">(</span><span class="s2">"Norm of 1st layer:"</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.norm.html#torch.norm" title="torch.norm"><span class="n">torch</span><span class="o">.</span><span class="n">norm</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential"><span class="n">modified_nn_light</span><span class="o">.</span><span class="n">features</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Norm of 1st layer for deep_nn: 7.500669956207275
-Norm of 1st layer for modified_deep_nn: 7.500669956207275
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Norm of 1st layer for deep_nn: 7.507744312286377
+Norm of 1st layer for modified_deep_nn: 7.507744312286377
 Norm of 1st layer: 2.327361822128296
 </pre></div>
 </div>
@@ -1082,17 +1082,17 @@ <h3>Cosine loss minimization run<a class="headerlink" href="#cosine-loss-minimiz
 <span class="n">test_accuracy_light_ce_and_cosine_loss</span> <span class="o">=</span> <span class="n">test_multiple_outputs</span><span class="p">(</span><span class="n">modified_nn_light</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><span class="n">test_loader</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 1.306663755565653
-Epoch 2/10, Loss: 1.0738007182355427
-Epoch 3/10, Loss: 0.9744930055440234
-Epoch 4/10, Loss: 0.8975373378495122
-Epoch 5/10, Loss: 0.8423850412868783
-Epoch 6/10, Loss: 0.7968010483190532
-Epoch 7/10, Loss: 0.7563447217502253
-Epoch 8/10, Loss: 0.7203674510007014
-Epoch 9/10, Loss: 0.6835664493958359
-Epoch 10/10, Loss: 0.6579263746890875
-Test Accuracy: 70.91%
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 1.3032945272562755
+Epoch 2/10, Loss: 1.0667807447635913
+Epoch 3/10, Loss: 0.9640445730570332
+Epoch 4/10, Loss: 0.88822200917222
+Epoch 5/10, Loss: 0.833785516526693
+Epoch 6/10, Loss: 0.7883921777805709
+Epoch 7/10, Loss: 0.7487654428347907
+Epoch 8/10, Loss: 0.7142888545380224
+Epoch 9/10, Loss: 0.6748507934458116
+Epoch 10/10, Loss: 0.6500329192337173
+Test Accuracy: 70.92%
 </pre></div>
 </div>
 </div>
@@ -1249,17 +1249,17 @@ <h3>Intermediate regressor run<a class="headerlink" href="#intermediate-regresso
 <span class="n">test_accuracy_light_ce_and_mse_loss</span> <span class="o">=</span> <span class="n">test_multiple_outputs</span><span class="p">(</span><span class="n">modified_nn_light_reg</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><span class="n">test_loader</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 1.692887835185546
-Epoch 2/10, Loss: 1.3222829390059956
-Epoch 3/10, Loss: 1.1805361286758462
-Epoch 4/10, Loss: 1.0865643943667107
-Epoch 5/10, Loss: 1.0102771583115657
-Epoch 6/10, Loss: 0.9460458064932957
-Epoch 7/10, Loss: 0.8926568333145297
-Epoch 8/10, Loss: 0.842905539990691
-Epoch 9/10, Loss: 0.798999123865991
-Epoch 10/10, Loss: 0.7600122499648873
-Test Accuracy: 70.84%
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/10, Loss: 1.7142535137093586
+Epoch 2/10, Loss: 1.3228392171128023
+Epoch 3/10, Loss: 1.1758271349055687
+Epoch 4/10, Loss: 1.079089686541301
+Epoch 5/10, Loss: 1.0010888690838728
+Epoch 6/10, Loss: 0.9352552056922327
+Epoch 7/10, Loss: 0.8804437144638022
+Epoch 8/10, Loss: 0.828696703362038
+Epoch 9/10, Loss: 0.7858170161161886
+Epoch 10/10, Loss: 0.7487682099537472
+Test Accuracy: 71.06%
 </pre></div>
 </div>
 <p>It is expected that the final method will work better than <code class="docutils literal notranslate"><span class="pre">CosineLoss</span></code> because now we have allowed a trainable layer between the teacher and the student,
@@ -1272,11 +1272,11 @@ <h3>Intermediate regressor run<a class="headerlink" href="#intermediate-regresso
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Student accuracy with CE + RegressorMSE: </span><span class="si">{</span><span class="n">test_accuracy_light_ce_and_mse_loss</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%"</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Teacher accuracy: 75.62%
-Student accuracy without teacher: 70.30%
-Student accuracy with CE + KD: 71.17%
-Student accuracy with CE + CosineLoss: 70.91%
-Student accuracy with CE + RegressorMSE: 70.84%
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Teacher accuracy: 74.97%
+Student accuracy without teacher: 71.13%
+Student accuracy with CE + KD: 70.41%
+Student accuracy with CE + CosineLoss: 70.92%
+Student accuracy with CE + RegressorMSE: 71.06%
 </pre></div>
 </div>
 </div>
@@ -1294,7 +1294,7 @@ <h3>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 <li><p><a class="reference external" href="https://arxiv.org/abs/1503.02531">Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: Neural Information Processing System Deep Learning Workshop (2015)</a></p></li>
 <li><p><a class="reference external" href="https://arxiv.org/abs/1412.6550">Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: Hints for thin deep nets. In: Proceedings of the International Conference on Learning Representations (2015)</a></p></li>
 </ul>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes  33.687 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-knowledge-distillation-tutorial-py">
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diff --git a/beginner/nlp/advanced_tutorial.html b/beginner/nlp/advanced_tutorial.html
index 2a73900a70..7a33f185f1 100644
--- a/beginner/nlp/advanced_tutorial.html
+++ b/beginner/nlp/advanced_tutorial.html
@@ -602,7 +602,7 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
 <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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 </pre></div>
 </div>
 <p>Helper functions to make the code more readable.</p>
@@ -847,7 +847,7 @@ <h2>Exercise: A new loss function for discriminative tagging<a class="headerlink
 implementing this in a static toolkit, I imagine that it is possible but
 much less straightforward.</p>
 <p>Pick up some real data and do a comparison!</p>
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diff --git a/beginner/nlp/deep_learning_tutorial.html b/beginner/nlp/deep_learning_tutorial.html
index 81ddf1ec31..2edc0c8174 100644
--- a/beginner/nlp/deep_learning_tutorial.html
+++ b/beginner/nlp/deep_learning_tutorial.html
@@ -516,7 +516,7 @@ <h3>Affine Maps<a class="headerlink" href="#affine-maps" title="Permalink to thi
 <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">lin</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># maps from R^5 to R^3, parameters A, b</span>
diff --git a/beginner/nlp/pytorch_tutorial.html b/beginner/nlp/pytorch_tutorial.html
index 18462885ac..adaee17788 100644
--- a/beginner/nlp/pytorch_tutorial.html
+++ b/beginner/nlp/pytorch_tutorial.html
@@ -497,7 +497,7 @@ <h2>Introduction to Torch’s tensor library<a class="headerlink" href="#introdu
 <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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@@ -695,7 +695,7 @@ <h2>Computation Graphs and Automatic Differentiation<a class="headerlink" href="
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([5., 7., 9.], grad_fn=&lt;AddBackward0&gt;)
-&lt;AddBackward0 object at 0x7f4b20d59bd0&gt;
+&lt;AddBackward0 object at 0x7f3bc42bee30&gt;
 </pre></div>
 </div>
 <p>So Tensors know what created them. z knows that it wasn’t read in from
@@ -710,7 +710,7 @@ <h2>Computation Graphs and Automatic Differentiation<a class="headerlink" href="
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(21., grad_fn=&lt;SumBackward0&gt;)
-&lt;SumBackward0 object at 0x7f4b20d58970&gt;
+&lt;SumBackward0 object at 0x7f3bc42bc460&gt;
 </pre></div>
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 <p>So now, what is the derivative of this sum with respect to the first
@@ -779,7 +779,7 @@ <h2>Computation Graphs and Automatic Differentiation<a class="headerlink" href="
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 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>False False
 None
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diff --git a/beginner/nlp/sequence_models_tutorial.html b/beginner/nlp/sequence_models_tutorial.html
index ee36c678e2..b20294974a 100644
--- a/beginner/nlp/sequence_models_tutorial.html
+++ b/beginner/nlp/sequence_models_tutorial.html
@@ -529,7 +529,7 @@ <h2>LSTMs in Pytorch<a class="headerlink" href="#lstms-in-pytorch" title="Permal
 <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html#torch.nn.LSTM" title="torch.nn.LSTM"><span class="n">lstm</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html#torch.nn.LSTM" title="torch.nn.LSTM"><span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># Input dim is 3, output dim is 3</span>
@@ -736,7 +736,7 @@ <h2>Exercise: Augmenting the LSTM part-of-speech tagger with character-level fea
 <li><p>To do a sequence model over characters, you will have to embed characters.
 The character embeddings will be the input to the character LSTM.</p></li>
 </ul>
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diff --git a/beginner/nlp/word_embeddings_tutorial.html b/beginner/nlp/word_embeddings_tutorial.html
index c32ebdc0f1..4159155767 100644
--- a/beginner/nlp/word_embeddings_tutorial.html
+++ b/beginner/nlp/word_embeddings_tutorial.html
@@ -622,7 +622,7 @@ <h2>Word Embeddings in Pytorch<a class="headerlink" href="#word-embeddings-in-py
 <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">word_to_ix</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"hello"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"world"</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
@@ -738,9 +738,9 @@ <h2>An Example: N-Gram Language Modeling<a class="headerlink" href="#an-example-
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(['forty', 'When'], 'winters'), (['winters', 'forty'], 'shall'), (['shall', 'winters'], 'besiege')]
-[521.698427438736, 519.1963937282562, 516.7113490104675, 514.2416417598724, 511.7873330116272, 509.3473746776581, 506.92100834846497, 504.5057735443115, 502.10225200653076, 499.70783376693726]
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+tensor([ 0.6867,  0.4197, -1.0219,  0.9891,  0.7819, -2.2055, -1.4977, -0.9028,
+         0.8012, -0.6630], grad_fn=&lt;SelectBackward0&gt;)
 </pre></div>
 </div>
 </div>
@@ -817,10 +817,10 @@ <h2>Exercise: Computing Word Embeddings: Continuous Bag-of-Words<a class="header
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(['are', 'We', 'to', 'study'], 'about'), (['about', 'are', 'study', 'the'], 'to'), (['to', 'about', 'the', 'idea'], 'study'), (['study', 'to', 'idea', 'of'], 'the'), (['the', 'study', 'of', 'a'], 'idea')]
 
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diff --git a/beginner/nn_tutorial.html b/beginner/nn_tutorial.html
index 9da4b019e2..4c4fff7144 100644
--- a/beginner/nn_tutorial.html
+++ b/beginner/nn_tutorial.html
@@ -1338,8 +1338,8 @@ <h2>Using your GPU<a class="headerlink" href="#using-your-gpu" title="Permalink
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fit</span><span class="p">(</span><span class="n">epochs</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential"><span class="n">model</span></a><span class="p">,</span> <span class="n">loss_func</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.optim.SGD.html#torch.optim.SGD" title="torch.optim.SGD"><span class="n">opt</span></a><span class="p">,</span> <span class="n">train_dl</span><span class="p">,</span> <span class="n">valid_dl</span><span class="p">)</span>
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diff --git a/beginner/onnx/export_simple_model_to_onnx_tutorial.html b/beginner/onnx/export_simple_model_to_onnx_tutorial.html
index b3dc33d03d..c08079e19b 100644
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 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-onnx-export-simple-model-to-onnx-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/1c960eb430ba8694b1655bb03904dac2/export_simple_model_to_onnx_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">export_simple_model_to_onnx_tutorial.py</span></code></a></p>
diff --git a/beginner/onnx/onnx_registry_tutorial.html b/beginner/onnx/onnx_registry_tutorial.html
index e43ef30638..d4bc66e901 100644
--- a/beginner/onnx/onnx_registry_tutorial.html
+++ b/beginner/onnx/onnx_registry_tutorial.html
@@ -546,13 +546,13 @@ <h2>Unsupported ATen operators<a class="headerlink" href="#unsupported-aten-oper
 <span class="kn">import</span> <span class="nn">onnxscript</span>
 <span class="kn">from</span> <span class="nn">onnxscript</span> <span class="kn">import</span> <span class="n">opset18</span>  <span class="c1"># opset 18 is the latest (and only) supported version for now</span>
 
-<span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+<span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_x</span><span class="p">,</span> <span class="n">input_y</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">input_x</span><span class="p">,</span> <span class="n">input_y</span><span class="p">)</span>  <span class="c1"># generates a aten::add.Tensor node</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_add_x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_add_y</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="n">aten_add_model</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">Model</span></a><span class="p">()</span>
+<span class="n">input_add_x</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">input_add_y</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">aten_add_model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">()</span>
 
 
 <span class="c1"># Now we create a ONNX Script function that implements ``aten::add.Tensor``.</span>
@@ -571,41 +571,31 @@ <h2>Unsupported ATen operators<a class="headerlink" href="#unsupported-aten-oper
 
 <span class="c1"># Now we have everything we need to support unsupported ATen operators.</span>
 <span class="c1"># Let's register the ``custom_aten_add`` function to ONNX registry, and export the model to ONNX again.</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">OnnxRegistry</span></a><span class="p">()</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry.register_op" title="torch.onnx.OnnxRegistry.register_op"><span class="n">onnx_registry</span><span class="o">.</span><span class="n">register_op</span></a><span class="p">(</span>
+<span class="n">onnx_registry</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">OnnxRegistry</span></a><span class="p">()</span>
+<span class="n">onnx_registry</span><span class="o">.</span><span class="n">register_op</span><span class="p">(</span>
     <span class="n">namespace</span><span class="o">=</span><span class="s2">"aten"</span><span class="p">,</span> <span class="n">op_name</span><span class="o">=</span><span class="s2">"add"</span><span class="p">,</span> <span class="n">overload</span><span class="o">=</span><span class="s2">"Tensor"</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">custom_aten_add</span>
     <span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"aten::add.Tensor is supported by ONNX registry: </span><span class="se">\</span>
-<span class="s2">      </span><span class="si">{</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry.is_registered_op" title="torch.onnx.OnnxRegistry.is_registered_op"><span class="n">onnx_registry</span><span class="o">.</span><span class="n">is_registered_op</span></a><span class="p">(</span><span class="n">namespace</span><span class="o">=</span><span class="s1">'aten'</span><span class="p">,</span><span class="w"> </span><span class="n">op_name</span><span class="o">=</span><span class="s1">'add'</span><span class="p">,</span><span class="w"> </span><span class="n">overload</span><span class="o">=</span><span class="s1">'Tensor'</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span>
+<span class="s2">      </span><span class="si">{</span><span class="n">onnx_registry</span><span class="o">.</span><span class="n">is_registered_op</span><span class="p">(</span><span class="n">namespace</span><span class="o">=</span><span class="s1">'aten'</span><span class="p">,</span><span class="w"> </span><span class="n">op_name</span><span class="o">=</span><span class="s1">'add'</span><span class="p">,</span><span class="w"> </span><span class="n">overload</span><span class="o">=</span><span class="s1">'Tensor'</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span>
       <span class="p">)</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">export_options</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">ExportOptions</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><span class="n">onnx_program</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span></a><span class="p">(</span>
-    <span class="n">aten_add_model</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_add_x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_add_y</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">export_options</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">export_options</span></a>
+<span class="n">export_options</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">ExportOptions</span></a><span class="p">(</span><span class="n">onnx_registry</span><span class="o">=</span><span class="n">onnx_registry</span><span class="p">)</span>
+<span class="n">onnx_program</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span></a><span class="p">(</span>
+    <span class="n">aten_add_model</span><span class="p">,</span> <span class="n">input_add_x</span><span class="p">,</span> <span class="n">input_add_y</span><span class="p">,</span> <span class="n">export_options</span><span class="o">=</span><span class="n">export_options</span>
     <span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:136: UserWarning:
-
-torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
-
-aten::add.Tensor is supported by ONNX registry:       True
-/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning:
-
-torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-</pre></div>
-</div>
 <p>Now let’s inspect the model and verify the model has a <code class="docutils literal notranslate"><span class="pre">custom_aten_add</span></code> instead of <code class="docutils literal notranslate"><span class="pre">aten::add.Tensor</span></code>.
 The graph has one graph node for <code class="docutils literal notranslate"><span class="pre">custom_aten_add</span></code>, and inside of it there are four function nodes, one for each
 operator, and one for constant attribute.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># graph node domain is the custom domain we registered</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"custom.aten"</span>
-<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span></a><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"custom.aten"</span>
+<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
 <span class="c1"># graph node name is the function name</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"custom_aten_add"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"custom_aten_add"</span>
 <span class="c1"># function node domain is empty because we use standard ONNX operators</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">""</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">""</span>
 <span class="c1"># function node name is the standard ONNX operator name</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"Add"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"Add"</span>
 </pre></div>
 </div>
 <p>This is how <code class="docutils literal notranslate"><span class="pre">custom_aten_add_model</span></code> looks in the ONNX graph using Netron:</p>
@@ -616,7 +606,7 @@ <h2>Unsupported ATen operators<a class="headerlink" href="#unsupported-aten-oper
 <p>This was all that we needed to register the new ATen operator into the ONNX Registry.
 As an additional step, we can use ONNX Runtime to run the model, and compare the results with PyTorch.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Use ONNX Runtime to run the model, and compare the results with PyTorch</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.save" title="torch.onnx.ONNXProgram.save"><span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span><span class="s2">"./custom_add_model.onnx"</span><span class="p">)</span>
+<span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"./custom_add_model.onnx"</span><span class="p">)</span>
 <span class="n">ort_session</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span>
     <span class="s2">"./custom_add_model.onnx"</span><span class="p">,</span> <span class="n">providers</span><span class="o">=</span><span class="p">[</span><span class="s1">'CPUExecutionProvider'</span><span class="p">]</span>
     <span class="p">)</span>
@@ -624,16 +614,16 @@ <h2>Unsupported ATen operators<a class="headerlink" href="#unsupported-aten-oper
 <span class="k">def</span> <span class="nf">to_numpy</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">requires_grad</span> <span class="k">else</span> <span class="n">tensor</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
 
-<span class="n">onnx_input</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx" title="torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx"><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_inputs_to_onnx</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_add_x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_add_y</span></a><span class="p">)</span>
+<span class="n">onnx_input</span> <span class="o">=</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_inputs_to_onnx</span><span class="p">(</span><span class="n">input_add_x</span><span class="p">,</span> <span class="n">input_add_y</span><span class="p">)</span>
 <span class="n">onnxruntime_input</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">to_numpy</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">ort_session</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">(),</span> <span class="n">onnx_input</span><span class="p">)}</span>
 <span class="n">onnxruntime_outputs</span> <span class="o">=</span> <span class="n">ort_session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">onnxruntime_input</span><span class="p">)</span>
 
-<span class="n">torch_outputs</span> <span class="o">=</span> <span class="n">aten_add_model</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_add_x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_add_y</span></a><span class="p">)</span>
-<span class="n">torch_outputs</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx" title="torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx"><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_outputs_to_onnx</span></a><span class="p">(</span><span class="n">torch_outputs</span><span class="p">)</span>
+<span class="n">torch_outputs</span> <span class="o">=</span> <span class="n">aten_add_model</span><span class="p">(</span><span class="n">input_add_x</span><span class="p">,</span> <span class="n">input_add_y</span><span class="p">)</span>
+<span class="n">torch_outputs</span> <span class="o">=</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_outputs_to_onnx</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">)</span>
 
 <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">onnxruntime_outputs</span><span class="p">)</span>
-<span class="k">for</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch_output</span></a><span class="p">,</span> <span class="n">onnxruntime_output</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">,</span> <span class="n">onnxruntime_outputs</span><span class="p">):</span>
-    <a class="sphx-glr-backref-module-torch-testing sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/testing.html#torch.testing.assert_close" title="torch.testing.assert_close"><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch_output</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="n">onnxruntime_output</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">torch_output</span><span class="p">,</span> <span class="n">onnxruntime_output</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">,</span> <span class="n">onnxruntime_outputs</span><span class="p">):</span>
+    <a class="sphx-glr-backref-module-torch-testing sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/testing.html#torch.testing.assert_close" title="torch.testing.assert_close"><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span></a><span class="p">(</span><span class="n">torch_output</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="n">onnxruntime_output</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -648,21 +638,14 @@ <h2>Custom operators with existing ONNX Runtime support<a class="headerlink" hre
 which is not the same <code class="docutils literal notranslate"><span class="pre">Gelu</span></code> from ONNX spec. Thus, we register the Gelu with
 the namespace <code class="docutils literal notranslate"><span class="pre">com.microsoft</span></code> and operator name <code class="docutils literal notranslate"><span class="pre">Gelu</span></code>.</p>
 <p>Before we begin, let’s check whether <code class="docutils literal notranslate"><span class="pre">aten::gelu.default</span></code> is really supported by the ONNX registry.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">OnnxRegistry</span></a><span class="p">()</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">onnx_registry</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">OnnxRegistry</span></a><span class="p">()</span>
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"aten::gelu.default is supported by ONNX registry: </span><span class="se">\</span>
-<span class="s2">    </span><span class="si">{</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry.is_registered_op" title="torch.onnx.OnnxRegistry.is_registered_op"><span class="n">onnx_registry</span><span class="o">.</span><span class="n">is_registered_op</span></a><span class="p">(</span><span class="n">namespace</span><span class="o">=</span><span class="s1">'aten'</span><span class="p">,</span><span class="w"> </span><span class="n">op_name</span><span class="o">=</span><span class="s1">'gelu'</span><span class="p">,</span><span class="w"> </span><span class="n">overload</span><span class="o">=</span><span class="s1">'default'</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:136: UserWarning:
-
-torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
-
-aten::gelu.default is supported by ONNX registry:     True
+<span class="s2">    </span><span class="si">{</span><span class="n">onnx_registry</span><span class="o">.</span><span class="n">is_registered_op</span><span class="p">(</span><span class="n">namespace</span><span class="o">=</span><span class="s1">'aten'</span><span class="p">,</span><span class="w"> </span><span class="n">op_name</span><span class="o">=</span><span class="s1">'gelu'</span><span class="p">,</span><span class="w"> </span><span class="n">overload</span><span class="o">=</span><span class="s1">'default'</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>In our example, <code class="docutils literal notranslate"><span class="pre">aten::gelu.default</span></code> operator is supported by the ONNX registry,
 so <code class="xref py py-meth docutils literal notranslate"><span class="pre">onnx_registry.is_registered_op()</span></code> returns <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">CustomGelu</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">CustomGelu</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_x</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">gelu</span><span class="p">(</span><span class="n">input_x</span><span class="p">)</span>
 
@@ -679,37 +662,31 @@ <h2>Custom operators with existing ONNX Runtime support<a class="headerlink" hre
     <span class="k">return</span> <span class="n">custom_ort</span><span class="o">.</span><span class="n">Gelu</span><span class="p">(</span><span class="n">input_x</span><span class="p">)</span>
 
 
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">OnnxRegistry</span></a><span class="p">()</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry.register_op" title="torch.onnx.OnnxRegistry.register_op"><span class="n">onnx_registry</span><span class="o">.</span><span class="n">register_op</span></a><span class="p">(</span>
+<span class="n">onnx_registry</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">OnnxRegistry</span></a><span class="p">()</span>
+<span class="n">onnx_registry</span><span class="o">.</span><span class="n">register_op</span><span class="p">(</span>
     <span class="n">namespace</span><span class="o">=</span><span class="s2">"aten"</span><span class="p">,</span> <span class="n">op_name</span><span class="o">=</span><span class="s2">"gelu"</span><span class="p">,</span> <span class="n">overload</span><span class="o">=</span><span class="s2">"default"</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">custom_aten_gelu</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">export_options</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">ExportOptions</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a><span class="p">)</span>
+<span class="n">export_options</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">ExportOptions</span></a><span class="p">(</span><span class="n">onnx_registry</span><span class="o">=</span><span class="n">onnx_registry</span><span class="p">)</span>
 
-<span class="n">aten_gelu_model</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">CustomGelu</span></a><span class="p">()</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_gelu_x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">aten_gelu_model</span> <span class="o">=</span> <span class="n">CustomGelu</span><span class="p">()</span>
+<span class="n">input_gelu_x</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><span class="n">onnx_program</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span></a><span class="p">(</span>
-    <span class="n">aten_gelu_model</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_gelu_x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">export_options</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">export_options</span></a>
+<span class="n">onnx_program</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span></a><span class="p">(</span>
+    <span class="n">aten_gelu_model</span><span class="p">,</span> <span class="n">input_gelu_x</span><span class="p">,</span> <span class="n">export_options</span><span class="o">=</span><span class="n">export_options</span>
     <span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'Gelu' is not a known op in 'com.microsoft'
-/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:136: UserWarning:
-
-torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
-</pre></div>
-</div>
 <p>Let’s inspect the model and verify the model uses <code class="xref py py-func docutils literal notranslate"><span class="pre">custom_aten_gelu()</span></code> instead of
 <code class="xref py py-class docutils literal notranslate"><span class="pre">aten::gelu</span></code>. Note the graph has one graph nodes for
 <code class="docutils literal notranslate"><span class="pre">custom_aten_gelu</span></code>, and inside <code class="docutils literal notranslate"><span class="pre">custom_aten_gelu</span></code>, there is a function
 node for <code class="docutils literal notranslate"><span class="pre">Gelu</span></code> with namespace <code class="docutils literal notranslate"><span class="pre">com.microsoft</span></code>.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># graph node domain is the custom domain we registered</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"com.microsoft"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"com.microsoft"</span>
 <span class="c1"># graph node name is the function name</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"custom_aten_gelu"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"custom_aten_gelu"</span>
 <span class="c1"># function node domain is the custom domain we registered</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"com.microsoft"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"com.microsoft"</span>
 <span class="c1"># function node name is the node name used in the function</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"Gelu"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"Gelu"</span>
 </pre></div>
 </div>
 <p>The following diagram shows <code class="docutils literal notranslate"><span class="pre">custom_aten_gelu_model</span></code> ONNX graph using Netron:</p>
@@ -719,7 +696,7 @@ <h2>Custom operators with existing ONNX Runtime support<a class="headerlink" hre
 <img alt="../../_images/custom_aten_gelu_function.png" src="../../_images/custom_aten_gelu_function.png"/>
 <p>That is all we need to do. As an additional step, we can use ONNX Runtime to run the model,
 and compare the results with PyTorch.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.save" title="torch.onnx.ONNXProgram.save"><span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span><span class="s2">"./custom_gelu_model.onnx"</span><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"./custom_gelu_model.onnx"</span><span class="p">)</span>
 <span class="n">ort_session</span> <span class="o">=</span> <span class="n">onnxruntime</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span>
     <span class="s2">"./custom_gelu_model.onnx"</span><span class="p">,</span> <span class="n">providers</span><span class="o">=</span><span class="p">[</span><span class="s1">'CPUExecutionProvider'</span><span class="p">]</span>
     <span class="p">)</span>
@@ -727,16 +704,16 @@ <h2>Custom operators with existing ONNX Runtime support<a class="headerlink" hre
 <span class="k">def</span> <span class="nf">to_numpy</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">requires_grad</span> <span class="k">else</span> <span class="n">tensor</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
 
-<span class="n">onnx_input</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx" title="torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx"><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_inputs_to_onnx</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_gelu_x</span></a><span class="p">)</span>
+<span class="n">onnx_input</span> <span class="o">=</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_inputs_to_onnx</span><span class="p">(</span><span class="n">input_gelu_x</span><span class="p">)</span>
 <span class="n">onnxruntime_input</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">to_numpy</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">ort_session</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">(),</span> <span class="n">onnx_input</span><span class="p">)}</span>
 <span class="n">onnxruntime_outputs</span> <span class="o">=</span> <span class="n">ort_session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">onnxruntime_input</span><span class="p">)</span>
 
-<span class="n">torch_outputs</span> <span class="o">=</span> <span class="n">aten_gelu_model</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_gelu_x</span></a><span class="p">)</span>
-<span class="n">torch_outputs</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx" title="torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx"><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_outputs_to_onnx</span></a><span class="p">(</span><span class="n">torch_outputs</span><span class="p">)</span>
+<span class="n">torch_outputs</span> <span class="o">=</span> <span class="n">aten_gelu_model</span><span class="p">(</span><span class="n">input_gelu_x</span><span class="p">)</span>
+<span class="n">torch_outputs</span> <span class="o">=</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_outputs_to_onnx</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">)</span>
 
 <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">onnxruntime_outputs</span><span class="p">)</span>
-<span class="k">for</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch_output</span></a><span class="p">,</span> <span class="n">onnxruntime_output</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">,</span> <span class="n">onnxruntime_outputs</span><span class="p">):</span>
-    <a class="sphx-glr-backref-module-torch-testing sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/testing.html#torch.testing.assert_close" title="torch.testing.assert_close"><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch_output</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="n">onnxruntime_output</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">torch_output</span><span class="p">,</span> <span class="n">onnxruntime_output</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">,</span> <span class="n">onnxruntime_outputs</span><span class="p">):</span>
+    <a class="sphx-glr-backref-module-torch-testing sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/testing.html#torch.testing.assert_close" title="torch.testing.assert_close"><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span></a><span class="p">(</span><span class="n">torch_output</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="n">onnxruntime_output</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -761,7 +738,7 @@ <h3>Custom Ops Registration in PyTorch FX Graph (Beta)<a class="headerlink" href
 <span class="kn">from</span> <span class="nn">torch._custom_op</span> <span class="kn">import</span> <span class="n">impl</span> <span class="k">as</span> <span class="n">custom_op</span>
 
 <span class="nd">@custom_op</span><span class="o">.</span><span class="n">custom_op</span><span class="p">(</span><span class="s2">"mylibrary::addandround_op"</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">addandround_op</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">:</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">)</span> <span class="o">-&gt;</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">:</span>
+<span class="k">def</span> <span class="nf">addandround_op</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">:</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">)</span> <span class="o">-&gt;</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">:</span>
     <span class="o">...</span>
 
 <span class="nd">@addandround_op</span><span class="o">.</span><span class="n">impl_abstract</span><span class="p">()</span>
@@ -774,12 +751,12 @@ <h3>Custom Ops Registration in PyTorch FX Graph (Beta)<a class="headerlink" href
 
 <span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">allow_in_graph</span><span class="p">(</span><span class="n">addandround_op</span><span class="p">)</span>
 
-<span class="k">class</span> <span class="nc">CustomFoo</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+<span class="k">class</span> <span class="nc">CustomFoo</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tensor_x</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">addandround_op</span><span class="p">(</span><span class="n">tensor_x</span><span class="p">)</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_addandround_x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">custom_addandround_model</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">CustomFoo</span></a><span class="p">()</span>
+<span class="n">input_addandround_x</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">custom_addandround_model</span> <span class="o">=</span> <span class="n">CustomFoo</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
@@ -805,34 +782,27 @@ <h3>Custom Ops Registration in ONNX Registry<a class="headerlink" href="#custom-
     <span class="k">return</span> <span class="n">opset18</span><span class="o">.</span><span class="n">Cast</span><span class="p">(</span><span class="n">round_x</span><span class="p">,</span> <span class="n">to</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 
 
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">OnnxRegistry</span></a><span class="p">()</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry.register_op" title="torch.onnx.OnnxRegistry.register_op"><span class="n">onnx_registry</span><span class="o">.</span><span class="n">register_op</span></a><span class="p">(</span>
+<span class="n">onnx_registry</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">OnnxRegistry</span></a><span class="p">()</span>
+<span class="n">onnx_registry</span><span class="o">.</span><span class="n">register_op</span><span class="p">(</span>
     <span class="n">namespace</span><span class="o">=</span><span class="s2">"mylibrary"</span><span class="p">,</span> <span class="n">op_name</span><span class="o">=</span><span class="s2">"addandround_op"</span><span class="p">,</span> <span class="n">overload</span><span class="o">=</span><span class="s2">"default"</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="n">custom_addandround</span>
     <span class="p">)</span>
 
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">export_options</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">ExportOptions</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.OnnxRegistry" title="torch.onnx.OnnxRegistry"><span class="n">onnx_registry</span></a><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram" title="torch.onnx.ONNXProgram"><span class="n">onnx_program</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span></a><span class="p">(</span>
-    <span class="n">custom_addandround_model</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_addandround_x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">export_options</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">export_options</span></a>
+<span class="n">export_options</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ExportOptions" title="torch.onnx.ExportOptions"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">ExportOptions</span></a><span class="p">(</span><span class="n">onnx_registry</span><span class="o">=</span><span class="n">onnx_registry</span><span class="p">)</span>
+<span class="n">onnx_program</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.dynamo_export" title="torch.onnx.dynamo_export"><span class="n">torch</span><span class="o">.</span><span class="n">onnx</span><span class="o">.</span><span class="n">dynamo_export</span></a><span class="p">(</span>
+    <span class="n">custom_addandround_model</span><span class="p">,</span> <span class="n">input_addandround_x</span><span class="p">,</span> <span class="n">export_options</span><span class="o">=</span><span class="n">export_options</span>
     <span class="p">)</span>
-<a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.save" title="torch.onnx.ONNXProgram.save"><span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span><span class="s2">"./custom_addandround_model.onnx"</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>'CustomOpOne' is not a known op in 'test.customop'
-'CustomOpTwo' is not a known op in 'test.customop'
-/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:136: UserWarning:
-
-torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
+<span class="n">onnx_program</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"./custom_addandround_model.onnx"</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>The <code class="docutils literal notranslate"><span class="pre">onnx_program</span></code> exposes the exported model as protobuf through <code class="docutils literal notranslate"><span class="pre">onnx_program.model_proto</span></code>.
 The graph has one graph nodes for <code class="docutils literal notranslate"><span class="pre">custom_addandround</span></code>, and inside <code class="docutils literal notranslate"><span class="pre">custom_addandround</span></code>,
 there are two function nodes, one for each operator.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"test.customop"</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"custom_addandround"</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"test.customop"</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"CustomOpOne"</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"test.customop"</span>
-<span class="k">assert</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.model_proto" title="torch.onnx.ONNXProgram.model_proto"><span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"CustomOpTwo"</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"test.customop"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"custom_addandround"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"test.customop"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"CustomOpOne"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">domain</span> <span class="o">==</span> <span class="s2">"test.customop"</span>
+<span class="k">assert</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">model_proto</span><span class="o">.</span><span class="n">functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">op_type</span> <span class="o">==</span> <span class="s2">"CustomOpTwo"</span>
 </pre></div>
 </div>
 <p>This is how <code class="docutils literal notranslate"><span class="pre">custom_addandround_model</span></code> ONNX graph looks using Netron:</p>
@@ -873,16 +843,16 @@ <h3>Custom Ops Registration in ONNX Runtime<a class="headerlink" href="#custom-o
 <span class="k">def</span> <span class="nf">to_numpy</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">requires_grad</span> <span class="k">else</span> <span class="n">tensor</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
 
-<span class="n">onnx_input</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx" title="torch.onnx.ONNXProgram.adapt_torch_inputs_to_onnx"><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_inputs_to_onnx</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_addandround_x</span></a><span class="p">)</span>
+<span class="n">onnx_input</span> <span class="o">=</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_inputs_to_onnx</span><span class="p">(</span><span class="n">input_addandround_x</span><span class="p">)</span>
 <span class="n">onnxruntime_input</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">to_numpy</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">ort_session</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">(),</span> <span class="n">onnx_input</span><span class="p">)}</span>
 <span class="n">onnxruntime_outputs</span> <span class="o">=</span> <span class="n">ort_session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">onnxruntime_input</span><span class="p">)</span>
 
-<span class="n">torch_outputs</span> <span class="o">=</span> <span class="n">custom_addandround_model</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">input_addandround_x</span></a><span class="p">)</span>
-<span class="n">torch_outputs</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-onnx sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/onnx_dynamo.html#torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx" title="torch.onnx.ONNXProgram.adapt_torch_outputs_to_onnx"><span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_outputs_to_onnx</span></a><span class="p">(</span><span class="n">torch_outputs</span><span class="p">)</span>
+<span class="n">torch_outputs</span> <span class="o">=</span> <span class="n">custom_addandround_model</span><span class="p">(</span><span class="n">input_addandround_x</span><span class="p">)</span>
+<span class="n">torch_outputs</span> <span class="o">=</span> <span class="n">onnx_program</span><span class="o">.</span><span class="n">adapt_torch_outputs_to_onnx</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">)</span>
 
 <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">onnxruntime_outputs</span><span class="p">)</span>
-<span class="k">for</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch_output</span></a><span class="p">,</span> <span class="n">onnxruntime_output</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">,</span> <span class="n">onnxruntime_outputs</span><span class="p">):</span>
-    <a class="sphx-glr-backref-module-torch-testing sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/testing.html#torch.testing.assert_close" title="torch.testing.assert_close"><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">torch_output</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="n">onnxruntime_output</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">torch_output</span><span class="p">,</span> <span class="n">onnxruntime_output</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">torch_outputs</span><span class="p">,</span> <span class="n">onnxruntime_outputs</span><span class="p">):</span>
+    <a class="sphx-glr-backref-module-torch-testing sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/testing.html#torch.testing.assert_close" title="torch.testing.assert_close"><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span></a><span class="p">(</span><span class="n">torch_output</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="n">onnxruntime_output</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
@@ -908,7 +878,7 @@ <h2>Further reading<a class="headerlink" href="#further-reading" title="Permalin
 </div>
 <div class="toctree-wrapper compound">
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.920 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-onnx-onnx-registry-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/94896e8c36969aff0b2abe5e3848a487/onnx_registry_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">onnx_registry_tutorial.py</span></code></a></p>
diff --git a/beginner/template_tutorial.html b/beginner/template_tutorial.html
index 1372c05a2e..26ad0debd5 100644
--- a/beginner/template_tutorial.html
+++ b/beginner/template_tutorial.html
@@ -574,7 +574,7 @@ <h2>Further Reading<a class="headerlink" href="#further-reading" title="Permalin
 <li><p>Link1</p></li>
 <li><p>Link2</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.021 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.023 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-template-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/822c69800d15c38ee5556b071c0d1eda/template_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">template_tutorial.py</span></code></a></p>
diff --git a/beginner/transfer_learning_tutorial.html b/beginner/transfer_learning_tutorial.html
index 51b0fb2ab4..e292562d5c 100644
--- a/beginner/transfer_learning_tutorial.html
+++ b/beginner/transfer_learning_tutorial.html
@@ -532,7 +532,7 @@
 <span class="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span>   <span class="c1"># interactive mode</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;contextlib.ExitStack object at 0x7f7f3a9de6b0&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;contextlib.ExitStack object at 0x7f495c89e650&gt;
 </pre></div>
 </div>
 <div class="section" id="load-data">
@@ -746,8 +746,8 @@ <h2>Finetuning the ConvNet<a class="headerlink" href="#finetuning-the-convnet" t
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
 
   0%|          | 0.00/44.7M [00:00&lt;?, ?B/s]
- 94%|#########3| 41.9M/44.7M [00:00&lt;00:00, 438MB/s]
-100%|##########| 44.7M/44.7M [00:00&lt;00:00, 433MB/s]
+ 95%|#########5| 42.6M/44.7M [00:00&lt;00:00, 447MB/s]
+100%|##########| 44.7M/44.7M [00:00&lt;00:00, 446MB/s]
 </pre></div>
 </div>
 <div class="section" id="train-and-evaluate">
@@ -760,131 +760,131 @@ <h3>Train and evaluate<a class="headerlink" href="#train-and-evaluate" title="Pe
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 0/24
 ----------
-train Loss: 0.4767 Acc: 0.7623
-val Loss: 0.2806 Acc: 0.8824
+train Loss: 0.4800 Acc: 0.7500
+val Loss: 0.3129 Acc: 0.8758
 
 Epoch 1/24
 ----------
-train Loss: 0.5237 Acc: 0.8033
-val Loss: 0.6379 Acc: 0.7255
+train Loss: 0.5424 Acc: 0.7951
+val Loss: 0.5827 Acc: 0.7712
 
 Epoch 2/24
 ----------
-train Loss: 0.4181 Acc: 0.8320
-val Loss: 0.2440 Acc: 0.9216
+train Loss: 0.4197 Acc: 0.8361
+val Loss: 0.1975 Acc: 0.9085
 
 Epoch 3/24
 ----------
-train Loss: 0.5908 Acc: 0.7746
-val Loss: 0.3013 Acc: 0.8758
+train Loss: 0.5670 Acc: 0.7623
+val Loss: 0.2927 Acc: 0.8693
 
 Epoch 4/24
 ----------
-train Loss: 0.4045 Acc: 0.8566
-val Loss: 0.1879 Acc: 0.9150
+train Loss: 0.3947 Acc: 0.8443
+val Loss: 0.3083 Acc: 0.8889
 
 Epoch 5/24
 ----------
-train Loss: 0.4965 Acc: 0.7992
-val Loss: 0.2498 Acc: 0.8889
+train Loss: 0.4595 Acc: 0.8156
+val Loss: 0.2687 Acc: 0.9150
 
 Epoch 6/24
 ----------
-train Loss: 0.3785 Acc: 0.8279
-val Loss: 0.2358 Acc: 0.9281
+train Loss: 0.3799 Acc: 0.8115
+val Loss: 0.2301 Acc: 0.9412
 
 Epoch 7/24
 ----------
-train Loss: 0.3876 Acc: 0.8607
-val Loss: 0.2037 Acc: 0.9150
+train Loss: 0.3627 Acc: 0.8197
+val Loss: 0.1722 Acc: 0.9477
 
 Epoch 8/24
 ----------
-train Loss: 0.2167 Acc: 0.9057
-val Loss: 0.2235 Acc: 0.9216
+train Loss: 0.2120 Acc: 0.9098
+val Loss: 0.1956 Acc: 0.9412
 
 Epoch 9/24
 ----------
-train Loss: 0.2616 Acc: 0.8852
-val Loss: 0.2264 Acc: 0.9216
+train Loss: 0.2721 Acc: 0.8770
+val Loss: 0.2000 Acc: 0.9346
 
 Epoch 10/24
 ----------
-train Loss: 0.3352 Acc: 0.8607
-val Loss: 0.1934 Acc: 0.9216
+train Loss: 0.3438 Acc: 0.8402
+val Loss: 0.1668 Acc: 0.9542
 
 Epoch 11/24
 ----------
-train Loss: 0.3193 Acc: 0.8566
-val Loss: 0.2572 Acc: 0.9216
+train Loss: 0.3142 Acc: 0.8238
+val Loss: 0.2695 Acc: 0.9020
 
 Epoch 12/24
 ----------
-train Loss: 0.2434 Acc: 0.9016
-val Loss: 0.2078 Acc: 0.9216
+train Loss: 0.2165 Acc: 0.8975
+val Loss: 0.2052 Acc: 0.9346
 
 Epoch 13/24
 ----------
-train Loss: 0.2875 Acc: 0.8484
-val Loss: 0.1963 Acc: 0.9150
+train Loss: 0.2812 Acc: 0.8689
+val Loss: 0.1586 Acc: 0.9542
 
 Epoch 14/24
 ----------
-train Loss: 0.2617 Acc: 0.8852
-val Loss: 0.2301 Acc: 0.9216
+train Loss: 0.2480 Acc: 0.9098
+val Loss: 0.2275 Acc: 0.9085
 
 Epoch 15/24
 ----------
-train Loss: 0.3066 Acc: 0.8689
-val Loss: 0.2791 Acc: 0.9085
+train Loss: 0.3120 Acc: 0.8689
+val Loss: 0.2508 Acc: 0.8954
 
 Epoch 16/24
 ----------
-train Loss: 0.2018 Acc: 0.9262
-val Loss: 0.2149 Acc: 0.9216
+train Loss: 0.2068 Acc: 0.9221
+val Loss: 0.1905 Acc: 0.9216
 
 Epoch 17/24
 ----------
-train Loss: 0.2667 Acc: 0.8975
-val Loss: 0.1902 Acc: 0.9150
+train Loss: 0.2502 Acc: 0.8934
+val Loss: 0.1703 Acc: 0.9542
 
 Epoch 18/24
 ----------
-train Loss: 0.2705 Acc: 0.8975
-val Loss: 0.2449 Acc: 0.9216
+train Loss: 0.2655 Acc: 0.8934
+val Loss: 0.2096 Acc: 0.9085
 
 Epoch 19/24
 ----------
-train Loss: 0.1940 Acc: 0.9180
-val Loss: 0.1963 Acc: 0.9150
+train Loss: 0.1975 Acc: 0.9303
+val Loss: 0.1733 Acc: 0.9346
 
 Epoch 20/24
 ----------
-train Loss: 0.2379 Acc: 0.8893
-val Loss: 0.2075 Acc: 0.9216
+train Loss: 0.2412 Acc: 0.8975
+val Loss: 0.1880 Acc: 0.9281
 
 Epoch 21/24
 ----------
-train Loss: 0.2309 Acc: 0.8770
-val Loss: 0.2516 Acc: 0.9150
+train Loss: 0.2323 Acc: 0.8934
+val Loss: 0.2280 Acc: 0.9150
 
 Epoch 22/24
 ----------
-train Loss: 0.3238 Acc: 0.8730
-val Loss: 0.2021 Acc: 0.9216
+train Loss: 0.3288 Acc: 0.8566
+val Loss: 0.1817 Acc: 0.9412
 
 Epoch 23/24
 ----------
-train Loss: 0.2599 Acc: 0.8689
-val Loss: 0.2126 Acc: 0.9216
+train Loss: 0.2776 Acc: 0.8770
+val Loss: 0.1916 Acc: 0.9281
 
 Epoch 24/24
 ----------
-train Loss: 0.2999 Acc: 0.8689
-val Loss: 0.1979 Acc: 0.9150
+train Loss: 0.2940 Acc: 0.8811
+val Loss: 0.1740 Acc: 0.9608
 
-Training complete in 0m 30s
-Best val Acc: 0.928105
+Training complete in 0m 31s
+Best val Acc: 0.960784
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">visualize_model</span><span class="p">(</span><span class="n">model_ft</span><span class="p">)</span>
@@ -940,7 +940,7 @@ <h3>Train and evaluate<a class="headerlink" href="#id1" title="Permalink to this
 
 Epoch 2/24
 ----------
-train Loss: 0.4602 Acc: 0.7869
+train Loss: 0.4603 Acc: 0.7869
 val Loss: 0.1847 Acc: 0.9477
 
 Epoch 3/24
@@ -956,7 +956,7 @@ <h3>Train and evaluate<a class="headerlink" href="#id1" title="Permalink to this
 Epoch 5/24
 ----------
 train Loss: 0.5505 Acc: 0.7828
-val Loss: 0.1642 Acc: 0.9477
+val Loss: 0.1643 Acc: 0.9477
 
 Epoch 6/24
 ----------
@@ -1045,7 +1045,7 @@ <h3>Train and evaluate<a class="headerlink" href="#id1" title="Permalink to this
 
 Epoch 23/24
 ----------
-train Loss: 0.3625 Acc: 0.8361
+train Loss: 0.3626 Acc: 0.8361
 val Loss: 0.1747 Acc: 0.9412
 
 Epoch 24/24
@@ -1053,7 +1053,7 @@ <h3>Train and evaluate<a class="headerlink" href="#id1" title="Permalink to this
 train Loss: 0.3840 Acc: 0.8320
 val Loss: 0.1768 Acc: 0.9412
 
-Training complete in 0m 23s
+Training complete in 0m 22s
 Best val Acc: 0.954248
 </pre></div>
 </div>
@@ -1104,7 +1104,7 @@ <h2>Inference on custom images<a class="headerlink" href="#inference-on-custom-i
 <h2>Further Learning<a class="headerlink" href="#further-learning" title="Permalink to this heading">¶</a></h2>
 <p>If you would like to learn more about the applications of transfer learning,
 checkout our <a class="reference external" href="https://pytorch.org/tutorials/intermediate/quantized_transfer_learning_tutorial.html">Quantized Transfer Learning for Computer Vision Tutorial</a>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  54.787 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  55.883 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-transfer-learning-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/d923ca53b1bfbeb3c222ae46d65d485e/transfer_learning_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">transfer_learning_tutorial.py</span></code></a></p>
diff --git a/beginner/vt_tutorial.html b/beginner/vt_tutorial.html
index 8fb86bcbdf..ee1bf707ed 100644
--- a/beginner/vt_tutorial.html
+++ b/beginner/vt_tutorial.html
@@ -597,26 +597,37 @@ <h2>Classifying Images with DeiT<a class="headerlink" href="#classifying-images-
 Downloading: "https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/deit_base_patch16_224-b5f2ef4d.pth
 
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 269
 </pre></div>
 </div>
@@ -740,11 +751,11 @@ <h2>Comparing Inference Speed<a class="headerlink" href="#comparing-inference-sp
 <span class="nb">print</span><span class="p">(</span><span class="s2">"lite model: </span><span class="si">{:.2f}</span><span class="s2">ms"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-autograd-profiler sphx-glr-backref-type-py-property" href="https://pytorch.org/docs/stable/generated/torch.autograd.profiler.profile.self_cpu_time_total.html#torch.autograd.profiler.profile.self_cpu_time_total" title="torch.autograd.profiler.profile.self_cpu_time_total"><span class="n">prof5</span><span class="o">.</span><span class="n">self_cpu_time_total</span></a><span class="o">/</span><span class="mi">1000</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>original model: 120.11ms
-scripted model: 104.93ms
-scripted &amp; quantized model: 129.52ms
-scripted &amp; quantized &amp; optimized model: 126.87ms
-lite model: 116.97ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>original model: 102.06ms
+scripted model: 107.77ms
+scripted &amp; quantized model: 133.82ms
+scripted &amp; quantized &amp; optimized model: 129.41ms
+lite model: 121.42ms
 </pre></div>
 </div>
 <p>The results running on a Google Colab are:</p>
@@ -788,10 +799,10 @@ <h2>Comparing Inference Speed<a class="headerlink" href="#comparing-inference-sp
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>                                    Model  ... Reduction
 0                          original model  ...        0%
-1                          scripted model  ...    12.64%
-2              scripted &amp; quantized model  ...    -7.83%
-3  scripted &amp; quantized &amp; optimized model  ...    -5.63%
-4                              lite model  ...     2.62%
+1                          scripted model  ...    -5.60%
+2              scripted &amp; quantized model  ...   -31.12%
+3  scripted &amp; quantized &amp; optimized model  ...   -26.80%
+4                              lite model  ...   -18.97%
 
 [5 rows x 3 columns]
 
@@ -805,7 +816,7 @@ <h3>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this
 <li><p><a class="reference external" href="https://github.com/pytorch/ios-demo-app/tree/master/ViT4MNIST">Vision Transformer with ImageNet and MNIST on iOS</a></p></li>
 <li><p><a class="reference external" href="https://github.com/pytorch/android-demo-app/tree/master/ViT4MNIST">Vision Transformer with ImageNet and MNIST on Android</a></p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  18.388 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  19.455 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-vt-tutorial-py">
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 <p><a class="reference download internal" download="" href="../_downloads/82714b1145e891e2eba191bec427b2dd/vt_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">vt_tutorial.py</span></code></a></p>
diff --git a/intermediate/autograd_saved_tensors_hooks_tutorial.html b/intermediate/autograd_saved_tensors_hooks_tutorial.html
index 22b876536a..b2e675c920 100644
--- a/intermediate/autograd_saved_tensors_hooks_tutorial.html
+++ b/intermediate/autograd_saved_tensors_hooks_tutorial.html
@@ -899,7 +899,7 @@ <h3>Saving tensors to disk<a class="headerlink" href="#saving-tensors-to-disk" t
 <p>If you’ve made it this far, congratulations! You now know how to use
 saved tensor hooks and how they can be useful in a few scenarios to
 tradeoff memory for compute.</p>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.033 seconds)</p>
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 <p><a class="reference download internal" download="" href="../_downloads/bc9c404ccbb9fd600e9d44a56fe16bee/autograd_saved_tensors_hooks_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">autograd_saved_tensors_hooks_tutorial.py</span></code></a></p>
diff --git a/intermediate/ax_multiobjective_nas_tutorial.html b/intermediate/ax_multiobjective_nas_tutorial.html
index a93447f1fc..45ea220f9e 100644
--- a/intermediate/ax_multiobjective_nas_tutorial.html
+++ b/intermediate/ax_multiobjective_nas_tutorial.html
@@ -803,12 +803,12 @@ <h2>Choosing the Generation Strategy<a class="headerlink" href="#choosing-the-ge
   <span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.
-[INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=48 use_batch_trials=False
-[INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=9
-[INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=9
-[INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.
-[INFO 06-25 21:33:15] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 9 trials, BoTorch for subsequent trials]). Iterations after 9 will take longer to generate due to model-fitting.
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.
+[INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=48 use_batch_trials=False
+[INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=9
+[INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=9
+[INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.
+[INFO 06-25 22:16:54] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 9 trials, BoTorch for subsequent trials]). Iterations after 9 will take longer to generate due to model-fitting.
 </pre></div>
 </div>
 </div>
@@ -841,8 +841,8 @@ <h2>Configuring the Scheduler<a class="headerlink" href="#configuring-the-schedu
 <span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[WARNING 06-25 21:33:15] ax.service.utils.with_db_settings_base: Ax currently requires a sqlalchemy version below 2.0. This will be addressed in a future release. Disabling SQL storage in Ax for now, if you would like to use SQL storage please install Ax with mysql extras via `pip install ax-platform[mysql]`.
-[INFO 06-25 21:33:15] Scheduler: `Scheduler` requires experiment to have immutable search space and optimization config. Setting property immutable_search_space_and_opt_config to `True` on experiment.
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[WARNING 06-25 22:16:55] ax.service.utils.with_db_settings_base: Ax currently requires a sqlalchemy version below 2.0. This will be addressed in a future release. Disabling SQL storage in Ax for now, if you would like to use SQL storage please install Ax with mysql extras via `pip install ax-platform[mysql]`.
+[INFO 06-25 22:16:55] Scheduler: `Scheduler` requires experiment to have immutable search space and optimization config. Setting property immutable_search_space_and_opt_config to `True` on experiment.
 </pre></div>
 </div>
 </div>
@@ -857,29 +857,29 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">scheduler</span><span class="o">.</span><span class="n">run_all_trials</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[INFO 06-25 21:33:15] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:33:15] Scheduler: Running trials [0]...
-[INFO 06-25 21:33:16] Scheduler: Running trials [1]...
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-[INFO 06-25 21:34:09] Scheduler: Fetching data for newly completed trials: [1].
-[INFO 06-25 21:34:09] Scheduler: Retrieved COMPLETED trials: [1].
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[INFO 06-25 22:16:55] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:16:55] Scheduler: Running trials [0]...
+[INFO 06-25 22:16:56] Scheduler: Running trials [1]...
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+[INFO 06-25 22:17:07] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
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+[INFO 06-25 22:17:12] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 4).
+[INFO 06-25 22:17:19] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:17:19] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 4).
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+[INFO 06-25 22:17:31] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 4).
+[INFO 06-25 22:17:48] Scheduler: Fetching data for newly completed trials: [1].
+[INFO 06-25 22:17:48] Scheduler: Retrieved COMPLETED trials: [1].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -888,11 +888,13 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:34:09] Scheduler: Running trials [4]...
-[INFO 06-25 21:34:10] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:34:10] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-[INFO 06-25 21:34:11] Scheduler: Fetching data for newly completed trials: [3].
-[INFO 06-25 21:34:11] Scheduler: Retrieved COMPLETED trials: [3].
+[INFO 06-25 22:17:48] Scheduler: Running trials [4]...
+[INFO 06-25 22:17:49] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:17:49] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+[INFO 06-25 22:17:50] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:17:50] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+[INFO 06-25 22:17:52] Scheduler: Fetching data for newly completed trials: [3].
+[INFO 06-25 22:17:52] Scheduler: Retrieved COMPLETED trials: [3].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -901,19 +903,17 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:34:11] Scheduler: Running trials [5]...
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-[INFO 06-25 21:34:19] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
-[INFO 06-25 21:34:24] Scheduler: Fetching data for newly completed trials: [2].
-[INFO 06-25 21:34:24] Scheduler: Retrieved COMPLETED trials: [2].
+[INFO 06-25 22:17:52] Scheduler: Running trials [5]...
+[INFO 06-25 22:17:53] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:17:53] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
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+[INFO 06-25 22:17:54] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+[INFO 06-25 22:17:55] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:17:55] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+[INFO 06-25 22:17:57] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:17:57] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+[INFO 06-25 22:18:01] Scheduler: Fetching data for newly completed trials: [2].
+[INFO 06-25 22:18:01] Scheduler: Retrieved COMPLETED trials: [2].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -922,17 +922,19 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:34:24] Scheduler: Running trials [6]...
-[INFO 06-25 21:34:25] Scheduler: Fetching data for newly completed trials: [].
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-[INFO 06-25 21:34:33] Scheduler: Fetching data for newly completed trials: [0].
-[INFO 06-25 21:34:33] Scheduler: Retrieved COMPLETED trials: [0].
+[INFO 06-25 22:18:01] Scheduler: Running trials [6]...
+[INFO 06-25 22:18:02] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:02] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
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+[INFO 06-25 22:18:03] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+[INFO 06-25 22:18:04] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:04] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+[INFO 06-25 22:18:07] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:07] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+[INFO 06-25 22:18:10] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:10] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
+[INFO 06-25 22:18:15] Scheduler: Fetching data for newly completed trials: [0].
+[INFO 06-25 22:18:15] Scheduler: Retrieved COMPLETED trials: [0].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -941,17 +943,15 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:34:33] Scheduler: Running trials [7]...
-[INFO 06-25 21:34:34] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:34:34] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
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-[INFO 06-25 21:34:39] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
-[INFO 06-25 21:34:43] Scheduler: Fetching data for newly completed trials: [4].
-[INFO 06-25 21:34:43] Scheduler: Retrieved COMPLETED trials: [4].
+[INFO 06-25 22:18:15] Scheduler: Running trials [7]...
+[INFO 06-25 22:18:16] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:16] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
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+[INFO 06-25 22:18:17] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+[INFO 06-25 22:18:19] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:19] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+[INFO 06-25 22:18:21] Scheduler: Fetching data for newly completed trials: [4].
+[INFO 06-25 22:18:21] Scheduler: Retrieved COMPLETED trials: [4].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -960,15 +960,17 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:34:43] Scheduler: Running trials [8]...
-[INFO 06-25 21:34:44] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:34:44] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
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-[INFO 06-25 21:34:46] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-[INFO 06-25 21:34:48] Scheduler: Fetching data for newly completed trials: [5].
-[INFO 06-25 21:34:48] Scheduler: Retrieved COMPLETED trials: [5].
+[INFO 06-25 22:18:21] Scheduler: Running trials [8]...
+[INFO 06-25 22:18:22] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:22] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
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+[INFO 06-25 22:18:23] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+[INFO 06-25 22:18:25] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:25] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+[INFO 06-25 22:18:27] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:27] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+[INFO 06-25 22:18:30] Scheduler: Fetching data for newly completed trials: [5].
+[INFO 06-25 22:18:30] Scheduler: Retrieved COMPLETED trials: [5].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -977,15 +979,9 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:34:51] Scheduler: Running trials [9]...
-[INFO 06-25 21:34:51] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:34:51] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
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-[INFO 06-25 21:34:52] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-[INFO 06-25 21:34:54] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:34:54] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-[INFO 06-25 21:34:56] Scheduler: Fetching data for newly completed trials: [6].
-[INFO 06-25 21:34:56] Scheduler: Retrieved COMPLETED trials: [6].
+[INFO 06-25 22:18:33] Scheduler: Running trials [9]...
+[INFO 06-25 22:18:35] Scheduler: Fetching data for newly completed trials: [6].
+[INFO 06-25 22:18:35] Scheduler: Retrieved COMPLETED trials: [6].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -994,19 +990,17 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:34:59] Scheduler: Running trials [10]...
-[INFO 06-25 21:35:00] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:00] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-[INFO 06-25 21:35:01] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:01] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-[INFO 06-25 21:35:02] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:02] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-[INFO 06-25 21:35:04] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:04] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
-[INFO 06-25 21:35:08] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:08] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
-[INFO 06-25 21:35:13] Scheduler: Fetching data for newly completed trials: [8].
-[INFO 06-25 21:35:13] Scheduler: Retrieved COMPLETED trials: [8].
+[INFO 06-25 22:18:39] Scheduler: Running trials [10]...
+[INFO 06-25 22:18:40] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+[INFO 06-25 22:18:41] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+[INFO 06-25 22:18:43] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:43] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+[INFO 06-25 22:18:45] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:45] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+[INFO 06-25 22:18:48] Scheduler: Fetching data for newly completed trials: [8].
+[INFO 06-25 22:18:48] Scheduler: Retrieved COMPLETED trials: [8].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1015,19 +1009,21 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:35:15] Scheduler: Running trials [11]...
-[INFO 06-25 21:35:16] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:16] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
-[INFO 06-25 21:35:17] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:17] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
-[INFO 06-25 21:35:19] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:19] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
-[INFO 06-25 21:35:21] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:21] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
-[INFO 06-25 21:35:25] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:25] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
-[INFO 06-25 21:35:30] Scheduler: Fetching data for newly completed trials: [7].
-[INFO 06-25 21:35:30] Scheduler: Retrieved COMPLETED trials: [7].
+[INFO 06-25 22:18:52] Scheduler: Running trials [11]...
+[INFO 06-25 22:18:53] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:53] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
+[INFO 06-25 22:18:54] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:54] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
+[INFO 06-25 22:18:55] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:55] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
+[INFO 06-25 22:18:57] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:18:57] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
+[INFO 06-25 22:19:01] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:01] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
+[INFO 06-25 22:19:06] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:06] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 4).
+[INFO 06-25 22:19:13] Scheduler: Fetching data for newly completed trials: [7].
+[INFO 06-25 22:19:13] Scheduler: Retrieved COMPLETED trials: [7].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1036,18 +1032,18 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:35:30] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:30] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:35:31] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:31] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:35:32] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:32] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:35:35] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:35] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:35:38] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:38] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:35:43] Scheduler: Fetching data for newly completed trials: [9].
-[INFO 06-25 21:35:43] Scheduler: Retrieved COMPLETED trials: [9].
+[INFO 06-25 22:19:14] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:14] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:19:15] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:15] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:19:16] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:16] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:19:18] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:18] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:19:22] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:22] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:19:27] Scheduler: Fetching data for newly completed trials: [9].
+[INFO 06-25 22:19:27] Scheduler: Retrieved COMPLETED trials: [9].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1056,27 +1052,23 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:35:45] Scheduler: Running trials [12]...
+[INFO 06-25 22:19:29] Scheduler: Running trials [12]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:35:46] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:35:46] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:35:46] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:46] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:35:47] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:47] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:35:49] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:49] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:35:51] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:51] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:35:54] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:54] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:35:59] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:35:59] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:36:07] Scheduler: Fetching data for newly completed trials: [10].
-[INFO 06-25 21:36:07] Scheduler: Retrieved COMPLETED trials: [10].
+[INFO 06-25 22:19:30] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:19:30] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:19:30] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:30] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:19:31] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:31] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:19:32] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:32] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:19:35] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:35] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:19:38] Scheduler: Fetching data for newly completed trials: [10].
+[INFO 06-25 22:19:38] Scheduler: Retrieved COMPLETED trials: [10].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1085,15 +1077,27 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:36:10] Scheduler: Running trials [13]...
+[INFO 06-25 22:19:41] Scheduler: Running trials [13]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:36:12] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:36:12] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:36:12] Scheduler: Fetching data for newly completed trials: [11].
-[INFO 06-25 21:36:12] Scheduler: Retrieved COMPLETED trials: [11].
+[INFO 06-25 22:19:42] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:19:42] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:19:42] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:42] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:19:43] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:43] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:19:44] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:44] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:19:47] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:47] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:19:50] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:50] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:19:55] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:19:55] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:20:03] Scheduler: Fetching data for newly completed trials: [11].
+[INFO 06-25 22:20:03] Scheduler: Retrieved COMPLETED trials: [11].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1102,27 +1106,15 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:36:15] Scheduler: Running trials [14]...
+[INFO 06-25 22:20:05] Scheduler: Running trials [14]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:36:16] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:36:16] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:36:16] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:16] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:36:17] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:17] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:36:18] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:18] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:36:20] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:20] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:36:24] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:24] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:36:29] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:29] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:36:37] Scheduler: Fetching data for newly completed trials: [12].
-[INFO 06-25 21:36:37] Scheduler: Retrieved COMPLETED trials: [12].
+[INFO 06-25 22:20:06] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:20:06] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:20:06] Scheduler: Fetching data for newly completed trials: [12].
+[INFO 06-25 22:20:06] Scheduler: Retrieved COMPLETED trials: [12].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1131,23 +1123,27 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:36:39] Scheduler: Running trials [15]...
+[INFO 06-25 22:20:09] Scheduler: Running trials [15]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:36:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:36:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:36:40] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:36:41] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:36:42] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:42] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:36:45] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:45] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:36:48] Scheduler: Fetching data for newly completed trials: [13].
-[INFO 06-25 21:36:48] Scheduler: Retrieved COMPLETED trials: [13].
+[INFO 06-25 22:20:10] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:20:10] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:20:10] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:10] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:20:11] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:11] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:20:12] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:12] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:20:15] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:15] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:20:18] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:18] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:20:23] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:23] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:20:31] Scheduler: Fetching data for newly completed trials: [13].
+[INFO 06-25 22:20:31] Scheduler: Retrieved COMPLETED trials: [13].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1156,23 +1152,27 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:36:51] Scheduler: Running trials [16]...
+[INFO 06-25 22:20:33] Scheduler: Running trials [16]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:36:52] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:36:52] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:36:52] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:52] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:36:53] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:53] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:36:55] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:55] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:36:57] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:36:57] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:37:00] Scheduler: Fetching data for newly completed trials: [14].
-[INFO 06-25 21:37:00] Scheduler: Retrieved COMPLETED trials: [14].
+[INFO 06-25 22:20:34] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:20:34] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:20:34] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:34] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:20:35] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:35] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:20:36] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:36] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:20:38] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:38] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:20:42] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:42] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:20:47] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:20:47] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:20:54] Scheduler: Fetching data for newly completed trials: [15].
+[INFO 06-25 22:20:54] Scheduler: Retrieved COMPLETED trials: [15].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1181,27 +1181,19 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:37:03] Scheduler: Running trials [17]...
+[INFO 06-25 22:20:59] Scheduler: Running trials [17]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:37:04] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:37:04] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:37:04] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:04] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:37:05] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:05] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:37:06] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:06] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:37:08] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:08] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:37:12] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:12] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:37:17] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:17] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:37:24] Scheduler: Fetching data for newly completed trials: [15].
-[INFO 06-25 21:37:24] Scheduler: Retrieved COMPLETED trials: [15].
+[INFO 06-25 22:21:00] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:21:00] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:21:00] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:00] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:21:01] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:01] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:21:02] Scheduler: Fetching data for newly completed trials: [14].
+[INFO 06-25 22:21:02] Scheduler: Retrieved COMPLETED trials: [14].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1210,23 +1202,25 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:37:27] Scheduler: Running trials [18]...
+[INFO 06-25 22:21:07] Scheduler: Running trials [18]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:37:28] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:37:28] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:37:28] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:28] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:37:29] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:29] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:37:30] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:30] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:37:33] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:33] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:37:36] Scheduler: Fetching data for newly completed trials: [16].
-[INFO 06-25 21:37:36] Scheduler: Retrieved COMPLETED trials: [16].
+[INFO 06-25 22:21:08] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:21:08] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:21:08] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:08] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:21:09] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:09] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:21:11] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:11] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:21:13] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:13] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:21:16] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:16] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:21:21] Scheduler: Fetching data for newly completed trials: [16].
+[INFO 06-25 22:21:21] Scheduler: Retrieved COMPLETED trials: [16].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1235,25 +1229,29 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:37:39] Scheduler: Running trials [19]...
+[INFO 06-25 22:21:26] Scheduler: Running trials [19]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:37:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:37:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:37:40] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:37:41] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:37:43] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:43] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:37:45] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:45] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:37:48] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:48] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:37:53] Scheduler: Fetching data for newly completed trials: [17].
-[INFO 06-25 21:37:53] Scheduler: Retrieved COMPLETED trials: [17].
+[INFO 06-25 22:21:27] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:21:27] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:21:27] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:27] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:21:28] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:28] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:21:30] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:30] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:21:32] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:32] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:21:36] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:36] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:21:41] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:41] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:21:48] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:21:48] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:22:00] Scheduler: Fetching data for newly completed trials: [17].
+[INFO 06-25 22:22:00] Scheduler: Retrieved COMPLETED trials: [17].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1262,29 +1260,15 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:37:56] Scheduler: Running trials [20]...
+[INFO 06-25 22:22:04] Scheduler: Running trials [20]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:37:57] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:37:57] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:37:57] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:57] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:37:58] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:37:58] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:38:00] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:00] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:38:02] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:02] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:38:06] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:06] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:38:11] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:11] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:38:18] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:18] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:38:30] Scheduler: Fetching data for newly completed trials: [18, 20].
-[INFO 06-25 21:38:30] Scheduler: Retrieved COMPLETED trials: [18, 20].
+[INFO 06-25 22:22:05] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:22:05] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:22:05] Scheduler: Fetching data for newly completed trials: [18].
+[INFO 06-25 22:22:05] Scheduler: Retrieved COMPLETED trials: [18].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1293,75 +1277,73 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:38:32] Scheduler: Running trials [21]...
+[INFO 06-25 22:22:11] Scheduler: Running trials [21]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:38:33] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:38:36] Scheduler: Running trials [22]...
+[INFO 06-25 22:22:12] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:22:12] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:22:12] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:22:12] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:22:13] Scheduler: Fetching data for newly completed trials: [19].
+[INFO 06-25 22:22:13] Scheduler: Retrieved COMPLETED trials: [19].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:38:37] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:38:37] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:38:37] Scheduler: Fetching data for newly completed trials: [19].
-[INFO 06-25 21:38:37] Scheduler: Retrieved COMPLETED trials: [19].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:22:19] Scheduler: Running trials [22]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:38:41] Scheduler: Running trials [23]...
+[INFO 06-25 22:22:20] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:22:20] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:22:20] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:22:20] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:22:21] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:22:21] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:22:22] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:22:22] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:22:25] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:22:25] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:22:28] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:22:28] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:22:33] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:22:33] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:22:41] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:22:41] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:22:52] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:22:52] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+[INFO 06-25 22:23:09] Scheduler: Fetching data for newly completed trials: 20 - 21.
+[INFO 06-25 22:23:09] Scheduler: Retrieved COMPLETED trials: 20 - 21.
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:38:42] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:38:42] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:38:42] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:42] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:38:43] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:43] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:38:44] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:44] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:38:47] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:47] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:38:50] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:50] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:38:55] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:38:55] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:39:03] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:39:03] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:39:14] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:39:14] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-[INFO 06-25 21:39:31] Scheduler: Fetching data for newly completed trials: [21].
-[INFO 06-25 21:39:31] Scheduler: Retrieved COMPLETED trials: [21].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:23:14] Scheduler: Running trials [23]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:39:35] Scheduler: Running trials [24]...
+[INFO 06-25 22:23:15] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:23:18] Scheduler: Running trials [24]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:39:36] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:39:36] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:39:36] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:39:36] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:39:37] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:39:37] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:39:39] Scheduler: Fetching data for newly completed trials: [22].
-[INFO 06-25 21:39:39] Scheduler: Retrieved COMPLETED trials: [22].
+[INFO 06-25 22:23:19] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:23:19] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:23:19] Scheduler: Fetching data for newly completed trials: [22].
+[INFO 06-25 22:23:19] Scheduler: Retrieved COMPLETED trials: [22].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1370,15 +1352,31 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:39:43] Scheduler: Running trials [25]...
+[INFO 06-25 22:23:23] Scheduler: Running trials [25]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:39:44] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:39:44] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:39:44] Scheduler: Fetching data for newly completed trials: [23].
-[INFO 06-25 21:39:44] Scheduler: Retrieved COMPLETED trials: [23].
+[INFO 06-25 22:23:24] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:23:24] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:23:24] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:23:24] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:23:25] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:23:25] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:23:27] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:23:27] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:23:29] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:23:29] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:23:33] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:23:33] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:23:38] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:23:38] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:23:45] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:23:45] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:23:57] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:23:57] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+[INFO 06-25 22:24:14] Scheduler: Fetching data for newly completed trials: [23].
+[INFO 06-25 22:24:14] Scheduler: Retrieved COMPLETED trials: [23].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1387,31 +1385,15 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:39:48] Scheduler: Running trials [26]...
+[INFO 06-25 22:24:18] Scheduler: Running trials [26]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:39:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:39:49] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:39:49] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:39:49] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:39:50] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:39:50] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:39:52] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:39:52] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:39:54] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:39:54] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:39:58] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:39:58] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:40:03] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:40:03] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:40:10] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:40:10] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:40:22] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:40:22] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-[INFO 06-25 21:40:39] Scheduler: Fetching data for newly completed trials: 24 - 25.
-[INFO 06-25 21:40:39] Scheduler: Retrieved COMPLETED trials: 24 - 25.
+[INFO 06-25 22:24:18] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:24:18] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:24:18] Scheduler: Fetching data for newly completed trials: 24 - 25.
+[INFO 06-25 22:24:18] Scheduler: Retrieved COMPLETED trials: 24 - 25.
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1420,21 +1402,37 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:40:43] Scheduler: Running trials [27]...
+[INFO 06-25 22:24:24] Scheduler: Running trials [27]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:40:44] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:40:48] Scheduler: Running trials [28]...
+[INFO 06-25 22:24:24] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:24:28] Scheduler: Running trials [28]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:40:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:40:49] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:40:49] Scheduler: Fetching data for newly completed trials: [26].
-[INFO 06-25 21:40:49] Scheduler: Retrieved COMPLETED trials: [26].
+[INFO 06-25 22:24:29] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:24:29] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:24:29] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:24:29] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:24:30] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:24:30] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:24:31] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:24:31] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:24:33] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:24:33] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:24:37] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:24:37] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:24:42] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:24:42] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:24:49] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:24:49] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:25:01] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:25:01] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+[INFO 06-25 22:25:18] Scheduler: Fetching data for newly completed trials: [26].
+[INFO 06-25 22:25:18] Scheduler: Retrieved COMPLETED trials: [26].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1443,31 +1441,15 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:40:54] Scheduler: Running trials [29]...
+[INFO 06-25 22:25:22] Scheduler: Running trials [29]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:40:55] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:40:55] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:40:55] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:40:55] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:40:56] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:40:56] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:40:58] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:40:58] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:41:00] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:41:00] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:41:03] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:41:03] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:41:08] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:41:08] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:41:16] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:41:16] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:41:27] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:41:27] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-[INFO 06-25 21:41:45] Scheduler: Fetching data for newly completed trials: 27 - 28.
-[INFO 06-25 21:41:45] Scheduler: Retrieved COMPLETED trials: 27 - 28.
+[INFO 06-25 22:25:23] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:25:23] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:25:23] Scheduler: Fetching data for newly completed trials: [27].
+[INFO 06-25 22:25:23] Scheduler: Retrieved COMPLETED trials: [27].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1476,338 +1458,429 @@ <h2>Running the optimization<a class="headerlink" href="#running-the-optimizatio
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:41:53] Scheduler: Running trials [30]...
+[INFO 06-25 22:25:29] Scheduler: Running trials [30]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:41:54] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:42:02] Scheduler: Running trials [31]...
+[INFO 06-25 22:25:30] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:25:30] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:25:30] Scheduler: Fetching data for newly completed trials: [28].
+[INFO 06-25 22:25:30] Scheduler: Retrieved COMPLETED trials: [28].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:42:03] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:42:03] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:42:04] Scheduler: Fetching data for newly completed trials: [29].
-[INFO 06-25 21:42:04] Scheduler: Retrieved COMPLETED trials: [29].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:25:39] Scheduler: Running trials [31]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:42:14] Scheduler: Running trials [32]...
+[INFO 06-25 22:25:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:25:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:25:40] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:25:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:25:41] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:25:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:25:42] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:25:42] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:25:45] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:25:45] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:25:48] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:25:48] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:25:53] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:25:53] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:26:01] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:26:01] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:26:12] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:26:12] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+[INFO 06-25 22:26:29] Scheduler: Fetching data for newly completed trials: 29 - 30.
+[INFO 06-25 22:26:29] Scheduler: Retrieved COMPLETED trials: 29 - 30.
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:42:15] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:42:15] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:42:15] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:42:15] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:42:16] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:42:16] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:42:17] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:42:17] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:42:20] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:42:20] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:42:23] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:42:23] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:42:28] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:42:28] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:42:36] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:42:36] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:42:47] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:42:47] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-[INFO 06-25 21:43:04] Scheduler: Fetching data for newly completed trials: 30 - 31.
-[INFO 06-25 21:43:04] Scheduler: Retrieved COMPLETED trials: 30 - 31.
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:26:35] Scheduler: Running trials [32]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:43:09] Scheduler: Running trials [33]...
+[INFO 06-25 22:26:36] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:26:40] Scheduler: Running trials [33]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:43:10] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:43:14] Scheduler: Running trials [34]...
+[INFO 06-25 22:26:41] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:26:41] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:26:41] Scheduler: Fetching data for newly completed trials: [31].
+[INFO 06-25 22:26:41] Scheduler: Retrieved COMPLETED trials: [31].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:43:15] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:43:15] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:43:16] Scheduler: Fetching data for newly completed trials: [32].
-[INFO 06-25 21:43:16] Scheduler: Retrieved COMPLETED trials: [32].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:26:46] Scheduler: Running trials [34]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:43:20] Scheduler: Running trials [35]...
+[INFO 06-25 22:26:47] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:26:47] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:26:47] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:26:47] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:26:48] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:26:48] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:26:50] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:26:50] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:26:52] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:26:52] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:26:55] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:26:55] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:27:00] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:27:00] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:27:08] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:27:08] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:27:19] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:27:19] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+[INFO 06-25 22:27:36] Scheduler: Fetching data for newly completed trials: [32].
+[INFO 06-25 22:27:36] Scheduler: Retrieved COMPLETED trials: [32].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:43:22] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:43:22] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:43:22] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:43:22] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:43:23] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:43:23] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:43:24] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:43:24] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:43:26] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:43:26] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:43:30] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:43:30] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:43:35] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:43:35] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:43:42] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:43:42] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:43:54] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:43:54] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-[INFO 06-25 21:44:11] Scheduler: Fetching data for newly completed trials: 33 - 34.
-[INFO 06-25 21:44:11] Scheduler: Retrieved COMPLETED trials: 33 - 34.
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:27:42] Scheduler: Running trials [35]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:44:17] Scheduler: Running trials [36]...
+[INFO 06-25 22:27:43] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:27:43] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:27:43] Scheduler: Fetching data for newly completed trials: [34].
+[INFO 06-25 22:27:43] Scheduler: Retrieved COMPLETED trials: [34].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:44:17] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:44:21] Scheduler: Running trials [37]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:44:22] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:44:22] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:44:22] Scheduler: Fetching data for newly completed trials: [35].
-[INFO 06-25 21:44:22] Scheduler: Retrieved COMPLETED trials: [35].
+[INFO 06-25 22:27:49] Scheduler: Running trials [36]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:27:50] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:27:50] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:27:50] Scheduler: Fetching data for newly completed trials: [33].
+[INFO 06-25 22:27:50] Scheduler: Retrieved COMPLETED trials: [33].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:44:27] Scheduler: Running trials [38]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:44:27] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:44:27] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:44:27] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:44:27] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:44:28] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:44:28] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:44:29] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:44:29] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:44:32] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:44:32] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:44:35] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:44:35] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:44:40] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:44:40] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:44:48] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:44:48] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:44:59] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:44:59] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-[INFO 06-25 21:45:16] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:45:16] Scheduler: Waiting for completed trials (for 25 sec, currently running trials: 3).
-[INFO 06-25 21:45:42] Scheduler: Fetching data for newly completed trials: 36 - 38.
-[INFO 06-25 21:45:42] Scheduler: Retrieved COMPLETED trials: 36 - 38.
+[INFO 06-25 22:27:55] Scheduler: Running trials [37]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:27:56] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:27:56] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:27:56] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:27:56] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:27:57] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:27:57] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:27:59] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:27:59] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:28:01] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:28:01] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:28:04] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:28:04] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:28:09] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:28:09] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:28:17] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:28:17] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:28:28] Scheduler: Fetching data for newly completed trials: [37].
+[INFO 06-25 22:28:28] Scheduler: Retrieved COMPLETED trials: [37].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:45:48] Scheduler: Running trials [39]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:45:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:45:53] Scheduler: Running trials [40]...
+[INFO 06-25 22:28:34] Scheduler: Running trials [38]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:45:54] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:45:59] Scheduler: Running trials [41]...
+[INFO 06-25 22:28:35] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:28:35] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:28:35] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:28:35] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:28:36] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:28:36] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:28:37] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:28:37] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:28:39] Scheduler: Fetching data for newly completed trials: [35].
+[INFO 06-25 22:28:39] Scheduler: Retrieved COMPLETED trials: [35].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:46:00] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:46:00] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:46:00] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:46:00] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:46:01] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:46:01] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:46:02] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:46:02] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:46:04] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:46:04] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:46:08] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:46:08] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:46:13] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:46:13] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:46:20] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:46:20] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:46:32] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:46:32] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-[INFO 06-25 21:46:49] Scheduler: Fetching data for newly completed trials: 39 - 40.
-[INFO 06-25 21:46:49] Scheduler: Retrieved COMPLETED trials: 39 - 40.
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:28:46] Scheduler: Running trials [39]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:46:54] Scheduler: Running trials [42]...
+[INFO 06-25 22:28:47] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:28:47] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:28:47] Scheduler: Fetching data for newly completed trials: [36].
+[INFO 06-25 22:28:47] Scheduler: Retrieved COMPLETED trials: [36].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:46:55] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:47:00] Scheduler: Running trials [43]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:47:01] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:47:01] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:47:01] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:01] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:47:02] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:02] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:47:04] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:04] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:47:06] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:06] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:47:09] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:09] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:47:14] Scheduler: Fetching data for newly completed trials: [41].
-[INFO 06-25 21:47:14] Scheduler: Retrieved COMPLETED trials: [41].
+[INFO 06-25 22:28:56] Scheduler: Running trials [40]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:28:57] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:28:57] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:28:57] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:28:57] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:28:58] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:28:58] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:29:00] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:29:00] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:29:02] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:29:02] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:29:05] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:29:05] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:29:10] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:29:10] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:29:18] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:29:18] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:29:29] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:29:29] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
+[INFO 06-25 22:29:46] Scheduler: Fetching data for newly completed trials: 38 - 39.
+[INFO 06-25 22:29:46] Scheduler: Retrieved COMPLETED trials: 38 - 39.
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:47:20] Scheduler: Running trials [44]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:47:21] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:47:21] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:47:21] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:21] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:47:22] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:22] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:47:24] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:24] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:47:26] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:26] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:47:29] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:29] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:47:34] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:34] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:47:42] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:42] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:47:53] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:47:53] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-[INFO 06-25 21:48:10] Scheduler: Fetching data for newly completed trials: 42 - 43.
-[INFO 06-25 21:48:10] Scheduler: Retrieved COMPLETED trials: 42 - 43.
+[INFO 06-25 22:29:54] Scheduler: Running trials [41]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:29:55] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:29:59] Scheduler: Running trials [42]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:48:17] Scheduler: Running trials [45]...
+[INFO 06-25 22:30:00] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:30:00] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:30:00] Scheduler: Fetching data for newly completed trials: [40].
+[INFO 06-25 22:30:00] Scheduler: Retrieved COMPLETED trials: [40].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:48:18] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:48:24] Scheduler: Running trials [46]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:48:25] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
-[INFO 06-25 21:48:25] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
-[INFO 06-25 21:48:25] Scheduler: Fetching data for newly completed trials: [44].
-[INFO 06-25 21:48:25] Scheduler: Retrieved COMPLETED trials: [44].
+[INFO 06-25 22:30:05] Scheduler: Running trials [43]...
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
+[INFO 06-25 22:30:06] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:30:06] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:30:06] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:06] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:30:07] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:07] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:30:08] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:08] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:30:10] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:10] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:30:14] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:14] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:30:19] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:19] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:30:26] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:26] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:30:38] Scheduler: Fetching data for newly completed trials: [42].
+[INFO 06-25 22:30:38] Scheduler: Retrieved COMPLETED trials: [42].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:48:31] Scheduler: Running trials [47]...
-[INFO 06-25 21:48:32] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:48:32] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
-[INFO 06-25 21:48:33] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:48:33] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
-[INFO 06-25 21:48:34] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:48:34] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
-[INFO 06-25 21:48:37] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:48:37] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
-[INFO 06-25 21:48:40] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:48:40] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
-[INFO 06-25 21:48:45] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:48:45] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
-[INFO 06-25 21:48:53] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:48:53] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
-[INFO 06-25 21:49:04] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:49:04] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
-[INFO 06-25 21:49:21] Scheduler: Fetching data for newly completed trials: 46 - 47.
-[INFO 06-25 21:49:21] Scheduler: Retrieved COMPLETED trials: 46 - 47.
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[INFO 06-25 21:49:21] Scheduler: Done submitting trials, waiting for remaining 1 running trials...
-[INFO 06-25 21:49:21] Scheduler: Fetching data for newly completed trials: [].
-[INFO 06-25 21:49:21] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 1).
-[INFO 06-25 21:49:23] Scheduler: Fetching data for newly completed trials: [45].
-[INFO 06-25 21:49:23] Scheduler: Retrieved COMPLETED trials: [45].
+[INFO 06-25 22:30:43] Scheduler: Running trials [44]...
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+[INFO 06-25 22:30:43] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:30:43] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:30:43] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:43] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:30:44] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:44] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:30:46] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:46] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:30:48] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:30:48] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:30:52] Scheduler: Fetching data for newly completed trials: [41].
+[INFO 06-25 22:30:52] Scheduler: Retrieved COMPLETED trials: [41].
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+[INFO 06-25 22:30:59] Scheduler: Running trials [45]...
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+[INFO 06-25 22:31:00] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:31:00] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:31:00] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:00] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:31:01] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:01] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:31:02] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:02] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:31:05] Scheduler: Fetching data for newly completed trials: [43].
+[INFO 06-25 22:31:05] Scheduler: Retrieved COMPLETED trials: [43].
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+[INFO 06-25 22:31:10] Scheduler: Running trials [46]...
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+[INFO 06-25 22:31:11] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
+[INFO 06-25 22:31:11] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
+[INFO 06-25 22:31:11] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:11] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:31:12] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:12] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:31:13] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:13] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
+[INFO 06-25 22:31:15] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:15] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
+[INFO 06-25 22:31:19] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:19] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
+[INFO 06-25 22:31:24] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:24] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
+[INFO 06-25 22:31:31] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:31] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
+[INFO 06-25 22:31:43] Scheduler: Fetching data for newly completed trials: [44].
+[INFO 06-25 22:31:43] Scheduler: Retrieved COMPLETED trials: [44].
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+[INFO 06-25 22:31:50] Scheduler: Running trials [47]...
+[INFO 06-25 22:31:51] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:51] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
+[INFO 06-25 22:31:52] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:52] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
+[INFO 06-25 22:31:53] Scheduler: Fetching data for newly completed trials: [45].
+[INFO 06-25 22:31:53] Scheduler: Retrieved COMPLETED trials: [45].
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+[INFO 06-25 22:31:53] Scheduler: Done submitting trials, waiting for remaining 2 running trials...
+[INFO 06-25 22:31:53] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:53] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 2).
+[INFO 06-25 22:31:54] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:54] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 2).
+[INFO 06-25 22:31:56] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:56] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 2).
+[INFO 06-25 22:31:58] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:31:58] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 2).
+[INFO 06-25 22:32:01] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:32:01] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 2).
+[INFO 06-25 22:32:06] Scheduler: Fetching data for newly completed trials: [46].
+[INFO 06-25 22:32:06] Scheduler: Retrieved COMPLETED trials: [46].
+/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
+
+The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
+
+[INFO 06-25 22:32:06] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:32:07] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 1).
+[INFO 06-25 22:32:08] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:32:08] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 1).
+[INFO 06-25 22:32:09] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:32:09] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 1).
+[INFO 06-25 22:32:11] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:32:11] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 1).
+[INFO 06-25 22:32:15] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:32:15] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 1).
+[INFO 06-25 22:32:20] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:32:20] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 1).
+[INFO 06-25 22:32:27] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:32:27] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 1).
+[INFO 06-25 22:32:39] Scheduler: Fetching data for newly completed trials: [].
+[INFO 06-25 22:32:39] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 1).
+[INFO 06-25 22:32:56] Scheduler: Fetching data for newly completed trials: [47].
+[INFO 06-25 22:32:56] Scheduler: Retrieved COMPLETED trials: [47].
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
@@ -1837,7 +1910,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[WARNING 06-25 21:49:23] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.
+[WARNING 06-25 22:32:56] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
@@ -1883,7 +1956,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>Sobol</td>
 <td>False</td>
 <td>16810.0</td>
-<td>0.897621</td>
+<td>0.911009</td>
 <td>19</td>
 <td>66</td>
 <td>0.003182</td>
@@ -1899,7 +1972,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>Sobol</td>
 <td>False</td>
 <td>21926.0</td>
-<td>0.879206</td>
+<td>0.883842</td>
 <td>23</td>
 <td>118</td>
 <td>0.000145</td>
@@ -1915,7 +1988,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>Sobol</td>
 <td>True</td>
 <td>37560.0</td>
-<td>0.954121</td>
+<td>0.949112</td>
 <td>40</td>
 <td>124</td>
 <td>0.002745</td>
@@ -1931,7 +2004,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>Sobol</td>
 <td>False</td>
 <td>14756.0</td>
-<td>0.892503</td>
+<td>0.899586</td>
 <td>18</td>
 <td>23</td>
 <td>0.000166</td>
@@ -1947,7 +2020,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>Sobol</td>
 <td>True</td>
 <td>71630.0</td>
-<td>0.952770</td>
+<td>0.948751</td>
 <td>80</td>
 <td>99</td>
 <td>0.000642</td>
@@ -1963,7 +2036,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>Sobol</td>
 <td>False</td>
 <td>13948.0</td>
-<td>0.925682</td>
+<td>0.923867</td>
 <td>16</td>
 <td>54</td>
 <td>0.000444</td>
@@ -1979,7 +2052,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>Sobol</td>
 <td>False</td>
 <td>24686.0</td>
-<td>0.855924</td>
+<td>0.862653</td>
 <td>29</td>
 <td>50</td>
 <td>0.000177</td>
@@ -1995,7 +2068,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>Sobol</td>
 <td>False</td>
 <td>18290.0</td>
-<td>0.877489</td>
+<td>0.875520</td>
 <td>20</td>
 <td>87</td>
 <td>0.000119</td>
@@ -2011,7 +2084,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>Sobol</td>
 <td>False</td>
 <td>20996.0</td>
-<td>0.723215</td>
+<td>0.841835</td>
 <td>26</td>
 <td>17</td>
 <td>0.005245</td>
@@ -2026,13 +2099,13 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <td>COMPLETED</td>
 <td>BoTorch</td>
 <td>True</td>
-<td>41030.0</td>
-<td>0.960493</td>
-<td>44</td>
-<td>121</td>
-<td>0.001528</td>
+<td>52088.0</td>
+<td>0.954132</td>
+<td>56</td>
+<td>124</td>
+<td>0.002046</td>
 <td>3</td>
-<td>0.089977</td>
+<td>0.168286</td>
 <td>64</td>
 </tr>
 </tbody>
@@ -2065,12 +2138,12 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 
 The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
 
-[WARNING 06-25 21:49:23] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.
+[WARNING 06-25 22:32:56] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.
 </pre></div>
 </div>
 <div class="output_subarea output_html rendered_html output_result">
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 <br/><p>To better understand what our surrogate models have learned about
@@ -2096,7 +2169,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
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60514.432]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002408052603520359\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.2510863887315102\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 11_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 20800.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 20662.752 [16835.653, 24489.851]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.001470133144187864\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21407980749240713\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 12_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 23520.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 23525.89 [19842.453, 27209.327]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245468034556\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.15340543119063924\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 32288.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 32161.228 [27213.832, 37108.624]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001994300938129328\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.21499235767270763\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 28658.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 28337.644 [25890.893, 30784.395]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.0008764775313422482\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.09728717173603192\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 20122.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 19795.554 [16161.168, 23429.941]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.0022267758514678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.058372345242382266\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 38672.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 38307.769 [35463.851, 41151.687]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237371701334\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.19449495772619033\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 115808.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 82800.604 [68565.568, 97035.641]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.0012017670755849175\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.13474598990397513\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 25904.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 25871.522 [24791.152, 26951.891]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012018965795618222\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.035270633844164935\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 34736.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 34788.758 [33510.965, 36066.551]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.0026075764359321427\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08965967639634911\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 27960.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 28848.862 [24012.134, 33685.590]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0009987284102201518\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 16800.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 16810.408 [15849.765, 17771.051]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.0010052246187516813\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 45673.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 45331.739 [43773.994, 46889.484]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.0010740830978628849\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08962285567515688\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 45968.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 46002.758 [45543.092, 46462.423]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0023031161968662373\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.07332857890409054\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21344.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21401.498 [18621.239, 24181.757]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0039016676642448078\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 50075.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 50608.467 [48806.372, 52410.561]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0012272060004844662\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10591940160087167\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 39584.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 39558.149 [38466.502, 40649.796]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0023557560376612557\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.013262435603067895\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 50528.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 50474.403 [50240.027, 50708.779]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0018959916188280854\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06769325117594031\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 49616.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 49570.345 [49338.109, 49802.582]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019116412526868863\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06882646818321762\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 49616.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 49621.102 [49341.551, 49900.653]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001896813466012091\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.047524591711452374\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 51440.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 51496.968 [51089.843, 51904.094]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002051537058037546\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.04398768200255323\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 23590.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 23645.386 [22896.294, 24394.478]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.0015065283662359535\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.002151874266435718\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 18110.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 17972.946 [16875.985, 19069.906]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.001448955255377498\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 43232.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 43242.989 [42741.968, 43744.010]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019186606244828539\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.07176473293061604\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 15872.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 16097.978 [14080.716, 18115.239]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0009895788143775668\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.029928865179771662\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 14104.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 14590.406 [12061.078, 17119.735]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.0024350447173329265\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 36_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 49175.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 49172.02 [48781.284, 49562.757]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0018066351219625165\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06526323924987067\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 37_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 50080.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 50073.438 [49694.783, 50452.093]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0018035678195813015\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06840431994084485\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 38_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 31276.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 31346.56 [30441.968, 32251.152]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762026758842844\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.028217772221820595\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 39_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 14728.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 14549.155 [13313.338, 15784.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.001289584449813646\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03270213326253294\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 40_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 16568.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 17598.368 [12654.782, 22541.953]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.0017306487601014113\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 41_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 12960.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 18009.735 [2212.615, 33806.856]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 42_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 59648.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 60476.205 [58722.373, 62230.037]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0015988439912958453\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.11538931071540572\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 43_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 56912.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 56882.199 [55632.346, 58132.051]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002192914937837011\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09898427583037986\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 44_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 26948.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 26896.183 [26033.799, 27758.566]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.001969449854904104\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.025489989406061484\u003cbr\u003ebatch_size: 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0_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.898 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.927 [0.905, 0.950]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816019941386738\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1909700185060501\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 1_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.879 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.907 [0.893, 0.921]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00014466425599951336\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.46575378254055977\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 2_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.951 [0.944, 0.959]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.002744711802661285\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19660000363364816\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 3_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.893 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.915 [0.888, 0.942]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.00016592709310927874\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16949627175927162\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 4_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.953 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.941 [0.923, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0006421366253755414\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2912765285000205\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 5_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.926 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.925 [0.904, 0.945]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.00044353892687835314\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.05755229713395238\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 6_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.856 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.908 [0.886, 0.930]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.00017699389919183936\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.4350298522040248\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 7_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.877 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.902 [0.887, 0.917]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.00011908174276707618\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.46274396777153015\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 8_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.723 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.912 [0.880, 0.944]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.005244787716785168\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.45581294130533934\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 9_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.96 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.952, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0015283801386478438\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.08997717482673702\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 10_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.959 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.949, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002408052603520359\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.2510863887315102\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 11_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.944 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.937, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.001470133144187864\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21407980749240713\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 12_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.945 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.944 [0.932, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245468034556\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.15340543119063924\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.941 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.945 [0.932, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001994300938129328\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.21499235767270763\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.952 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.95 [0.942, 0.959]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.0008764775313422482\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.09728717173603192\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.952 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.940, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.0022267758514678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.058372345242382266\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.953 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.956 [0.947, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237371701334\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.19449495772619033\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.97 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.962 [0.945, 0.978]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.0012017670755849175\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.13474598990397513\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.959 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.957 [0.953, 0.962]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012018965795618222\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.035270633844164935\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.962 [0.956, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.0026075764359321427\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08965967639634911\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.944 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.95 [0.937, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0009987284102201518\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.95 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.955 [0.950, 0.959]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.0010052246187516813\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.966 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.963 [0.956, 0.970]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.0010740830978628849\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08962285567515688\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.969 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.964, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0023031161968662373\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.07332857890409054\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.949 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.953 [0.942, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0039016676642448078\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.965 [0.958, 0.971]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0012272060004844662\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10591940160087167\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.968 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0023557560376612557\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.013262435603067895\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.968 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.965, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0018959916188280854\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06769325117594031\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.969 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.965, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019116412526868863\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06882646818321762\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.967 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.965, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001896813466012091\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.047524591711452374\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.968 [0.966, 0.970]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002051537058037546\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.04398768200255323\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.957 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.957, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.0015065283662359535\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.002151874266435718\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.958 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.948, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.001448955255377498\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.965, 0.970]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019186606244828539\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.07176473293061604\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.95 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.947 [0.940, 0.954]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0009895788143775668\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.029928865179771662\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.94 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.938, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.0024350447173329265\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 36_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0018066351219625165\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06526323924987067\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 37_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.963, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.0018035678195813015\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.06840431994084485\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 38_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.963 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.963 [0.960, 0.966]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762026758842844\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.028217772221820595\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 39_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.951 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.95 [0.945, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.001289584449813646\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03270213326253294\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 40_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.952 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.946 [0.930, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.0017306487601014113\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 41_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.912 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.915 [0.885, 0.946]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 42_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.969 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.963, 0.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0015988439912958453\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.11538931071540572\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 43_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.968 [0.964, 0.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.002192914937837011\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09898427583037986\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 44_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.96 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.962 [0.959, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.001969449854904104\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.025489989406061484\u003cbr\u003ebatch_size: 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32631.107]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0015836311118018933\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2050776715403858\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 30792.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 30669.976 [28497.180, 32842.771]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030924955346424113\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.15425080038036754\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 35024.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 35203.43 [33938.688, 36468.172]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000892939773706933\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15377686031759935\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21344.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21136.647 [17769.926, 24503.367]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019122437399990496\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.11648592327558678\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 34112.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 34058.052 [32483.707, 35632.397]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588023688155876\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.20693801163974998\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21695.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21669.082 [19793.036, 23545.129]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.001428892513377131\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21430085808858387\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 46267.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 46121.589 [45379.665, 46863.513]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.001435040428753664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15967120130756182\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 40390.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 40552.374 [38408.298, 42696.450]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.0007441019683474586\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.1588948086860703\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 24498.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 24461.116 [23536.777, 25385.455]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.0008458653529265809\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08591265458457016\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 60560.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 59662.281 [57846.557, 61478.004]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0017804197438138333\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16868798731941295\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 38544.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 38531.59 [37820.736, 39242.444]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.001550017812211206\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.12840093075010264\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 18290.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 18109.863 [16412.513, 19807.213]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.0008563855724641313\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0788539938881646\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 44611.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 44642.676 [44082.406, 45202.946]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0014959402865701906\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09098484746501616\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 44729.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 44145.835 [41711.023, 46580.646]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0019242342360913701\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13264347854218717\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 29204.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 29182.626 [28348.227, 30017.026]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.0012059060451300881\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554013848979968\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 48545.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 48590.181 [48122.257, 49058.105]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0016147538184019287\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1240463086440828\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 20544.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 20394.164 [17696.083, 23092.245]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.0006538314475705554\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 112772.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 81782.272 [68274.375, 95290.169]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185006900815838\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13265672941473142\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 21167.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 21185.222 [20386.478, 21983.966]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.0012526312430088306\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09609853986651441\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 26360.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 26397.009 [25818.325, 26975.693]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0011707340084095397\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875025543199334\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 57576.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 57741.722 [56784.042, 58699.402]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0015427188728687946\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1294948811057008\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 15872.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 16824.721 [11469.780, 22179.662]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001066663459652777\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.043526285037224215\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 30254.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 30255.76 [29913.634, 30597.886]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.0013965235130177253\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03764783823261509\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 22964.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 23036.958 [21886.346, 24187.570]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.0013783106638542864\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.004069116579453136\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 36_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 34112.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 34098.252 [33627.857, 34568.647]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0016649690964076766\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03710676543833276\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 37_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 15872.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 20355.606 [7902.620, 32808.593]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 38_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 60956.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 60902.426 [60519.563, 61285.289]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.002003639791986216\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16358667872355087\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 39_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 61917.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 63621.52 [60681.618, 66561.421]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0019158548962992022\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16659106176553426\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 40_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 32288.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 32262.155 [31948.900, 32575.411]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014188925033155881\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.04812832697870953\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 41_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 62800.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 62958.154 [62378.440, 63537.868]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.002291288856676227\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156049268867353\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 42_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 42320.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 43106.393 [34832.601, 51380.186]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.28284327697220896\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 43_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 39277.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 39438.787 [38345.090, 40532.485]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.002311018317884243\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0031601680139129963\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 44_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 48736.0 [nan, nan]\u003cbr\u003ePredicted Outcome: 48420.349 [46908.512, 49932.186]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.002697593907691842\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.01955848196111164\u003cbr\u003ebatch_size: 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0_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.911 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.93 [0.909, 0.951]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816019941386738\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1909700185060501\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 1_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.884 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.907 [0.893, 0.921]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00014466425599951336\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.46575378254055977\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 2_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.949 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.940, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.002744711802661285\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19660000363364816\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 3_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.9 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.915 [0.888, 0.943]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.00016592709310927874\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16949627175927162\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 4_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.949 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.944 [0.925, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0006421366253755414\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2912765285000205\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 5_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.924 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.92 [0.899, 0.941]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.00044353892687835314\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.05755229713395238\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 6_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.863 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.909 [0.887, 0.931]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.00017699389919183936\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.4350298522040248\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 7_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.876 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.903 [0.887, 0.918]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.00011908174276707618\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.46274396777153015\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 8_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.842 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.915 [0.883, 0.947]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.005244787716785168\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.45581294130533934\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 9_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.957 [0.947, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.002046487133026912\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.16828606332032023\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 10_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.955 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.953 [0.946, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.003139271388033848\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16521781376806371\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 11_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.949 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.948 [0.938, 0.957]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.0008286510325344759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15904629532238485\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 12_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.945 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.937 [0.923, 0.950]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0015836311118018933\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.2050776715403858\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 13_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.951 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.95 [0.941, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030924955346424113\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.15425080038036754\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 14_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.959 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.954, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000892939773706933\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15377686031759935\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 15_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.942 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.951 [0.943, 0.960]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019122437399990496\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.11648592327558678\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 16_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.956 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.945, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588023688155876\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.20693801163974998\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 17_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.942 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.948 [0.940, 0.956]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.001428892513377131\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21430085808858387\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 18_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.001435040428753664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.15967120130756182\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 19_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.957 [0.949, 0.965]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.0007441019683474586\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.1588948086860703\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 20_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.955 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.955 [0.951, 0.958]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.0008458653529265809\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.08591265458457016\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 21_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.966 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.962, 0.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0017804197438138333\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16868798731941295\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 22_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.964 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.962, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.001550017812211206\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.12840093075010264\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 23_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.948 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.949 [0.944, 0.954]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.0008563855724641313\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0788539938881646\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 24_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.964, 0.969]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0014959402865701906\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09098484746501616\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 25_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.962 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.961 [0.954, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.0019242342360913701\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13264347854218717\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 26_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.96 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.957, 0.962]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.0012059060451300881\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554013848979968\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 27_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.967 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.965, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.0016147538184019287\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1240463086440828\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 28_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.952 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.942, 0.962]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.0006538314475705554\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 29_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.972 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.96 [0.946, 0.973]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185006900815838\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.13265672941473142\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 30_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.954 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.952 [0.949, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.0012526312430088306\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.09609853986651441\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 31_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.961 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.961 [0.958, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.0011707340084095397\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875025543199334\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 32_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.966 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.967 [0.964, 0.970]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.0015427188728687946\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1294948811057008\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 33_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.95 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.946 [0.936, 0.955]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.001066663459652777\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.043526285037224215\u003cbr\u003ebatch_size: 64","\u003cb\u003eArm 34_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.962 [0.961, 0.964]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.0013965235130177253\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03764783823261509\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 35_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.958 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.958 [0.954, 0.962]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.0013783106638542864\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.004069116579453136\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 36_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.963 [0.960, 0.965]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0016649690964076766\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.03710676543833276\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 37_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.89 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.923 [0.894, 0.951]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.0\u003cbr\u003ebatch_size: 32","\u003cb\u003eArm 38_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.968 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.963, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.002003639791986216\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16358667872355087\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 39_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.964 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.966 [0.961, 0.972]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.0019158548962992022\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16659106176553426\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 40_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.962 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.965 [0.963, 0.967]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014188925033155881\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.04812832697870953\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 41_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.968 [0.964, 0.971]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.002291288856676227\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156049268867353\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 42_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.933 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.938 [0.913, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.28284327697220896\u003cbr\u003ebatch_size: 256","\u003cb\u003eArm 43_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.958 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.964 [0.961, 0.968]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.002311018317884243\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0031601680139129963\u003cbr\u003ebatch_size: 128","\u003cb\u003eArm 44_0\u003c\u002fb\u003e\u003cbr\u003e\u003cbr\u003eActual Outcome: 0.965 [nan, nan]\u003cbr\u003ePredicted Outcome: 0.959 [0.954, 0.963]\u003cbr\u003e\u003cbr\u003e\u003cem\u003eParameterization:\u003c\u002fem\u003e\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.002697593907691842\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.01955848196111164\u003cbr\u003ebatch_size: 128"],"visible":false,"x":[0.9110090136528015,0.8838416934013367,0.949111819267273,0.8995856642723083,0.9487510919570923,0.9238665103912354,0.8626527190208435,0.8755195140838623,0.841835081577301,0.9541319608688354,0.9554736614227295,0.9485170841217041,0.9454241991043091,0.9510927796363831,0.9593484401702881,0.9415989518165588,0.9559780955314636,0.9424334764480591,0.965461790561676,0.9542149305343628,0.9547032117843628,0.9659853577613831,0.9639917016029358,0.9475871920585632,0.9646998047828674,0.9615045785903931,0.9603331089019775,0.9668697118759155,0.9517737030982971,0.9717813730239868,0.9538366198539734,0.9607595801353455,0.9664375185966492,0.950124204158783,0.9649875164031982,0.958263099193573,0.9654144644737244,0.890011727809906,0.9678809642791748,0.9641238451004028,0.961800754070282,0.9649840593338013,0.9334069490432739,0.958203136920929,0.9648357629776001],"y":[0.9302869338684696,0.9068851772723312,0.9492996048089083,0.9153267640320253,0.9440346187276889,0.9196338531633792,0.9087370977191331,0.9026026163174592,0.9151194215494712,0.9572354181645552,0.9528517713785435,0.9476897091619653,0.93653768498881,0.949654632170069,0.9591645340494209,0.9514429451237414,0.951562656674562,0.9478116549562078,0.96409759569689,0.9571556229174486,0.9547147852762838,0.9668779333717318,0.9644084850132388,0.9489648735906442,0.9664131889728305,0.9605767941644683,0.9597463871792846,0.9664066445599353,0.9517655036143808,0.9596350147814909,0.95212191185513,0.9606549707842262,0.967321595509326,0.9457039394866652,0.96238654163058,0.9582810232201464,0.9628552836994768,0.9226201313265748,0.9655613792361888,0.9662937956799548,0.9647381873941672,0.967746511424018,0.937955439040303,0.9644348312707393,0.958591468745544],"type":"scatter"}], 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0_0\u003cbr\u003eval_acc: 0.911009 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003eval_acc: 0.955474 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003eval_acc: 0.948517 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003eval_acc: 0.945424 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003eval_acc: 0.951093 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003eval_acc: 0.959348 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003eval_acc: 0.941599 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003eval_acc: 0.955978 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003eval_acc: 0.942433 (SEM: None)\u003cbr\u003ehidden_size_1: 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 <br/>
 <br/><p>Similarly, we show the number of model parameters as a function of
@@ -2123,7 +2196,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012019\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0352706\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 34736 (SEM: None)\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00260758\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896597\u003cbr\u003ebatch_size: 128","Arm 20_0\u003cbr\u003enum_params: 27960 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.000998728\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 16800 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.00100522\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 45673 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00107408\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896229\u003cbr\u003ebatch_size: 64","Arm 23_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00230312\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0733286\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00390167\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0475246\u003cbr\u003ebatch_size: 128","Arm 30_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00205154\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0439877\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.00150653\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00215187\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 18110 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.00144896\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 43232 (SEM: None)\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191866\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0717647\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000989579\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0299289\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 14104 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.00243504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 49175 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0652632\u003cbr\u003ebatch_size: 64","Arm 37_0\u003cbr\u003enum_params: 50080 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180357\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0684043\u003cbr\u003ebatch_size: 64","Arm 38_0\u003cbr\u003enum_params: 31276 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0282178\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 14728 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.00128958\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0327021\u003cbr\u003ebatch_size: 128","Arm 40_0\u003cbr\u003enum_params: 16568 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.00173065\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 41_0\u003cbr\u003enum_params: 12960 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 42_0\u003cbr\u003enum_params: 59648 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00159884\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.115389\u003cbr\u003ebatch_size: 64","Arm 43_0\u003cbr\u003enum_params: 56912 (SEM: None)\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00219291\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989843\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 26948 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00196945\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.02549\u003cbr\u003ebatch_size: 128"],"visible":false,"x":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"xaxis":"x","y":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 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None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012019\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0352706\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 34736 (SEM: None)\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00260758\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896597\u003cbr\u003ebatch_size: 128","Arm 20_0\u003cbr\u003enum_params: 27960 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.000998728\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 16800 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.00100522\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 45673 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00107408\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896229\u003cbr\u003ebatch_size: 64","Arm 23_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00230312\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0733286\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00390167\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0475246\u003cbr\u003ebatch_size: 128","Arm 30_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00205154\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0439877\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.00150653\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00215187\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 18110 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.00144896\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 43232 (SEM: None)\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191866\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0717647\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000989579\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0299289\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 14104 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.00243504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 49175 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0652632\u003cbr\u003ebatch_size: 64","Arm 37_0\u003cbr\u003enum_params: 50080 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180357\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0684043\u003cbr\u003ebatch_size: 64","Arm 38_0\u003cbr\u003enum_params: 31276 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0282178\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 14728 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.00128958\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0327021\u003cbr\u003ebatch_size: 128","Arm 40_0\u003cbr\u003enum_params: 16568 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.00173065\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 41_0\u003cbr\u003enum_params: 12960 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 42_0\u003cbr\u003enum_params: 59648 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00159884\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.115389\u003cbr\u003ebatch_size: 64","Arm 43_0\u003cbr\u003enum_params: 56912 (SEM: None)\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00219291\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989843\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 26948 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00196945\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.02549\u003cbr\u003ebatch_size: 128"],"visible":true,"x":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"xaxis":"x","y":[66,118,124,23,99,54,50,87,17,121,128,111,112,128,85,118,128,112,128,103,111,96,123,128,128,107,128,128,128,128,128,114,81,128,128,60,121,121,105,84,120,16,128,128,108],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 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112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012019\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0352706\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 34736 (SEM: None)\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00260758\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896597\u003cbr\u003ebatch_size: 128","Arm 20_0\u003cbr\u003enum_params: 27960 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.000998728\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 16800 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.00100522\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 45673 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00107408\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896229\u003cbr\u003ebatch_size: 64","Arm 23_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00230312\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0733286\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00390167\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0475246\u003cbr\u003ebatch_size: 128","Arm 30_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00205154\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0439877\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.00150653\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00215187\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 18110 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.00144896\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 43232 (SEM: None)\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191866\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0717647\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000989579\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0299289\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 14104 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.00243504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 49175 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0652632\u003cbr\u003ebatch_size: 64","Arm 37_0\u003cbr\u003enum_params: 50080 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180357\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0684043\u003cbr\u003ebatch_size: 64","Arm 38_0\u003cbr\u003enum_params: 31276 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0282178\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 14728 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.00128958\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0327021\u003cbr\u003ebatch_size: 128","Arm 40_0\u003cbr\u003enum_params: 16568 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.00173065\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 41_0\u003cbr\u003enum_params: 12960 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 42_0\u003cbr\u003enum_params: 59648 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00159884\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.115389\u003cbr\u003ebatch_size: 64","Arm 43_0\u003cbr\u003enum_params: 56912 (SEM: None)\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00219291\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989843\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 26948 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00196945\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.02549\u003cbr\u003ebatch_size: 128"],"visible":false,"x":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"xaxis":"x","y":[0.0031816019941386738,0.00014466425599951336,0.002744711802661285,0.00016592709310927874,0.0006421366253755414,0.00044353892687835314,0.00017699389919183936,0.00011908174276707618,0.005244787716785168,0.0015283801386478438,0.002408052603520359,0.001470133144187864,0.000780245468034556,0.001994300938129328,0.0008764775313422482,0.0022267758514678,0.00110237371701334,0.0012017670755849175,0.0012018965795618222,0.0026075764359321427,0.0009987284102201518,0.0010052246187516813,0.0010740830978628849,0.0023031161968662373,0.0039016676642448078,0.0012272060004844662,0.0023557560376612557,0.0018959916188280854,0.0019116412526868863,0.001896813466012091,0.002051537058037546,0.0015065283662359535,0.001448955255377498,0.0019186606244828539,0.0009895788143775668,0.0024350447173329265,0.0018066351219625165,0.0018035678195813015,0.0016762026758842844,0.001289584449813646,0.0017306487601014113,0.0001,0.0015988439912958453,0.002192914937837011,0.001969449854904104],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 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128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 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None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 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112\u003cbr\u003elearning_rate: 0.000780245\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.153405\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0019943\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.214992\u003cbr\u003ebatch_size: 64","Arm 14_0\u003cbr\u003enum_params: 28658 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 85\u003cbr\u003elearning_rate: 0.000876478\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0972872\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 20122 (SEM: None)\u003cbr\u003ehidden_size_1: 21\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.00222678\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0583723\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 38672 (SEM: None)\u003cbr\u003ehidden_size_1: 41\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00110237\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.194495\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 115808 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 112\u003cbr\u003elearning_rate: 0.00120177\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.134746\u003cbr\u003ebatch_size: 64","Arm 18_0\u003cbr\u003enum_params: 25904 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0012019\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0352706\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 34736 (SEM: None)\u003cbr\u003ehidden_size_1: 38\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00260758\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896597\u003cbr\u003ebatch_size: 128","Arm 20_0\u003cbr\u003enum_params: 27960 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.000998728\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 16800 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 96\u003cbr\u003elearning_rate: 0.00100522\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 45673 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00107408\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0896229\u003cbr\u003ebatch_size: 64","Arm 23_0\u003cbr\u003enum_params: 45968 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00230312\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0733286\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00390167\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 50075 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00122721\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.105919\u003cbr\u003ebatch_size: 128","Arm 26_0\u003cbr\u003enum_params: 39584 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00235576\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0132624\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 50528 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189599\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0676933\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191164\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0688265\u003cbr\u003ebatch_size: 128","Arm 29_0\u003cbr\u003enum_params: 49616 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00189681\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0475246\u003cbr\u003ebatch_size: 128","Arm 30_0\u003cbr\u003enum_params: 51440 (SEM: None)\u003cbr\u003ehidden_size_1: 55\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00205154\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0439877\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.00150653\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00215187\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 18110 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 81\u003cbr\u003elearning_rate: 0.00144896\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 43232 (SEM: None)\u003cbr\u003ehidden_size_1: 46\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191866\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0717647\u003cbr\u003ebatch_size: 128","Arm 34_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.000989579\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0299289\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 14104 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 60\u003cbr\u003elearning_rate: 0.00243504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 49175 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180664\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0652632\u003cbr\u003ebatch_size: 64","Arm 37_0\u003cbr\u003enum_params: 50080 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00180357\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0684043\u003cbr\u003ebatch_size: 64","Arm 38_0\u003cbr\u003enum_params: 31276 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.0016762\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0282178\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 14728 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 84\u003cbr\u003elearning_rate: 0.00128958\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0327021\u003cbr\u003ebatch_size: 128","Arm 40_0\u003cbr\u003enum_params: 16568 (SEM: None)\u003cbr\u003ehidden_size_1: 17\u003cbr\u003ehidden_size_2: 120\u003cbr\u003elearning_rate: 0.00173065\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 256","Arm 41_0\u003cbr\u003enum_params: 12960 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 16\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 42_0\u003cbr\u003enum_params: 59648 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00159884\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.115389\u003cbr\u003ebatch_size: 64","Arm 43_0\u003cbr\u003enum_params: 56912 (SEM: None)\u003cbr\u003ehidden_size_1: 61\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00219291\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0989843\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 26948 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00196945\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.02549\u003cbr\u003ebatch_size: 128"],"visible":false,"x":[19,23,40,18,80,16,29,20,26,44,62,22,25,34,32,21,41,128,27,38,30,18,49,49,22,55,42,54,53,53,55,25,20,46,16,16,53,54,34,16,17,16,64,61,29],"xaxis":"x","y":[0.1909700185060501,0.46575378254055977,0.19660000363364816,0.16949627175927162,0.2912765285000205,0.05755229713395238,0.4350298522040248,0.46274396777153015,0.45581294130533934,0.08997717482673702,0.2510863887315102,0.21407980749240713,0.15340543119063924,0.21499235767270763,0.09728717173603192,0.058372345242382266,0.19449495772619033,0.13474598990397513,0.035270633844164935,0.08965967639634911,0.0,0.0,0.08962285567515688,0.07332857890409054,0.0,0.10591940160087167,0.013262435603067895,0.06769325117594031,0.06882646818321762,0.047524591711452374,0.04398768200255323,0.002151874266435718,0.0,0.07176473293061604,0.029928865179771662,0.0,0.06526323924987067,0.06840431994084485,0.028217772221820595,0.03270213326253294,0.0,0.0,0.11538931071540572,0.09898427583037986,0.025489989406061484],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 41030 (SEM: None)\u003cbr\u003ehidden_size_1: 44\u003cbr\u003ehidden_size_2: 121\u003cbr\u003elearning_rate: 0.00152838\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.0899772\u003cbr\u003ebatch_size: 64","Arm 10_0\u003cbr\u003enum_params: 57824 (SEM: None)\u003cbr\u003ehidden_size_1: 62\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00240805\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.251086\u003cbr\u003ebatch_size: 64","Arm 11_0\u003cbr\u003enum_params: 20800 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00147013\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.21408\u003cbr\u003ebatch_size: 128","Arm 12_0\u003cbr\u003enum_params: 23520 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00120591\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 48545 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00161475\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.124046\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 20544 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.000653831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 64","Arm 29_0\u003cbr\u003enum_params: 112772 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185007\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132657\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 21167 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.00125263\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0960985\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 26360 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00117073\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 57576 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00154272\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.129495\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00106666\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0435263\u003cbr\u003ebatch_size: 64","Arm 34_0\u003cbr\u003enum_params: 30254 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00139652\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0376478\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 22964 (SEM: None)\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.00137831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00406912\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00141889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0481283\u003cbr\u003ebatch_size: 128","Arm 41_0\u003cbr\u003enum_params: 62800 (SEM: None)\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00229129\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 42320 (SEM: None)\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.282843\u003cbr\u003ebatch_size: 256","Arm 43_0\u003cbr\u003enum_params: 39277 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00231102\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00316017\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 48736 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00269759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0195585\u003cbr\u003ebatch_size: 128","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 52088 (SEM: None)\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00204649\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.168286\u003cbr\u003ebatch_size: 64"],"visible":false,"x":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"xaxis":"x","y":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00120591\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 48545 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00161475\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.124046\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 20544 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.000653831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 64","Arm 29_0\u003cbr\u003enum_params: 112772 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185007\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132657\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 21167 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.00125263\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0960985\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 26360 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00117073\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 57576 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00154272\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.129495\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00106666\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0435263\u003cbr\u003ebatch_size: 64","Arm 34_0\u003cbr\u003enum_params: 30254 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00139652\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0376478\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 22964 (SEM: None)\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.00137831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00406912\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00141889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0481283\u003cbr\u003ebatch_size: 128","Arm 41_0\u003cbr\u003enum_params: 62800 (SEM: None)\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00229129\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 42320 (SEM: None)\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.282843\u003cbr\u003ebatch_size: 256","Arm 43_0\u003cbr\u003enum_params: 39277 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00231102\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00316017\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 48736 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00269759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0195585\u003cbr\u003ebatch_size: 128","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 52088 (SEM: None)\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00204649\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.168286\u003cbr\u003ebatch_size: 64"],"visible":true,"x":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"xaxis":"x","y":[66,128,114,116,94,128,128,128,111,103,126,118,90,128,108,87,105,107,98,111,103,90,124,95,116,100,128,123,122,128,128,98,99,23,128,99,128,105,100,99,54,50,87,17,124],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00120591\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 48545 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00161475\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.124046\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 20544 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.000653831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 64","Arm 29_0\u003cbr\u003enum_params: 112772 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185007\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132657\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 21167 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.00125263\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0960985\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 26360 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00117073\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 57576 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00154272\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.129495\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00106666\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0435263\u003cbr\u003ebatch_size: 64","Arm 34_0\u003cbr\u003enum_params: 30254 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00139652\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0376478\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 22964 (SEM: None)\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.00137831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00406912\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00141889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0481283\u003cbr\u003ebatch_size: 128","Arm 41_0\u003cbr\u003enum_params: 62800 (SEM: None)\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00229129\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 42320 (SEM: None)\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.282843\u003cbr\u003ebatch_size: 256","Arm 43_0\u003cbr\u003enum_params: 39277 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00231102\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00316017\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 48736 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00269759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0195585\u003cbr\u003ebatch_size: 128","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 52088 (SEM: None)\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00204649\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.168286\u003cbr\u003ebatch_size: 64"],"visible":false,"x":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"xaxis":"x","y":[0.0031816019941386738,0.003139271388033848,0.0008286510325344759,0.0015836311118018933,0.0030924955346424113,0.000892939773706933,0.0019122437399990496,0.0014588023688155876,0.001428892513377131,0.001435040428753664,0.0007441019683474586,0.00014466425599951336,0.0008458653529265809,0.0017804197438138333,0.001550017812211206,0.0008563855724641313,0.0014959402865701906,0.0019242342360913701,0.0012059060451300881,0.0016147538184019287,0.0006538314475705554,0.00185006900815838,0.002744711802661285,0.0012526312430088306,0.0011707340084095397,0.0015427188728687946,0.001066663459652777,0.0013965235130177253,0.0013783106638542864,0.0016649690964076766,0.0001,0.002003639791986216,0.0019158548962992022,0.00016592709310927874,0.0014188925033155881,0.002291288856676227,0.01,0.002311018317884243,0.002697593907691842,0.0006421366253755414,0.00044353892687835314,0.00017699389919183936,0.00011908174276707618,0.005244787716785168,0.002046487133026912],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 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128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: 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0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.000845865\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0859127\u003cbr\u003ebatch_size: 128","Arm 21_0\u003cbr\u003enum_params: 60560 (SEM: None)\u003cbr\u003ehidden_size_1: 65\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00178042\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.168688\u003cbr\u003ebatch_size: 128","Arm 22_0\u003cbr\u003enum_params: 38544 (SEM: None)\u003cbr\u003ehidden_size_1: 42\u003cbr\u003ehidden_size_2: 108\u003cbr\u003elearning_rate: 0.00155002\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.128401\u003cbr\u003ebatch_size: 128","Arm 23_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000856386\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.078854\u003cbr\u003ebatch_size: 128","Arm 24_0\u003cbr\u003enum_params: 44611 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00149594\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0909848\u003cbr\u003ebatch_size: 128","Arm 25_0\u003cbr\u003enum_params: 44729 (SEM: None)\u003cbr\u003ehidden_size_1: 49\u003cbr\u003ehidden_size_2: 107\u003cbr\u003elearning_rate: 0.00192423\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132643\u003cbr\u003ebatch_size: 256","Arm 26_0\u003cbr\u003enum_params: 29204 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00120591\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.10554\u003cbr\u003ebatch_size: 128","Arm 27_0\u003cbr\u003enum_params: 48545 (SEM: None)\u003cbr\u003ehidden_size_1: 53\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00161475\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.124046\u003cbr\u003ebatch_size: 128","Arm 28_0\u003cbr\u003enum_params: 20544 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.000653831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 64","Arm 29_0\u003cbr\u003enum_params: 112772 (SEM: None)\u003cbr\u003ehidden_size_1: 128\u003cbr\u003ehidden_size_2: 90\u003cbr\u003elearning_rate: 0.00185007\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.132657\u003cbr\u003ebatch_size: 256","Arm 2_0\u003cbr\u003enum_params: 37560 (SEM: None)\u003cbr\u003ehidden_size_1: 40\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00274471\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.1966\u003cbr\u003ebatch_size: 64","Arm 30_0\u003cbr\u003enum_params: 21167 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 95\u003cbr\u003elearning_rate: 0.00125263\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0960985\u003cbr\u003ebatch_size: 128","Arm 31_0\u003cbr\u003enum_params: 26360 (SEM: None)\u003cbr\u003ehidden_size_1: 28\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00117073\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.015875\u003cbr\u003ebatch_size: 128","Arm 32_0\u003cbr\u003enum_params: 57576 (SEM: None)\u003cbr\u003ehidden_size_1: 64\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00154272\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.129495\u003cbr\u003ebatch_size: 128","Arm 33_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00106666\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0435263\u003cbr\u003ebatch_size: 64","Arm 34_0\u003cbr\u003enum_params: 30254 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 123\u003cbr\u003elearning_rate: 0.00139652\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0376478\u003cbr\u003ebatch_size: 128","Arm 35_0\u003cbr\u003enum_params: 22964 (SEM: None)\u003cbr\u003ehidden_size_1: 24\u003cbr\u003ehidden_size_2: 122\u003cbr\u003elearning_rate: 0.00137831\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00406912\u003cbr\u003ebatch_size: 128","Arm 36_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00166497\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0371068\u003cbr\u003ebatch_size: 128","Arm 37_0\u003cbr\u003enum_params: 15872 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0001\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0\u003cbr\u003ebatch_size: 32","Arm 38_0\u003cbr\u003enum_params: 60956 (SEM: None)\u003cbr\u003ehidden_size_1: 68\u003cbr\u003ehidden_size_2: 98\u003cbr\u003elearning_rate: 0.00200364\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.163587\u003cbr\u003ebatch_size: 128","Arm 39_0\u003cbr\u003enum_params: 61917 (SEM: None)\u003cbr\u003ehidden_size_1: 69\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00191585\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.166591\u003cbr\u003ebatch_size: 256","Arm 3_0\u003cbr\u003enum_params: 14756 (SEM: None)\u003cbr\u003ehidden_size_1: 18\u003cbr\u003ehidden_size_2: 23\u003cbr\u003elearning_rate: 0.000165927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.169496\u003cbr\u003ebatch_size: 256","Arm 40_0\u003cbr\u003enum_params: 32288 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00141889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0481283\u003cbr\u003ebatch_size: 128","Arm 41_0\u003cbr\u003enum_params: 62800 (SEM: None)\u003cbr\u003ehidden_size_1: 70\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.00229129\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.16156\u003cbr\u003ebatch_size: 128","Arm 42_0\u003cbr\u003enum_params: 42320 (SEM: None)\u003cbr\u003ehidden_size_1: 45\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.01\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.282843\u003cbr\u003ebatch_size: 256","Arm 43_0\u003cbr\u003enum_params: 39277 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 105\u003cbr\u003elearning_rate: 0.00231102\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.00316017\u003cbr\u003ebatch_size: 128","Arm 44_0\u003cbr\u003enum_params: 48736 (SEM: None)\u003cbr\u003ehidden_size_1: 54\u003cbr\u003ehidden_size_2: 100\u003cbr\u003elearning_rate: 0.00269759\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.0195585\u003cbr\u003ebatch_size: 128","Arm 4_0\u003cbr\u003enum_params: 71630 (SEM: None)\u003cbr\u003ehidden_size_1: 80\u003cbr\u003ehidden_size_2: 99\u003cbr\u003elearning_rate: 0.000642137\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.291277\u003cbr\u003ebatch_size: 128","Arm 5_0\u003cbr\u003enum_params: 13948 (SEM: None)\u003cbr\u003ehidden_size_1: 16\u003cbr\u003ehidden_size_2: 54\u003cbr\u003elearning_rate: 0.000443539\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.0575523\u003cbr\u003ebatch_size: 64","Arm 6_0\u003cbr\u003enum_params: 24686 (SEM: None)\u003cbr\u003ehidden_size_1: 29\u003cbr\u003ehidden_size_2: 50\u003cbr\u003elearning_rate: 0.000176994\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.43503\u003cbr\u003ebatch_size: 256","Arm 7_0\u003cbr\u003enum_params: 18290 (SEM: None)\u003cbr\u003ehidden_size_1: 20\u003cbr\u003ehidden_size_2: 87\u003cbr\u003elearning_rate: 0.000119082\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.462744\u003cbr\u003ebatch_size: 256","Arm 8_0\u003cbr\u003enum_params: 20996 (SEM: None)\u003cbr\u003ehidden_size_1: 26\u003cbr\u003ehidden_size_2: 17\u003cbr\u003elearning_rate: 0.00524479\u003cbr\u003eepochs: 1\u003cbr\u003edropout: 0.455813\u003cbr\u003ebatch_size: 32","Arm 9_0\u003cbr\u003enum_params: 52088 (SEM: None)\u003cbr\u003ehidden_size_1: 56\u003cbr\u003ehidden_size_2: 124\u003cbr\u003elearning_rate: 0.00204649\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.168286\u003cbr\u003ebatch_size: 64"],"visible":false,"x":[19,32,25,30,34,37,22,36,23,51,43,23,27,65,42,20,49,49,32,53,22,128,40,23,28,64,16,32,24,36,16,68,69,18,34,70,45,43,54,80,16,29,20,26,56],"xaxis":"x","y":[0.1909700185060501,0.16521781376806371,0.15904629532238485,0.2050776715403858,0.15425080038036754,0.15377686031759935,0.11648592327558678,0.20693801163974998,0.21430085808858387,0.15967120130756182,0.1588948086860703,0.46575378254055977,0.08591265458457016,0.16868798731941295,0.12840093075010264,0.0788539938881646,0.09098484746501616,0.13264347854218717,0.10554013848979968,0.1240463086440828,0.0,0.13265672941473142,0.19660000363364816,0.09609853986651441,0.015875025543199334,0.1294948811057008,0.043526285037224215,0.03764783823261509,0.004069116579453136,0.03710676543833276,0.0,0.16358667872355087,0.16659106176553426,0.16949627175927162,0.04812832697870953,0.16156049268867353,0.28284327697220896,0.0031601680139129963,0.01955848196111164,0.2912765285000205,0.05755229713395238,0.4350298522040248,0.46274396777153015,0.45581294130533934,0.16828606332032023],"yaxis":"y","type":"scatter"},{"hoverinfo":"text","legendgroup":"In-sample","marker":{"color":"black","opacity":0.5,"symbol":1},"mode":"markers","name":"In-sample","showlegend":false,"text":["Arm 0_0\u003cbr\u003enum_params: 16810 (SEM: None)\u003cbr\u003ehidden_size_1: 19\u003cbr\u003ehidden_size_2: 66\u003cbr\u003elearning_rate: 0.0031816\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.19097\u003cbr\u003ebatch_size: 32","Arm 10_0\u003cbr\u003enum_params: 30464 (SEM: None)\u003cbr\u003ehidden_size_1: 32\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00313927\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.165218\u003cbr\u003ebatch_size: 128","Arm 11_0\u003cbr\u003enum_params: 23590 (SEM: None)\u003cbr\u003ehidden_size_1: 25\u003cbr\u003ehidden_size_2: 114\u003cbr\u003elearning_rate: 0.000828651\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159046\u003cbr\u003ebatch_size: 64","Arm 12_0\u003cbr\u003enum_params: 28160 (SEM: None)\u003cbr\u003ehidden_size_1: 30\u003cbr\u003ehidden_size_2: 116\u003cbr\u003elearning_rate: 0.00158363\u003cbr\u003eepochs: 2\u003cbr\u003edropout: 0.205078\u003cbr\u003ebatch_size: 64","Arm 13_0\u003cbr\u003enum_params: 30792 (SEM: None)\u003cbr\u003ehidden_size_1: 34\u003cbr\u003ehidden_size_2: 94\u003cbr\u003elearning_rate: 0.0030925\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.154251\u003cbr\u003ebatch_size: 128","Arm 14_0\u003cbr\u003enum_params: 35024 (SEM: None)\u003cbr\u003ehidden_size_1: 37\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00089294\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.153777\u003cbr\u003ebatch_size: 128","Arm 15_0\u003cbr\u003enum_params: 21344 (SEM: None)\u003cbr\u003ehidden_size_1: 22\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.00191224\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.116486\u003cbr\u003ebatch_size: 128","Arm 16_0\u003cbr\u003enum_params: 34112 (SEM: None)\u003cbr\u003ehidden_size_1: 36\u003cbr\u003ehidden_size_2: 128\u003cbr\u003elearning_rate: 0.0014588\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.206938\u003cbr\u003ebatch_size: 128","Arm 17_0\u003cbr\u003enum_params: 21695 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 111\u003cbr\u003elearning_rate: 0.00142889\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.214301\u003cbr\u003ebatch_size: 128","Arm 18_0\u003cbr\u003enum_params: 46267 (SEM: None)\u003cbr\u003ehidden_size_1: 51\u003cbr\u003ehidden_size_2: 103\u003cbr\u003elearning_rate: 0.00143504\u003cbr\u003eepochs: 4\u003cbr\u003edropout: 0.159671\u003cbr\u003ebatch_size: 128","Arm 19_0\u003cbr\u003enum_params: 40390 (SEM: None)\u003cbr\u003ehidden_size_1: 43\u003cbr\u003ehidden_size_2: 126\u003cbr\u003elearning_rate: 0.000744102\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.158895\u003cbr\u003ebatch_size: 128","Arm 1_0\u003cbr\u003enum_params: 21926 (SEM: None)\u003cbr\u003ehidden_size_1: 23\u003cbr\u003ehidden_size_2: 118\u003cbr\u003elearning_rate: 0.000144664\u003cbr\u003eepochs: 3\u003cbr\u003edropout: 0.465754\u003cbr\u003ebatch_size: 256","Arm 20_0\u003cbr\u003enum_params: 24498 (SEM: None)\u003cbr\u003ehidden_size_1: 27\u003cbr\u003ehidden_size_2: 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@@ -2131,7 +2204,7 @@ <h2>Evaluating the results<a class="headerlink" href="#evaluating-the-results" t
 <h2>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this heading">¶</a></h2>
 <p>We thank the TorchX team (in particular Kiuk Chung and Tristan Rice)
 for their help with integrating TorchX with Ax.</p>
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diff --git a/intermediate/char_rnn_classification_tutorial.html b/intermediate/char_rnn_classification_tutorial.html
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-80000 80% (9m 30s) 0.4456 Maessen / Dutch ✓
-85000 85% (10m 6s) 0.0239 Gan / Chinese ✓
-90000 90% (10m 41s) 0.0521 Bellomi / Italian ✓
-95000 95% (11m 16s) 0.0867 Vozgov / Russian ✓
-100000 100% (11m 52s) 0.2730 Tong / Vietnamese ✓
+15000 15% (1m 47s) 0.1778 Yukhvidov / Russian ✓
+20000 20% (2m 22s) 1.5856 Mclaughlin / Irish ✗ (Scottish)
+25000 25% (2m 58s) 0.6552 Banh / Vietnamese ✓
+30000 30% (3m 34s) 1.5547 Machado / Japanese ✗ (Portuguese)
+35000 35% (4m 10s) 0.0168 Fotopoulos / Greek ✓
+40000 40% (4m 49s) 1.1464 Quirke / Irish ✓
+45000 45% (5m 24s) 1.7532 Reier / French ✗ (German)
+50000 50% (6m 0s) 0.8413 Hou / Chinese ✓
+55000 55% (6m 36s) 0.8587 Duan / Vietnamese ✗ (Chinese)
+60000 60% (7m 12s) 0.2047 Giang / Vietnamese ✓
+65000 65% (7m 48s) 2.5534 Cober / French ✗ (Czech)
+70000 70% (8m 23s) 1.5163 Mateus / Arabic ✗ (Portuguese)
+75000 75% (8m 59s) 0.2217 Hamilton / Scottish ✓
+80000 80% (9m 34s) 0.4456 Maessen / Dutch ✓
+85000 85% (10m 10s) 0.0239 Gan / Chinese ✓
+90000 90% (10m 46s) 0.0521 Bellomi / Italian ✓
+95000 95% (11m 21s) 0.0867 Vozgov / Russian ✓
+100000 100% (11m 57s) 0.2730 Tong / Vietnamese ✓
 </pre></div>
 </div>
 </div>
@@ -889,7 +889,7 @@ <h3>Plotting the Results<a class="headerlink" href="#plotting-the-results" title
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">all_losses</span><span class="p">)</span>
 </pre></div>
 </div>
-<img alt="char rnn classification tutorial" class="sphx-glr-single-img" src="../_images/sphx_glr_char_rnn_classification_tutorial_001.png" srcset="../_images/sphx_glr_char_rnn_classification_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D object at 0x7f8ba2ebbf40&gt;]
+<img alt="char rnn classification tutorial" class="sphx-glr-single-img" src="../_images/sphx_glr_char_rnn_classification_tutorial_001.png" srcset="../_images/sphx_glr_char_rnn_classification_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D object at 0x7fc876581f60&gt;]
 </pre></div>
 </div>
 </div>
@@ -1027,7 +1027,7 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this he
 </ul>
 </li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 12 minutes  4.757 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 12 minutes  10.610 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-char-rnn-classification-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/37c8905519d3fd3f437b783a48d06eac/char_rnn_classification_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">char_rnn_classification_tutorial.py</span></code></a></p>
diff --git a/intermediate/char_rnn_generation_tutorial.html b/intermediate/char_rnn_generation_tutorial.html
index 5fbeffe4b9..bebb53e990 100644
--- a/intermediate/char_rnn_generation_tutorial.html
+++ b/intermediate/char_rnn_generation_tutorial.html
@@ -789,26 +789,26 @@ <h3>Training the Network<a class="headerlink" href="#training-the-network" title
         <span class="n">total_loss</span> <span class="o">=</span> <span class="mi">0</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0m 39s (5000 5%) 3.1506
-1m 19s (10000 10%) 2.5070
-1m 59s (15000 15%) 3.3047
-2m 39s (20000 20%) 2.4247
-3m 20s (25000 25%) 2.6406
-4m 0s (30000 30%) 2.0266
-4m 40s (35000 35%) 2.6520
-5m 20s (40000 40%) 2.4261
-6m 0s (45000 45%) 2.2302
-6m 41s (50000 50%) 1.6496
-7m 22s (55000 55%) 2.7101
-8m 3s (60000 60%) 2.5396
-8m 43s (65000 65%) 2.5978
-9m 23s (70000 70%) 1.6029
-10m 4s (75000 75%) 0.9634
-10m 44s (80000 80%) 3.0950
-11m 24s (85000 85%) 2.0512
-12m 4s (90000 90%) 2.5302
-12m 45s (95000 95%) 3.2365
-13m 25s (100000 100%) 1.7113
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0m 38s (5000 5%) 3.1506
+1m 17s (10000 10%) 2.5070
+1m 57s (15000 15%) 3.3047
+2m 37s (20000 20%) 2.4247
+3m 17s (25000 25%) 2.6406
+3m 57s (30000 30%) 2.0266
+4m 37s (35000 35%) 2.6520
+5m 17s (40000 40%) 2.4261
+5m 57s (45000 45%) 2.2302
+6m 37s (50000 50%) 1.6496
+7m 17s (55000 55%) 2.7101
+7m 58s (60000 60%) 2.5396
+8m 38s (65000 65%) 2.5978
+9m 18s (70000 70%) 1.6029
+9m 58s (75000 75%) 0.9634
+10m 38s (80000 80%) 3.0950
+11m 18s (85000 85%) 2.0512
+11m 59s (90000 90%) 2.5302
+12m 40s (95000 95%) 3.2365
+13m 20s (100000 100%) 1.7113
 </pre></div>
 </div>
 </div>
@@ -822,7 +822,7 @@ <h3>Plotting the Losses<a class="headerlink" href="#plotting-the-losses" title="
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">all_losses</span><span class="p">)</span>
 </pre></div>
 </div>
-<img alt="char rnn generation tutorial" class="sphx-glr-single-img" src="../_images/sphx_glr_char_rnn_generation_tutorial_001.png" srcset="../_images/sphx_glr_char_rnn_generation_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D object at 0x7f46681c4070&gt;]
+<img alt="char rnn generation tutorial" class="sphx-glr-single-img" src="../_images/sphx_glr_char_rnn_generation_tutorial_001.png" srcset="../_images/sphx_glr_char_rnn_generation_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D object at 0x7f20c0c74100&gt;]
 </pre></div>
 </div>
 </div>
@@ -923,7 +923,7 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this he
 </ul>
 </li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 13 minutes  25.694 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 13 minutes  21.102 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-char-rnn-generation-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/322506af160d5e2056afd75de1fd34ee/char_rnn_generation_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">char_rnn_generation_tutorial.py</span></code></a></p>
diff --git a/intermediate/custom_function_conv_bn_tutorial.html b/intermediate/custom_function_conv_bn_tutorial.html
index f796ace6dc..5c2925285d 100644
--- a/intermediate/custom_function_conv_bn_tutorial.html
+++ b/intermediate/custom_function_conv_bn_tutorial.html
@@ -900,22 +900,22 @@ <h2>A Comparison of Memory Usage<a class="headerlink" href="#a-comparison-of-mem
 Test set: Average loss: 0.4197, Accuracy: 8681/10000 (87%)
 
 Train Epoch: 0 [0/60000 (0%)]   Loss: 2.349030
-Train Epoch: 0 [4096/60000 (7%)]        Loss: 7.435157
-Train Epoch: 0 [8192/60000 (13%)]       Loss: 5.443542
-Train Epoch: 0 [12288/60000 (20%)]      Loss: 2.457858
-Train Epoch: 0 [16384/60000 (27%)]      Loss: 1.739216
-Train Epoch: 0 [20480/60000 (33%)]      Loss: 1.448291
-Train Epoch: 0 [24576/60000 (40%)]      Loss: 1.312150
-Train Epoch: 0 [28672/60000 (47%)]      Loss: 1.145356
-Train Epoch: 0 [32768/60000 (53%)]      Loss: 1.496005
-Train Epoch: 0 [36864/60000 (60%)]      Loss: 1.251128
-Train Epoch: 0 [40960/60000 (67%)]      Loss: 1.076815
-Train Epoch: 0 [45056/60000 (73%)]      Loss: 0.892333
-Train Epoch: 0 [49152/60000 (80%)]      Loss: 0.829717
-Train Epoch: 0 [53248/60000 (87%)]      Loss: 0.740473
-Train Epoch: 0 [57344/60000 (93%)]      Loss: 0.789344
+Train Epoch: 0 [4096/60000 (7%)]        Loss: 7.435156
+Train Epoch: 0 [8192/60000 (13%)]       Loss: 5.443526
+Train Epoch: 0 [12288/60000 (20%)]      Loss: 2.457884
+Train Epoch: 0 [16384/60000 (27%)]      Loss: 1.739214
+Train Epoch: 0 [20480/60000 (33%)]      Loss: 1.448314
+Train Epoch: 0 [24576/60000 (40%)]      Loss: 1.312073
+Train Epoch: 0 [28672/60000 (47%)]      Loss: 1.144974
+Train Epoch: 0 [32768/60000 (53%)]      Loss: 1.495681
+Train Epoch: 0 [36864/60000 (60%)]      Loss: 1.249685
+Train Epoch: 0 [40960/60000 (67%)]      Loss: 1.080824
+Train Epoch: 0 [45056/60000 (73%)]      Loss: 0.894487
+Train Epoch: 0 [49152/60000 (80%)]      Loss: 0.845663
+Train Epoch: 0 [53248/60000 (87%)]      Loss: 0.700515
+Train Epoch: 0 [57344/60000 (93%)]      Loss: 0.743951
 
-Test set: Average loss: 0.4183, Accuracy: 8869/10000 (89%)
+Test set: Average loss: 0.4384, Accuracy: 8719/10000 (87%)
 
 cuDNN version: 8902
 
@@ -925,7 +925,7 @@ <h2>A Comparison of Memory Usage<a class="headerlink" href="#a-comparison-of-mem
 fused: 0.59GB, unfused: 0.96GB
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  36.520 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  37.817 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-custom-function-conv-bn-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/187aea79daf1552dd05cdde1f4b4e34d/custom_function_conv_bn_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">custom_function_conv_bn_tutorial.py</span></code></a></p>
diff --git a/intermediate/dqn_with_rnn_tutorial.html b/intermediate/dqn_with_rnn_tutorial.html
index c47a51392d..842a04bf4f 100644
--- a/intermediate/dqn_with_rnn_tutorial.html
+++ b/intermediate/dqn_with_rnn_tutorial.html
@@ -980,15 +980,15 @@ <h2>Training loop<a class="headerlink" href="#training-loop" title="Permalink to
     device=cpu,
     is_shared=False)
 
-  0%|          | 50/1000000 [00:00&lt;2:26:56, 113.41it/s]
-  0%|          | 50/1000000 [00:15&lt;2:26:56, 113.41it/s]
-steps: 16, loss_val:  0.0002, action_spread: tensor([ 8, 42]):   0%|          | 50/1000000 [00:25&lt;2:26:56, 113.41it/s]
-steps: 16, loss_val:  0.0002, action_spread: tensor([ 8, 42]):   0%|          | 100/1000000 [00:26&lt;85:13:26,  3.26it/s]
-steps: 16, loss_val:  0.0003, action_spread: tensor([40, 10]):   0%|          | 100/1000000 [00:50&lt;85:13:26,  3.26it/s]
-steps: 16, loss_val:  0.0003, action_spread: tensor([40, 10]):   0%|          | 150/1000000 [00:51&lt;110:30:44,  2.51it/s]
-steps: 16, loss_val:  0.0003, action_spread: tensor([ 8, 42]):   0%|          | 150/1000000 [01:16&lt;110:30:44,  2.51it/s]
-steps: 16, loss_val:  0.0003, action_spread: tensor([ 8, 42]):   0%|          | 200/1000000 [01:16&lt;122:26:43,  2.27it/s]
-steps: 16, loss_val:  0.0003, action_spread: tensor([43,  7]):   0%|          | 200/1000000 [01:41&lt;122:26:43,  2.27it/s]
+  0%|          | 50/1000000 [00:00&lt;2:24:15, 115.53it/s]
+  0%|          | 50/1000000 [00:10&lt;2:24:15, 115.53it/s]
+steps: 14, loss_val:  0.0005, action_spread: tensor([ 9, 41]):   0%|          | 50/1000000 [00:26&lt;2:24:15, 115.53it/s]
+steps: 14, loss_val:  0.0005, action_spread: tensor([ 9, 41]):   0%|          | 100/1000000 [00:27&lt;87:57:07,  3.16it/s]
+steps: 14, loss_val:  0.0006, action_spread: tensor([42,  8]):   0%|          | 100/1000000 [00:52&lt;87:57:07,  3.16it/s]
+steps: 14, loss_val:  0.0006, action_spread: tensor([42,  8]):   0%|          | 150/1000000 [00:53&lt;114:57:29,  2.42it/s]
+steps: 14, loss_val:  0.0007, action_spread: tensor([ 7, 43]):   0%|          | 150/1000000 [01:19&lt;114:57:29,  2.42it/s]
+steps: 14, loss_val:  0.0007, action_spread: tensor([ 7, 43]):   0%|          | 200/1000000 [01:20&lt;127:54:56,  2.17it/s]
+steps: 14, loss_val:  0.0007, action_spread: tensor([41,  9]):   0%|          | 200/1000000 [01:45&lt;127:54:56,  2.17it/s]
 </pre></div>
 </div>
 <p>Let’s plot our results:</p>
@@ -1020,7 +1020,7 @@ <h2>Further Reading<a class="headerlink" href="#further-reading" title="Permalin
 <ul class="simple">
 <li><p>The TorchRL documentation can be found <a class="reference external" href="https://pytorch.org/rl/">here</a>.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  46.960 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  51.213 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-dqn-with-rnn-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/14b68b9764c79afe8ef88b11fc27bff7/dqn_with_rnn_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">dqn_with_rnn_tutorial.py</span></code></a></p>
diff --git a/intermediate/ensembling.html b/intermediate/ensembling.html
index ec40fe082f..4be03d46cd 100644
--- a/intermediate/ensembling.html
+++ b/intermediate/ensembling.html
@@ -633,13 +633,13 @@ <h2>Performance<a class="headerlink" href="#performance" title="Permalink to thi
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Predictions with vmap </span><span class="si">{</span><a class="sphx-glr-backref-module-torch-utils-benchmark sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Timer.timeit" title="torch.utils.benchmark.Timer.timeit"><span class="n">with_vmap</span><span class="o">.</span><span class="n">timeit</span></a><span class="p">(</span><span class="mi">100</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Predictions without vmap &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f6a06751600&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Predictions without vmap &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f2f8a322890&gt;
 [model(minibatch) for model, minibatch in zip(models, minibatches)]
-  2.08 ms
+  2.26 ms
   1 measurement, 100 runs , 1 thread
-Predictions with vmap &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f6a06752c80&gt;
+Predictions with vmap &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f2f8a322710&gt;
 vmap(fmodel)(params, buffers, minibatches)
-  795.37 us
+  858.11 us
   1 measurement, 100 runs , 1 thread
 </pre></div>
 </div>
@@ -650,7 +650,7 @@ <h2>Performance<a class="headerlink" href="#performance" title="Permalink to thi
 operation or if the underlying kernels weren’t optimized for older hardware
 (GPUs). If you see any of these cases, please let us know by opening an issue
 on GitHub.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.835 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.879 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-ensembling-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/626f23350a6d0b457ded1932a69ec7eb/ensembling.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">ensembling.py</span></code></a></p>
diff --git a/intermediate/forward_ad_usage.html b/intermediate/forward_ad_usage.html
index 8afd388a15..ef546ee4ca 100644
--- a/intermediate/forward_ad_usage.html
+++ b/intermediate/forward_ad_usage.html
@@ -734,7 +734,7 @@ <h2>Using the functional API with Modules<a class="headerlink" href="#using-the-
 </pre></div>
 </div>
 <p>[0] <a class="reference external" href="https://en.wikipedia.org/wiki/Dual_number">https://en.wikipedia.org/wiki/Dual_number</a></p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.152 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.155 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-forward-ad-usage-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/3a285734c191abde60d7db0362f294b1/forward_ad_usage.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">forward_ad_usage.py</span></code></a></p>
diff --git a/intermediate/fx_profiling_tutorial.html b/intermediate/fx_profiling_tutorial.html
index 4830eb4240..5cdaa7d3ab 100644
--- a/intermediate/fx_profiling_tutorial.html
+++ b/intermediate/fx_profiling_tutorial.html
@@ -843,77 +843,77 @@ <h2>Investigating the Performance of ResNet18<a class="headerlink" href="#invest
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Op type        Op                       Average runtime (s)    Pct total runtime
 -------------  ---------------------  ---------------------  -------------------
-call_module    maxpool                          0.00575376             8.70941
-call_module    layer4_1_conv2                   0.00491071             7.4333
-call_module    conv1                            0.00479746             7.26187
-call_module    layer4_0_conv2                   0.00369048             5.58625
-call_module    layer1_0_conv1                   0.00338578             5.12503
-call_module    layer1_1_conv2                   0.00320458             4.85075
-call_module    layer4_1_conv1                   0.00319433             4.83523
-call_module    layer1_0_conv2                   0.00295234             4.46893
-call_module    layer2_1_conv1                   0.0028708              4.3455
-call_module    layer3_1_conv1                   0.00283694             4.29426
-call_module    layer3_1_conv2                   0.00275111             4.16434
-call_module    layer3_0_conv2                   0.00265932             4.02539
-call_module    layer1_1_conv1                   0.00259066             3.92146
-call_module    layer2_1_conv2                   0.0025208              3.81571
-call_module    layer2_0_conv2                   0.00239658             3.62769
-call_module    layer4_0_conv1                   0.00222993             3.37543
-call_module    layer3_0_conv1                   0.00175953             2.66338
-call_module    bn1                              0.00150132             2.27254
-call_module    layer2_0_conv1                   0.00146842             2.22274
-call_module    layer2_0_downsample_0            0.000971079            1.46991
-call_module    layer3_0_downsample_0            0.000450134            0.681365
-call_module    layer4_0_downsample_0            0.000447512            0.677395
-call_function  add                              0.000422955            0.640223
-call_function  add_1                            0.00041914             0.634449
-call_module    relu                             0.000286579            0.433793
-call_function  add_3                            0.000248194            0.375689
-call_module    layer1_0_bn1                     0.000195265            0.295571
-call_module    fc                               0.000192881            0.291962
-call_module    layer1_1_bn2                     0.000168562            0.255151
-call_module    layer1_0_bn2                     0.000168085            0.254429
-call_module    avgpool                          0.000153065            0.231693
-call_module    layer1_1_bn1                     0.000148773            0.225197
-call_module    layer4_1_bn2                     0.000141859            0.214731
-call_module    layer2_0_downsample_1            0.000134468            0.203543
-call_module    layer2_1_bn1                     0.000132799            0.201017
-call_module    layer2_0_bn2                     0.000128746            0.194882
-call_module    layer1_1_relu_1                  0.000128508            0.194521
-call_module    layer3_1_bn1                     0.000123501            0.186942
-call_module    layer2_0_bn1                     0.000122309            0.185138
-call_module    layer3_1_bn2                     0.000121117            0.183333
-call_module    layer2_1_bn2                     0.000119686            0.181168
-call_module    layer4_1_bn1                     0.000119448            0.180807
-call_module    layer3_0_bn1                     0.000116348            0.176115
-call_module    layer4_0_bn2                     0.000114679            0.173589
-call_module    layer1_0_relu                    0.000113726            0.172146
-call_module    layer3_0_bn2                     0.00011301             0.171063
-call_module    layer4_0_bn1                     0.000112057            0.169619
-call_module    layer3_0_downsample_1            0.00011158             0.168898
-call_module    layer4_0_downsample_1            0.000108957            0.164928
-call_module    layer1_1_relu                    9.84669e-05            0.149049
-call_module    layer1_0_relu_1                  9.75132e-05            0.147605
-call_module    layer4_1_relu                    8.63075e-05            0.130643
-call_function  add_2                            8.05855e-05            0.121982
-call_module    layer2_1_relu                    8.03471e-05            0.121621
-call_module    layer4_0_relu                    8.01086e-05            0.12126
-call_function  add_7                            8.01086e-05            0.12126
-call_module    layer2_0_relu                    7.96318e-05            0.120538
-call_module    layer2_1_relu_1                  7.82013e-05            0.118373
-call_module    layer2_0_relu_1                  7.67708e-05            0.116207
-call_module    layer3_1_relu                    7.39098e-05            0.111877
-call_module    layer3_0_relu                    7.31945e-05            0.110794
-call_function  add_4                            7.29561e-05            0.110433
-call_module    layer4_1_relu_1                  7.27177e-05            0.110072
-call_function  add_6                            7.15256e-05            0.108268
-call_module    layer4_0_relu_1                  7.10487e-05            0.107546
-call_module    layer3_1_relu_1                  6.98566e-05            0.105741
-call_function  add_5                            6.96182e-05            0.105381
-call_module    layer3_0_relu_1                  6.93798e-05            0.10502
-call_function  flatten                          4.29153e-05            0.0649606
-placeholder    x                                2.5034e-05             0.0378937
-output         output                           1.85966e-05            0.0281496
+call_module    maxpool                          0.00839758             8.8298
+call_module    conv1                            0.00785089             8.25497
+call_module    layer1_1_conv1                   0.00579381             6.09202
+call_module    layer1_0_conv2                   0.00575066             6.04664
+call_module    layer1_0_conv1                   0.00556517             5.85161
+call_module    layer2_1_conv2                   0.00535631             5.632
+call_module    layer2_1_conv1                   0.00509667             5.359
+call_module    layer1_1_conv2                   0.00482035             5.06845
+call_module    layer2_0_conv2                   0.00472021             4.96316
+call_module    layer4_0_conv2                   0.00393105             4.13338
+call_module    layer4_1_conv1                   0.00334334             3.51543
+call_module    layer4_1_conv2                   0.00333142             3.50289
+call_module    layer2_0_conv1                   0.00315976             3.32239
+call_module    layer3_1_conv2                   0.00291038             3.06017
+call_module    layer3_1_conv1                   0.00289321             3.04212
+call_module    layer3_0_conv1                   0.00289011             3.03886
+call_module    layer3_0_conv2                   0.00272608             2.86639
+call_module    bn1                              0.0023737              2.49587
+call_module    layer4_0_conv1                   0.00233245             2.4525
+call_module    layer2_0_downsample_0            0.00140858             1.48108
+call_function  add_1                            0.000641108            0.674105
+call_module    relu                             0.000547171            0.575334
+call_module    layer3_0_downsample_0            0.000541925            0.569818
+call_module    layer4_0_downsample_0            0.000512362            0.538733
+call_function  add                              0.000449419            0.472551
+call_module    layer1_1_bn1                     0.000344038            0.361746
+call_module    layer1_0_bn2                     0.00033474             0.351969
+call_module    layer1_0_bn1                     0.00032258             0.339184
+call_module    layer1_1_bn2                     0.000280857            0.295313
+call_module    layer2_1_bn2                     0.000243187            0.255704
+call_module    layer2_1_bn1                     0.000236273            0.248434
+call_module    layer2_0_bn1                     0.000234127            0.246178
+call_module    layer2_0_bn2                     0.000226974            0.238657
+call_module    layer2_0_downsample_1            0.000204802            0.215343
+call_module    fc                               0.000177145            0.186263
+call_module    layer1_0_relu                    0.000175714            0.184759
+call_module    layer4_0_bn2                     0.000174522            0.183505
+call_module    layer1_1_relu                    0.000173569            0.182502
+call_module    layer1_0_relu_1                  0.000165939            0.17448
+call_function  add_3                            0.000164747            0.173227
+call_module    layer3_0_bn1                     0.00016427             0.172725
+call_module    layer1_1_relu_1                  0.000163555            0.171973
+call_module    layer4_1_bn1                     0.000163078            0.171472
+call_module    layer4_1_bn2                     0.000160694            0.168965
+call_module    layer4_0_downsample_1            0.00015974             0.167962
+call_module    avgpool                          0.000156164            0.164202
+call_function  add_2                            0.000155926            0.163951
+call_module    layer3_1_bn1                     0.000144958            0.15242
+call_module    layer2_1_relu                    0.000142813            0.150163
+call_module    layer3_1_bn2                     0.000141621            0.14891
+call_module    layer2_1_relu_1                  0.000133753            0.140637
+call_module    layer4_0_bn1                     0.000132799            0.139634
+call_module    layer3_0_bn2                     0.000131845            0.138632
+call_module    layer2_0_relu_1                  0.000128746            0.135373
+call_module    layer2_0_relu                    0.000125408            0.131863
+call_module    layer3_0_downsample_1            0.000123739            0.130108
+call_module    layer4_1_relu                    0.000109196            0.114816
+call_function  add_6                            0.000101089            0.106293
+call_module    layer4_0_relu_1                  9.13143e-05            0.0960143
+call_module    layer4_0_relu                    8.82149e-05            0.0927553
+call_function  add_7                            8.79765e-05            0.0925046
+call_function  add_5                            8.44002e-05            0.0887443
+call_module    layer4_1_relu_1                  8.32081e-05            0.0874908
+call_module    layer3_0_relu                    8.13007e-05            0.0854853
+call_module    layer3_1_relu                    7.9155e-05             0.0832291
+call_module    layer3_0_relu_1                  7.65324e-05            0.0804715
+call_module    layer3_1_relu_1                  7.43866e-05            0.0782153
+call_function  add_4                            7.27177e-05            0.0764605
+call_function  flatten                          4.48227e-05            0.0471297
+placeholder    x                                2.93255e-05            0.0308349
+output         output                           1.90735e-05            0.0200552
 </pre></div>
 </div>
 <p>There are two things we should call out here:</p>
@@ -937,7 +937,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 PyTorch Forums (<a class="reference external" href="https://discuss.pytorch.org/">https://discuss.pytorch.org/</a>) and the issue tracker
 (<a class="reference external" href="https://github.com/pytorch/pytorch/issues">https://github.com/pytorch/pytorch/issues</a>) to provide any feedback
 you might have.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.418 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.493 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-fx-profiling-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/8c575aa36ad9a61584ec0ddf11cbe84d/fx_profiling_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">fx_profiling_tutorial.py</span></code></a></p>
diff --git a/intermediate/inductor_debug_cpu.html b/intermediate/inductor_debug_cpu.html
index dedc8d4d04..9a2fc92dce 100644
--- a/intermediate/inductor_debug_cpu.html
+++ b/intermediate/inductor_debug_cpu.html
@@ -510,17 +510,17 @@ <h2>Debugging<a class="headerlink" href="#debugging" title="Permalink to this he
 <p>Here is a simple example to run the <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> using Inductor and compare its result with eager mode:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 
-<span class="k">def</span> <span class="nf">foo1</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x1</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x2</span></a><span class="p">):</span>
-    <span class="n">a</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.neg.html#torch.neg" title="torch.neg"><span class="n">torch</span><span class="o">.</span><span class="n">neg</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x1</span></a><span class="p">)</span>
-    <span class="n">b</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum" title="torch.maximum"><span class="n">torch</span><span class="o">.</span><span class="n">maximum</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x2</span></a><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">foo1</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">):</span>
+    <span class="n">a</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.neg.html#torch.neg" title="torch.neg"><span class="n">torch</span><span class="o">.</span><span class="n">neg</span></a><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
+    <span class="n">b</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum" title="torch.maximum"><span class="n">torch</span><span class="o">.</span><span class="n">maximum</span></a><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
     <span class="n">y</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat" title="torch.cat"><span class="n">torch</span><span class="o">.</span><span class="n">cat</span></a><span class="p">([</span><span class="n">b</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">y</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x1</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randint.html#torch.randint" title="torch.randint"><span class="n">torch</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span></a><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x2</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randint.html#torch.randint" title="torch.randint"><span class="n">torch</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="p">(</span><span class="mi">8390</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span></a><span class="p">)</span>
+<span class="n">x1</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randint.html#torch.randint" title="torch.randint"><span class="n">torch</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
+<span class="n">x2</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randint.html#torch.randint" title="torch.randint"><span class="n">torch</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="p">(</span><span class="mi">8390</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
 
 <span class="n">compiled_foo1</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.compile.html#torch.compile" title="torch.compile"><span class="n">torch</span><span class="o">.</span><span class="n">compile</span></a><span class="p">(</span><span class="n">foo1</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">result</span></a> <span class="o">=</span> <span class="n">compiled_foo1</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x1</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x2</span></a><span class="p">)</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">compiled_foo1</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>The correct implementation of <code class="docutils literal notranslate"><span class="pre">neg</span></code> in the <code class="docutils literal notranslate"><span class="pre">cpp</span></code> codegen is as follows:</p>
@@ -735,21 +735,21 @@ <h3>Accuracy debugging<a class="headerlink" href="#accuracy-debugging" title="Pe
 <p>For instance, we modify the example like this:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch._dynamo.utils</span> <span class="kn">import</span> <span class="n">same</span>
 
-<span class="k">def</span> <span class="nf">foo2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x1</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x2</span></a><span class="p">):</span>
-    <span class="n">a</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.neg.html#torch.neg" title="torch.neg"><span class="n">torch</span><span class="o">.</span><span class="n">neg</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x1</span></a><span class="p">)</span>
-    <span class="n">b</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum" title="torch.maximum"><span class="n">torch</span><span class="o">.</span><span class="n">maximum</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x2</span></a><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">foo2</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">):</span>
+    <span class="n">a</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.neg.html#torch.neg" title="torch.neg"><span class="n">torch</span><span class="o">.</span><span class="n">neg</span></a><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
+    <span class="n">b</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.maximum.html#torch.maximum" title="torch.maximum"><span class="n">torch</span><span class="o">.</span><span class="n">maximum</span></a><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
     <span class="n">y</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat" title="torch.cat"><span class="n">torch</span><span class="o">.</span><span class="n">cat</span></a><span class="p">([</span><span class="n">b</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">y</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x1</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">float32</span></a><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x2</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">((</span><span class="mi">8390</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">float32</span></a><span class="p">)</span>
+<span class="n">x1</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">x2</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">((</span><span class="mi">8390</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">expected_result</span></a> <span class="o">=</span> <span class="n">foo2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x1</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x2</span></a><span class="p">)</span>
+<span class="n">expected_result</span> <span class="o">=</span> <span class="n">foo2</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
 
 <span class="n">compiled_foo2</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.compile.html#torch.compile" title="torch.compile"><span class="n">torch</span><span class="o">.</span><span class="n">compile</span></a><span class="p">(</span><span class="n">foo2</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">actual_result</span></a> <span class="o">=</span> <span class="n">compiled_foo2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x1</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x2</span></a><span class="p">)</span>
+<span class="n">actual_result</span> <span class="o">=</span> <span class="n">compiled_foo2</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
 
-<span class="k">assert</span> <span class="n">same</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">expected_result</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">actual_result</span></a><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span>
+<span class="k">assert</span> <span class="n">same</span><span class="p">(</span><span class="n">expected_result</span><span class="p">,</span> <span class="n">actual_result</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span>
 </pre></div>
 </div>
 <p>And also modify the <code class="docutils literal notranslate"><span class="pre">neg</span></code> function:</p>
@@ -812,23 +812,23 @@ <h2>Performance profiling<a class="headerlink" href="#performance-profiling" tit
 <span class="n">seq_length</span> <span class="o">=</span> <span class="mi">128</span>
 <span class="n">bs</span> <span class="o">=</span> <span class="mi">128</span>
 <span class="n">vocab_size</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">vocab_size</span>
-<span class="nb">input</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randint.html#torch.randint" title="torch.randint"><span class="n">torch</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="p">(</span><span class="n">bs</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">int64</span></a><span class="p">)</span>
+<span class="nb">input</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randint.html#torch.randint" title="torch.randint"><span class="n">torch</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="p">(</span><span class="n">bs</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
 <span class="n">input_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"input_ids"</span><span class="p">:</span> <span class="nb">input</span><span class="p">}</span>
 
 <span class="c1"># Initialize the inductor model</span>
 <span class="n">compiled_model</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.compile.html#torch.compile" title="torch.compile"><span class="n">torch</span><span class="o">.</span><span class="n">compile</span></a><span class="p">(</span><span class="n">model</span><span class="p">)</span>
-<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
+<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
     <span class="n">compiled_model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span>
 
 <span class="n">NUM_ITERS</span><span class="o">=</span><span class="mi">50</span>
 <span class="kn">import</span> <span class="nn">timeit</span>
-<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
+<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
     <span class="c1"># warmup</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
         <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span>
     <span class="n">eager_t</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="s2">"model(**input_dict)"</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="n">NUM_ITERS</span><span class="p">,</span> <span class="nb">globals</span><span class="o">=</span><span class="nb">globals</span><span class="p">())</span>
 
-<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
+<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
     <span class="c1"># warmup</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
         <span class="n">compiled_model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span>
@@ -838,24 +838,6 @@ <h2>Performance profiling<a class="headerlink" href="#performance-profiling" tit
 <span class="c1"># print(f"speed up ratio: {eager_t / inductor_t}")</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning:
-
-torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-
-/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning:
-
-torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
-
-
-Downloading config.json:   0%|          | 0.00/765 [00:00&lt;?, ?B/s]
-Downloading config.json: 100%|##########| 765/765 [00:00&lt;00:00, 4.69MB/s]
-
-Downloading model.safetensors:   0%|          | 0.00/98.5M [00:00&lt;?, ?B/s]
-Downloading model.safetensors:  43%|####2     | 41.9M/98.5M [00:00&lt;00:00, 373MB/s]
-Downloading model.safetensors:  85%|########5 | 83.9M/98.5M [00:00&lt;00:00, 297MB/s]
-Downloading model.safetensors: 100%|##########| 98.5M/98.5M [00:00&lt;00:00, 311MB/s]
-</pre></div>
-</div>
 <p>Output:</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>eager<span class="w"> </span>use:<span class="w"> </span><span class="m">802</span>.1023553796113<span class="w"> </span>ms/iter
 inductor<span class="w"> </span>use:<span class="w"> </span><span class="m">339</span>.95180135127157<span class="w"> </span>ms/iter
@@ -873,7 +855,7 @@ <h2>Performance profiling<a class="headerlink" href="#performance-profiling" tit
 <p>Following the steps in <a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html">Pytorch Profiler</a>
 We are able to get the profiling table and trace files.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># bench.py</span>
-<span class="kn">from</span> <span class="nn">torch.profiler</span> <span class="kn">import</span> <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.profile" title="torch.profiler.profile"><span class="n">profile</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.schedule" title="torch.profiler.schedule"><span class="n">schedule</span></a><span class="p">,</span> <span class="n">ProfilerActivity</span>
+<span class="kn">from</span> <span class="nn">torch.profiler</span> <span class="kn">import</span> <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.profile" title="torch.profiler.profile"><span class="n">profile</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.schedule" title="torch.profiler.schedule"><span class="n">schedule</span></a><span class="p">,</span> <span class="n">ProfilerActivity</span>
 <span class="n">RESULT_DIR</span> <span class="o">=</span> <span class="s2">"./prof_trace"</span>
 <span class="n">my_schedule</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.schedule" title="torch.profiler.schedule"><span class="n">schedule</span></a><span class="p">(</span>
     <span class="n">skip_first</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
@@ -882,23 +864,23 @@ <h2>Performance profiling<a class="headerlink" href="#performance-profiling" tit
     <span class="n">active</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
     <span class="n">repeat</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
 
-<span class="k">def</span> <span class="nf">trace_handler</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.profile" title="torch.profiler.profile"><span class="n">p</span></a><span class="p">):</span>
-    <span class="n">output</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler._KinetoProfile.key_averages" title="torch.profiler._KinetoProfile.key_averages"><span class="n">p</span><span class="o">.</span><span class="n">key_averages</span></a><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">"self_cpu_time_total"</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">trace_handler</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
+    <span class="n">output</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">"self_cpu_time_total"</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
     <span class="c1"># print(output)</span>
-    <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler._KinetoProfile.export_chrome_trace" title="torch.profiler._KinetoProfile.export_chrome_trace"><span class="n">p</span><span class="o">.</span><span class="n">export_chrome_trace</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">RESULT_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.profile" title="torch.profiler.profile"><span class="n">p</span></a><span class="o">.</span><span class="n">step_num</span><span class="si">}</span><span class="s2">.json"</span><span class="p">)</span>
+    <span class="n">p</span><span class="o">.</span><span class="n">export_chrome_trace</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">RESULT_DIR</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">p</span><span class="o">.</span><span class="n">step_num</span><span class="si">}</span><span class="s2">.json"</span><span class="p">)</span>
 
 <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
     <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span>  <span class="c1"># compiled_model(**input_dict) to get inductor model profiling</span>
 
 <span class="n">total</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="k">with</span> <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.profile" title="torch.profiler.profile"><span class="n">profile</span></a><span class="p">(</span>
-    <span class="n">activities</span><span class="o">=</span><span class="p">[</span><a class="sphx-glr-backref-module-torch-_C-_profiler sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.ProfilerActivity" title="torch._C._profiler.ProfilerActivity"><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CPU</span></a><span class="p">],</span>
+<span class="k">with</span> <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.profile" title="torch.profiler.profile"><span class="n">profile</span></a><span class="p">(</span>
+    <span class="n">activities</span><span class="o">=</span><span class="p">[</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CPU</span><span class="p">],</span>
     <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.schedule" title="torch.profiler.schedule"><span class="n">schedule</span></a><span class="o">=</span><span class="n">my_schedule</span><span class="p">,</span>
     <span class="n">on_trace_ready</span><span class="o">=</span><span class="n">trace_handler</span>
-<span class="p">)</span> <span class="k">as</span> <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.profile" title="torch.profiler.profile"><span class="n">p</span></a><span class="p">:</span>
+<span class="p">)</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">):</span>
         <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">input_dict</span><span class="p">)</span>  <span class="c1"># compiled_model(**input_dict) to get inductor model profiling</span>
-        <a class="sphx-glr-backref-module-torch-profiler sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/profiler.html#torch.profiler.profile.step" title="torch.profiler.profile.step"><span class="n">p</span><span class="o">.</span><span class="n">step</span></a><span class="p">()</span>
+        <span class="n">p</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
 </pre></div>
 </div>
 <p>We get the following performance profiling table for the eager-mode model (omitting some columns):</p>
@@ -1007,34 +989,34 @@ <h2>Performance profiling<a class="headerlink" href="#performance-profiling" tit
 This is a memory-bound bottle neck preventing good performance. To get a more intuitive feeling about this optimization,
 we can infer the sizes and stride of the inputs and further benchmark this <code class="docutils literal notranslate"><span class="pre">[add,</span> <span class="pre">add,</span> <span class="pre">mul,</span> <span class="pre">add]</span></code> pattern.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># bench.py</span>
-<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_0</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_1</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_2</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_3</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_4</span></a><span class="p">):</span>
-    <span class="n">add_0</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_0</span></a> <span class="o">+</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_1</span></a>
-    <span class="n">add_1</span> <span class="o">=</span> <span class="n">add_0</span> <span class="o">+</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_2</span></a>
-    <span class="n">mul_1</span> <span class="o">=</span> <span class="n">add_1</span> <span class="o">*</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_3</span></a>
-    <span class="n">add_2</span> <span class="o">=</span> <span class="n">mul_1</span> <span class="o">+</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_4</span></a>
-    <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_2</span></a> <span class="o">=</span> <span class="n">add_2</span>
-    <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_2</span></a>
+<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">arg_0</span><span class="p">,</span> <span class="n">arg_1</span><span class="p">,</span> <span class="n">arg_2</span><span class="p">,</span> <span class="n">arg_3</span><span class="p">,</span> <span class="n">arg_4</span><span class="p">):</span>
+    <span class="n">add_0</span> <span class="o">=</span> <span class="n">arg_0</span> <span class="o">+</span> <span class="n">arg_1</span>
+    <span class="n">add_1</span> <span class="o">=</span> <span class="n">add_0</span> <span class="o">+</span> <span class="n">arg_2</span>
+    <span class="n">mul_1</span> <span class="o">=</span> <span class="n">add_1</span> <span class="o">*</span> <span class="n">arg_3</span>
+    <span class="n">add_2</span> <span class="o">=</span> <span class="n">mul_1</span> <span class="o">+</span> <span class="n">arg_4</span>
+    <span class="n">arg_2</span> <span class="o">=</span> <span class="n">add_2</span>
+    <span class="k">return</span> <span class="n">arg_2</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_0</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.rand.html#torch.rand" title="torch.rand"><span class="n">torch</span><span class="o">.</span><span class="n">rand</span></a><span class="p">(</span><span class="mi">16384</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_1</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.rand.html#torch.rand" title="torch.rand"><span class="n">torch</span><span class="o">.</span><span class="n">rand</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_2</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.zeros.html#torch.zeros" title="torch.zeros"><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="mi">16384</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_3</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.rand.html#torch.rand" title="torch.rand"><span class="n">torch</span><span class="o">.</span><span class="n">rand</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_4</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.rand.html#torch.rand" title="torch.rand"><span class="n">torch</span><span class="o">.</span><span class="n">rand</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
+<span class="n">arg_0</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.rand.html#torch.rand" title="torch.rand"><span class="n">torch</span><span class="o">.</span><span class="n">rand</span></a><span class="p">(</span><span class="mi">16384</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
+<span class="n">arg_1</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.rand.html#torch.rand" title="torch.rand"><span class="n">torch</span><span class="o">.</span><span class="n">rand</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
+<span class="n">arg_2</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.zeros.html#torch.zeros" title="torch.zeros"><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="mi">16384</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
+<span class="n">arg_3</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.rand.html#torch.rand" title="torch.rand"><span class="n">torch</span><span class="o">.</span><span class="n">rand</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
+<span class="n">arg_4</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.rand.html#torch.rand" title="torch.rand"><span class="n">torch</span><span class="o">.</span><span class="n">rand</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">)</span>
 
-<span class="nb">input</span> <span class="o">=</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_0</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_1</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_2</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_3</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">arg_4</span></a><span class="p">)</span>
+<span class="nb">input</span> <span class="o">=</span> <span class="p">(</span><span class="n">arg_0</span><span class="p">,</span> <span class="n">arg_1</span><span class="p">,</span> <span class="n">arg_2</span><span class="p">,</span> <span class="n">arg_3</span><span class="p">,</span> <span class="n">arg_4</span><span class="p">)</span>
 <span class="n">inductor_func</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.compile.html#torch.compile" title="torch.compile"><span class="n">torch</span><span class="o">.</span><span class="n">compile</span></a><span class="p">(</span><span class="n">func</span><span class="p">)</span>
-<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
+<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
     <span class="n">inductor_func</span><span class="p">(</span><span class="o">*</span><span class="nb">input</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">timeit</span>
 <span class="n">NUM_ITERS</span><span class="o">=</span><span class="mi">100</span>
-<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
+<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
     <span class="c1"># warmup</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
         <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="nb">input</span><span class="p">)</span>
     <span class="n">eager_t</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="s2">"func(*input)"</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="n">NUM_ITERS</span><span class="p">,</span> <span class="nb">globals</span><span class="o">=</span><span class="nb">globals</span><span class="p">())</span>
 
-<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
+<span class="k">with</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
     <span class="c1"># warmup</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
         <span class="n">inductor_func</span><span class="p">(</span><span class="o">*</span><span class="nb">input</span><span class="p">)</span>
@@ -1062,7 +1044,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 Firstly determine which component the failure occurs in and then try to generate the smallest snippet of code that can reproduce the failure.</p>
 <p>When the performance with Inductor is better than that of eager mode, we provide a solid analytical method for performance profiling.
 We show how to find the time-consuming hotspot with PyTorch Profiler and figure out the operator-level or kernel-level reason to explain the phenomenon.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 9 minutes  30.097 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-inductor-debug-cpu-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/864b90f09a798ba06b420b737cd463b1/inductor_debug_cpu.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">inductor_debug_cpu.py</span></code></a></p>
diff --git a/intermediate/jacobians_hessians.html b/intermediate/jacobians_hessians.html
index 96055deeac..dc9f1db9e7 100644
--- a/intermediate/jacobians_hessians.html
+++ b/intermediate/jacobians_hessians.html
@@ -607,13 +607,13 @@ <h2>Computing the Jacobian<a class="headerlink" href="#computing-the-jacobian" t
 <span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">with_vmap_timer</span></a><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba2eb5a20&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;torch.utils.benchmark.utils.common.Measurement object at 0x7fc877b78460&gt;
 compute_jac(xp)
-  2.41 ms
+  2.40 ms
   1 measurement, 500 runs , 1 thread
-&lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1bdca30&gt;
+&lt;torch.utils.benchmark.utils.common.Measurement object at 0x7fc876894820&gt;
 jacrev(predict, argnums=2)(weight, bias, x)
-  678.92 us
+  680.98 us
   1 measurement, 500 runs , 1 thread
 </pre></div>
 </div>
@@ -621,7 +621,7 @@ <h2>Computing the Jacobian<a class="headerlink" href="#computing-the-jacobian" t
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">get_perf</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">no_vmap_timer</span></a><span class="p">,</span> <span class="s2">"without vmap"</span><span class="p">,</span>  <a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">with_vmap_timer</span></a><span class="p">,</span> <span class="s2">"vmap"</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Performance delta: 71.8454 percent improvement with vmap
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Performance delta: 71.6275 percent improvement with vmap
 </pre></div>
 </div>
 <p>Furthermore, it’s pretty easy to flip the problem around and say we want to
@@ -676,13 +676,13 @@ <h2>Reverse-mode Jacobian (<code class="docutils literal notranslate"><span clas
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>torch.Size([2048, 32])
-jacfwd time: &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1a77fd0&gt;
+jacfwd time: &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7fc877b29c60&gt;
 jacfwd(predict, argnums=2)(weight, bias, x)
-  1.23 ms
+  1.31 ms
   1 measurement, 500 runs , 1 thread
-jacrev time: &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1bdd030&gt;
+jacrev time: &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7fc876895990&gt;
 jacrev(predict, argnums=2)(weight, bias, x)
-  13.41 ms
+  13.13 ms
   1 measurement, 500 runs , 1 thread
 </pre></div>
 </div>
@@ -690,7 +690,7 @@ <h2>Reverse-mode Jacobian (<code class="docutils literal notranslate"><span clas
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">get_perf</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">jacfwd_timing</span></a><span class="p">,</span> <span class="s2">"jacfwd"</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">jacrev_timing</span></a><span class="p">,</span> <span class="s2">"jacrev"</span><span class="p">,</span> <span class="p">);</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Performance delta: 985.8145 percent improvement with jacrev
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Performance delta: 904.8346 percent improvement with jacrev
 </pre></div>
 </div>
 <p>and now the reverse - more outputs (M) than inputs (N):</p>
@@ -710,13 +710,13 @@ <h2>Reverse-mode Jacobian (<code class="docutils literal notranslate"><span clas
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'jacrev time: </span><span class="si">{</span><a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">jacrev_timing</span></a><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>jacfwd time: &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1a9a530&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>jacfwd time: &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7fc8765927d0&gt;
 jacfwd(predict, argnums=2)(weight, bias, x)
-  6.73 ms
+  8.65 ms
   1 measurement, 500 runs , 1 thread
-jacrev time: &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f8ba1a741c0&gt;
+jacrev time: &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7fc876573a90&gt;
 jacrev(predict, argnums=2)(weight, bias, x)
-  783.92 us
+  822.61 us
   1 measurement, 500 runs , 1 thread
 </pre></div>
 </div>
@@ -724,7 +724,7 @@ <h2>Reverse-mode Jacobian (<code class="docutils literal notranslate"><span clas
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">get_perf</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">jacrev_timing</span></a><span class="p">,</span> <span class="s2">"jacrev"</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">jacfwd_timing</span></a><span class="p">,</span> <span class="s2">"jacfwd"</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Performance delta: 758.4283 percent improvement with jacfwd
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Performance delta: 951.6169 percent improvement with jacfwd
 </pre></div>
 </div>
 </div>
@@ -853,7 +853,7 @@ <h2>Computing Hessian-vector products<a class="headerlink" href="#computing-hess
 <span class="k">assert</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.allclose.html#torch.allclose" title="torch.allclose"><span class="n">torch</span><span class="o">.</span><span class="n">allclose</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">result</span></a><span class="p">,</span> <span class="n">result_hvp_revrev</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  13.847 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  14.813 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-jacobians-hessians-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/089b69a49b6eb4080d35c4b983b939a5/jacobians_hessians.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">jacobians_hessians.py</span></code></a></p>
diff --git a/intermediate/mario_rl_tutorial.html b/intermediate/mario_rl_tutorial.html
index ae8ad693cf..9fea9ce3cd 100644
--- a/intermediate/mario_rl_tutorial.html
+++ b/intermediate/mario_rl_tutorial.html
@@ -1184,9 +1184,9 @@ <h2>Let’s play!<a class="headerlink" href="#lets-play" title="Permalink to thi
 </div>
 <img alt="mario rl tutorial" class="sphx-glr-single-img" src="../_images/sphx_glr_mario_rl_tutorial_001.png" srcset="../_images/sphx_glr_mario_rl_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using CUDA: True
 
-Episode 0 - Step 163 - Epsilon 0.9999592508251706 - Mean Reward 635.0 - Mean Length 163.0 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 1.995 - Time 2024-06-25T20:49:45
-Episode 20 - Step 5007 - Epsilon 0.9987490329557962 - Mean Reward 667.429 - Mean Length 238.429 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 59.381 - Time 2024-06-25T20:50:45
-Episode 39 - Step 8854 - Epsilon 0.9977889477081997 - Mean Reward 656.6 - Mean Length 221.35 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 47.646 - Time 2024-06-25T20:51:32
+Episode 0 - Step 163 - Epsilon 0.9999592508251706 - Mean Reward 635.0 - Mean Length 163.0 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 2.018 - Time 2024-06-25T21:23:13
+Episode 20 - Step 5007 - Epsilon 0.9987490329557962 - Mean Reward 667.429 - Mean Length 238.429 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 59.836 - Time 2024-06-25T21:24:12
+Episode 39 - Step 8854 - Epsilon 0.9977889477081997 - Mean Reward 656.6 - Mean Length 221.35 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 48.305 - Time 2024-06-25T21:25:01
 </pre></div>
 </div>
 </div>
@@ -1195,7 +1195,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 <p>In this tutorial, we saw how we can use PyTorch to train a game-playing AI. You can use the same methods
 to train an AI to play any of the games at the <a class="reference external" href="https://gym.openai.com/">OpenAI gym</a>. Hope you enjoyed this tutorial, feel free to reach us at
 <a class="reference external" href="https://github.com/yuansongFeng/MadMario/">our github</a>!</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  50.056 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  51.191 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-mario-rl-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/38360df5715ca8f0d232e62f3a303840/mario_rl_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">mario_rl_tutorial.py</span></code></a></p>
diff --git a/intermediate/memory_format_tutorial.html b/intermediate/memory_format_tutorial.html
index e2bf06e865..e4c0477e5d 100644
--- a/intermediate/memory_format_tutorial.html
+++ b/intermediate/memory_format_tutorial.html
@@ -868,7 +868,7 @@ <h2>Work to do<a class="headerlink" href="#work-to-do" title="Permalink to this
 </ul>
 <p>If you have feedback and/or suggestions for improvement, please let us
 know by creating <a class="reference external" href="https://github.com/pytorch/pytorch/issues">an issue</a>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.064 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.062 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-memory-format-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/591028d309d0401740cd71eb6b14bf93/memory_format_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">memory_format_tutorial.py</span></code></a></p>
diff --git a/intermediate/model_parallel_tutorial.html b/intermediate/model_parallel_tutorial.html
index 4e0c9d8400..10430bfb85 100644
--- a/intermediate/model_parallel_tutorial.html
+++ b/intermediate/model_parallel_tutorial.html
@@ -817,7 +817,7 @@ <h2>Speed Up by Pipelining Inputs<a class="headerlink" href="#speed-up-by-pipeli
 for your environment, a proper approach is to first generate the curve to
 figure out the best split size, and then use that split size to pipeline
 inputs.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  42.800 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  42.102 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-model-parallel-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/84ab670fda2216116ac8e3ecd5805f0b/model_parallel_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">model_parallel_tutorial.py</span></code></a></p>
diff --git a/intermediate/neural_tangent_kernels.html b/intermediate/neural_tangent_kernels.html
index 23c2af51f5..a8df91d265 100644
--- a/intermediate/neural_tangent_kernels.html
+++ b/intermediate/neural_tangent_kernels.html
@@ -714,7 +714,7 @@ <h2>Compute the NTK: method 2 (NTK-vector products)<a class="headerlink" href="#
 Memory-wise, both methods should be comparable. See section 3.3 in
 <a class="reference external" href="https://arxiv.org/abs/2206.08720">Fast Finite Width Neural Tangent Kernel</a>
 for details.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.538 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.528 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-neural-tangent-kernels-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/57e71bcf0a6c2280481b4e79ca070e22/neural_tangent_kernels.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">neural_tangent_kernels.py</span></code></a></p>
diff --git a/intermediate/optimizer_step_in_backward_tutorial.html b/intermediate/optimizer_step_in_backward_tutorial.html
index 88e0568720..3c9c3ef421 100644
--- a/intermediate/optimizer_step_in_backward_tutorial.html
+++ b/intermediate/optimizer_step_in_backward_tutorial.html
@@ -518,127 +518,34 @@
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/vit_l_16-852ce7e3.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vit_l_16-852ce7e3.pth
 
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 </pre></div>
 </div>
 <p>Now let’s define our typical training loop. You should use real images when
@@ -861,7 +768,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 <code class="docutils literal notranslate"><span class="pre">Tensor.register_post_accumulate_grad_hook()</span></code> API and <em>when</em> to apply this
 technique (when gradients memory is significant). Along the way, we also learned
 about memory snapshots, which are generally useful in memory optimization.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  28.177 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  8.820 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-optimizer-step-in-backward-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/678a3ee2efc3fad76b565fdd254b297d/optimizer_step_in_backward_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">optimizer_step_in_backward_tutorial.py</span></code></a></p>
diff --git a/intermediate/parametrizations.html b/intermediate/parametrizations.html
index 260c746bb3..824722b8aa 100644
--- a/intermediate/parametrizations.html
+++ b/intermediate/parametrizations.html
@@ -1025,7 +1025,7 @@ <h2>Removing parametrizations<a class="headerlink" href="#removing-parametrizati
         [ 0.0000,  0.0000,  0.0000]], requires_grad=True)
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.057 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.052 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-parametrizations-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/621174a140b9f76910c50ed4afb0e621/parametrizations.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">parametrizations.py</span></code></a></p>
diff --git a/intermediate/per_sample_grads.html b/intermediate/per_sample_grads.html
index d0132ba95d..fbc25c5e21 100644
--- a/intermediate/per_sample_grads.html
+++ b/intermediate/per_sample_grads.html
@@ -678,15 +678,15 @@ <h2>Performance comparison<a class="headerlink" href="#performance-comparison" t
 <span class="n">get_perf</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">with_vmap_timing</span></a><span class="p">,</span> <span class="s2">"vmap"</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch-utils-benchmark-utils-common sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/benchmark_utils.html#torch.utils.benchmark.Measurement" title="torch.utils.benchmark.utils.common.Measurement"><span class="n">no_vmap_timing</span></a><span class="p">,</span> <span class="s2">"no vmap"</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Per-sample-grads without vmap &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f0799583820&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Per-sample-grads without vmap &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f3ba69d0c10&gt;
 compute_sample_grads(data, targets)
-  57.70 ms
+  57.50 ms
   1 measurement, 100 runs , 1 thread
-Per-sample-grads with vmap &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f07995180d0&gt;
+Per-sample-grads with vmap &lt;torch.utils.benchmark.utils.common.Measurement object at 0x7f3ba6980160&gt;
 ft_compute_sample_grad(params, buffers, data, targets)
-  2.88 ms
+  2.84 ms
   1 measurement, 100 runs , 1 thread
-Performance delta: 1904.7805 percent improvement with vmap
+Performance delta: 1921.5607 percent improvement with vmap
 </pre></div>
 </div>
 <p>There are other optimized solutions (like in <a class="reference external" href="https://github.com/pytorch/opacus">https://github.com/pytorch/opacus</a>)
@@ -699,7 +699,7 @@ <h2>Performance comparison<a class="headerlink" href="#performance-comparison" t
 operation or if the underlying kernels weren’t optimized for older hardware
 (GPUs). If you see any of these cases, please let us know by opening an issue
 at on GitHub.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  7.223 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  7.194 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-per-sample-grads-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/bb0e78bec4d7a6e9b86b2e285cd06671/per_sample_grads.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">per_sample_grads.py</span></code></a></p>
diff --git a/intermediate/pruning_tutorial.html b/intermediate/pruning_tutorial.html
index d3c2cdf573..f75b4858d9 100644
--- a/intermediate/pruning_tutorial.html
+++ b/intermediate/pruning_tutorial.html
@@ -787,7 +787,7 @@ <h2>Pruning a Module<a class="headerlink" href="#pruning-a-module" title="Permal
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">module</span></a><span class="o">.</span><span class="n">_forward_pre_hooks</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>OrderedDict([(3, &lt;torch.nn.utils.prune.RandomUnstructured object at 0x7f31756338b0&gt;)])
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>OrderedDict([(3, &lt;torch.nn.utils.prune.RandomUnstructured object at 0x7ff77eb07400&gt;)])
 </pre></div>
 </div>
 <p>For completeness, we can now prune the <code class="docutils literal notranslate"><span class="pre">bias</span></code> too, to see how the
@@ -909,7 +909,7 @@ <h2>Pruning a Module<a class="headerlink" href="#pruning-a-module" title="Permal
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">module</span></a><span class="o">.</span><span class="n">_forward_pre_hooks</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>OrderedDict([(3, &lt;torch.nn.utils.prune.RandomUnstructured object at 0x7f31756338b0&gt;), (4, &lt;torch.nn.utils.prune.L1Unstructured object at 0x7f31756338e0&gt;)])
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>OrderedDict([(3, &lt;torch.nn.utils.prune.RandomUnstructured object at 0x7ff77eb07400&gt;), (4, &lt;torch.nn.utils.prune.L1Unstructured object at 0x7ff77eb07eb0&gt;)])
 </pre></div>
 </div>
 </div>
@@ -986,7 +986,7 @@ <h2>Iterative Pruning<a class="headerlink" href="#iterative-pruning" title="Perm
 <span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn-utils-prune sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.utils.prune.PruningContainer.html#torch.nn.utils.prune.PruningContainer" title="torch.nn.utils.prune.PruningContainer"><span class="n">hook</span></a><span class="p">))</span>  <span class="c1"># pruning history in the container</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;torch.nn.utils.prune.RandomUnstructured object at 0x7f31756338b0&gt;, &lt;torch.nn.utils.prune.LnStructured object at 0x7f3175633cd0&gt;]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;torch.nn.utils.prune.RandomUnstructured object at 0x7ff77eb07400&gt;, &lt;torch.nn.utils.prune.LnStructured object at 0x7ff77eb07c40&gt;]
 </pre></div>
 </div>
 </div>
@@ -1401,7 +1401,7 @@ <h2>Extending <code class="docutils literal notranslate"><span class="pre">torch
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([0., 1., 0., 1., 0., 1., 0., 1., 0., 1.])
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.218 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.211 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-pruning-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/ef3541eb2ef78e22efa65b3d6f4ba737/pruning_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">pruning_tutorial.py</span></code></a></p>
diff --git a/intermediate/reinforcement_ppo.html b/intermediate/reinforcement_ppo.html
index d31fde5aed..ce31135499 100644
--- a/intermediate/reinforcement_ppo.html
+++ b/intermediate/reinforcement_ppo.html
@@ -1177,106 +1177,106 @@ <h2>Training loop<a class="headerlink" href="#training-loop" title="Permalink to
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  0%|          | 0/50000 [00:00&lt;?, ?it/s]
-  2%|2         | 1000/50000 [00:04&lt;04:03, 201.03it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.0852 (init= 9.0852), step count (max): 10, lr policy:  0.0003:   2%|2         | 1000/50000 [00:05&lt;04:03, 201.03it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.0852 (init= 9.0852), step count (max): 10, lr policy:  0.0003:   4%|4         | 2000/50000 [00:09&lt;03:58, 201.58it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1155 (init= 9.0852), step count (max): 14, lr policy:  0.0003:   4%|4         | 2000/50000 [00:09&lt;03:58, 201.58it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1155 (init= 9.0852), step count (max): 14, lr policy:  0.0003:   6%|6         | 3000/50000 [00:14&lt;03:51, 202.60it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1584 (init= 9.0852), step count (max): 18, lr policy:  0.0003:   6%|6         | 3000/50000 [00:14&lt;03:51, 202.60it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1584 (init= 9.0852), step count (max): 18, lr policy:  0.0003:   8%|8         | 4000/50000 [00:19&lt;03:46, 203.40it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1788 (init= 9.0852), step count (max): 23, lr policy:  0.0003:   8%|8         | 4000/50000 [00:19&lt;03:46, 203.40it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1788 (init= 9.0852), step count (max): 23, lr policy:  0.0003:  10%|#         | 5000/50000 [00:24&lt;03:43, 201.45it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1961 (init= 9.0852), step count (max): 23, lr policy:  0.0003:  10%|#         | 5000/50000 [00:24&lt;03:43, 201.45it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.1961 (init= 9.0852), step count (max): 23, lr policy:  0.0003:  12%|#2        | 6000/50000 [00:29&lt;03:36, 202.94it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2111 (init= 9.0852), step count (max): 27, lr policy:  0.0003:  12%|#2        | 6000/50000 [00:29&lt;03:36, 202.94it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2111 (init= 9.0852), step count (max): 27, lr policy:  0.0003:  14%|#4        | 7000/50000 [00:34&lt;03:31, 203.68it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2304 (init= 9.0852), step count (max): 38, lr policy:  0.0003:  14%|#4        | 7000/50000 [00:34&lt;03:31, 203.68it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2304 (init= 9.0852), step count (max): 38, lr policy:  0.0003:  16%|#6        | 8000/50000 [00:39&lt;03:25, 204.42it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2350 (init= 9.0852), step count (max): 48, lr policy:  0.0003:  16%|#6        | 8000/50000 [00:39&lt;03:25, 204.42it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2350 (init= 9.0852), step count (max): 48, lr policy:  0.0003:  18%|#8        | 9000/50000 [00:44&lt;03:19, 205.22it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2473 (init= 9.0852), step count (max): 60, lr policy:  0.0003:  18%|#8        | 9000/50000 [00:44&lt;03:19, 205.22it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2473 (init= 9.0852), step count (max): 60, lr policy:  0.0003:  20%|##        | 10000/50000 [00:49&lt;03:14, 205.92it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2587 (init= 9.0852), step count (max): 47, lr policy:  0.0003:  20%|##        | 10000/50000 [00:49&lt;03:14, 205.92it/s]
-eval cumulative reward:  129.4761 (init:  129.4761), eval step-count: 13, average reward= 9.2587 (init= 9.0852), step count (max): 47, lr policy:  0.0003:  22%|##2       | 11000/50000 [00:53&lt;03:08, 206.55it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2583 (init= 9.0852), step count (max): 61, lr policy:  0.0003:  22%|##2       | 11000/50000 [00:53&lt;03:08, 206.55it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2583 (init= 9.0852), step count (max): 61, lr policy:  0.0003:  24%|##4       | 12000/50000 [00:58&lt;03:06, 203.79it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2613 (init= 9.0852), step count (max): 57, lr policy:  0.0003:  24%|##4       | 12000/50000 [00:58&lt;03:06, 203.79it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2613 (init= 9.0852), step count (max): 57, lr policy:  0.0003:  26%|##6       | 13000/50000 [01:03&lt;03:00, 204.90it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2651 (init= 9.0852), step count (max): 65, lr policy:  0.0003:  26%|##6       | 13000/50000 [01:03&lt;03:00, 204.90it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2651 (init= 9.0852), step count (max): 65, lr policy:  0.0003:  28%|##8       | 14000/50000 [01:08&lt;02:54, 205.79it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2737 (init= 9.0852), step count (max): 73, lr policy:  0.0003:  28%|##8       | 14000/50000 [01:08&lt;02:54, 205.79it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2737 (init= 9.0852), step count (max): 73, lr policy:  0.0003:  30%|###       | 15000/50000 [01:13&lt;02:49, 206.24it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2758 (init= 9.0852), step count (max): 66, lr policy:  0.0002:  30%|###       | 15000/50000 [01:13&lt;02:49, 206.24it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2758 (init= 9.0852), step count (max): 66, lr policy:  0.0002:  32%|###2      | 16000/50000 [01:18&lt;02:44, 206.71it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2918 (init= 9.0852), step count (max): 110, lr policy:  0.0002:  32%|###2      | 16000/50000 [01:18&lt;02:44, 206.71it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2918 (init= 9.0852), step count (max): 110, lr policy:  0.0002:  34%|###4      | 17000/50000 [01:22&lt;02:39, 207.02it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2895 (init= 9.0852), step count (max): 79, lr policy:  0.0002:  34%|###4      | 17000/50000 [01:22&lt;02:39, 207.02it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2895 (init= 9.0852), step count (max): 79, lr policy:  0.0002:  36%|###6      | 18000/50000 [01:27&lt;02:36, 204.79it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2775 (init= 9.0852), step count (max): 60, lr policy:  0.0002:  36%|###6      | 18000/50000 [01:27&lt;02:36, 204.79it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2775 (init= 9.0852), step count (max): 60, lr policy:  0.0002:  38%|###8      | 19000/50000 [01:32&lt;02:31, 205.13it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2841 (init= 9.0852), step count (max): 50, lr policy:  0.0002:  38%|###8      | 19000/50000 [01:32&lt;02:31, 205.13it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2841 (init= 9.0852), step count (max): 50, lr policy:  0.0002:  40%|####      | 20000/50000 [01:37&lt;02:25, 205.97it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2983 (init= 9.0852), step count (max): 92, lr policy:  0.0002:  40%|####      | 20000/50000 [01:37&lt;02:25, 205.97it/s]
-eval cumulative reward:  334.4898 (init:  129.4761), eval step-count: 35, average reward= 9.2983 (init= 9.0852), step count (max): 92, lr policy:  0.0002:  42%|####2     | 21000/50000 [01:42&lt;02:20, 206.38it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2955 (init= 9.0852), step count (max): 74, lr policy:  0.0002:  42%|####2     | 21000/50000 [01:42&lt;02:20, 206.38it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2955 (init= 9.0852), step count (max): 74, lr policy:  0.0002:  44%|####4     | 22000/50000 [01:47&lt;02:16, 205.64it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2818 (init= 9.0852), step count (max): 54, lr policy:  0.0002:  44%|####4     | 22000/50000 [01:47&lt;02:16, 205.64it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2818 (init= 9.0852), step count (max): 54, lr policy:  0.0002:  46%|####6     | 23000/50000 [01:52&lt;02:10, 206.21it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2908 (init= 9.0852), step count (max): 64, lr policy:  0.0002:  46%|####6     | 23000/50000 [01:52&lt;02:10, 206.21it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2908 (init= 9.0852), step count (max): 64, lr policy:  0.0002:  48%|####8     | 24000/50000 [01:56&lt;02:05, 206.63it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.3003 (init= 9.0852), step count (max): 82, lr policy:  0.0002:  48%|####8     | 24000/50000 [01:56&lt;02:05, 206.63it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.3003 (init= 9.0852), step count (max): 82, lr policy:  0.0002:  50%|#####     | 25000/50000 [02:01&lt;02:02, 204.46it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2980 (init= 9.0852), step count (max): 80, lr policy:  0.0002:  50%|#####     | 25000/50000 [02:01&lt;02:02, 204.46it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2980 (init= 9.0852), step count (max): 80, lr policy:  0.0002:  52%|#####2    | 26000/50000 [02:06&lt;01:56, 205.34it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.3047 (init= 9.0852), step count (max): 109, lr policy:  0.0001:  52%|#####2    | 26000/50000 [02:06&lt;01:56, 205.34it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.3047 (init= 9.0852), step count (max): 109, lr policy:  0.0001:  54%|#####4    | 27000/50000 [02:11&lt;01:51, 205.62it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2958 (init= 9.0852), step count (max): 67, lr policy:  0.0001:  54%|#####4    | 27000/50000 [02:11&lt;01:51, 205.62it/s]
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-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2958 (init= 9.0852), step count (max): 76, lr policy:  0.0001:  56%|#####6    | 28000/50000 [02:16&lt;01:47, 205.55it/s]
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-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2914 (init= 9.0852), step count (max): 68, lr policy:  0.0001:  58%|#####8    | 29000/50000 [02:21&lt;01:42, 205.79it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2914 (init= 9.0852), step count (max): 68, lr policy:  0.0001:  60%|######    | 30000/50000 [02:26&lt;01:37, 205.97it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2848 (init= 9.0852), step count (max): 57, lr policy:  0.0001:  60%|######    | 30000/50000 [02:26&lt;01:37, 205.97it/s]
-eval cumulative reward:  353.5143 (init:  129.4761), eval step-count: 37, average reward= 9.2848 (init= 9.0852), step count (max): 57, lr policy:  0.0001:  62%|######2   | 31000/50000 [02:31&lt;01:32, 206.32it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2850 (init= 9.0852), step count (max): 58, lr policy:  0.0001:  62%|######2   | 31000/50000 [02:31&lt;01:32, 206.32it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2850 (init= 9.0852), step count (max): 58, lr policy:  0.0001:  64%|######4   | 32000/50000 [02:36&lt;01:28, 202.65it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2849 (init= 9.0852), step count (max): 59, lr policy:  0.0001:  64%|######4   | 32000/50000 [02:36&lt;01:28, 202.65it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2849 (init= 9.0852), step count (max): 59, lr policy:  0.0001:  66%|######6   | 33000/50000 [02:40&lt;01:23, 204.11it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2935 (init= 9.0852), step count (max): 63, lr policy:  0.0001:  66%|######6   | 33000/50000 [02:41&lt;01:23, 204.11it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2935 (init= 9.0852), step count (max): 63, lr policy:  0.0001:  68%|######8   | 34000/50000 [02:45&lt;01:18, 205.06it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2876 (init= 9.0852), step count (max): 63, lr policy:  0.0001:  68%|######8   | 34000/50000 [02:45&lt;01:18, 205.06it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2876 (init= 9.0852), step count (max): 63, lr policy:  0.0001:  70%|#######   | 35000/50000 [02:50&lt;01:12, 205.62it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2912 (init= 9.0852), step count (max): 83, lr policy:  0.0001:  70%|#######   | 35000/50000 [02:50&lt;01:12, 205.62it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2912 (init= 9.0852), step count (max): 83, lr policy:  0.0001:  72%|#######2  | 36000/50000 [02:55&lt;01:07, 206.24it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2952 (init= 9.0852), step count (max): 66, lr policy:  0.0001:  72%|#######2  | 36000/50000 [02:55&lt;01:07, 206.24it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2952 (init= 9.0852), step count (max): 66, lr policy:  0.0001:  74%|#######4  | 37000/50000 [03:00&lt;01:02, 206.51it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2947 (init= 9.0852), step count (max): 76, lr policy:  0.0001:  74%|#######4  | 37000/50000 [03:00&lt;01:02, 206.51it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2947 (init= 9.0852), step count (max): 76, lr policy:  0.0001:  76%|#######6  | 38000/50000 [03:05&lt;00:58, 206.78it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.3014 (init= 9.0852), step count (max): 87, lr policy:  0.0000:  76%|#######6  | 38000/50000 [03:05&lt;00:58, 206.78it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.3014 (init= 9.0852), step count (max): 87, lr policy:  0.0000:  78%|#######8  | 39000/50000 [03:10&lt;00:53, 204.68it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2982 (init= 9.0852), step count (max): 87, lr policy:  0.0000:  78%|#######8  | 39000/50000 [03:10&lt;00:53, 204.68it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.2982 (init= 9.0852), step count (max): 87, lr policy:  0.0000:  80%|########  | 40000/50000 [03:14&lt;00:48, 205.46it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.3103 (init= 9.0852), step count (max): 109, lr policy:  0.0000:  80%|########  | 40000/50000 [03:14&lt;00:48, 205.46it/s]
-eval cumulative reward:  587.5851 (init:  129.4761), eval step-count: 62, average reward= 9.3103 (init= 9.0852), step count (max): 109, lr policy:  0.0000:  82%|########2 | 41000/50000 [03:19&lt;00:43, 206.00it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3071 (init= 9.0852), step count (max): 90, lr policy:  0.0000:  82%|########2 | 41000/50000 [03:19&lt;00:43, 206.00it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3071 (init= 9.0852), step count (max): 90, lr policy:  0.0000:  84%|########4 | 42000/50000 [03:24&lt;00:39, 204.74it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3171 (init= 9.0852), step count (max): 107, lr policy:  0.0000:  84%|########4 | 42000/50000 [03:24&lt;00:39, 204.74it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3171 (init= 9.0852), step count (max): 107, lr policy:  0.0000:  86%|########6 | 43000/50000 [03:29&lt;00:34, 205.55it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3191 (init= 9.0852), step count (max): 109, lr policy:  0.0000:  86%|########6 | 43000/50000 [03:29&lt;00:34, 205.55it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3191 (init= 9.0852), step count (max): 109, lr policy:  0.0000:  88%|########8 | 44000/50000 [03:34&lt;00:29, 206.19it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3165 (init= 9.0852), step count (max): 102, lr policy:  0.0000:  88%|########8 | 44000/50000 [03:34&lt;00:29, 206.19it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3165 (init= 9.0852), step count (max): 102, lr policy:  0.0000:  90%|######### | 45000/50000 [03:39&lt;00:24, 206.50it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3110 (init= 9.0852), step count (max): 133, lr policy:  0.0000:  90%|######### | 45000/50000 [03:39&lt;00:24, 206.50it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3110 (init= 9.0852), step count (max): 133, lr policy:  0.0000:  92%|#########2| 46000/50000 [03:44&lt;00:19, 203.96it/s]
-eval cumulative reward:  512.6136 (init:  129.4761), eval step-count: 54, average reward= 9.3312 (init= 9.0852), step count (max): 120, lr policy:  0.0000:  92%|#########2| 46000/50000 [03:44&lt;00:19, 203.96it/s]
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+eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3165 (init= 9.0863), step count (max): 164, lr policy:  0.0000:  80%|########  | 40000/50000 [03:25&lt;00:51, 196.03it/s]
+eval cumulative reward:  1317.3445 (init:  92.0841), eval step-count: 140, average reward= 9.3165 (init= 9.0863), step count (max): 164, lr policy:  0.0000:  82%|########2 | 41000/50000 [03:30&lt;00:45, 196.18it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3179 (init= 9.0863), step count (max): 189, lr policy:  0.0000:  82%|########2 | 41000/50000 [03:30&lt;00:45, 196.18it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3179 (init= 9.0863), step count (max): 189, lr policy:  0.0000:  84%|########4 | 42000/50000 [03:36&lt;00:41, 192.22it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3320 (init= 9.0863), step count (max): 128, lr policy:  0.0000:  84%|########4 | 42000/50000 [03:36&lt;00:41, 192.22it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3320 (init= 9.0863), step count (max): 128, lr policy:  0.0000:  86%|########6 | 43000/50000 [03:41&lt;00:36, 193.16it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3237 (init= 9.0863), step count (max): 206, lr policy:  0.0000:  86%|########6 | 43000/50000 [03:41&lt;00:36, 193.16it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3237 (init= 9.0863), step count (max): 206, lr policy:  0.0000:  88%|########8 | 44000/50000 [03:46&lt;00:30, 194.54it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3119 (init= 9.0863), step count (max): 117, lr policy:  0.0000:  88%|########8 | 44000/50000 [03:46&lt;00:30, 194.54it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3119 (init= 9.0863), step count (max): 117, lr policy:  0.0000:  90%|######### | 45000/50000 [03:51&lt;00:25, 195.59it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3173 (init= 9.0863), step count (max): 213, lr policy:  0.0000:  90%|######### | 45000/50000 [03:51&lt;00:25, 195.59it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3173 (init= 9.0863), step count (max): 213, lr policy:  0.0000:  92%|#########2| 46000/50000 [03:56&lt;00:20, 195.84it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3211 (init= 9.0863), step count (max): 275, lr policy:  0.0000:  92%|#########2| 46000/50000 [03:56&lt;00:20, 195.84it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3211 (init= 9.0863), step count (max): 275, lr policy:  0.0000:  94%|#########3| 47000/50000 [04:01&lt;00:15, 195.74it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3222 (init= 9.0863), step count (max): 347, lr policy:  0.0000:  94%|#########3| 47000/50000 [04:01&lt;00:15, 195.74it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3222 (init= 9.0863), step count (max): 347, lr policy:  0.0000:  96%|#########6| 48000/50000 [04:06&lt;00:10, 194.25it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3258 (init= 9.0863), step count (max): 165, lr policy:  0.0000:  96%|#########6| 48000/50000 [04:06&lt;00:10, 194.25it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3258 (init= 9.0863), step count (max): 165, lr policy:  0.0000:  98%|#########8| 49000/50000 [04:11&lt;00:05, 194.96it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3254 (init= 9.0863), step count (max): 169, lr policy:  0.0000:  98%|#########8| 49000/50000 [04:11&lt;00:05, 194.96it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3254 (init= 9.0863), step count (max): 169, lr policy:  0.0000: 100%|##########| 50000/50000 [04:17&lt;00:00, 195.42it/s]
+eval cumulative reward:  699.6022 (init:  92.0841), eval step-count: 74, average reward= 9.3143 (init= 9.0863), step count (max): 130, lr policy:  0.0000: 100%|##########| 50000/50000 [04:17&lt;00:00, 195.42it/s]
 </pre></div>
 </div>
 </div>
@@ -1320,7 +1320,7 @@ <h2>Conclusion and next steps<a class="headerlink" href="#conclusion-and-next-st
 inverted pendulum in action. Check <code class="xref py py-mod docutils literal notranslate"><span class="pre">torchrl.record</span></code> to
 know more.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes  5.041 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes  18.572 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-reinforcement-ppo-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/7ed508ed54ec36ee5c1d3fa1e8ceede0/reinforcement_ppo.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">reinforcement_ppo.py</span></code></a></p>
diff --git a/intermediate/reinforcement_q_learning.html b/intermediate/reinforcement_q_learning.html
index 39832b55d6..4d86f126d5 100644
--- a/intermediate/reinforcement_q_learning.html
+++ b/intermediate/reinforcement_q_learning.html
@@ -719,8 +719,8 @@ <h3>Hyperparameters and utilities<a class="headerlink" href="#hyperparameters-an
 <span class="c1"># Get number of actions from gym action space</span>
 <span class="n">n_actions</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">n</span>
 <span class="c1"># Get the number of state observations</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
-<span class="n">n_observations</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a><span class="p">)</span>
+<span class="n">state</span><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
+<span class="n">n_observations</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
 
 <span class="n">policy_net</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DQN</span></a><span class="p">(</span><span class="n">n_observations</span><span class="p">,</span> <span class="n">n_actions</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span>
 <span class="n">target_net</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DQN</span></a><span class="p">(</span><span class="n">n_observations</span><span class="p">,</span> <span class="n">n_actions</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span>
@@ -733,7 +733,7 @@ <h3>Hyperparameters and utilities<a class="headerlink" href="#hyperparameters-an
 <span class="n">steps_done</span> <span class="o">=</span> <span class="mi">0</span>
 
 
-<span class="k">def</span> <span class="nf">select_action</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a><span class="p">):</span>
+<span class="k">def</span> <span class="nf">select_action</span><span class="p">(</span><span class="n">state</span><span class="p">):</span>
     <span class="k">global</span> <span class="n">steps_done</span>
     <span class="n">sample</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span>
     <span class="n">eps_threshold</span> <span class="o">=</span> <span class="n">EPS_END</span> <span class="o">+</span> <span class="p">(</span><span class="n">EPS_START</span> <span class="o">-</span> <span class="n">EPS_END</span><span class="p">)</span> <span class="o">*</span> \
@@ -744,7 +744,7 @@ <h3>Hyperparameters and utilities<a class="headerlink" href="#hyperparameters-an
             <span class="c1"># t.max(1) will return the largest column value of each row.</span>
             <span class="c1"># second column on max result is index of where max element was</span>
             <span class="c1"># found, so we pick action with the larger expected reward.</span>
-            <span class="k">return</span> <span class="n">policy_net</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">policy_net</span><span class="p">(</span><span class="n">state</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">([[</span><span class="n">env</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">sample</span><span class="p">()]],</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">long</span></a><span class="p">)</span>
 
@@ -803,10 +803,10 @@ <h3>Training loop<a class="headerlink" href="#training-loop" title="Permalink to
     <span class="c1"># Compute a mask of non-final states and concatenate the batch elements</span>
     <span class="c1"># (a final state would've been the one after which simulation ended)</span>
     <span class="n">non_final_mask</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span>
-                                          <span class="n">batch</span><span class="o">.</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">next_state</span></a><span class="p">)),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">bool</span></a><span class="p">)</span>
-    <span class="n">non_final_next_states</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat" title="torch.cat"><span class="n">torch</span><span class="o">.</span><span class="n">cat</span></a><span class="p">([</span><span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">batch</span><span class="o">.</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">next_state</span></a>
+                                          <span class="n">batch</span><span class="o">.</span><span class="n">next_state</span><span class="p">)),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">bool</span></a><span class="p">)</span>
+    <span class="n">non_final_next_states</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat" title="torch.cat"><span class="n">torch</span><span class="o">.</span><span class="n">cat</span></a><span class="p">([</span><span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">batch</span><span class="o">.</span><span class="n">next_state</span>
                                                 <span class="k">if</span> <span class="n">s</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">])</span>
-    <span class="n">state_batch</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat" title="torch.cat"><span class="n">torch</span><span class="o">.</span><span class="n">cat</span></a><span class="p">(</span><span class="n">batch</span><span class="o">.</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a><span class="p">)</span>
+    <span class="n">state_batch</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat" title="torch.cat"><span class="n">torch</span><span class="o">.</span><span class="n">cat</span></a><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">state</span><span class="p">)</span>
     <span class="n">action_batch</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat" title="torch.cat"><span class="n">torch</span><span class="o">.</span><span class="n">cat</span></a><span class="p">(</span><span class="n">batch</span><span class="o">.</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">action</span></a><span class="p">)</span>
     <span class="n">reward_batch</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cat.html#torch.cat" title="torch.cat"><span class="n">torch</span><span class="o">.</span><span class="n">cat</span></a><span class="p">(</span><span class="n">batch</span><span class="o">.</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">reward</span></a><span class="p">)</span>
 
@@ -856,24 +856,24 @@ <h3>Training loop<a class="headerlink" href="#training-loop" title="Permalink to
 
 <span class="k">for</span> <span class="n">i_episode</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_episodes</span><span class="p">):</span>
     <span class="c1"># Initialize the environment and get its state</span>
-    <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
-    <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">float32</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">state</span><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
+    <span class="n">state</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">float32</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
     <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">count</span><span class="p">():</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">action</span></a> <span class="o">=</span> <span class="n">select_action</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a><span class="p">)</span>
+        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">action</span></a> <span class="o">=</span> <span class="n">select_action</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
         <span class="n">observation</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">reward</span></a><span class="p">,</span> <span class="n">terminated</span><span class="p">,</span> <span class="n">truncated</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">action</span></a><span class="o">.</span><span class="n">item</span><span class="p">())</span>
         <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">reward</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">([</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">reward</span></a><span class="p">],</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span>
         <span class="n">done</span> <span class="o">=</span> <span class="n">terminated</span> <span class="ow">or</span> <span class="n">truncated</span>
 
         <span class="k">if</span> <span class="n">terminated</span><span class="p">:</span>
-            <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">next_state</span></a> <span class="o">=</span> <span class="kc">None</span>
+            <span class="n">next_state</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">else</span><span class="p">:</span>
-            <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">next_state</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="n">observation</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">float32</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">next_state</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor" title="torch.tensor"><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span></a><span class="p">(</span><span class="n">observation</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="n">torch</span><span class="o">.</span><span class="n">float32</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="o">=</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="torch.device"><span class="n">device</span></a><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 
         <span class="c1"># Store the transition in memory</span>
-        <span class="n">memory</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">action</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">next_state</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">reward</span></a><span class="p">)</span>
+        <span class="n">memory</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">action</span></a><span class="p">,</span> <span class="n">next_state</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">reward</span></a><span class="p">)</span>
 
         <span class="c1"># Move to the next state</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">state</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">next_state</span></a>
+        <span class="n">state</span> <span class="o">=</span> <span class="n">next_state</span>
 
         <span class="c1"># Perform one step of the optimization (on the policy network)</span>
         <span class="n">optimize_model</span><span class="p">()</span>
@@ -914,7 +914,7 @@ <h3>Training loop<a class="headerlink" href="#training-loop" title="Permalink to
 Optimization picks a random batch from the replay memory to do training of the
 new policy. The “older” target_net is also used in optimization to compute the
 expected Q values. A soft update of its weights are performed at every step.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 12 minutes  47.007 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes  27.406 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-reinforcement-q-learning-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/6ea0e49c7d0da2713588ef1a3b64eb35/reinforcement_q_learning.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">reinforcement_q_learning.py</span></code></a></p>
diff --git a/intermediate/scaled_dot_product_attention_tutorial.html b/intermediate/scaled_dot_product_attention_tutorial.html
index e60dbbdc4d..75cf53b5d7 100644
--- a/intermediate/scaled_dot_product_attention_tutorial.html
+++ b/intermediate/scaled_dot_product_attention_tutorial.html
@@ -597,10 +597,10 @@ <h2>Explicit Dispatcher Control<a class="headerlink" href="#explicit-dispatcher-
         <span class="nb">print</span><span class="p">(</span><span class="s2">"EfficientAttention is not supported. See warnings for reasons."</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>The default implementation runs in 2303.418 microseconds
-The math implementation runs in 19246.958 microseconds
-The flash attention implementation runs in 2304.079 microseconds
-The memory efficient implementation runs in 4235.871 microseconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>The default implementation runs in 2305.492 microseconds
+The math implementation runs in 19256.061 microseconds
+The flash attention implementation runs in 2304.993 microseconds
+The memory efficient implementation runs in 4304.684 microseconds
 </pre></div>
 </div>
 </div>
@@ -738,8 +738,8 @@ <h3><code class="docutils literal notranslate"><span class="pre">NestedTensor</s
 
 The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
 
-Random NT runs in 561.716 microseconds
-Random Dense runs in 938.942 microseconds
+Random NT runs in 560.097 microseconds
+Random Dense runs in 935.449 microseconds
 </pre></div>
 </div>
 </div>
@@ -768,8 +768,8 @@ <h1>Using SDPA with <code class="docutils literal notranslate"><span class="pre"
     <span class="sa">f</span><span class="s2">"The compiled module runs in  </span><span class="si">{</span><span class="n">benchmark_torch_function_in_microseconds</span><span class="p">(</span><span class="n">compiled_model</span><span class="p">,</span><span class="w"> </span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> microseconds"</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>The non compiled module runs in  408.355 microseconds
-The compiled module runs in  518.208 microseconds
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>The non compiled module runs in  408.374 microseconds
+The compiled module runs in  518.703 microseconds
 </pre></div>
 </div>
 <p>The exact execution time is dependent on machine, however the results for mine:
@@ -806,36 +806,36 @@ <h1>Using SDPA with <code class="docutils literal notranslate"><span class="pre"
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
                                                    Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls
 -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
-                         Non-Compilied Causal Attention        20.56%       2.233ms        77.56%       8.424ms       8.424ms       0.000us         0.00%      11.081ms      11.081ms             1
-                         Non-Compilied Causal Attention         0.00%       0.000us         0.00%       0.000us       0.000us      10.325ms        50.44%      10.325ms      10.325ms             1
-                                           aten::matmul         2.74%     298.000us        24.06%       2.613ms      52.260us       0.000us         0.00%       8.140ms     162.800us            50
-                                               aten::mm        14.94%       1.623ms        19.41%       2.108ms      42.160us       7.736ms        37.79%       8.140ms     162.800us            50
-                                           aten::linear         2.87%     312.000us        27.88%       3.028ms      60.560us       0.000us         0.00%       7.522ms     150.440us            50
-         ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_tn         0.00%       0.000us         0.00%       0.000us       0.000us       5.539ms        27.06%       5.539ms     221.560us            25
-                     aten::scaled_dot_product_attention         2.28%     248.000us        19.66%       2.135ms      85.400us       0.000us         0.00%       2.941ms     117.640us            25
-              aten::_scaled_dot_product_flash_attention         3.40%     369.000us        17.37%       1.887ms      75.480us       0.000us         0.00%       2.941ms     117.640us            25
-                         aten::_flash_attention_forward         5.05%     548.000us        12.86%       1.397ms      55.880us       2.410ms        11.77%       2.941ms     117.640us            25
-void pytorch_flash::flash_fwd_kernel&lt;pytorch_flash::...         0.00%       0.000us         0.00%       0.000us       0.000us       2.410ms        11.77%       2.410ms      96.400us            25
+                         Non-Compilied Causal Attention        21.06%       2.291ms        73.84%       8.032ms       8.032ms       0.000us         0.00%      11.101ms      11.101ms             1
+                         Non-Compilied Causal Attention         0.00%       0.000us         0.00%       0.000us       0.000us      10.367ms        50.50%      10.367ms      10.367ms             1
+                                           aten::linear         1.28%     139.000us        26.54%       2.887ms      57.740us       0.000us         0.00%       8.146ms     162.920us            50
+                                           aten::matmul         2.62%     285.000us        22.84%       2.484ms      49.680us       0.000us         0.00%       8.146ms     162.920us            50
+                                               aten::mm        13.90%       1.512ms        18.27%       1.987ms      39.740us       7.738ms        37.69%       8.146ms     162.920us            50
+         ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_tn         0.00%       0.000us         0.00%       0.000us       0.000us       5.543ms        27.00%       5.543ms     221.720us            25
+                     aten::scaled_dot_product_attention         2.08%     226.000us        18.37%       1.998ms      79.920us       0.000us         0.00%       2.955ms     118.200us            25
+              aten::_scaled_dot_product_flash_attention         3.40%     370.000us        16.29%       1.772ms      70.880us       0.000us         0.00%       2.955ms     118.200us            25
+                         aten::_flash_attention_forward         4.73%     514.000us        11.94%       1.299ms      51.960us       2.423ms        11.80%       2.955ms     118.200us            25
+void pytorch_flash::flash_fwd_kernel&lt;pytorch_flash::...         0.00%       0.000us         0.00%       0.000us       0.000us       2.423ms        11.80%       2.423ms      96.920us            25
 -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
-Self CPU time total: 10.861ms
-Self CUDA time total: 20.471ms
+Self CPU time total: 10.878ms
+Self CUDA time total: 20.528ms
 
 -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
                                                    Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls
 -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
-                              Compiled Causal Attention         6.72%     746.000us        93.75%      10.410ms      10.410ms       0.000us         0.00%      10.969ms      10.969ms             1
-                                  Torch-Compiled Region        11.24%       1.248ms        85.71%       9.517ms     380.680us       0.000us         0.00%      10.969ms     438.760us            25
-                                       CompiledFunction        42.37%       4.705ms        73.17%       8.125ms     325.000us       0.000us         0.00%      10.969ms     438.760us            25
-                              Compiled Causal Attention         0.00%       0.000us         0.00%       0.000us       0.000us      10.415ms        50.64%      10.415ms      10.415ms             1
-                                               aten::mm         8.88%     986.000us        13.86%       1.539ms      30.780us       7.742ms        37.65%       8.148ms     162.960us            50
-         ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_tn         0.00%       0.000us         0.00%       0.000us       0.000us       5.542ms        26.95%       5.542ms     221.680us            25
-              aten::_scaled_dot_product_flash_attention         2.50%     278.000us        15.92%       1.768ms      70.720us       0.000us         0.00%       2.821ms     112.840us            25
-                         aten::_flash_attention_forward         4.71%     523.000us        11.95%       1.327ms      53.080us       2.408ms        11.71%       2.821ms     112.840us            25
-void pytorch_flash::flash_fwd_kernel&lt;pytorch_flash::...         0.00%       0.000us         0.00%       0.000us       0.000us       2.408ms        11.71%       2.408ms      96.320us            25
-ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_stages_3...         0.00%       0.000us         0.00%       0.000us       0.000us       2.200ms        10.70%       2.200ms      88.000us            25
+                              Compiled Causal Attention         6.78%     744.000us        89.35%       9.811ms       9.811ms       0.000us         0.00%      10.984ms      10.984ms             1
+                                  Torch-Compiled Region        10.83%       1.189ms        81.32%       8.929ms     357.160us       0.000us         0.00%      10.984ms     439.360us            25
+                                       CompiledFunction        39.49%       4.336ms        69.36%       7.616ms     304.640us       0.000us         0.00%      10.984ms     439.360us            25
+                              Compiled Causal Attention         0.00%       0.000us         0.00%       0.000us       0.000us      10.345ms        50.46%      10.345ms      10.345ms             1
+                                               aten::mm         8.70%     955.000us        13.47%       1.479ms      29.580us       7.746ms        37.78%       8.155ms     163.100us            50
+         ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_tn         0.00%       0.000us         0.00%       0.000us       0.000us       5.548ms        27.06%       5.548ms     221.920us            25
+              aten::_scaled_dot_product_flash_attention         2.47%     271.000us        15.43%       1.694ms      67.760us       0.000us         0.00%       2.829ms     113.160us            25
+                         aten::_flash_attention_forward         4.64%     510.000us        11.60%       1.274ms      50.960us       2.412ms        11.76%       2.829ms     113.160us            25
+void pytorch_flash::flash_fwd_kernel&lt;pytorch_flash::...         0.00%       0.000us         0.00%       0.000us       0.000us       2.412ms        11.76%       2.412ms      96.480us            25
+ampere_fp16_s1688gemm_fp16_128x128_ldg8_f2f_stages_3...         0.00%       0.000us         0.00%       0.000us       0.000us       2.198ms        10.72%       2.198ms      87.920us            25
 -------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
-Self CPU time total: 11.104ms
-Self CUDA time total: 20.565ms
+Self CPU time total: 10.980ms
+Self CUDA time total: 20.503ms
 </pre></div>
 </div>
 <p>The previous code snippet generates a report of the top 10 PyTorch functions
@@ -939,7 +939,7 @@ <h1>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 compilable. In the process we have shown how to the profiling tools can
 be used to explore the performance characteristics of a user defined
 module.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  6.228 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  7.838 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-scaled-dot-product-attention-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/e40ced94a143a49f0f8745e10c981139/scaled_dot_product_attention_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">scaled_dot_product_attention_tutorial.py</span></code></a></p>
diff --git a/intermediate/seq2seq_translation_tutorial.html b/intermediate/seq2seq_translation_tutorial.html
index 45ea7a82c5..3906780903 100644
--- a/intermediate/seq2seq_translation_tutorial.html
+++ b/intermediate/seq2seq_translation_tutorial.html
@@ -1208,22 +1208,22 @@ <h2>Training and Evaluating<a class="headerlink" href="#training-and-evaluating"
 Counted words:
 fra 4601
 eng 2991
-0m 27s (- 6m 50s) (5 6%) 1.5304
-0m 54s (- 6m 21s) (10 12%) 0.6776
-1m 21s (- 5m 54s) (15 18%) 0.3528
-1m 48s (- 5m 26s) (20 25%) 0.1946
-2m 16s (- 4m 59s) (25 31%) 0.1205
-2m 43s (- 4m 31s) (30 37%) 0.0841
-3m 10s (- 4m 4s) (35 43%) 0.0639
-3m 37s (- 3m 37s) (40 50%) 0.0521
-4m 4s (- 3m 10s) (45 56%) 0.0452
-4m 31s (- 2m 42s) (50 62%) 0.0395
-4m 58s (- 2m 15s) (55 68%) 0.0377
-5m 25s (- 1m 48s) (60 75%) 0.0349
-5m 52s (- 1m 21s) (65 81%) 0.0324
-6m 20s (- 0m 54s) (70 87%) 0.0316
-6m 47s (- 0m 27s) (75 93%) 0.0298
-7m 14s (- 0m 0s) (80 100%) 0.0291
+0m 28s (- 7m 1s) (5 6%) 1.5304
+0m 56s (- 6m 32s) (10 12%) 0.6776
+1m 23s (- 6m 3s) (15 18%) 0.3528
+1m 51s (- 5m 35s) (20 25%) 0.1946
+2m 19s (- 5m 7s) (25 31%) 0.1205
+2m 47s (- 4m 39s) (30 37%) 0.0841
+3m 15s (- 4m 11s) (35 43%) 0.0639
+3m 43s (- 3m 43s) (40 50%) 0.0521
+4m 11s (- 3m 15s) (45 56%) 0.0452
+4m 39s (- 2m 47s) (50 62%) 0.0395
+5m 7s (- 2m 19s) (55 68%) 0.0377
+5m 35s (- 1m 51s) (60 75%) 0.0349
+6m 3s (- 1m 23s) (65 81%) 0.0324
+6m 31s (- 0m 55s) (70 87%) 0.0316
+6m 59s (- 0m 27s) (75 93%) 0.0298
+7m 27s (- 0m 0s) (80 100%) 0.0291
 </pre></div>
 </div>
 <p>Set dropout layers to <code class="docutils literal notranslate"><span class="pre">eval</span></code> mode</p>
@@ -1390,7 +1390,7 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this he
 </ul>
 </li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes  23.294 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes  35.981 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-seq2seq-translation-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/3baf9960a4be104931872ff3ffda07b7/seq2seq_translation_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">seq2seq_translation_tutorial.py</span></code></a></p>
diff --git a/intermediate/spatial_transformer_tutorial.html b/intermediate/spatial_transformer_tutorial.html
index 6055ba867d..afe7837d4e 100644
--- a/intermediate/spatial_transformer_tutorial.html
+++ b/intermediate/spatial_transformer_tutorial.html
@@ -520,7 +520,7 @@
 <span class="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span>   <span class="c1"># interactive mode</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;contextlib.ExitStack object at 0x7f8ba1bec700&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;contextlib.ExitStack object at 0x7fc87682f700&gt;
 </pre></div>
 </div>
 <div class="section" id="loading-the-data">
@@ -558,7 +558,7 @@ <h2>Loading the data<a class="headerlink" href="#loading-the-data" title="Permal
 Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz
 
   0%|          | 0/9912422 [00:00&lt;?, ?it/s]
-100%|##########| 9912422/9912422 [00:00&lt;00:00, 134663407.95it/s]
+100%|##########| 9912422/9912422 [00:00&lt;00:00, 130659054.82it/s]
 Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw
 
 Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
@@ -569,7 +569,7 @@ <h2>Loading the data<a class="headerlink" href="#loading-the-data" title="Permal
 Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz
 
   0%|          | 0/28881 [00:00&lt;?, ?it/s]
-100%|##########| 28881/28881 [00:00&lt;00:00, 17903590.57it/s]
+100%|##########| 28881/28881 [00:00&lt;00:00, 17124073.20it/s]
 Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw
 
 Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
@@ -580,7 +580,7 @@ <h2>Loading the data<a class="headerlink" href="#loading-the-data" title="Permal
 Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz
 
   0%|          | 0/1648877 [00:00&lt;?, ?it/s]
-100%|##########| 1648877/1648877 [00:00&lt;00:00, 88552880.28it/s]
+100%|##########| 1648877/1648877 [00:00&lt;00:00, 198305129.65it/s]
 Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw
 
 Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
@@ -591,7 +591,7 @@ <h2>Loading the data<a class="headerlink" href="#loading-the-data" title="Permal
 Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz
 
   0%|          | 0/4542 [00:00&lt;?, ?it/s]
-100%|##########| 4542/4542 [00:00&lt;00:00, 6028648.34it/s]
+100%|##########| 4542/4542 [00:00&lt;00:00, 3616273.49it/s]
 Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw
 </pre></div>
 </div>
@@ -780,107 +780,107 @@ <h2>Visualizing the STN results<a class="headerlink" href="#visualizing-the-stn-
 </pre></div>
 </div>
 <img alt="Dataset Images, Transformed Images" class="sphx-glr-single-img" src="../_images/sphx_glr_spatial_transformer_tutorial_001.png" srcset="../_images/sphx_glr_spatial_transformer_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Train Epoch: 1 [0/60000 (0%)]   Loss: 2.315648
-Train Epoch: 1 [32000/60000 (53%)]      Loss: 1.056052
+Train Epoch: 1 [32000/60000 (53%)]      Loss: 1.094919
 
-Test set: Average loss: 0.2696, Accuracy: 9226/10000 (92%)
+Test set: Average loss: 0.2729, Accuracy: 9232/10000 (92%)
 
-Train Epoch: 2 [0/60000 (0%)]   Loss: 0.522132
-Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.309089
+Train Epoch: 2 [0/60000 (0%)]   Loss: 0.538398
+Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.362289
 
-Test set: Average loss: 0.1487, Accuracy: 9554/10000 (96%)
+Test set: Average loss: 0.1911, Accuracy: 9415/10000 (94%)
 
-Train Epoch: 3 [0/60000 (0%)]   Loss: 0.378176
-Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.235657
+Train Epoch: 3 [0/60000 (0%)]   Loss: 0.516258
+Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.190172
 
-Test set: Average loss: 0.1079, Accuracy: 9676/10000 (97%)
+Test set: Average loss: 0.1147, Accuracy: 9647/10000 (96%)
 
-Train Epoch: 4 [0/60000 (0%)]   Loss: 0.367314
-Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.143735
+Train Epoch: 4 [0/60000 (0%)]   Loss: 0.394187
+Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.112920
 
-Test set: Average loss: 0.1514, Accuracy: 9518/10000 (95%)
+Test set: Average loss: 0.1002, Accuracy: 9703/10000 (97%)
 
-Train Epoch: 5 [0/60000 (0%)]   Loss: 0.299169
-Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.104072
+Train Epoch: 5 [0/60000 (0%)]   Loss: 0.227524
+Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.141361
 
-Test set: Average loss: 0.0886, Accuracy: 9709/10000 (97%)
+Test set: Average loss: 0.0825, Accuracy: 9732/10000 (97%)
 
-Train Epoch: 6 [0/60000 (0%)]   Loss: 0.117600
-Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.091880
+Train Epoch: 6 [0/60000 (0%)]   Loss: 0.139464
+Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.125213
 
-Test set: Average loss: 0.0683, Accuracy: 9795/10000 (98%)
+Test set: Average loss: 0.0734, Accuracy: 9779/10000 (98%)
 
-Train Epoch: 7 [0/60000 (0%)]   Loss: 0.108650
-Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.147451
+Train Epoch: 7 [0/60000 (0%)]   Loss: 0.062508
+Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.187347
 
-Test set: Average loss: 0.0671, Accuracy: 9799/10000 (98%)
+Test set: Average loss: 0.2350, Accuracy: 9267/10000 (93%)
 
-Train Epoch: 8 [0/60000 (0%)]   Loss: 0.204512
-Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.088205
+Train Epoch: 8 [0/60000 (0%)]   Loss: 0.571213
+Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.073750
 
-Test set: Average loss: 0.0626, Accuracy: 9809/10000 (98%)
+Test set: Average loss: 0.0892, Accuracy: 9732/10000 (97%)
 
-Train Epoch: 9 [0/60000 (0%)]   Loss: 0.106027
-Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.052493
+Train Epoch: 9 [0/60000 (0%)]   Loss: 0.216066
+Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.163309
 
-Test set: Average loss: 0.0636, Accuracy: 9809/10000 (98%)
+Test set: Average loss: 0.0666, Accuracy: 9813/10000 (98%)
 
-Train Epoch: 10 [0/60000 (0%)]  Loss: 0.123162
-Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.247202
+Train Epoch: 10 [0/60000 (0%)]  Loss: 0.072257
+Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.208973
 
-Test set: Average loss: 0.0611, Accuracy: 9824/10000 (98%)
+Test set: Average loss: 0.0573, Accuracy: 9830/10000 (98%)
 
-Train Epoch: 11 [0/60000 (0%)]  Loss: 0.172087
-Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.097818
+Train Epoch: 11 [0/60000 (0%)]  Loss: 0.115926
+Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.167152
 
-Test set: Average loss: 0.0509, Accuracy: 9844/10000 (98%)
+Test set: Average loss: 0.0731, Accuracy: 9784/10000 (98%)
 
-Train Epoch: 12 [0/60000 (0%)]  Loss: 0.147289
-Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.156528
+Train Epoch: 12 [0/60000 (0%)]  Loss: 0.121299
+Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.198820
 
-Test set: Average loss: 0.0573, Accuracy: 9834/10000 (98%)
+Test set: Average loss: 0.0576, Accuracy: 9832/10000 (98%)
 
-Train Epoch: 13 [0/60000 (0%)]  Loss: 0.069093
-Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.100166
+Train Epoch: 13 [0/60000 (0%)]  Loss: 0.064778
+Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.087009
 
-Test set: Average loss: 0.0731, Accuracy: 9781/10000 (98%)
+Test set: Average loss: 0.0509, Accuracy: 9842/10000 (98%)
 
-Train Epoch: 14 [0/60000 (0%)]  Loss: 0.099262
-Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.164480
+Train Epoch: 14 [0/60000 (0%)]  Loss: 0.073423
+Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.080678
 
-Test set: Average loss: 0.0515, Accuracy: 9837/10000 (98%)
+Test set: Average loss: 0.0574, Accuracy: 9826/10000 (98%)
 
-Train Epoch: 15 [0/60000 (0%)]  Loss: 0.041433
-Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.082513
+Train Epoch: 15 [0/60000 (0%)]  Loss: 0.108524
+Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.085530
 
-Test set: Average loss: 0.0479, Accuracy: 9855/10000 (99%)
+Test set: Average loss: 0.0560, Accuracy: 9834/10000 (98%)
 
-Train Epoch: 16 [0/60000 (0%)]  Loss: 0.057918
-Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.216040
+Train Epoch: 16 [0/60000 (0%)]  Loss: 0.077510
+Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.159949
 
-Test set: Average loss: 0.0464, Accuracy: 9861/10000 (99%)
+Test set: Average loss: 0.0489, Accuracy: 9860/10000 (99%)
 
-Train Epoch: 17 [0/60000 (0%)]  Loss: 0.229464
-Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.150280
+Train Epoch: 17 [0/60000 (0%)]  Loss: 0.322598
+Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.156583
 
-Test set: Average loss: 0.0998, Accuracy: 9698/10000 (97%)
+Test set: Average loss: 0.0610, Accuracy: 9816/10000 (98%)
 
-Train Epoch: 18 [0/60000 (0%)]  Loss: 0.117676
-Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.135358
+Train Epoch: 18 [0/60000 (0%)]  Loss: 0.038340
+Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.087965
 
-Test set: Average loss: 0.0471, Accuracy: 9871/10000 (99%)
+Test set: Average loss: 0.0597, Accuracy: 9813/10000 (98%)
 
-Train Epoch: 19 [0/60000 (0%)]  Loss: 0.056745
-Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.123519
+Train Epoch: 19 [0/60000 (0%)]  Loss: 0.070837
+Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.089139
 
-Test set: Average loss: 0.0493, Accuracy: 9856/10000 (99%)
+Test set: Average loss: 0.0424, Accuracy: 9876/10000 (99%)
 
-Train Epoch: 20 [0/60000 (0%)]  Loss: 0.050441
-Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.043098
+Train Epoch: 20 [0/60000 (0%)]  Loss: 0.069807
+Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.144610
 
-Test set: Average loss: 0.0817, Accuracy: 9764/10000 (98%)
+Test set: Average loss: 0.0543, Accuracy: 9844/10000 (98%)
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  0.273 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  12.932 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-spatial-transformer-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/a4f07fecba75b5e84fe9e56cac0c7b71/spatial_transformer_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">spatial_transformer_tutorial.py</span></code></a></p>
diff --git a/intermediate/torch_compile_tutorial.html b/intermediate/torch_compile_tutorial.html
index f87159b6e4..dfb42dc9b9 100644
--- a/intermediate/torch_compile_tutorial.html
+++ b/intermediate/torch_compile_tutorial.html
@@ -695,12 +695,12 @@ <h2><a class="toc-backref" href="#id2">Demonstrating Speedups</a><a class="heade
     <span class="nb">print</span><span class="p">(</span><span class="s2">"compile:"</span><span class="p">,</span> <span class="n">timed</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">model_opt</span><span class="p">(</span><span class="n">inp</span><span class="p">))[</span><span class="mi">1</span><span class="p">])</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>eager: 0.19579493713378907
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>eager: 0.19602336120605468
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:124: UserWarning:
 
 TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
 
-compile: 85.2317265625
+compile: 84.86965625
 </pre></div>
 </div>
 <p>Notice that <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> takes a lot longer to complete
@@ -737,29 +737,29 @@ <h2><a class="toc-backref" href="#id2">Demonstrating Speedups</a><a class="heade
 <span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>eager eval time 0: 0.018774015426635742
-eager eval time 1: 0.017271808624267578
-eager eval time 2: 0.016611328125
-eager eval time 3: 0.01659699249267578
-eager eval time 4: 0.016461824417114256
-eager eval time 5: 0.01663692855834961
-eager eval time 6: 0.01643622398376465
-eager eval time 7: 0.016538623809814454
-eager eval time 8: 0.01648646354675293
-eager eval time 9: 0.01640345573425293
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>eager eval time 0: 0.017921215057373048
+eager eval time 1: 0.016335872650146483
+eager eval time 2: 0.016227327346801757
+eager eval time 3: 0.01622425651550293
+eager eval time 4: 0.016085920333862306
+eager eval time 5: 0.016227327346801757
+eager eval time 6: 0.01606649589538574
+eager eval time 7: 0.016202751159667968
+eager eval time 8: 0.016100351333618163
+eager eval time 9: 0.01623961639404297
 ~~~~~~~~~~
-compile eval time 0: 0.6135510864257813
-compile eval time 1: 0.008589311599731446
-compile eval time 2: 0.008352767944335938
-compile eval time 3: 0.008348671913146973
-compile eval time 4: 0.0083374080657959
-compile eval time 5: 0.008338432312011718
-compile eval time 6: 0.008353792190551757
-compile eval time 7: 0.008361984252929687
-compile eval time 8: 0.008352767944335938
-compile eval time 9: 0.008350720405578613
+compile eval time 0: 0.6061260986328125
+compile eval time 1: 0.008586239814758301
+compile eval time 2: 0.008363007545471191
+compile eval time 3: 0.008343551635742187
+compile eval time 4: 0.008356863975524903
+compile eval time 5: 0.008348671913146973
+compile eval time 6: 0.008343551635742187
+compile eval time 7: 0.008351743698120117
+compile eval time 8: 0.008342528343200683
+compile eval time 9: 0.008336319923400878
 ~~~~~~~~~~
-(eval) eager median: 0.016567808151245117, compile median: 0.008352767944335938, speedup: 1.9835111260908245x
+(eval) eager median: 0.016225791931152346, compile median: 0.008350207805633544, speedup: 1.9431602552699905x
 ~~~~~~~~~~
 </pre></div>
 </div>
@@ -819,29 +819,29 @@ <h2><a class="toc-backref" href="#id2">Demonstrating Speedups</a><a class="heade
 <span class="nb">print</span><span class="p">(</span><span class="s2">"~"</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>eager train time 0: 0.30075186157226563
-eager train time 1: 0.05288857650756836
-eager train time 2: 0.05173759841918945
-eager train time 3: 0.05079558563232422
-eager train time 4: 0.05079244613647461
-eager train time 5: 0.051041217803955076
-eager train time 6: 0.050733055114746094
-eager train time 7: 0.05195980834960937
-eager train time 8: 0.05063167953491211
-eager train time 9: 0.0510648307800293
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>eager train time 0: 0.29824200439453125
+eager train time 1: 0.053937152862548826
+eager train time 2: 0.05155632019042969
+eager train time 3: 0.05148979187011719
+eager train time 4: 0.05138227081298828
+eager train time 5: 0.052450302124023435
+eager train time 6: 0.05168025588989258
+eager train time 7: 0.05139148712158203
+eager train time 8: 0.05126758575439453
+eager train time 9: 0.05125737762451172
 ~~~~~~~~~~
-compile train time 0: 210.392171875
-compile train time 1: 4.5687314453125
-compile train time 2: 0.030623743057250977
-compile train time 3: 0.02369024085998535
-compile train time 4: 0.02368409538269043
-compile train time 5: 0.023356416702270507
-compile train time 6: 0.02330521583557129
-compile train time 7: 0.024222719192504884
-compile train time 8: 0.023751680374145507
-compile train time 9: 0.023669759750366212
+compile train time 0: 209.48853125
+compile train time 1: 4.540728515625
+compile train time 2: 0.02995199966430664
+compile train time 3: 0.023593984603881835
+compile train time 4: 0.023359487533569336
+compile train time 5: 0.023391231536865235
+compile train time 6: 0.023404544830322265
+compile train time 7: 0.024204191207885743
+compile train time 8: 0.02351411247253418
+compile train time 9: 0.023315391540527343
 ~~~~~~~~~~
-(train) eager median: 0.05105302429199218, compile median: 0.02372096061706543, speedup: 2.1522325809715905x
+(train) eager median: 0.051523056030273434, compile median: 0.023554048538208007, speedup: 2.18743949460305x
 ~~~~~~~~~~
 </pre></div>
 </div>
@@ -1128,432 +1128,432 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
 call_module    l__self___features_norm0                           L__self___features_norm0                                    (l__self___features_conv0,)                                                                                                                                                                                                                                                                                                                                                                                                                                         {}
 call_module    l__self___features_relu0                           L__self___features_relu0                                    (l__self___features_norm0,)                                                                                                                                                                                                                                                                                                                                                                                                                                         {}
 call_module    l__self___features_pool0                           L__self___features_pool0                                    (l__self___features_relu0,)                                                                                                                                                                                                                                                                                                                                                                                                                                         {}
-call_function  concated_features                                  &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_pool0], 1)                                                                                                                                                                                                                                                                                                                                                                                                                                     {}
+call_function  concated_features                                  &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_pool0], 1)                                                                                                                                                                                                                                                                                                                                                                                                                                     {}
 call_module    l__self___features_denseblock1_denselayer1_norm1   L__self___features_denseblock1_denselayer1_norm1            (concated_features,)                                                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock1_denselayer1_relu1   L__self___features_denseblock1_denselayer1_relu1            (l__self___features_denseblock1_denselayer1_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output                                  L__self___features_denseblock1_denselayer1_conv1            (l__self___features_denseblock1_denselayer1_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock1_denselayer1_norm2   L__self___features_denseblock1_denselayer1_norm2            (bottleneck_output,)                                                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock1_denselayer1_relu2   L__self___features_denseblock1_denselayer1_relu2            (l__self___features_denseblock1_denselayer1_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features                                       L__self___features_denseblock1_denselayer1_conv2            (l__self___features_denseblock1_denselayer1_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_1                                &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_pool0, new_features], 1)                                                                                                                                                                                                                                                                                                                                                                                                                       {}
+call_function  concated_features_1                                &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_pool0, new_features], 1)                                                                                                                                                                                                                                                                                                                                                                                                                       {}
 call_module    l__self___features_denseblock1_denselayer2_norm1   L__self___features_denseblock1_denselayer2_norm1            (concated_features_1,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock1_denselayer2_relu1   L__self___features_denseblock1_denselayer2_relu1            (l__self___features_denseblock1_denselayer2_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_2                                L__self___features_denseblock1_denselayer2_conv1            (l__self___features_denseblock1_denselayer2_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock1_denselayer2_norm2   L__self___features_denseblock1_denselayer2_norm2            (bottleneck_output_2,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock1_denselayer2_relu2   L__self___features_denseblock1_denselayer2_relu2            (l__self___features_denseblock1_denselayer2_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_2                                     L__self___features_denseblock1_denselayer2_conv2            (l__self___features_denseblock1_denselayer2_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_2                                &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_pool0, new_features, new_features_2], 1)                                                                                                                                                                                                                                                                                                                                                                                                       {}
+call_function  concated_features_2                                &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_pool0, new_features, new_features_2], 1)                                                                                                                                                                                                                                                                                                                                                                                                       {}
 call_module    l__self___features_denseblock1_denselayer3_norm1   L__self___features_denseblock1_denselayer3_norm1            (concated_features_2,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock1_denselayer3_relu1   L__self___features_denseblock1_denselayer3_relu1            (l__self___features_denseblock1_denselayer3_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_4                                L__self___features_denseblock1_denselayer3_conv1            (l__self___features_denseblock1_denselayer3_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock1_denselayer3_norm2   L__self___features_denseblock1_denselayer3_norm2            (bottleneck_output_4,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock1_denselayer3_relu2   L__self___features_denseblock1_denselayer3_relu2            (l__self___features_denseblock1_denselayer3_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_4                                     L__self___features_denseblock1_denselayer3_conv2            (l__self___features_denseblock1_denselayer3_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_3                                &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_pool0, new_features, new_features_2, new_features_4], 1)                                                                                                                                                                                                                                                                                                                                                                                       {}
+call_function  concated_features_3                                &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_pool0, new_features, new_features_2, new_features_4], 1)                                                                                                                                                                                                                                                                                                                                                                                       {}
 call_module    l__self___features_denseblock1_denselayer4_norm1   L__self___features_denseblock1_denselayer4_norm1            (concated_features_3,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock1_denselayer4_relu1   L__self___features_denseblock1_denselayer4_relu1            (l__self___features_denseblock1_denselayer4_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_6                                L__self___features_denseblock1_denselayer4_conv1            (l__self___features_denseblock1_denselayer4_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock1_denselayer4_norm2   L__self___features_denseblock1_denselayer4_norm2            (bottleneck_output_6,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock1_denselayer4_relu2   L__self___features_denseblock1_denselayer4_relu2            (l__self___features_denseblock1_denselayer4_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_6                                     L__self___features_denseblock1_denselayer4_conv2            (l__self___features_denseblock1_denselayer4_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_4                                &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
+call_function  concated_features_4                                &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
 call_module    l__self___features_denseblock1_denselayer5_norm1   L__self___features_denseblock1_denselayer5_norm1            (concated_features_4,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock1_denselayer5_relu1   L__self___features_denseblock1_denselayer5_relu1            (l__self___features_denseblock1_denselayer5_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_8                                L__self___features_denseblock1_denselayer5_conv1            (l__self___features_denseblock1_denselayer5_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock1_denselayer5_norm2   L__self___features_denseblock1_denselayer5_norm2            (bottleneck_output_8,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock1_denselayer5_relu2   L__self___features_denseblock1_denselayer5_relu2            (l__self___features_denseblock1_denselayer5_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_8                                     L__self___features_denseblock1_denselayer5_conv2            (l__self___features_denseblock1_denselayer5_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_5                                &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8], 1)                                                                                                                                                                                                                                                                                                                                                       {}
+call_function  concated_features_5                                &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8], 1)                                                                                                                                                                                                                                                                                                                                                       {}
 call_module    l__self___features_denseblock1_denselayer6_norm1   L__self___features_denseblock1_denselayer6_norm1            (concated_features_5,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock1_denselayer6_relu1   L__self___features_denseblock1_denselayer6_relu1            (l__self___features_denseblock1_denselayer6_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_10                               L__self___features_denseblock1_denselayer6_conv1            (l__self___features_denseblock1_denselayer6_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock1_denselayer6_norm2   L__self___features_denseblock1_denselayer6_norm2            (bottleneck_output_10,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock1_denselayer6_relu2   L__self___features_denseblock1_denselayer6_relu2            (l__self___features_denseblock1_denselayer6_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_10                                    L__self___features_denseblock1_denselayer6_conv2            (l__self___features_denseblock1_denselayer6_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  cat_6                                              &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8, new_features_10], 1)                                                                                                                                                                                                                                                                                                                                      {}
+call_function  cat_6                                              &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_pool0, new_features, new_features_2, new_features_4, new_features_6, new_features_8, new_features_10], 1)                                                                                                                                                                                                                                                                                                                                      {}
 call_module    l__self___features_transition1_norm                L__self___features_transition1_norm                         (cat_6,)                                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 call_module    l__self___features_transition1_relu                L__self___features_transition1_relu                         (l__self___features_transition1_norm,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_transition1_conv                L__self___features_transition1_conv                         (l__self___features_transition1_relu,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_transition1_pool                L__self___features_transition1_pool                         (l__self___features_transition1_conv,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
-call_function  concated_features_6                                &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
+call_function  concated_features_6                                &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
 call_module    l__self___features_denseblock2_denselayer1_norm1   L__self___features_denseblock2_denselayer1_norm1            (concated_features_6,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock2_denselayer1_relu1   L__self___features_denseblock2_denselayer1_relu1            (l__self___features_denseblock2_denselayer1_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_12                               L__self___features_denseblock2_denselayer1_conv1            (l__self___features_denseblock2_denselayer1_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer1_norm2   L__self___features_denseblock2_denselayer1_norm2            (bottleneck_output_12,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer1_relu2   L__self___features_denseblock2_denselayer1_relu2            (l__self___features_denseblock2_denselayer1_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_12                                    L__self___features_denseblock2_denselayer1_conv2            (l__self___features_denseblock2_denselayer1_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_7                                &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
+call_function  concated_features_7                                &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
 call_module    l__self___features_denseblock2_denselayer2_norm1   L__self___features_denseblock2_denselayer2_norm1            (concated_features_7,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock2_denselayer2_relu1   L__self___features_denseblock2_denselayer2_relu1            (l__self___features_denseblock2_denselayer2_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_14                               L__self___features_denseblock2_denselayer2_conv1            (l__self___features_denseblock2_denselayer2_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer2_norm2   L__self___features_denseblock2_denselayer2_norm2            (bottleneck_output_14,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer2_relu2   L__self___features_denseblock2_denselayer2_relu2            (l__self___features_denseblock2_denselayer2_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_14                                    L__self___features_denseblock2_denselayer2_conv2            (l__self___features_denseblock2_denselayer2_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_8                                &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
+call_function  concated_features_8                                &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
 call_module    l__self___features_denseblock2_denselayer3_norm1   L__self___features_denseblock2_denselayer3_norm1            (concated_features_8,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock2_denselayer3_relu1   L__self___features_denseblock2_denselayer3_relu1            (l__self___features_denseblock2_denselayer3_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_16                               L__self___features_denseblock2_denselayer3_conv1            (l__self___features_denseblock2_denselayer3_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer3_norm2   L__self___features_denseblock2_denselayer3_norm2            (bottleneck_output_16,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer3_relu2   L__self___features_denseblock2_denselayer3_relu2            (l__self___features_denseblock2_denselayer3_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_16                                    L__self___features_denseblock2_denselayer3_conv2            (l__self___features_denseblock2_denselayer3_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_9                                &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
+call_function  concated_features_9                                &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
 call_module    l__self___features_denseblock2_denselayer4_norm1   L__self___features_denseblock2_denselayer4_norm1            (concated_features_9,)                                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock2_denselayer4_relu1   L__self___features_denseblock2_denselayer4_relu1            (l__self___features_denseblock2_denselayer4_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_18                               L__self___features_denseblock2_denselayer4_conv1            (l__self___features_denseblock2_denselayer4_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer4_norm2   L__self___features_denseblock2_denselayer4_norm2            (bottleneck_output_18,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer4_relu2   L__self___features_denseblock2_denselayer4_relu2            (l__self___features_denseblock2_denselayer4_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_18                                    L__self___features_denseblock2_denselayer4_conv2            (l__self___features_denseblock2_denselayer4_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_10                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18], 1)                                                                                                                                                                                                                                                                                                                                                      {}
+call_function  concated_features_10                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18], 1)                                                                                                                                                                                                                                                                                                                                                      {}
 call_module    l__self___features_denseblock2_denselayer5_norm1   L__self___features_denseblock2_denselayer5_norm1            (concated_features_10,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer5_relu1   L__self___features_denseblock2_denselayer5_relu1            (l__self___features_denseblock2_denselayer5_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_20                               L__self___features_denseblock2_denselayer5_conv1            (l__self___features_denseblock2_denselayer5_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer5_norm2   L__self___features_denseblock2_denselayer5_norm2            (bottleneck_output_20,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer5_relu2   L__self___features_denseblock2_denselayer5_relu2            (l__self___features_denseblock2_denselayer5_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_20                                    L__self___features_denseblock2_denselayer5_conv2            (l__self___features_denseblock2_denselayer5_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_11                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20], 1)                                                                                                                                                                                                                                                                                                                                     {}
+call_function  concated_features_11                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20], 1)                                                                                                                                                                                                                                                                                                                                     {}
 call_module    l__self___features_denseblock2_denselayer6_norm1   L__self___features_denseblock2_denselayer6_norm1            (concated_features_11,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer6_relu1   L__self___features_denseblock2_denselayer6_relu1            (l__self___features_denseblock2_denselayer6_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_22                               L__self___features_denseblock2_denselayer6_conv1            (l__self___features_denseblock2_denselayer6_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer6_norm2   L__self___features_denseblock2_denselayer6_norm2            (bottleneck_output_22,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer6_relu2   L__self___features_denseblock2_denselayer6_relu2            (l__self___features_denseblock2_denselayer6_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_22                                    L__self___features_denseblock2_denselayer6_conv2            (l__self___features_denseblock2_denselayer6_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_12                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22], 1)                                                                                                                                                                                                                                                                                                                    {}
+call_function  concated_features_12                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22], 1)                                                                                                                                                                                                                                                                                                                    {}
 call_module    l__self___features_denseblock2_denselayer7_norm1   L__self___features_denseblock2_denselayer7_norm1            (concated_features_12,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer7_relu1   L__self___features_denseblock2_denselayer7_relu1            (l__self___features_denseblock2_denselayer7_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_24                               L__self___features_denseblock2_denselayer7_conv1            (l__self___features_denseblock2_denselayer7_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer7_norm2   L__self___features_denseblock2_denselayer7_norm2            (bottleneck_output_24,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer7_relu2   L__self___features_denseblock2_denselayer7_relu2            (l__self___features_denseblock2_denselayer7_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_24                                    L__self___features_denseblock2_denselayer7_conv2            (l__self___features_denseblock2_denselayer7_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_13                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24], 1)                                                                                                                                                                                                                                                                                                   {}
+call_function  concated_features_13                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24], 1)                                                                                                                                                                                                                                                                                                   {}
 call_module    l__self___features_denseblock2_denselayer8_norm1   L__self___features_denseblock2_denselayer8_norm1            (concated_features_13,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer8_relu1   L__self___features_denseblock2_denselayer8_relu1            (l__self___features_denseblock2_denselayer8_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_26                               L__self___features_denseblock2_denselayer8_conv1            (l__self___features_denseblock2_denselayer8_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer8_norm2   L__self___features_denseblock2_denselayer8_norm2            (bottleneck_output_26,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer8_relu2   L__self___features_denseblock2_denselayer8_relu2            (l__self___features_denseblock2_denselayer8_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_26                                    L__self___features_denseblock2_denselayer8_conv2            (l__self___features_denseblock2_denselayer8_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_14                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26], 1)                                                                                                                                                                                                                                                                                  {}
+call_function  concated_features_14                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26], 1)                                                                                                                                                                                                                                                                                  {}
 call_module    l__self___features_denseblock2_denselayer9_norm1   L__self___features_denseblock2_denselayer9_norm1            (concated_features_14,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer9_relu1   L__self___features_denseblock2_denselayer9_relu1            (l__self___features_denseblock2_denselayer9_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_28                               L__self___features_denseblock2_denselayer9_conv1            (l__self___features_denseblock2_denselayer9_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer9_norm2   L__self___features_denseblock2_denselayer9_norm2            (bottleneck_output_28,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer9_relu2   L__self___features_denseblock2_denselayer9_relu2            (l__self___features_denseblock2_denselayer9_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_28                                    L__self___features_denseblock2_denselayer9_conv2            (l__self___features_denseblock2_denselayer9_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_15                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28], 1)                                                                                                                                                                                                                                                                 {}
+call_function  concated_features_15                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28], 1)                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock2_denselayer10_norm1  L__self___features_denseblock2_denselayer10_norm1           (concated_features_15,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer10_relu1  L__self___features_denseblock2_denselayer10_relu1           (l__self___features_denseblock2_denselayer10_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_30                               L__self___features_denseblock2_denselayer10_conv1           (l__self___features_denseblock2_denselayer10_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock2_denselayer10_norm2  L__self___features_denseblock2_denselayer10_norm2           (bottleneck_output_30,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer10_relu2  L__self___features_denseblock2_denselayer10_relu2           (l__self___features_denseblock2_denselayer10_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_30                                    L__self___features_denseblock2_denselayer10_conv2           (l__self___features_denseblock2_denselayer10_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_16                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30], 1)                                                                                                                                                                                                                                                {}
+call_function  concated_features_16                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30], 1)                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock2_denselayer11_norm1  L__self___features_denseblock2_denselayer11_norm1           (concated_features_16,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer11_relu1  L__self___features_denseblock2_denselayer11_relu1           (l__self___features_denseblock2_denselayer11_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_32                               L__self___features_denseblock2_denselayer11_conv1           (l__self___features_denseblock2_denselayer11_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock2_denselayer11_norm2  L__self___features_denseblock2_denselayer11_norm2           (bottleneck_output_32,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer11_relu2  L__self___features_denseblock2_denselayer11_relu2           (l__self___features_denseblock2_denselayer11_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_32                                    L__self___features_denseblock2_denselayer11_conv2           (l__self___features_denseblock2_denselayer11_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_17                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32], 1)                                                                                                                                                                                                                               {}
+call_function  concated_features_17                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32], 1)                                                                                                                                                                                                                               {}
 call_module    l__self___features_denseblock2_denselayer12_norm1  L__self___features_denseblock2_denselayer12_norm1           (concated_features_17,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer12_relu1  L__self___features_denseblock2_denselayer12_relu1           (l__self___features_denseblock2_denselayer12_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_34                               L__self___features_denseblock2_denselayer12_conv1           (l__self___features_denseblock2_denselayer12_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock2_denselayer12_norm2  L__self___features_denseblock2_denselayer12_norm2           (bottleneck_output_34,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock2_denselayer12_relu2  L__self___features_denseblock2_denselayer12_relu2           (l__self___features_denseblock2_denselayer12_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_34                                    L__self___features_denseblock2_denselayer12_conv2           (l__self___features_denseblock2_denselayer12_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  cat_19                                             &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32, new_features_34], 1)                                                                                                                                                                                                              {}
+call_function  cat_19                                             &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition1_pool, new_features_12, new_features_14, new_features_16, new_features_18, new_features_20, new_features_22, new_features_24, new_features_26, new_features_28, new_features_30, new_features_32, new_features_34], 1)                                                                                                                                                                                                              {}
 call_module    l__self___features_transition2_norm                L__self___features_transition2_norm                         (cat_19,)                                                                                                                                                                                                                                                                                                                                                                                                                                                           {}
 call_module    l__self___features_transition2_relu                L__self___features_transition2_relu                         (l__self___features_transition2_norm,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_transition2_conv                L__self___features_transition2_conv                         (l__self___features_transition2_relu,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_transition2_pool                L__self___features_transition2_pool                         (l__self___features_transition2_conv,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
-call_function  concated_features_18                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
+call_function  concated_features_18                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
 call_module    l__self___features_denseblock3_denselayer1_norm1   L__self___features_denseblock3_denselayer1_norm1            (concated_features_18,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer1_relu1   L__self___features_denseblock3_denselayer1_relu1            (l__self___features_denseblock3_denselayer1_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_36                               L__self___features_denseblock3_denselayer1_conv1            (l__self___features_denseblock3_denselayer1_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer1_norm2   L__self___features_denseblock3_denselayer1_norm2            (bottleneck_output_36,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer1_relu2   L__self___features_denseblock3_denselayer1_relu2            (l__self___features_denseblock3_denselayer1_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_36                                    L__self___features_denseblock3_denselayer1_conv2            (l__self___features_denseblock3_denselayer1_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_19                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
+call_function  concated_features_19                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
 call_module    l__self___features_denseblock3_denselayer2_norm1   L__self___features_denseblock3_denselayer2_norm1            (concated_features_19,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer2_relu1   L__self___features_denseblock3_denselayer2_relu1            (l__self___features_denseblock3_denselayer2_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_38                               L__self___features_denseblock3_denselayer2_conv1            (l__self___features_denseblock3_denselayer2_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer2_norm2   L__self___features_denseblock3_denselayer2_norm2            (bottleneck_output_38,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer2_relu2   L__self___features_denseblock3_denselayer2_relu2            (l__self___features_denseblock3_denselayer2_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_38                                    L__self___features_denseblock3_denselayer2_conv2            (l__self___features_denseblock3_denselayer2_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_20                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
+call_function  concated_features_20                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
 call_module    l__self___features_denseblock3_denselayer3_norm1   L__self___features_denseblock3_denselayer3_norm1            (concated_features_20,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer3_relu1   L__self___features_denseblock3_denselayer3_relu1            (l__self___features_denseblock3_denselayer3_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_40                               L__self___features_denseblock3_denselayer3_conv1            (l__self___features_denseblock3_denselayer3_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer3_norm2   L__self___features_denseblock3_denselayer3_norm2            (bottleneck_output_40,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer3_relu2   L__self___features_denseblock3_denselayer3_relu2            (l__self___features_denseblock3_denselayer3_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_40                                    L__self___features_denseblock3_denselayer3_conv2            (l__self___features_denseblock3_denselayer3_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_21                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
+call_function  concated_features_21                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
 call_module    l__self___features_denseblock3_denselayer4_norm1   L__self___features_denseblock3_denselayer4_norm1            (concated_features_21,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer4_relu1   L__self___features_denseblock3_denselayer4_relu1            (l__self___features_denseblock3_denselayer4_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_42                               L__self___features_denseblock3_denselayer4_conv1            (l__self___features_denseblock3_denselayer4_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer4_norm2   L__self___features_denseblock3_denselayer4_norm2            (bottleneck_output_42,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer4_relu2   L__self___features_denseblock3_denselayer4_relu2            (l__self___features_denseblock3_denselayer4_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_42                                    L__self___features_denseblock3_denselayer4_conv2            (l__self___features_denseblock3_denselayer4_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_22                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42], 1)                                                                                                                                                                                                                                                                                                                                                      {}
+call_function  concated_features_22                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42], 1)                                                                                                                                                                                                                                                                                                                                                      {}
 call_module    l__self___features_denseblock3_denselayer5_norm1   L__self___features_denseblock3_denselayer5_norm1            (concated_features_22,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer5_relu1   L__self___features_denseblock3_denselayer5_relu1            (l__self___features_denseblock3_denselayer5_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_44                               L__self___features_denseblock3_denselayer5_conv1            (l__self___features_denseblock3_denselayer5_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer5_norm2   L__self___features_denseblock3_denselayer5_norm2            (bottleneck_output_44,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer5_relu2   L__self___features_denseblock3_denselayer5_relu2            (l__self___features_denseblock3_denselayer5_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_44                                    L__self___features_denseblock3_denselayer5_conv2            (l__self___features_denseblock3_denselayer5_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_23                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44], 1)                                                                                                                                                                                                                                                                                                                                     {}
+call_function  concated_features_23                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44], 1)                                                                                                                                                                                                                                                                                                                                     {}
 call_module    l__self___features_denseblock3_denselayer6_norm1   L__self___features_denseblock3_denselayer6_norm1            (concated_features_23,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer6_relu1   L__self___features_denseblock3_denselayer6_relu1            (l__self___features_denseblock3_denselayer6_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_46                               L__self___features_denseblock3_denselayer6_conv1            (l__self___features_denseblock3_denselayer6_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer6_norm2   L__self___features_denseblock3_denselayer6_norm2            (bottleneck_output_46,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer6_relu2   L__self___features_denseblock3_denselayer6_relu2            (l__self___features_denseblock3_denselayer6_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_46                                    L__self___features_denseblock3_denselayer6_conv2            (l__self___features_denseblock3_denselayer6_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_24                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46], 1)                                                                                                                                                                                                                                                                                                                    {}
+call_function  concated_features_24                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46], 1)                                                                                                                                                                                                                                                                                                                    {}
 call_module    l__self___features_denseblock3_denselayer7_norm1   L__self___features_denseblock3_denselayer7_norm1            (concated_features_24,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer7_relu1   L__self___features_denseblock3_denselayer7_relu1            (l__self___features_denseblock3_denselayer7_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_48                               L__self___features_denseblock3_denselayer7_conv1            (l__self___features_denseblock3_denselayer7_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer7_norm2   L__self___features_denseblock3_denselayer7_norm2            (bottleneck_output_48,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer7_relu2   L__self___features_denseblock3_denselayer7_relu2            (l__self___features_denseblock3_denselayer7_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_48                                    L__self___features_denseblock3_denselayer7_conv2            (l__self___features_denseblock3_denselayer7_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_25                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48], 1)                                                                                                                                                                                                                                                                                                   {}
+call_function  concated_features_25                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48], 1)                                                                                                                                                                                                                                                                                                   {}
 call_module    l__self___features_denseblock3_denselayer8_norm1   L__self___features_denseblock3_denselayer8_norm1            (concated_features_25,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer8_relu1   L__self___features_denseblock3_denselayer8_relu1            (l__self___features_denseblock3_denselayer8_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_50                               L__self___features_denseblock3_denselayer8_conv1            (l__self___features_denseblock3_denselayer8_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer8_norm2   L__self___features_denseblock3_denselayer8_norm2            (bottleneck_output_50,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer8_relu2   L__self___features_denseblock3_denselayer8_relu2            (l__self___features_denseblock3_denselayer8_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_50                                    L__self___features_denseblock3_denselayer8_conv2            (l__self___features_denseblock3_denselayer8_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_26                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50], 1)                                                                                                                                                                                                                                                                                  {}
+call_function  concated_features_26                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50], 1)                                                                                                                                                                                                                                                                                  {}
 call_module    l__self___features_denseblock3_denselayer9_norm1   L__self___features_denseblock3_denselayer9_norm1            (concated_features_26,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer9_relu1   L__self___features_denseblock3_denselayer9_relu1            (l__self___features_denseblock3_denselayer9_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_52                               L__self___features_denseblock3_denselayer9_conv1            (l__self___features_denseblock3_denselayer9_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer9_norm2   L__self___features_denseblock3_denselayer9_norm2            (bottleneck_output_52,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer9_relu2   L__self___features_denseblock3_denselayer9_relu2            (l__self___features_denseblock3_denselayer9_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_52                                    L__self___features_denseblock3_denselayer9_conv2            (l__self___features_denseblock3_denselayer9_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_27                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52], 1)                                                                                                                                                                                                                                                                 {}
+call_function  concated_features_27                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52], 1)                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock3_denselayer10_norm1  L__self___features_denseblock3_denselayer10_norm1           (concated_features_27,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer10_relu1  L__self___features_denseblock3_denselayer10_relu1           (l__self___features_denseblock3_denselayer10_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_54                               L__self___features_denseblock3_denselayer10_conv1           (l__self___features_denseblock3_denselayer10_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer10_norm2  L__self___features_denseblock3_denselayer10_norm2           (bottleneck_output_54,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer10_relu2  L__self___features_denseblock3_denselayer10_relu2           (l__self___features_denseblock3_denselayer10_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_54                                    L__self___features_denseblock3_denselayer10_conv2           (l__self___features_denseblock3_denselayer10_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_28                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54], 1)                                                                                                                                                                                                                                                {}
+call_function  concated_features_28                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54], 1)                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer11_norm1  L__self___features_denseblock3_denselayer11_norm1           (concated_features_28,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer11_relu1  L__self___features_denseblock3_denselayer11_relu1           (l__self___features_denseblock3_denselayer11_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_56                               L__self___features_denseblock3_denselayer11_conv1           (l__self___features_denseblock3_denselayer11_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer11_norm2  L__self___features_denseblock3_denselayer11_norm2           (bottleneck_output_56,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer11_relu2  L__self___features_denseblock3_denselayer11_relu2           (l__self___features_denseblock3_denselayer11_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_56                                    L__self___features_denseblock3_denselayer11_conv2           (l__self___features_denseblock3_denselayer11_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_29                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56], 1)                                                                                                                                                                                                                               {}
+call_function  concated_features_29                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56], 1)                                                                                                                                                                                                                               {}
 call_module    l__self___features_denseblock3_denselayer12_norm1  L__self___features_denseblock3_denselayer12_norm1           (concated_features_29,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer12_relu1  L__self___features_denseblock3_denselayer12_relu1           (l__self___features_denseblock3_denselayer12_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_58                               L__self___features_denseblock3_denselayer12_conv1           (l__self___features_denseblock3_denselayer12_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer12_norm2  L__self___features_denseblock3_denselayer12_norm2           (bottleneck_output_58,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer12_relu2  L__self___features_denseblock3_denselayer12_relu2           (l__self___features_denseblock3_denselayer12_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_58                                    L__self___features_denseblock3_denselayer12_conv2           (l__self___features_denseblock3_denselayer12_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_30                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58], 1)                                                                                                                                                                                                              {}
+call_function  concated_features_30                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58], 1)                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock3_denselayer13_norm1  L__self___features_denseblock3_denselayer13_norm1           (concated_features_30,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer13_relu1  L__self___features_denseblock3_denselayer13_relu1           (l__self___features_denseblock3_denselayer13_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_60                               L__self___features_denseblock3_denselayer13_conv1           (l__self___features_denseblock3_denselayer13_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer13_norm2  L__self___features_denseblock3_denselayer13_norm2           (bottleneck_output_60,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer13_relu2  L__self___features_denseblock3_denselayer13_relu2           (l__self___features_denseblock3_denselayer13_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_60                                    L__self___features_denseblock3_denselayer13_conv2           (l__self___features_denseblock3_denselayer13_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_31                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60], 1)                                                                                                                                                                                             {}
+call_function  concated_features_31                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60], 1)                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer14_norm1  L__self___features_denseblock3_denselayer14_norm1           (concated_features_31,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer14_relu1  L__self___features_denseblock3_denselayer14_relu1           (l__self___features_denseblock3_denselayer14_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_62                               L__self___features_denseblock3_denselayer14_conv1           (l__self___features_denseblock3_denselayer14_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer14_norm2  L__self___features_denseblock3_denselayer14_norm2           (bottleneck_output_62,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer14_relu2  L__self___features_denseblock3_denselayer14_relu2           (l__self___features_denseblock3_denselayer14_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_62                                    L__self___features_denseblock3_denselayer14_conv2           (l__self___features_denseblock3_denselayer14_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_32                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62], 1)                                                                                                                                                                            {}
+call_function  concated_features_32                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62], 1)                                                                                                                                                                            {}
 call_module    l__self___features_denseblock3_denselayer15_norm1  L__self___features_denseblock3_denselayer15_norm1           (concated_features_32,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer15_relu1  L__self___features_denseblock3_denselayer15_relu1           (l__self___features_denseblock3_denselayer15_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_64                               L__self___features_denseblock3_denselayer15_conv1           (l__self___features_denseblock3_denselayer15_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer15_norm2  L__self___features_denseblock3_denselayer15_norm2           (bottleneck_output_64,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer15_relu2  L__self___features_denseblock3_denselayer15_relu2           (l__self___features_denseblock3_denselayer15_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_64                                    L__self___features_denseblock3_denselayer15_conv2           (l__self___features_denseblock3_denselayer15_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_33                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64], 1)                                                                                                                                                           {}
+call_function  concated_features_33                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64], 1)                                                                                                                                                           {}
 call_module    l__self___features_denseblock3_denselayer16_norm1  L__self___features_denseblock3_denselayer16_norm1           (concated_features_33,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer16_relu1  L__self___features_denseblock3_denselayer16_relu1           (l__self___features_denseblock3_denselayer16_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_66                               L__self___features_denseblock3_denselayer16_conv1           (l__self___features_denseblock3_denselayer16_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer16_norm2  L__self___features_denseblock3_denselayer16_norm2           (bottleneck_output_66,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer16_relu2  L__self___features_denseblock3_denselayer16_relu2           (l__self___features_denseblock3_denselayer16_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_66                                    L__self___features_denseblock3_denselayer16_conv2           (l__self___features_denseblock3_denselayer16_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_34                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66], 1)                                                                                                                                          {}
+call_function  concated_features_34                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66], 1)                                                                                                                                          {}
 call_module    l__self___features_denseblock3_denselayer17_norm1  L__self___features_denseblock3_denselayer17_norm1           (concated_features_34,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer17_relu1  L__self___features_denseblock3_denselayer17_relu1           (l__self___features_denseblock3_denselayer17_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_68                               L__self___features_denseblock3_denselayer17_conv1           (l__self___features_denseblock3_denselayer17_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer17_norm2  L__self___features_denseblock3_denselayer17_norm2           (bottleneck_output_68,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer17_relu2  L__self___features_denseblock3_denselayer17_relu2           (l__self___features_denseblock3_denselayer17_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_68                                    L__self___features_denseblock3_denselayer17_conv2           (l__self___features_denseblock3_denselayer17_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_35                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68], 1)                                                                                                                         {}
+call_function  concated_features_35                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68], 1)                                                                                                                         {}
 call_module    l__self___features_denseblock3_denselayer18_norm1  L__self___features_denseblock3_denselayer18_norm1           (concated_features_35,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer18_relu1  L__self___features_denseblock3_denselayer18_relu1           (l__self___features_denseblock3_denselayer18_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_70                               L__self___features_denseblock3_denselayer18_conv1           (l__self___features_denseblock3_denselayer18_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer18_norm2  L__self___features_denseblock3_denselayer18_norm2           (bottleneck_output_70,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer18_relu2  L__self___features_denseblock3_denselayer18_relu2           (l__self___features_denseblock3_denselayer18_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_70                                    L__self___features_denseblock3_denselayer18_conv2           (l__self___features_denseblock3_denselayer18_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_36                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70], 1)                                                                                                        {}
+call_function  concated_features_36                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70], 1)                                                                                                        {}
 call_module    l__self___features_denseblock3_denselayer19_norm1  L__self___features_denseblock3_denselayer19_norm1           (concated_features_36,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer19_relu1  L__self___features_denseblock3_denselayer19_relu1           (l__self___features_denseblock3_denselayer19_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_72                               L__self___features_denseblock3_denselayer19_conv1           (l__self___features_denseblock3_denselayer19_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer19_norm2  L__self___features_denseblock3_denselayer19_norm2           (bottleneck_output_72,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer19_relu2  L__self___features_denseblock3_denselayer19_relu2           (l__self___features_denseblock3_denselayer19_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_72                                    L__self___features_denseblock3_denselayer19_conv2           (l__self___features_denseblock3_denselayer19_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_37                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72], 1)                                                                                       {}
+call_function  concated_features_37                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72], 1)                                                                                       {}
 call_module    l__self___features_denseblock3_denselayer20_norm1  L__self___features_denseblock3_denselayer20_norm1           (concated_features_37,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer20_relu1  L__self___features_denseblock3_denselayer20_relu1           (l__self___features_denseblock3_denselayer20_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_74                               L__self___features_denseblock3_denselayer20_conv1           (l__self___features_denseblock3_denselayer20_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer20_norm2  L__self___features_denseblock3_denselayer20_norm2           (bottleneck_output_74,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer20_relu2  L__self___features_denseblock3_denselayer20_relu2           (l__self___features_denseblock3_denselayer20_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_74                                    L__self___features_denseblock3_denselayer20_conv2           (l__self___features_denseblock3_denselayer20_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_38                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74], 1)                                                                      {}
+call_function  concated_features_38                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74], 1)                                                                      {}
 call_module    l__self___features_denseblock3_denselayer21_norm1  L__self___features_denseblock3_denselayer21_norm1           (concated_features_38,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer21_relu1  L__self___features_denseblock3_denselayer21_relu1           (l__self___features_denseblock3_denselayer21_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_76                               L__self___features_denseblock3_denselayer21_conv1           (l__self___features_denseblock3_denselayer21_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer21_norm2  L__self___features_denseblock3_denselayer21_norm2           (bottleneck_output_76,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer21_relu2  L__self___features_denseblock3_denselayer21_relu2           (l__self___features_denseblock3_denselayer21_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_76                                    L__self___features_denseblock3_denselayer21_conv2           (l__self___features_denseblock3_denselayer21_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_39                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76], 1)                                                     {}
+call_function  concated_features_39                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76], 1)                                                     {}
 call_module    l__self___features_denseblock3_denselayer22_norm1  L__self___features_denseblock3_denselayer22_norm1           (concated_features_39,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer22_relu1  L__self___features_denseblock3_denselayer22_relu1           (l__self___features_denseblock3_denselayer22_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_78                               L__self___features_denseblock3_denselayer22_conv1           (l__self___features_denseblock3_denselayer22_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer22_norm2  L__self___features_denseblock3_denselayer22_norm2           (bottleneck_output_78,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer22_relu2  L__self___features_denseblock3_denselayer22_relu2           (l__self___features_denseblock3_denselayer22_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_78                                    L__self___features_denseblock3_denselayer22_conv2           (l__self___features_denseblock3_denselayer22_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_40                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78], 1)                                    {}
+call_function  concated_features_40                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78], 1)                                    {}
 call_module    l__self___features_denseblock3_denselayer23_norm1  L__self___features_denseblock3_denselayer23_norm1           (concated_features_40,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer23_relu1  L__self___features_denseblock3_denselayer23_relu1           (l__self___features_denseblock3_denselayer23_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_80                               L__self___features_denseblock3_denselayer23_conv1           (l__self___features_denseblock3_denselayer23_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer23_norm2  L__self___features_denseblock3_denselayer23_norm2           (bottleneck_output_80,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer23_relu2  L__self___features_denseblock3_denselayer23_relu2           (l__self___features_denseblock3_denselayer23_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_80                                    L__self___features_denseblock3_denselayer23_conv2           (l__self___features_denseblock3_denselayer23_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_41                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80], 1)                   {}
+call_function  concated_features_41                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80], 1)                   {}
 call_module    l__self___features_denseblock3_denselayer24_norm1  L__self___features_denseblock3_denselayer24_norm1           (concated_features_41,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer24_relu1  L__self___features_denseblock3_denselayer24_relu1           (l__self___features_denseblock3_denselayer24_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_82                               L__self___features_denseblock3_denselayer24_conv1           (l__self___features_denseblock3_denselayer24_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock3_denselayer24_norm2  L__self___features_denseblock3_denselayer24_norm2           (bottleneck_output_82,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock3_denselayer24_relu2  L__self___features_denseblock3_denselayer24_relu2           (l__self___features_denseblock3_denselayer24_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_82                                    L__self___features_denseblock3_denselayer24_conv2           (l__self___features_denseblock3_denselayer24_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  cat_44                                             &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80, new_features_82], 1)  {}
+call_function  cat_44                                             &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition2_pool, new_features_36, new_features_38, new_features_40, new_features_42, new_features_44, new_features_46, new_features_48, new_features_50, new_features_52, new_features_54, new_features_56, new_features_58, new_features_60, new_features_62, new_features_64, new_features_66, new_features_68, new_features_70, new_features_72, new_features_74, new_features_76, new_features_78, new_features_80, new_features_82], 1)  {}
 call_module    l__self___features_transition3_norm                L__self___features_transition3_norm                         (cat_44,)                                                                                                                                                                                                                                                                                                                                                                                                                                                           {}
 call_module    l__self___features_transition3_relu                L__self___features_transition3_relu                         (l__self___features_transition3_norm,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_transition3_conv                L__self___features_transition3_conv                         (l__self___features_transition3_relu,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
 call_module    l__self___features_transition3_pool                L__self___features_transition3_pool                         (l__self___features_transition3_conv,)                                                                                                                                                                                                                                                                                                                                                                                                                              {}
-call_function  concated_features_42                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
+call_function  concated_features_42                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool], 1)                                                                                                                                                                                                                                                                                                                                                                                                                          {}
 call_module    l__self___features_denseblock4_denselayer1_norm1   L__self___features_denseblock4_denselayer1_norm1            (concated_features_42,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer1_relu1   L__self___features_denseblock4_denselayer1_relu1            (l__self___features_denseblock4_denselayer1_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_84                               L__self___features_denseblock4_denselayer1_conv1            (l__self___features_denseblock4_denselayer1_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock4_denselayer1_norm2   L__self___features_denseblock4_denselayer1_norm2            (bottleneck_output_84,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer1_relu2   L__self___features_denseblock4_denselayer1_relu2            (l__self___features_denseblock4_denselayer1_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_84                                    L__self___features_denseblock4_denselayer1_conv2            (l__self___features_denseblock4_denselayer1_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_43                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
+call_function  concated_features_43                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84], 1)                                                                                                                                                                                                                                                                                                                                                                                                         {}
 call_module    l__self___features_denseblock4_denselayer2_norm1   L__self___features_denseblock4_denselayer2_norm1            (concated_features_43,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer2_relu1   L__self___features_denseblock4_denselayer2_relu1            (l__self___features_denseblock4_denselayer2_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_86                               L__self___features_denseblock4_denselayer2_conv1            (l__self___features_denseblock4_denselayer2_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock4_denselayer2_norm2   L__self___features_denseblock4_denselayer2_norm2            (bottleneck_output_86,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer2_relu2   L__self___features_denseblock4_denselayer2_relu2            (l__self___features_denseblock4_denselayer2_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_86                                    L__self___features_denseblock4_denselayer2_conv2            (l__self___features_denseblock4_denselayer2_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_44                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
+call_function  concated_features_44                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86], 1)                                                                                                                                                                                                                                                                                                                                                                                        {}
 call_module    l__self___features_denseblock4_denselayer3_norm1   L__self___features_denseblock4_denselayer3_norm1            (concated_features_44,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer3_relu1   L__self___features_denseblock4_denselayer3_relu1            (l__self___features_denseblock4_denselayer3_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_88                               L__self___features_denseblock4_denselayer3_conv1            (l__self___features_denseblock4_denselayer3_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock4_denselayer3_norm2   L__self___features_denseblock4_denselayer3_norm2            (bottleneck_output_88,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer3_relu2   L__self___features_denseblock4_denselayer3_relu2            (l__self___features_denseblock4_denselayer3_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_88                                    L__self___features_denseblock4_denselayer3_conv2            (l__self___features_denseblock4_denselayer3_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_45                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
+call_function  concated_features_45                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88], 1)                                                                                                                                                                                                                                                                                                                                                                       {}
 call_module    l__self___features_denseblock4_denselayer4_norm1   L__self___features_denseblock4_denselayer4_norm1            (concated_features_45,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer4_relu1   L__self___features_denseblock4_denselayer4_relu1            (l__self___features_denseblock4_denselayer4_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_90                               L__self___features_denseblock4_denselayer4_conv1            (l__self___features_denseblock4_denselayer4_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock4_denselayer4_norm2   L__self___features_denseblock4_denselayer4_norm2            (bottleneck_output_90,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer4_relu2   L__self___features_denseblock4_denselayer4_relu2            (l__self___features_denseblock4_denselayer4_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_90                                    L__self___features_denseblock4_denselayer4_conv2            (l__self___features_denseblock4_denselayer4_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_46                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90], 1)                                                                                                                                                                                                                                                                                                                                                      {}
+call_function  concated_features_46                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90], 1)                                                                                                                                                                                                                                                                                                                                                      {}
 call_module    l__self___features_denseblock4_denselayer5_norm1   L__self___features_denseblock4_denselayer5_norm1            (concated_features_46,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer5_relu1   L__self___features_denseblock4_denselayer5_relu1            (l__self___features_denseblock4_denselayer5_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_92                               L__self___features_denseblock4_denselayer5_conv1            (l__self___features_denseblock4_denselayer5_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock4_denselayer5_norm2   L__self___features_denseblock4_denselayer5_norm2            (bottleneck_output_92,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer5_relu2   L__self___features_denseblock4_denselayer5_relu2            (l__self___features_denseblock4_denselayer5_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_92                                    L__self___features_denseblock4_denselayer5_conv2            (l__self___features_denseblock4_denselayer5_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_47                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92], 1)                                                                                                                                                                                                                                                                                                                                     {}
+call_function  concated_features_47                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92], 1)                                                                                                                                                                                                                                                                                                                                     {}
 call_module    l__self___features_denseblock4_denselayer6_norm1   L__self___features_denseblock4_denselayer6_norm1            (concated_features_47,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer6_relu1   L__self___features_denseblock4_denselayer6_relu1            (l__self___features_denseblock4_denselayer6_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_94                               L__self___features_denseblock4_denselayer6_conv1            (l__self___features_denseblock4_denselayer6_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock4_denselayer6_norm2   L__self___features_denseblock4_denselayer6_norm2            (bottleneck_output_94,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer6_relu2   L__self___features_denseblock4_denselayer6_relu2            (l__self___features_denseblock4_denselayer6_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_94                                    L__self___features_denseblock4_denselayer6_conv2            (l__self___features_denseblock4_denselayer6_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_48                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94], 1)                                                                                                                                                                                                                                                                                                                    {}
+call_function  concated_features_48                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94], 1)                                                                                                                                                                                                                                                                                                                    {}
 call_module    l__self___features_denseblock4_denselayer7_norm1   L__self___features_denseblock4_denselayer7_norm1            (concated_features_48,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer7_relu1   L__self___features_denseblock4_denselayer7_relu1            (l__self___features_denseblock4_denselayer7_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_96                               L__self___features_denseblock4_denselayer7_conv1            (l__self___features_denseblock4_denselayer7_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock4_denselayer7_norm2   L__self___features_denseblock4_denselayer7_norm2            (bottleneck_output_96,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer7_relu2   L__self___features_denseblock4_denselayer7_relu2            (l__self___features_denseblock4_denselayer7_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_96                                    L__self___features_denseblock4_denselayer7_conv2            (l__self___features_denseblock4_denselayer7_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_49                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96], 1)                                                                                                                                                                                                                                                                                                   {}
+call_function  concated_features_49                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96], 1)                                                                                                                                                                                                                                                                                                   {}
 call_module    l__self___features_denseblock4_denselayer8_norm1   L__self___features_denseblock4_denselayer8_norm1            (concated_features_49,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer8_relu1   L__self___features_denseblock4_denselayer8_relu1            (l__self___features_denseblock4_denselayer8_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_98                               L__self___features_denseblock4_denselayer8_conv1            (l__self___features_denseblock4_denselayer8_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock4_denselayer8_norm2   L__self___features_denseblock4_denselayer8_norm2            (bottleneck_output_98,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer8_relu2   L__self___features_denseblock4_denselayer8_relu2            (l__self___features_denseblock4_denselayer8_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_98                                    L__self___features_denseblock4_denselayer8_conv2            (l__self___features_denseblock4_denselayer8_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_50                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98], 1)                                                                                                                                                                                                                                                                                  {}
+call_function  concated_features_50                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98], 1)                                                                                                                                                                                                                                                                                  {}
 call_module    l__self___features_denseblock4_denselayer9_norm1   L__self___features_denseblock4_denselayer9_norm1            (concated_features_50,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer9_relu1   L__self___features_denseblock4_denselayer9_relu1            (l__self___features_denseblock4_denselayer9_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    bottleneck_output_100                              L__self___features_denseblock4_denselayer9_conv1            (l__self___features_denseblock4_denselayer9_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    l__self___features_denseblock4_denselayer9_norm2   L__self___features_denseblock4_denselayer9_norm2            (bottleneck_output_100,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 call_module    l__self___features_denseblock4_denselayer9_relu2   L__self___features_denseblock4_denselayer9_relu2            (l__self___features_denseblock4_denselayer9_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
 call_module    new_features_100                                   L__self___features_denseblock4_denselayer9_conv2            (l__self___features_denseblock4_denselayer9_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                 {}
-call_function  concated_features_51                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100], 1)                                                                                                                                                                                                                                                                {}
+call_function  concated_features_51                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100], 1)                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock4_denselayer10_norm1  L__self___features_denseblock4_denselayer10_norm1           (concated_features_51,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer10_relu1  L__self___features_denseblock4_denselayer10_relu1           (l__self___features_denseblock4_denselayer10_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_102                              L__self___features_denseblock4_denselayer10_conv1           (l__self___features_denseblock4_denselayer10_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock4_denselayer10_norm2  L__self___features_denseblock4_denselayer10_norm2           (bottleneck_output_102,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 call_module    l__self___features_denseblock4_denselayer10_relu2  L__self___features_denseblock4_denselayer10_relu2           (l__self___features_denseblock4_denselayer10_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_102                                   L__self___features_denseblock4_denselayer10_conv2           (l__self___features_denseblock4_denselayer10_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_52                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102], 1)                                                                                                                                                                                                                                              {}
+call_function  concated_features_52                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102], 1)                                                                                                                                                                                                                                              {}
 call_module    l__self___features_denseblock4_denselayer11_norm1  L__self___features_denseblock4_denselayer11_norm1           (concated_features_52,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer11_relu1  L__self___features_denseblock4_denselayer11_relu1           (l__self___features_denseblock4_denselayer11_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_104                              L__self___features_denseblock4_denselayer11_conv1           (l__self___features_denseblock4_denselayer11_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock4_denselayer11_norm2  L__self___features_denseblock4_denselayer11_norm2           (bottleneck_output_104,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 call_module    l__self___features_denseblock4_denselayer11_relu2  L__self___features_denseblock4_denselayer11_relu2           (l__self___features_denseblock4_denselayer11_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_104                                   L__self___features_denseblock4_denselayer11_conv2           (l__self___features_denseblock4_denselayer11_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_53                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104], 1)                                                                                                                                                                                                                            {}
+call_function  concated_features_53                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104], 1)                                                                                                                                                                                                                            {}
 call_module    l__self___features_denseblock4_denselayer12_norm1  L__self___features_denseblock4_denselayer12_norm1           (concated_features_53,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer12_relu1  L__self___features_denseblock4_denselayer12_relu1           (l__self___features_denseblock4_denselayer12_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_106                              L__self___features_denseblock4_denselayer12_conv1           (l__self___features_denseblock4_denselayer12_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock4_denselayer12_norm2  L__self___features_denseblock4_denselayer12_norm2           (bottleneck_output_106,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 call_module    l__self___features_denseblock4_denselayer12_relu2  L__self___features_denseblock4_denselayer12_relu2           (l__self___features_denseblock4_denselayer12_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_106                                   L__self___features_denseblock4_denselayer12_conv2           (l__self___features_denseblock4_denselayer12_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_54                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106], 1)                                                                                                                                                                                                          {}
+call_function  concated_features_54                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106], 1)                                                                                                                                                                                                          {}
 call_module    l__self___features_denseblock4_denselayer13_norm1  L__self___features_denseblock4_denselayer13_norm1           (concated_features_54,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer13_relu1  L__self___features_denseblock4_denselayer13_relu1           (l__self___features_denseblock4_denselayer13_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_108                              L__self___features_denseblock4_denselayer13_conv1           (l__self___features_denseblock4_denselayer13_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock4_denselayer13_norm2  L__self___features_denseblock4_denselayer13_norm2           (bottleneck_output_108,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 call_module    l__self___features_denseblock4_denselayer13_relu2  L__self___features_denseblock4_denselayer13_relu2           (l__self___features_denseblock4_denselayer13_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_108                                   L__self___features_denseblock4_denselayer13_conv2           (l__self___features_denseblock4_denselayer13_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_55                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108], 1)                                                                                                                                                                                        {}
+call_function  concated_features_55                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108], 1)                                                                                                                                                                                        {}
 call_module    l__self___features_denseblock4_denselayer14_norm1  L__self___features_denseblock4_denselayer14_norm1           (concated_features_55,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer14_relu1  L__self___features_denseblock4_denselayer14_relu1           (l__self___features_denseblock4_denselayer14_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_110                              L__self___features_denseblock4_denselayer14_conv1           (l__self___features_denseblock4_denselayer14_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock4_denselayer14_norm2  L__self___features_denseblock4_denselayer14_norm2           (bottleneck_output_110,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 call_module    l__self___features_denseblock4_denselayer14_relu2  L__self___features_denseblock4_denselayer14_relu2           (l__self___features_denseblock4_denselayer14_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_110                                   L__self___features_denseblock4_denselayer14_conv2           (l__self___features_denseblock4_denselayer14_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_56                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110], 1)                                                                                                                                                                      {}
+call_function  concated_features_56                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110], 1)                                                                                                                                                                      {}
 call_module    l__self___features_denseblock4_denselayer15_norm1  L__self___features_denseblock4_denselayer15_norm1           (concated_features_56,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer15_relu1  L__self___features_denseblock4_denselayer15_relu1           (l__self___features_denseblock4_denselayer15_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_112                              L__self___features_denseblock4_denselayer15_conv1           (l__self___features_denseblock4_denselayer15_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock4_denselayer15_norm2  L__self___features_denseblock4_denselayer15_norm2           (bottleneck_output_112,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 call_module    l__self___features_denseblock4_denselayer15_relu2  L__self___features_denseblock4_denselayer15_relu2           (l__self___features_denseblock4_denselayer15_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_112                                   L__self___features_denseblock4_denselayer15_conv2           (l__self___features_denseblock4_denselayer15_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  concated_features_57                               &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112], 1)                                                                                                                                                    {}
+call_function  concated_features_57                               &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112], 1)                                                                                                                                                    {}
 call_module    l__self___features_denseblock4_denselayer16_norm1  L__self___features_denseblock4_denselayer16_norm1           (concated_features_57,)                                                                                                                                                                                                                                                                                                                                                                                                                                             {}
 call_module    l__self___features_denseblock4_denselayer16_relu1  L__self___features_denseblock4_denselayer16_relu1           (l__self___features_denseblock4_denselayer16_norm1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    bottleneck_output_114                              L__self___features_denseblock4_denselayer16_conv1           (l__self___features_denseblock4_denselayer16_relu1,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    l__self___features_denseblock4_denselayer16_norm2  L__self___features_denseblock4_denselayer16_norm2           (bottleneck_output_114,)                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 call_module    l__self___features_denseblock4_denselayer16_relu2  L__self___features_denseblock4_denselayer16_relu2           (l__self___features_denseblock4_denselayer16_norm2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
 call_module    new_features_114                                   L__self___features_denseblock4_denselayer16_conv2           (l__self___features_denseblock4_denselayer16_relu2,)                                                                                                                                                                                                                                                                                                                                                                                                                {}
-call_function  cat_61                                             &lt;built-in method cat of type object at 0x7f0913c11760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112, new_features_114], 1)                                                                                                                                  {}
+call_function  cat_61                                             &lt;built-in method cat of type object at 0x7f3d04955760&gt;      ([l__self___features_transition3_pool, new_features_84, new_features_86, new_features_88, new_features_90, new_features_92, new_features_94, new_features_96, new_features_98, new_features_100, new_features_102, new_features_104, new_features_106, new_features_108, new_features_110, new_features_112, new_features_114], 1)                                                                                                                                  {}
 call_module    features                                           L__self___features_norm5                                    (cat_61,)                                                                                                                                                                                                                                                                                                                                                                                                                                                           {}
-call_function  out                                                &lt;function relu at 0x7f083bd6d510&gt;                           (features,)                                                                                                                                                                                                                                                                                                                                                                                                                                                         {'inplace': True}
-call_function  out_1                                              &lt;function adaptive_avg_pool2d at 0x7f083bd6d000&gt;            (out, (1, 1))                                                                                                                                                                                                                                                                                                                                                                                                                                                       {}
-call_function  out_2                                              &lt;built-in method flatten of type object at 0x7f0913c11760&gt;  (out_1, 1)                                                                                                                                                                                                                                                                                                                                                                                                                                                          {}
+call_function  out                                                &lt;function relu at 0x7f3c2cab1510&gt;                           (features,)                                                                                                                                                                                                                                                                                                                                                                                                                                                         {'inplace': True}
+call_function  out_1                                              &lt;function adaptive_avg_pool2d at 0x7f3c2cab1000&gt;            (out, (1, 1))                                                                                                                                                                                                                                                                                                                                                                                                                                                       {}
+call_function  out_2                                              &lt;built-in method flatten of type object at 0x7f3d04955760&gt;  (out_1, 1)                                                                                                                                                                                                                                                                                                                                                                                                                                                          {}
 call_module    out_3                                              L__self___classifier                                        (out_2,)                                                                                                                                                                                                                                                                                                                                                                                                                                                            {}
 output         output                                             output                                                      ((out_3,),)                                                                                                                                                                                                                                                                                                                                                                                                                                                         {}
 
@@ -1588,7 +1588,7 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
 -------------  ------  ------------------------------------------------------  -----------  --------
 placeholder    l_a_    L_a_                                                    ()           {}
 placeholder    l_b_    L_b_                                                    ()           {}
-call_function  abs_1   &lt;built-in method abs of type object at 0x7f0913c11760&gt;  (l_a_,)      {}
+call_function  abs_1   &lt;built-in method abs of type object at 0x7f3d04955760&gt;  (l_a_,)      {}
 call_function  add     &lt;built-in function add&gt;                                 (abs_1, 1)   {}
 call_function  x       &lt;built-in function truediv&gt;                             (l_a_, add)  {}
 call_method    sum_1   sum                                                     (l_b_,)      {}
@@ -1651,7 +1651,7 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
       &lt;FrameSummary file /var/lib/workspace/intermediate_source/torch_compile_tutorial.py, line 434 in bar&gt;
 Ops per Graph:
   Ops 1:
-    &lt;built-in method abs of type object at 0x7f0913c11760&gt;
+    &lt;built-in method abs of type object at 0x7f3d04955760&gt;
     &lt;built-in function add&gt;
     &lt;built-in function truediv&gt;
     &lt;built-in function lt&gt;
@@ -1660,17 +1660,17 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
     &lt;built-in function mul&gt;
 Out Guards:
   Guard 1:
-    Name: "L['b']"
+    Name: "L['a']"
     Source: local
     Create Function: TENSOR_MATCH
     Guard Types: ['TENSOR_MATCH']
-    Code List: ["hasattr(L['b'], '_dynamo_dynamic_indices') == False"]
-    Object Weakref: &lt;weakref at 0x7f072277a8e0; dead&gt;
-    Guarded Class Weakref: &lt;weakref at 0x7f083c3ffe20; to 'torch._C._TensorMeta' at 0x61ca680 (Tensor)&gt;
+    Code List: ["hasattr(L['a'], '_dynamo_dynamic_indices') == False"]
+    Object Weakref: &lt;weakref at 0x7f3b1385c450; dead&gt;
+    Guarded Class Weakref: &lt;weakref at 0x7f3c2d143e20; to 'torch._C._TensorMeta' at 0x5447680 (Tensor)&gt;
   Guard 2:
     Name: ''
     Source: global
-    Create Function: DETERMINISTIC_ALGORITHMS
+    Create Function: TORCH_FUNCTION_STATE
     Guard Types: None
     Code List: None
     Object Weakref: None
@@ -1684,22 +1684,14 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
     Object Weakref: None
     Guarded Class Weakref: None
   Guard 4:
-    Name: ''
+    Name: "G['torch'].abs"
     Source: global
-    Create Function: TORCH_FUNCTION_STATE
+    Create Function: FUNCTION_MATCH
     Guard Types: None
     Code List: None
     Object Weakref: None
     Guarded Class Weakref: None
   Guard 5:
-    Name: ''
-    Source: shape_env
-    Create Function: SHAPE_ENV
-    Guard Types: None
-    Code List: None
-    Object Weakref: None
-    Guarded Class Weakref: None
-  Guard 6:
     Name: ''
     Source: global
     Create Function: DEFAULT_DEVICE
@@ -1707,31 +1699,23 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
     Code List: ['utils_device.CURRENT_DEVICE == None']
     Object Weakref: None
     Guarded Class Weakref: None
-  Guard 7:
-    Name: "G['torch'].abs"
-    Source: global
-    Create Function: FUNCTION_MATCH
+  Guard 6:
+    Name: ''
+    Source: shape_env
+    Create Function: SHAPE_ENV
     Guard Types: None
     Code List: None
     Object Weakref: None
     Guarded Class Weakref: None
-  Guard 8:
+  Guard 7:
     Name: ''
     Source: global
     Create Function: BACKEND_MATCH
     Guard Types: ['BACKEND_MATCH']
-    Code List: ['___check_current_backend(139670084077168)']
+    Code List: ['___check_current_backend(139891651057920)']
     Object Weakref: None
     Guarded Class Weakref: None
-  Guard 9:
-    Name: "L['a']"
-    Source: local
-    Create Function: TENSOR_MATCH
-    Guard Types: ['TENSOR_MATCH']
-    Code List: ["hasattr(L['a'], '_dynamo_dynamic_indices') == False"]
-    Object Weakref: &lt;weakref at 0x7f078d95ee30; dead&gt;
-    Guarded Class Weakref: &lt;weakref at 0x7f083c3ffe20; to 'torch._C._TensorMeta' at 0x61ca680 (Tensor)&gt;
-  Guard 10:
+  Guard 8:
     Name: ''
     Source: global
     Create Function: GRAD_MODE
@@ -1739,28 +1723,44 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
     Code List: None
     Object Weakref: None
     Guarded Class Weakref: None
-  Guard 11:
+  Guard 9:
+    Name: ''
+    Source: global
+    Create Function: DETERMINISTIC_ALGORITHMS
+    Guard Types: None
+    Code List: None
+    Object Weakref: None
+    Guarded Class Weakref: None
+  Guard 10:
     Name: "L['b']"
     Source: local
     Create Function: TENSOR_MATCH
     Guard Types: ['TENSOR_MATCH']
     Code List: ["hasattr(L['b'], '_dynamo_dynamic_indices') == False"]
-    Object Weakref: &lt;weakref at 0x7f072277a8e0; dead&gt;
-    Guarded Class Weakref: &lt;weakref at 0x7f083c3ffe20; to 'torch._C._TensorMeta' at 0x61ca680 (Tensor)&gt;
-  Guard 12:
+    Object Weakref: &lt;weakref at 0x7f3b69ec48b0; dead&gt;
+    Guarded Class Weakref: &lt;weakref at 0x7f3c2d143e20; to 'torch._C._TensorMeta' at 0x5447680 (Tensor)&gt;
+  Guard 11:
     Name: ''
     Source: global
-    Create Function: DETERMINISTIC_ALGORITHMS
+    Create Function: TORCH_FUNCTION_STATE
     Guard Types: None
     Code List: None
     Object Weakref: None
     Guarded Class Weakref: None
+  Guard 12:
+    Name: "L['x']"
+    Source: local
+    Create Function: TENSOR_MATCH
+    Guard Types: ['TENSOR_MATCH']
+    Code List: ["hasattr(L['x'], '_dynamo_dynamic_indices') == False"]
+    Object Weakref: &lt;weakref at 0x7f3b109e8bd0; dead&gt;
+    Guarded Class Weakref: &lt;weakref at 0x7f3c2d143e20; to 'torch._C._TensorMeta' at 0x5447680 (Tensor)&gt;
   Guard 13:
     Name: ''
     Source: global
-    Create Function: TORCH_FUNCTION_STATE
-    Guard Types: None
-    Code List: None
+    Create Function: DEFAULT_DEVICE
+    Guard Types: ['DEFAULT_DEVICE']
+    Code List: ['utils_device.CURRENT_DEVICE == None']
     Object Weakref: None
     Guarded Class Weakref: None
   Guard 14:
@@ -1772,30 +1772,22 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
     Object Weakref: None
     Guarded Class Weakref: None
   Guard 15:
-    Name: ''
-    Source: global
-    Create Function: DEFAULT_DEVICE
-    Guard Types: ['DEFAULT_DEVICE']
-    Code List: ['utils_device.CURRENT_DEVICE == None']
-    Object Weakref: None
-    Guarded Class Weakref: None
-  Guard 16:
     Name: ''
     Source: global
     Create Function: BACKEND_MATCH
     Guard Types: ['BACKEND_MATCH']
-    Code List: ['___check_current_backend(139670084077168)']
+    Code List: ['___check_current_backend(139891651057920)']
     Object Weakref: None
     Guarded Class Weakref: None
-  Guard 17:
-    Name: "L['x']"
+  Guard 16:
+    Name: "L['b']"
     Source: local
     Create Function: TENSOR_MATCH
     Guard Types: ['TENSOR_MATCH']
-    Code List: ["hasattr(L['x'], '_dynamo_dynamic_indices') == False"]
-    Object Weakref: &lt;weakref at 0x7f084adb68e0; dead&gt;
-    Guarded Class Weakref: &lt;weakref at 0x7f083c3ffe20; to 'torch._C._TensorMeta' at 0x61ca680 (Tensor)&gt;
-  Guard 18:
+    Code List: ["hasattr(L['b'], '_dynamo_dynamic_indices') == False"]
+    Object Weakref: &lt;weakref at 0x7f3b69ec48b0; dead&gt;
+    Guarded Class Weakref: &lt;weakref at 0x7f3c2d143e20; to 'torch._C._TensorMeta' at 0x5447680 (Tensor)&gt;
+  Guard 17:
     Name: ''
     Source: global
     Create Function: GRAD_MODE
@@ -1803,10 +1795,18 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
     Code List: None
     Object Weakref: None
     Guarded Class Weakref: None
+  Guard 18:
+    Name: ''
+    Source: global
+    Create Function: DETERMINISTIC_ALGORITHMS
+    Guard Types: None
+    Code List: None
+    Object Weakref: None
+    Guarded Class Weakref: None
 Compile Times: TorchDynamo compilation metrics:
 Function                         Runtimes (s)
 -------------------------------  --------------
-_compile.&lt;locals&gt;.compile_inner  0.0111, 0.0067
+_compile.&lt;locals&gt;.compile_inner  0.0109, 0.0067
 OutputGraph.call_user_compiler   0.0001, 0.0000
 </pre></div>
 </div>
@@ -1871,13 +1871,13 @@ <h2><a class="toc-backref" href="#id4">TorchDynamo and FX Graphs</a><a class="he
 <span class="nb">print</span><span class="p">(</span><span class="n">opt_model</span><span class="p">(</span><span class="n">generate_data</span><span class="p">(</span><span class="mi">16</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[ 0.1344,  0.1976, -0.2056,  ...,  0.1861, -0.1632, -0.1642],
-        [ 0.2680,  0.2349, -0.1903,  ...,  0.2170, -0.0055,  0.0309],
-        [ 0.0164,  0.2180, -0.1115,  ...,  0.1709, -0.1681, -0.0635],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[ 0.1343,  0.1977, -0.2055,  ...,  0.1862, -0.1632, -0.1640],
+        [ 0.2683,  0.2349, -0.1905,  ...,  0.2167, -0.0055,  0.0311],
+        [ 0.0162,  0.2181, -0.1115,  ...,  0.1708, -0.1679, -0.0636],
         ...,
-        [ 0.0807,  0.2680, -0.1889,  ...,  0.0381, -0.2073, -0.1444],
-        [-0.0209,  0.0858, -0.2457,  ...,  0.1862, -0.1280, -0.0282],
-        [-0.0386,  0.0730, -0.1961,  ...,  0.0864, -0.2199, -0.1485]],
+        [ 0.0805,  0.2680, -0.1888,  ...,  0.0382, -0.2072, -0.1445],
+        [-0.0210,  0.0859, -0.2458,  ...,  0.1863, -0.1280, -0.0282],
+        [-0.0387,  0.0729, -0.1960,  ...,  0.0863, -0.2200, -0.1486]],
        device='cuda:0', grad_fn=&lt;CompiledFunctionBackward&gt;)
 </pre></div>
 </div>
@@ -1894,7 +1894,7 @@ <h2><a class="toc-backref" href="#id5">Conclusion</a><a class="headerlink" href=
 basic usage, demonstrating speedups over eager mode, comparing to previous
 PyTorch compiler solutions, and briefly investigating TorchDynamo and its interactions
 with FX graphs. We hope that you will give <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> a try!</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes  11.441 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes  9.834 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-torch-compile-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/6b019e0b5f84b568fcca1120bd28e230/torch_compile_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">torch_compile_tutorial.py</span></code></a></p>
diff --git a/intermediate/torch_export_tutorial.html b/intermediate/torch_export_tutorial.html
index 9d453fa58d..3024e70f9a 100644
--- a/intermediate/torch_export_tutorial.html
+++ b/intermediate/torch_export_tutorial.html
@@ -539,37 +539,18 @@ <h2><a class="toc-backref" href="#id1">Basic Usage</a><a class="headerlink" href
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">from</span> <span class="nn">torch.export</span> <span class="kn">import</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a>
 
-<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lin</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lin</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">):</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.nn.functional.relu.html#torch.nn.functional.relu" title="torch.nn.functional.relu"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span></a><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lin</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">),</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="k">return</span> <a class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.nn.functional.relu.html#torch.nn.functional.relu" title="torch.nn.functional.relu"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span></a><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lin</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">),</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
-<span class="n">mod</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">MyModule</span></a><span class="p">()</span>
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_mod</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">)))</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_mod</span></a><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_mod</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">)))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;class 'torch.export.exported_program.ExportedProgram'&gt;
-tensor([[0.8632, 0.8407, 0.0407, 0.0000, 0.4132, 0.0000, 0.0000, 0.1538, 0.6111,
-         0.0000],
-        [0.0000, 0.0000, 0.0273, 0.8057, 0.0000, 1.0162, 0.8042, 0.0000, 0.2660,
-         0.0000],
-        [0.9481, 0.1396, 1.0225, 0.9563, 0.5832, 0.2546, 0.4095, 0.4591, 0.0000,
-         2.0053],
-        [1.1300, 0.4873, 0.0000, 0.9663, 1.2275, 1.4015, 0.0000, 0.9444, 0.0000,
-         0.0000],
-        [0.0000, 0.8724, 1.1648, 0.6867, 0.0000, 0.2833, 0.3202, 0.5848, 0.0000,
-         0.0833],
-        [1.1311, 0.1324, 0.0000, 1.7842, 0.0000, 0.3474, 0.9916, 0.3571, 0.0000,
-         0.0000],
-        [1.4348, 1.0570, 0.1771, 0.0000, 0.9510, 0.0000, 0.0000, 0.0000, 0.2618,
-         0.0000],
-        [0.8853, 0.0000, 0.0000, 0.4486, 0.0000, 0.0000, 0.5841, 0.7604, 0.0000,
-         0.0000]], grad_fn=&lt;ReluBackward0&gt;)
+<span class="n">mod</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
+<span class="n">exported_mod</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">exported_mod</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exported_mod</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">)))</span>
 </pre></div>
 </div>
 <p>Let’s review some attributes of <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> that are of interest.</p>
@@ -582,35 +563,8 @@ <h2><a class="toc-backref" href="#id1">Basic Usage</a><a class="headerlink" href
 </ul>
 <p>The <code class="docutils literal notranslate"><span class="pre">graph_module</span></code> attribute is the <code class="docutils literal notranslate"><span class="pre">GraphModule</span></code> that wraps the <code class="docutils literal notranslate"><span class="pre">graph</span></code> attribute
 so that it can be ran as a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_mod</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_mod</span></a><span class="o">.</span><span class="n">graph_module</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>ExportedProgram:
-    class GraphModule(torch.nn.Module):
-        def forward(self, arg0_1: "f32[10, 100]", arg1_1: "f32[10]", arg2_1: "f32[8, 100]", arg3_1: "f32[8, 100]"):
-            # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:70 in forward, code: return torch.nn.functional.relu(self.lin(x + y), inplace=True)
-            add: "f32[8, 100]" = torch.ops.aten.add.Tensor(arg2_1, arg3_1);  arg2_1 = arg3_1 = None
-            t: "f32[100, 10]" = torch.ops.aten.t.default(arg0_1);  arg0_1 = None
-            addmm: "f32[8, 10]" = torch.ops.aten.addmm.default(arg1_1, add, t);  arg1_1 = add = t = None
-            relu: "f32[8, 10]" = torch.ops.aten.relu.default(addmm);  addmm = None
-            return (relu,)
-
-Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=&lt;InputKind.PARAMETER: 2&gt;, arg=TensorArgument(name='arg0_1'), target='lin.weight', persistent=None), InputSpec(kind=&lt;InputKind.PARAMETER: 2&gt;, arg=TensorArgument(name='arg1_1'), target='lin.bias', persistent=None), InputSpec(kind=&lt;InputKind.USER_INPUT: 1&gt;, arg=TensorArgument(name='arg2_1'), target=None, persistent=None), InputSpec(kind=&lt;InputKind.USER_INPUT: 1&gt;, arg=TensorArgument(name='arg3_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=&lt;OutputKind.USER_OUTPUT: 1&gt;, arg=TensorArgument(name='relu'), target=None)])
-Range constraints: {}
-
-GraphModule()
-
-
-
-def forward(self, arg0_1, arg1_1, arg2_1, arg3_1):
-    add = torch.ops.aten.add.Tensor(arg2_1, arg3_1);  arg2_1 = arg3_1 = None
-    t = torch.ops.aten.t.default(arg0_1);  arg0_1 = None
-    addmm = torch.ops.aten.addmm.default(arg1_1, add, t);  arg1_1 = add = t = None
-    relu = torch.ops.aten.relu.default(addmm);  addmm = None
-    return (relu,)
-
-# To see more debug info, please use `graph_module.print_readable()`
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">exported_mod</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exported_mod</span><span class="o">.</span><span class="n">graph_module</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>The printed code shows that FX graph only contains ATen-level ops (such as <code class="docutils literal notranslate"><span class="pre">torch.ops.aten</span></code>)
@@ -623,10 +577,7 @@ <h2><a class="toc-backref" href="#id1">Basic Usage</a><a class="headerlink" href
 <li><p><code class="docutils literal notranslate"><span class="pre">graph_signature</span></code> – the inputs, outputs, parameters, buffers, etc. of the exported graph.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">range_constraints</span></code> – constraints, covered later</p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_mod</span></a><span class="o">.</span><span class="n">graph_signature</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>ExportGraphSignature(input_specs=[InputSpec(kind=&lt;InputKind.PARAMETER: 2&gt;, arg=TensorArgument(name='arg0_1'), target='lin.weight', persistent=None), InputSpec(kind=&lt;InputKind.PARAMETER: 2&gt;, arg=TensorArgument(name='arg1_1'), target='lin.bias', persistent=None), InputSpec(kind=&lt;InputKind.USER_INPUT: 1&gt;, arg=TensorArgument(name='arg2_1'), target=None, persistent=None), InputSpec(kind=&lt;InputKind.USER_INPUT: 1&gt;, arg=TensorArgument(name='arg3_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=&lt;OutputKind.USER_OUTPUT: 1&gt;, arg=TensorArgument(name='relu'), target=None)])
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">exported_mod</span><span class="o">.</span><span class="n">graph_signature</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>See the <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> <a class="reference external" href="https://pytorch.org/docs/main/export.html#torch.export.export">documentation</a>
@@ -645,91 +596,29 @@ <h2><a class="toc-backref" href="#id2">Graph Breaks</a><a class="headerlink" hre
 <ul class="simple">
 <li><p>data-dependent control flow</p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad1</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <span class="k">if</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.sin.html#torch.sin" title="torch.sin"><span class="n">torch</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cos.html#torch.cos" title="torch.cos"><span class="n">torch</span><span class="o">.</span><span class="n">cos</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad1</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.sin.html#torch.sin" title="torch.sin"><span class="n">torch</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cos.html#torch.cos" title="torch.cos"><span class="n">torch</span><span class="o">.</span><span class="n">cos</span></a><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">traceback</span> <span class="k">as</span> <span class="nn">tb</span>
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">Bad1</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
+    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">Bad1</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 136, in &lt;module&gt;
-    export(Bad1(), (torch.randn(3, 3),))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-    return _export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-    ep = fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-    gm_torch_level = _export_to_torch_ir(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-    gm_torch_level, _ = torch._dynamo.export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-    result_traced = opt_f(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-    return callback(frame, cache_entry, hooks, frame_state, skip=1)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-    return _compile(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-    return func(*args, **kwds)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-    guarded_code = compile_inner(code, one_graph, hooks, transform)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-    r = func(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
-    out_code = transform_code_object(code, transform)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
-    transformations(instructions, code_options)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 500, in transform
-    tracer.run()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2149, in run
-    super().run()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
-    and self.step()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
-    getattr(self, inst.opname)(inst)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 464, in inner
-    raise exc.UserError(
-torch._dynamo.exc.UserError: Dynamic control flow is not supported at the moment. Please use functorch.experimental.control_flow.cond to explicitly capture the control flow. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#cond-operands
-
-from user code:
-   File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 130, in forward
-    if x.sum() &gt; 0:
-
-Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-</pre></div>
-</div>
 <ul class="simple">
 <li><p>accessing tensor data with <code class="docutils literal notranslate"><span class="pre">.data</span></code></p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span><span class="o">.</span><span class="n">data</span></a><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>
+        <span class="k">return</span> <span class="n">x</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">Bad2</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
+    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">Bad2</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 </pre></div>
@@ -737,179 +626,35 @@ <h2><a class="toc-backref" href="#id2">Graph Breaks</a><a class="headerlink" hre
 <ul class="simple">
 <li><p>calling unsupported functions (such as many built-in functions)</p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad3</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <span class="mi">1</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <span class="nb">id</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad3</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">1</span>
+        <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">Bad3</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
+    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">Bad3</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 162, in &lt;module&gt;
-    export(Bad3(), (torch.randn(3, 3),))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-    return _export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-    ep = fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-    gm_torch_level = _export_to_torch_ir(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-    gm_torch_level, _ = torch._dynamo.export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-    result_traced = opt_f(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-    return callback(frame, cache_entry, hooks, frame_state, skip=1)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-    return _compile(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-    return func(*args, **kwds)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-    guarded_code = compile_inner(code, one_graph, hooks, transform)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-    r = func(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
-    out_code = transform_code_object(code, transform)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
-    transformations(instructions, code_options)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 500, in transform
-    tracer.run()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2149, in run
-    super().run()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
-    and self.step()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
-    getattr(self, inst.opname)(inst)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
-    return inner_fn(self, inst)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1219, in CALL_FUNCTION
-    self.call_function(fn, args, {})
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
-    self.push(fn.call_function(self, args, kwargs))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 687, in call_function
-    result = handler(tx, *args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 1520, in call_id
-    unimplemented(f"call_id with args {args}")
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 190, in unimplemented
-    raise Unsupported(msg)
-torch._dynamo.exc.Unsupported: call_id with args (TensorVariable(),)
-
-from user code:
-   File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 159, in forward
-    return x + id(x)
-
-Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-</pre></div>
-</div>
 <ul class="simple">
 <li><p>unsupported Python language features (e.g. throwing exceptions, match statements)</p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad4</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad4</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
         <span class="k">try</span><span class="p">:</span>
-            <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <span class="mi">1</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">1</span>
             <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"bad"</span><span class="p">)</span>
         <span class="k">except</span><span class="p">:</span>
-            <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <span class="mi">2</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">2</span>
+        <span class="k">return</span> <span class="n">x</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">Bad4</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
+    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">Bad4</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 179, in &lt;module&gt;
-    export(Bad4(), (torch.randn(3, 3),))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-    return _export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-    ep = fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-    gm_torch_level = _export_to_torch_ir(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-    gm_torch_level, _ = torch._dynamo.export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-    result_traced = opt_f(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-    return callback(frame, cache_entry, hooks, frame_state, skip=1)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-    return _compile(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-    return func(*args, **kwds)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-    guarded_code = compile_inner(code, one_graph, hooks, transform)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-    r = func(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
-    out_code = transform_code_object(code, transform)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
-    transformations(instructions, code_options)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 500, in transform
-    tracer.run()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2149, in run
-    super().run()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
-    and self.step()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
-    getattr(self, inst.opname)(inst)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 489, in wrapper
-    return inner_fn(self, inst)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1219, in CALL_FUNCTION
-    self.call_function(fn, args, {})
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 674, in call_function
-    self.push(fn.call_function(self, args, kwargs))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 730, in call_function
-    return super().call_function(tx, args, kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 349, in call_function
-    unimplemented(f"call_function {self} {args} {kwargs}")
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 190, in unimplemented
-    raise Unsupported(msg)
-torch._dynamo.exc.Unsupported: call_function BuiltinVariable(RuntimeError) [ConstantVariable()] {}
-
-from user code:
-   File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 173, in forward
-    raise RuntimeError("bad")
-
-Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-</pre></div>
-</div>
 </div>
 <div class="section" id="non-strict-export">
 <h2><a class="toc-backref" href="#id3">Non-Strict Export</a><a class="headerlink" href="#non-strict-export" title="Permalink to this heading">¶</a></h2>
@@ -927,26 +672,13 @@ <h2><a class="toc-backref" href="#id3">Non-Strict Export</a><a class="headerlink
 <ul class="simple">
 <li><p>Accessing tensor data with <code class="docutils literal notranslate"><span class="pre">.data</span></code> now works correctly</p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span><span class="o">.</span><span class="n">data</span></a><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a>
-
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">bad2_nonstrict</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">Bad2</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),),</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">bad2_nonstrict</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py:58: UserWarning:
-
-Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer
-
-/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph.py:1460: UserWarning:
-
-Node _lifted_tensor_constant0_1 target _lifted_tensor_constant0 _lifted_tensor_constant0 of  does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>
+        <span class="k">return</span> <span class="n">x</span>
 
-tensor([[3., 1., 1.],
-        [1., 1., 1.],
-        [1., 1., 1.]])
+<span class="n">bad2_nonstrict</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">Bad2</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),),</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">bad2_nonstrict</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
 </pre></div>
 </div>
 <ul class="simple">
@@ -955,52 +687,31 @@ <h2><a class="toc-backref" href="#id3">Non-Strict Export</a><a class="headerlink
 <p>through, but in this case, <code class="docutils literal notranslate"><span class="pre">id(x)</span></code> gets specialized as a constant integer in
 the graph. This is because <code class="docutils literal notranslate"><span class="pre">id(x)</span></code> is not a tensor operation, so the
 operation is not recorded in the graph.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad3</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <span class="mi">1</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <span class="nb">id</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
-
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">bad3_nonstrict</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">Bad3</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),),</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">bad3_nonstrict</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">bad3_nonstrict</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>ExportedProgram:
-    class GraphModule(torch.nn.Module):
-        def forward(self, arg0_1: "f32[3, 3]"):
-            # No stacktrace found for following nodes
-            add: "f32[3, 3]" = torch.ops.aten.add.Tensor(arg0_1, 1);  arg0_1 = None
-            add_1: "f32[3, 3]" = torch.ops.aten.add.Tensor(add, 139668425840400);  add = None
-            return (add_1,)
-
-Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=&lt;InputKind.USER_INPUT: 1&gt;, arg=TensorArgument(name='arg0_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=&lt;OutputKind.USER_OUTPUT: 1&gt;, arg=TensorArgument(name='add_1'), target=None)])
-Range constraints: {}
-
-tensor([[1.3967e+14, 1.3967e+14, 1.3967e+14],
-        [1.3967e+14, 1.3967e+14, 1.3967e+14],
-        [1.3967e+14, 1.3967e+14, 1.3967e+14]])
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad3</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">1</span>
+        <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="nb">id</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+<span class="n">bad3_nonstrict</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">Bad3</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),),</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">bad3_nonstrict</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">bad3_nonstrict</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
 </pre></div>
 </div>
 <ul class="simple">
 <li><p>Unsupported Python language features (such as throwing exceptions, match</p></li>
 </ul>
 <p>statements) now also get traced through.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad4</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Bad4</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
         <span class="k">try</span><span class="p">:</span>
-            <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <span class="mi">1</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">1</span>
             <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"bad"</span><span class="p">)</span>
         <span class="k">except</span><span class="p">:</span>
-            <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <span class="mi">2</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">2</span>
+        <span class="k">return</span> <span class="n">x</span>
 
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">bad4_nonstrict</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">Bad4</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),),</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">bad4_nonstrict</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[4., 4., 4.],
-        [4., 4., 4.],
-        [4., 4., 4.]])
+<span class="n">bad4_nonstrict</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">Bad4</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),),</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">bad4_nonstrict</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
 </pre></div>
 </div>
 <p>However, there are still some features that require rewrites to the original
@@ -1013,25 +724,17 @@ <h2><a class="toc-backref" href="#id4">Control Flow Ops</a><a class="headerlink"
 we can fix the control flow example above using the <code class="docutils literal notranslate"><span class="pre">cond</span></code> op, like so:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
 
-<span class="k">class</span> <span class="nc">Bad1Fixed</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <span class="k">def</span> <span class="nf">true_fn</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-            <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.sin.html#torch.sin" title="torch.sin"><span class="n">torch</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
-        <span class="k">def</span> <span class="nf">false_fn</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-            <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cos.html#torch.cos" title="torch.cos"><span class="n">torch</span><span class="o">.</span><span class="n">cos</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
-        <span class="k">return</span> <span class="n">cond</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="n">true_fn</span><span class="p">,</span> <span class="n">false_fn</span><span class="p">,</span> <span class="p">[</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">])</span>
-
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_bad1_fixed</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">Bad1Fixed</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_bad1_fixed</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_bad1_fixed</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><span class="o">-</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[0.8415, 0.8415, 0.8415],
-        [0.8415, 0.8415, 0.8415],
-        [0.8415, 0.8415, 0.8415]])
-tensor([[0.5403, 0.5403, 0.5403],
-        [0.5403, 0.5403, 0.5403],
-        [0.5403, 0.5403, 0.5403]])
+<span class="k">class</span> <span class="nc">Bad1Fixed</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">def</span> <span class="nf">true_fn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.sin.html#torch.sin" title="torch.sin"><span class="n">torch</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">def</span> <span class="nf">false_fn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cos.html#torch.cos" title="torch.cos"><span class="n">torch</span><span class="o">.</span><span class="n">cos</span></a><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">cond</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="n">true_fn</span><span class="p">,</span> <span class="n">false_fn</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">])</span>
+
+<span class="n">exported_bad1_fixed</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">Bad1Fixed</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exported_bad1_fixed</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exported_bad1_fixed</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><span class="o">-</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.ones.html#torch.ones" title="torch.ones"><span class="n">torch</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
 </pre></div>
 </div>
 <p>There are limitations to <code class="docutils literal notranslate"><span class="pre">cond</span></code> that one should be aware of:</p>
@@ -1053,47 +756,23 @@ <h2><a class="toc-backref" href="#id5">Constraints/Dynamic Shapes</a><a class="h
 example inputs given to the initial <code class="docutils literal notranslate"><span class="pre">torch.export.export()</span></code> call.
 If we try to run the <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> in the example below with a tensor
 with a different shape, we get an error:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModule2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModule2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lin</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lin</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">):</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.nn.functional.relu.html#torch.nn.functional.relu" title="torch.nn.functional.relu"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span></a><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lin</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">),</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="k">return</span> <a class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.nn.functional.relu.html#torch.nn.functional.relu" title="torch.nn.functional.relu"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span></a><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lin</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">),</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
-<span class="n">mod2</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">MyModule2</span></a><span class="p">()</span>
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_mod2</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">mod2</span><span class="p">,</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">)))</span>
+<span class="n">mod2</span> <span class="o">=</span> <span class="n">MyModule2</span><span class="p">()</span>
+<span class="n">exported_mod2</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">mod2</span><span class="p">,</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">100</span><span class="p">)))</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_mod2</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
+    <span class="n">exported_mod2</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 325, in &lt;module&gt;
-    exported_mod2.module()(torch.randn(10, 100), torch.randn(10, 100))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-    return self._wrapped_call(self, *args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-    return _check_input_constraints_for_graph(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 129, in _check_input_constraints_for_graph
-    raise RuntimeError(
-RuntimeError: Expected input at *args[0].shape[0] to be equal to 8, but got 10
-</pre></div>
-</div>
 <p>We can relax this constraint using the <code class="docutils literal notranslate"><span class="pre">dynamic_shapes</span></code> argument of
 <code class="docutils literal notranslate"><span class="pre">torch.export.export()</span></code>, which allows us to specify, using <code class="docutils literal notranslate"><span class="pre">torch.export.Dim</span></code>
 (<a class="reference external" href="https://pytorch.org/docs/main/export.html#torch.export.Dim">documentation</a>),
@@ -1113,12 +792,12 @@ <h2><a class="toc-backref" href="#id5">Constraints/Dynamic Shapes</a><a class="h
 dimension must be in the interval [4, 18].</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.export</span> <span class="kn">import</span> <span class="n">Dim</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp1</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">inp1</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
 
-<span class="k">class</span> <span class="nc">DynamicShapesExample1</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">[:,</span> <span class="mi">2</span><span class="p">:]</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
+<span class="k">class</span> <span class="nc">DynamicShapesExample1</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:]</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 
 <span class="n">inp1_dim0</span> <span class="o">=</span> <span class="n">Dim</span><span class="p">(</span><span class="s2">"inp1_dim0"</span><span class="p">)</span>
 <span class="n">inp1_dim1</span> <span class="o">=</span> <span class="n">Dim</span><span class="p">(</span><span class="s2">"inp1_dim1"</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
@@ -1126,113 +805,26 @@ <h2><a class="toc-backref" href="#id5">Constraints/Dynamic Shapes</a><a class="h
     <span class="s2">"x"</span><span class="p">:</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">inp1_dim0</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span> <span class="n">inp1_dim1</span><span class="p">},</span>
 <span class="p">}</span>
 
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_dynamic_shapes_example1</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DynamicShapesExample1</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp1</span></a><span class="p">,),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes1</span><span class="p">)</span>
+<span class="n">exported_dynamic_shapes_example1</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">DynamicShapesExample1</span><span class="p">(),</span> <span class="p">(</span><span class="n">inp1</span><span class="p">,),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes1</span><span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_dynamic_shapes_example1</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exported_dynamic_shapes_example1</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_dynamic_shapes_example1</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">exported_dynamic_shapes_example1</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_dynamic_shapes_example1</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">exported_dynamic_shapes_example1</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_dynamic_shapes_example1</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="n">exported_dynamic_shapes_example1</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[[0.0000, 0.0000],
-         [0.8850, 1.2371],
-         [0.0000, 0.0000]],
-
-        [[0.0000, 0.0000],
-         [0.0000, 0.3487],
-         [0.2520, 1.2545]],
-
-        [[0.5863, 0.2831],
-         [0.0000, 0.4669],
-         [0.1059, 0.0000]],
-
-        [[0.7833, 0.0000],
-         [0.4480, 0.0523],
-         [0.0000, 0.0000]],
-
-        [[0.9306, 0.0000],
-         [0.0000, 0.7895],
-         [0.1160, 0.0000]]])
-Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 372, in &lt;module&gt;
-    exported_dynamic_shapes_example1.module()(torch.randn(8, 1, 2))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-    return self._wrapped_call(self, *args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-    return _check_input_constraints_for_graph(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 117, in _check_input_constraints_for_graph
-    raise RuntimeError(
-RuntimeError: Expected input at *args[0].shape[1] to be &gt;= 4, but got 1
-Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 377, in &lt;module&gt;
-    exported_dynamic_shapes_example1.module()(torch.randn(8, 20, 2))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-    return self._wrapped_call(self, *args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-    return _check_input_constraints_for_graph(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 123, in _check_input_constraints_for_graph
-    raise RuntimeError(
-RuntimeError: Expected input at *args[0].shape[1] to be &lt;= 18, but got 20
-Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 382, in &lt;module&gt;
-    exported_dynamic_shapes_example1.module()(torch.randn(8, 8, 3))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-    return self._wrapped_call(self, *args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-    return _check_input_constraints_for_graph(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 129, in _check_input_constraints_for_graph
-    raise RuntimeError(
-RuntimeError: Expected input at *args[0].shape[2] to be equal to 2, but got 3
-</pre></div>
-</div>
 <p>Note that if our example inputs to <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> do not satisfy the constraints
 given by <code class="docutils literal notranslate"><span class="pre">dynamic_shapes</span></code>, then we get an error.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">inp1_dim1_bad</span> <span class="o">=</span> <span class="n">Dim</span><span class="p">(</span><span class="s2">"inp1_dim1_bad"</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
@@ -1241,135 +833,19 @@ <h2><a class="toc-backref" href="#id5">Constraints/Dynamic Shapes</a><a class="h
 <span class="p">}</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DynamicShapesExample1</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp1</span></a><span class="p">,),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes1_bad</span><span class="p">)</span>
+    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">DynamicShapesExample1</span><span class="p">(),</span> <span class="p">(</span><span class="n">inp1</span><span class="p">,),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes1_bad</span><span class="p">)</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Traceback (most recent call last):
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-    gm_torch_level, _ = torch._dynamo.export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1354, in inner
-    raise constraint_violation_error
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-    result_traced = opt_f(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-    return callback(frame, cache_entry, hooks, frame_state, skip=1)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-    return _compile(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-    return func(*args, **kwds)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-    guarded_code = compile_inner(code, one_graph, hooks, transform)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-    r = func(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
-    out_code = transform_code_object(code, transform)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
-    transformations(instructions, code_options)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 482, in transform
-    tracer = InstructionTranslator(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2115, in __init__
-    self.symbolic_locals = VariableTracker.apply(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 217, in apply
-    result = {
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 218, in &lt;dictcomp&gt;
-    k: cls.apply(fn, v, cache, skip_fn) for k, v in list(value.items())
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 203, in apply
-    result = fn(update_object_dict(value))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2116, in &lt;lambda&gt;
-    lambda x: x.realize(), self.symbolic_locals
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/lazy.py", line 58, in realize
-    self._cache.realize(self.parents_tracker)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/lazy.py", line 24, in realize
-    self.vt = VariableBuilder(tx, self.source)(self.value)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 269, in __call__
-    vt = self._wrap(value)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 402, in _wrap
-    return type_dispatch(self, value)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1073, in wrap_tensor
-    tensor_variable = wrap_fx_proxy(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1330, in wrap_fx_proxy
-    return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1440, in wrap_fx_proxy_cls
-    example_value = wrap_to_fake_tensor_and_record(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1880, in wrap_to_fake_tensor_and_record
-    fake_e = wrap_fake_exception(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 1190, in wrap_fake_exception
-    return fn()
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 1881, in &lt;lambda&gt;
-    lambda: tx.fake_mode.from_tensor(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 1666, in from_tensor
-    return self.fake_tensor_converter(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 349, in __call__
-    return self.from_real_tensor(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 306, in from_real_tensor
-    out = self.meta_converter(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py", line 967, in __call__
-    r = self.meta_tensor(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py", line 782, in meta_tensor
-    ) = sym_sizes_strides_storage_offset(t, source, symbolic_context)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py", line 269, in sym_sizes_strides_storage_offset
-    return shape_env.create_symbolic_sizes_strides_storage_offset(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 2339, in create_symbolic_sizes_strides_storage_offset
-    return self._create_symbolic_sizes_strides_storage_offset(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/recording.py", line 231, in wrapper
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 2395, in _create_symbolic_sizes_strides_storage_offset
-    size: List[sympy.Expr] = self._produce_dyn_sizes_from_int_tuple(ex_size, source, symbolic_context)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 2271, in _produce_dyn_sizes_from_int_tuple
-    size.append(self.create_symbol(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/recording.py", line 231, in wrapper
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 2709, in create_symbol
-    raise ConstraintViolationError(f"{val} not in range [{vr.lower}, {vr.upper}]")
-torch.fx.experimental.symbolic_shapes.ConstraintViolationError: 10 not in range [11, 18]
-
-Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 396, in &lt;module&gt;
-    export(DynamicShapesExample1(), (inp1,), dynamic_shapes=dynamic_shapes1_bad)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-    return _export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-    ep = fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-    gm_torch_level = _export_to_torch_ir(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 359, in _export_to_torch_ir
-    raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e))  # noqa: TRY200
-torch._dynamo.exc.UserError: 10 not in range [11, 18]
-
-Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
-</pre></div>
-</div>
 <p>We can enforce that equalities between dimensions of different tensors
 by using the same <code class="docutils literal notranslate"><span class="pre">torch.export.Dim</span></code> object, for example, in matrix multiplication:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp2</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp3</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">inp2</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
+<span class="n">inp3</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
 
-<span class="k">class</span> <span class="nc">DynamicShapesExample2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">):</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">@</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a>
+<span class="k">class</span> <span class="nc">DynamicShapesExample2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">x</span> <span class="o">@</span> <span class="n">y</span>
 
 <span class="n">inp2_dim0</span> <span class="o">=</span> <span class="n">Dim</span><span class="p">(</span><span class="s2">"inp2_dim0"</span><span class="p">)</span>
 <span class="n">inner_dim</span> <span class="o">=</span> <span class="n">Dim</span><span class="p">(</span><span class="s2">"inner_dim"</span><span class="p">)</span>
@@ -1380,106 +856,54 @@ <h2><a class="toc-backref" href="#id5">Constraints/Dynamic Shapes</a><a class="h
     <span class="s2">"y"</span><span class="p">:</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">inner_dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span> <span class="n">inp3_dim1</span><span class="p">},</span>
 <span class="p">}</span>
 
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_dynamic_shapes_example2</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DynamicShapesExample2</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp2</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp3</span></a><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes2</span><span class="p">)</span>
+<span class="n">exported_dynamic_shapes_example2</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">DynamicShapesExample2</span><span class="p">(),</span> <span class="p">(</span><span class="n">inp2</span><span class="p">,</span> <span class="n">inp3</span><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes2</span><span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_dynamic_shapes_example2</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">16</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">4</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exported_dynamic_shapes_example2</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">16</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">4</span><span class="p">)))</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_dynamic_shapes_example2</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">exported_dynamic_shapes_example2</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-2.9354, -2.2066, -0.2080,  4.6121],
-        [-0.5658, -0.6108,  0.8887,  1.5908]])
-Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 425, in &lt;module&gt;
-    exported_dynamic_shapes_example2.module()(torch.randn(4, 8), torch.randn(4, 2))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-    return self._wrapped_call(self, *args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-    return _check_input_constraints_for_graph(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 85, in _check_input_constraints_for_graph
-    raise RuntimeError(
-RuntimeError: Expected input at *args[1].shape[0] to be equal to 8, but got 4
-</pre></div>
-</div>
 <p>We can also describe one dimension in terms of other. There are some
 restrictions to how detailed we can specify one dimension in terms of another,
 but generally, those in the form of <code class="docutils literal notranslate"><span class="pre">A</span> <span class="pre">*</span> <span class="pre">Dim</span> <span class="pre">+</span> <span class="pre">B</span></code> should work.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DerivedDimExample1</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">):</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">+</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DerivedDimExample1</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
 
-<span class="n">foo</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DerivedDimExample1</span></a><span class="p">()</span>
+<span class="n">foo</span> <span class="o">=</span> <span class="n">DerivedDimExample1</span><span class="p">()</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
+<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
 <span class="n">dimx</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-module" href="https://pytorch.org/docs/stable/export.html#module-torch.export" title="torch.export"><span class="n">torch</span><span class="o">.</span><span class="n">export</span></a><span class="o">.</span><span class="n">Dim</span><span class="p">(</span><span class="s2">"dimx"</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
 <span class="n">dimy</span> <span class="o">=</span> <span class="n">dimx</span> <span class="o">+</span> <span class="mi">1</span>
 <span class="n">derived_dynamic_shapes1</span> <span class="o">=</span> <span class="p">({</span><span class="mi">0</span><span class="p">:</span> <span class="n">dimx</span><span class="p">},</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">dimy</span><span class="p">})</span>
 
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">derived_dim_example1</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">foo</span><span class="p">,</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">derived_dynamic_shapes1</span><span class="p">)</span>
+<span class="n">derived_dim_example1</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">foo</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">derived_dynamic_shapes1</span><span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">derived_dim_example1</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">5</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">derived_dim_example1</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">5</span><span class="p">)))</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">derived_dim_example1</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">6</span><span class="p">))</span>
+    <span class="n">derived_dim_example1</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">6</span><span class="p">))</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 
 
-<span class="k">class</span> <span class="nc">DerivedDimExample2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">z</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">):</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">z</span></a><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">3</span><span class="p">]</span>
+<span class="k">class</span> <span class="nc">DerivedDimExample2</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">z</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">3</span><span class="p">]</span>
 
-<span class="n">foo</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DerivedDimExample2</span></a><span class="p">()</span>
+<span class="n">foo</span> <span class="o">=</span> <span class="n">DerivedDimExample2</span><span class="p">()</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">z</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">z</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
 <span class="n">dx</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-module" href="https://pytorch.org/docs/stable/export.html#module-torch.export" title="torch.export"><span class="n">torch</span><span class="o">.</span><span class="n">export</span></a><span class="o">.</span><span class="n">Dim</span><span class="p">(</span><span class="s2">"dx"</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
 <span class="n">dz</span> <span class="o">=</span> <span class="n">dx</span> <span class="o">+</span> <span class="mi">1</span>
 <span class="n">dy</span> <span class="o">=</span> <span class="n">dx</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">+</span> <span class="mi">1</span>
 <span class="n">derived_dynamic_shapes2</span> <span class="o">=</span> <span class="p">({</span><span class="mi">0</span><span class="p">:</span> <span class="n">dz</span><span class="p">},</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">dy</span><span class="p">})</span>
 
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">derived_dim_example2</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">foo</span><span class="p">,</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">z</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">derived_dynamic_shapes2</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">derived_dim_example2</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">7</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">19</span><span class="p">)))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([ 0.3007, -1.7282, -0.0729,  0.1139])
-Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 450, in &lt;module&gt;
-    derived_dim_example1.module()(torch.randn(4), torch.randn(6))
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 737, in call_wrapped
-    return self._wrapped_call(self, *args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 317, in __call__
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/graph_module.py", line 304, in __call__
-    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
-    args_kwargs_result = hook(self, args, kwargs)  # type: ignore[misc]
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/external_utils.py", line 36, in inner
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_unlift.py", line 32, in _check_input_constraints_pre_hook
-    return _check_input_constraints_for_graph(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_export/utils.py", line 85, in _check_input_constraints_for_graph
-    raise RuntimeError(
-RuntimeError: Expected input at *args[1].shape[0] to be equal to 5, but got 6
-tensor([ 2.5416, -0.2760,  0.9003, -1.7479, -2.9716,  0.1013])
+<span class="n">derived_dim_example2</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">foo</span><span class="p">,</span> <span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">derived_dynamic_shapes2</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">derived_dim_example2</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">7</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">19</span><span class="p">)))</span>
 </pre></div>
 </div>
 <p>We can actually use <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> to guide us as to which <code class="docutils literal notranslate"><span class="pre">dynamic_shapes</span></code> constraints
@@ -1487,107 +911,26 @@ <h2><a class="toc-backref" href="#id5">Constraints/Dynamic Shapes</a><a class="h
 do not provide constraints for a dimension, the default behavior is to constrain
 to the exact shape value of the example input) and letting <code class="docutils literal notranslate"><span class="pre">torch.export</span></code>
 error out.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp4</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp5</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">inp4</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span>
+<span class="n">inp5</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
 
-<span class="k">class</span> <span class="nc">DynamicShapesExample3</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">):</span>
-        <span class="k">if</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">16</span><span class="p">:</span>
-            <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">@</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">[:,</span> <span class="p">:</span><span class="mi">16</span><span class="p">]</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a>
+<span class="k">class</span> <span class="nc">DynamicShapesExample3</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">16</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">x</span> <span class="o">@</span> <span class="n">y</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">16</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">y</span>
 
 <span class="n">dynamic_shapes3</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">"x"</span><span class="p">:</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">Dim</span><span class="p">(</span><span class="sa">f</span><span class="s2">"inp4_dim</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp4</span></a><span class="o">.</span><span class="n">dim</span><span class="p">())},</span>
-    <span class="s2">"y"</span><span class="p">:</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">Dim</span><span class="p">(</span><span class="sa">f</span><span class="s2">"inp5_dim</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp5</span></a><span class="o">.</span><span class="n">dim</span><span class="p">())},</span>
+    <span class="s2">"x"</span><span class="p">:</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">Dim</span><span class="p">(</span><span class="sa">f</span><span class="s2">"inp4_dim</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">inp4</span><span class="o">.</span><span class="n">dim</span><span class="p">())},</span>
+    <span class="s2">"y"</span><span class="p">:</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">Dim</span><span class="p">(</span><span class="sa">f</span><span class="s2">"inp5_dim</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">inp5</span><span class="o">.</span><span class="n">dim</span><span class="p">())},</span>
 <span class="p">}</span>
 
 <span class="k">try</span><span class="p">:</span>
-    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DynamicShapesExample3</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp4</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp5</span></a><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes3</span><span class="p">)</span>
+    <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">DynamicShapesExample3</span><span class="p">(),</span> <span class="p">(</span><span class="n">inp4</span><span class="p">,</span> <span class="n">inp5</span><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes3</span><span class="p">)</span>
 <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
     <span class="n">tb</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Traceback (most recent call last):
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
-    gm_torch_level, _ = torch._dynamo.export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1354, in inner
-    raise constraint_violation_error
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
-    result_traced = opt_f(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
-    return self._call_impl(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
-    return forward_call(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
-    return callback(frame, cache_entry, hooks, frame_state, skip=1)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
-    return _compile(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/contextlib.py", line 79, in inner
-    return func(*args, **kwds)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
-    guarded_code = compile_inner(code, one_graph, hooks, transform)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
-    r = func(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 634, in compile_inner
-    check_fn = CheckFunctionManager(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/guards.py", line 1048, in __init__
-    guard.create(builder)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_guards.py", line 249, in create
-    return self.create_fn(builder, self)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/guards.py", line 705, in SHAPE_ENV
-    guards = output_graph.shape_env.produce_guards(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py", line 3308, in produce_guards
-    raise ConstraintViolationError(
-torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated (inp4_dim0, inp5_dim0, inp5_dim1)! For more information, run with TORCH_LOGS="+dynamic".
-  - The values of inp5_dim0 = L['y'].size()[0] and inp4_dim1 = L['x'].size()[1] must always be equal.
-  - Not all values of inp5_dim1 = L['y'].size()[1] in the specified range satisfy the generated guard Ne(L['y'].size()[1], 16).
-  - Not all values of inp4_dim0 = L['x'].size()[0] in the specified range satisfy the generated guard 2 &lt;= L['x'].size()[0] and L['x'].size()[0] &lt;= 16
-  - Not all values of inp5_dim1 = L['y'].size()[1] in the specified range satisfy the generated guard 16 &lt;= L['y'].size()[1] and L['y'].size()[1] &lt;= 9223372036854775806
-
-Suggested fixes:
-  inp4_dim0 = Dim('inp4_dim0', max=16)
-  inp4_dim1 = Dim('inp4_dim1')
-  inp4_dim1 = Dim('inp4_dim1', min=2, max=9223372036854775806)  # 2 &lt;= inp4_dim1 &lt;= 9223372036854775806
-  inp5_dim1 = Dim('inp5_dim1', min=16)
-  inp5_dim0 = inp4_dim1
-
-During handling of the above exception, another exception occurred:
-
-Traceback (most recent call last):
-  File "/var/lib/workspace/intermediate_source/torch_export_tutorial.py", line 492, in &lt;module&gt;
-    export(DynamicShapesExample3(), (inp4, inp5), dynamic_shapes=dynamic_shapes3)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/__init__.py", line 174, in export
-    return _export(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 635, in wrapper
-    raise e
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 618, in wrapper
-    ep = fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/exported_program.py", line 83, in wrapper
-    return fn(*args, **kwargs)
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 860, in _export
-    gm_torch_level = _export_to_torch_ir(
-  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/export/_trace.py", line 359, in _export_to_torch_ir
-    raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e))  # noqa: TRY200
-torch._dynamo.exc.UserError: Constraints violated (inp4_dim0, inp5_dim0, inp5_dim1)! For more information, run with TORCH_LOGS="+dynamic".
-  - The values of inp5_dim0 = L['y'].size()[0] and inp4_dim1 = L['x'].size()[1] must always be equal.
-  - Not all values of inp5_dim1 = L['y'].size()[1] in the specified range satisfy the generated guard Ne(L['y'].size()[1], 16).
-  - Not all values of inp4_dim0 = L['x'].size()[0] in the specified range satisfy the generated guard 2 &lt;= L['x'].size()[0] and L['x'].size()[0] &lt;= 16
-  - Not all values of inp5_dim1 = L['y'].size()[1] in the specified range satisfy the generated guard 16 &lt;= L['y'].size()[1] and L['y'].size()[1] &lt;= 9223372036854775806
-
-Suggested fixes:
-  inp4_dim0 = Dim('inp4_dim0', max=16)
-  inp4_dim1 = Dim('inp4_dim1')
-  inp4_dim1 = Dim('inp4_dim1', min=2, max=9223372036854775806)  # 2 &lt;= inp4_dim1 &lt;= 9223372036854775806
-  inp5_dim1 = Dim('inp5_dim1', min=16)
-  inp5_dim0 = inp4_dim1
-</pre></div>
-</div>
 <p>We can see that the error message gives us suggested fixes to our
 dynamic shape constraints. Let us follow those suggestions (exact
 suggestions may differ slightly):</p>
@@ -1604,22 +947,8 @@ <h2><a class="toc-backref" href="#id5">Constraints/Dynamic Shapes</a><a class="h
     <span class="p">}</span>
 
 <span class="n">dynamic_shapes3_fixed</span> <span class="o">=</span> <span class="n">suggested_fixes</span><span class="p">()</span>
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_dynamic_shapes_example3</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DynamicShapesExample3</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp4</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp5</span></a><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes3_fixed</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_dynamic_shapes_example3</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">)))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[ 12.5915,  -0.7265,  -1.8981,  -8.0323,  -2.1447,  14.4020,  13.7854,
-           1.5568,   3.7933,   7.2591,  -2.2477,   1.6366,  -5.9276,   8.5279,
-           7.9349,  -1.1328],
-        [ -5.2210,   9.4576,  -0.2372,   9.0035,   6.2572,  -8.4716,   6.0191,
-           4.8424,  -0.4486,   0.1885,  -0.1749,   2.4314,   3.8271,   8.1822,
-           6.5064,   0.6512],
-        [ -3.6856,   7.5222,   4.8073,  13.1255,   3.6440,  -4.1587,   2.9806,
-           0.3689,   1.1133,  -1.7169,  -2.1537,   1.1841,   6.7619,   9.3401,
-          -1.1372,  -8.9628],
-        [ -4.3608,   5.1219,  -0.6240,  -6.8640,   2.3344,  -2.0273,   0.2769,
-          -0.9930,   2.0298, -10.3922,  -1.7186,  -5.0928,  11.7383,   6.4864,
-           8.0827,   0.5863]])
+<span class="n">exported_dynamic_shapes_example3</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">DynamicShapesExample3</span><span class="p">(),</span> <span class="p">(</span><span class="n">inp4</span><span class="p">,</span> <span class="n">inp5</span><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes3_fixed</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exported_dynamic_shapes_example3</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">)))</span>
 </pre></div>
 </div>
 <p>Note that in the example above, because we constrained the value of <code class="docutils literal notranslate"><span class="pre">x.shape[0]</span></code> in
@@ -1630,54 +959,15 @@ <h2><a class="toc-backref" href="#id5">Constraints/Dynamic Shapes</a><a class="h
 or use <code class="docutils literal notranslate"><span class="pre">torch._logging.set_logs</span></code>.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span>
 <a class="sphx-glr-backref-module-torch-_logging sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch._logging.set_logs.html#torch._logging.set_logs" title="torch._logging.set_logs"><span class="n">torch</span><span class="o">.</span><span class="n">_logging</span><span class="o">.</span><span class="n">set_logs</span></a><span class="p">(</span><span class="n">dynamic</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span> <span class="n">dynamo</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_dynamic_shapes_example3</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">DynamicShapesExample3</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp4</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp5</span></a><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes3_fixed</span><span class="p">)</span>
+<span class="n">exported_dynamic_shapes_example3</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">DynamicShapesExample3</span><span class="p">(),</span> <span class="p">(</span><span class="n">inp4</span><span class="p">,</span> <span class="n">inp5</span><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes3_fixed</span><span class="p">)</span>
 
 <span class="c1"># reset to previous values</span>
 <a class="sphx-glr-backref-module-torch-_logging sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch._logging.set_logs.html#torch._logging.set_logs" title="torch._logging.set_logs"><span class="n">torch</span><span class="o">.</span><span class="n">_logging</span><span class="o">.</span><span class="n">set_logs</span></a><span class="p">(</span><span class="n">dynamic</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">WARNING</span><span class="p">,</span> <span class="n">dynamo</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">WARNING</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>I0625 21:38:32.787000 139677001331328 torch/_dynamo/logging.py:55] [16/0] Step 1: torchdynamo start tracing forward /var/lib/workspace/intermediate_source/torch_export_tutorial.py:481
-I0625 21:38:32.790000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2724] [16/0] create_symbol s0 = 8 for L['x'].size()[0] [2, 16] (_dynamo/variables/builder.py:1881 in &lt;lambda&gt;)
-I0625 21:38:32.791000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2724] [16/0] create_symbol s1 = 16 for L['x'].size()[1] [2, 9223372036854775806] (_dynamo/variables/builder.py:1881 in &lt;lambda&gt;)
-I0625 21:38:32.793000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2724] [16/0] create_symbol s2 = 16 for L['y'].size()[0] [2, 9223372036854775806] (_dynamo/variables/builder.py:1881 in &lt;lambda&gt;)
-I0625 21:38:32.794000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2724] [16/0] create_symbol s3 = 32 for L['y'].size()[1] [17, 9223372036854775806] (_dynamo/variables/builder.py:1881 in &lt;lambda&gt;)
-I0625 21:38:32.802000 139677001331328 torch/fx/experimental/symbolic_shapes.py:3809] [16/0] set_replacement s2 = s1 (solve_backed) ValueRanges(lower=2, upper=9223372036854775806, is_bool=False)
-I0625 21:38:32.802000 139677001331328 torch/fx/experimental/symbolic_shapes.py:4035] [16/0] eval Eq(s1, s2) [guard added] at ar/lib/workspace/intermediate_source/torch_export_tutorial.py:483 in forward (_meta_registrations.py:2014 in meta_mm)
-I0625 21:38:32.803000 139677001331328 torch/_dynamo/logging.py:55] [16/0] Step 1: torchdynamo done tracing forward (RETURN_VALUE)
-I0625 21:38:32.805000 139677001331328 torch/fx/experimental/symbolic_shapes.py:3809] [16/0] set_replacement s2 = s1 (find) ValueRanges(lower=2, upper=9223372036854775806, is_bool=False)
-I0625 21:38:32.805000 139677001331328 torch/_dynamo/logging.py:55] [16/0] Step 2: calling compiler function dynamo_normalization_capturing_compiler
-I0625 21:38:32.805000 139677001331328 torch/_dynamo/logging.py:55] [16/0] Step 2: done compiler function dynamo_normalization_capturing_compiler
-I0625 21:38:32.807000 139677001331328 torch/fx/experimental/symbolic_shapes.py:2806] [16/0] produce_guards
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339] Summary of dimension constraints:
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339] Suggested fixes:
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339]   inp4_dim0 = Dim('inp4_dim0', max=16)
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339]   inp5_dim1 = Dim('inp5_dim1', min=17)
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1339]   shared_dim = Dim('shared_dim')
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363] Dynamo captured graph:
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363] class GraphModule(torch.nn.Module):
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]     def forward(self, L_x_ : torch.Tensor, L_y_ : torch.Tensor):
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         l_x_ = L_x_
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         l_y_ = L_y_
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:482 in forward, code: if x.shape[0] &lt;= 16:
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         size = l_x_.size()
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         getitem = size[0];  size = None
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         le = getitem &lt;= 16;  getitem = None
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:483 in forward, code: return x @ y[:, :16]
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         getitem_2 = l_y_[(slice(None, None, None), slice(None, 16, None))];  l_y_ = None
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         matmul = l_x_ @ getitem_2;  l_x_ = getitem_2 = None
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]         return (matmul,)
-I0625 21:38:32.834000 139677001331328 torch/_dynamo/eval_frame.py:1363]
-</pre></div>
-</div>
 <p>We can view an <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code>’s symbolic shape ranges using the
 <code class="docutils literal notranslate"><span class="pre">range_constraints</span></code> field.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_dynamic_shapes_example3</span></a><span class="o">.</span><span class="n">range_constraints</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{s0: ValueRanges(lower=2, upper=16, is_bool=False), s1: ValueRanges(lower=2, upper=oo, is_bool=False), s3: ValueRanges(lower=17, upper=oo, is_bool=False)}
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">exported_dynamic_shapes_example3</span><span class="o">.</span><span class="n">range_constraints</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1689,16 +979,16 @@ <h2><a class="toc-backref" href="#id6">Custom Ops</a><a class="headerlink" href=
 <li><p>Define the custom op using <code class="docutils literal notranslate"><span class="pre">torch.library</span></code> (<a class="reference external" href="https://pytorch.org/docs/main/library.html">reference</a>)
 as with any other custom op</p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.library</span> <span class="kn">import</span> <a class="sphx-glr-backref-module-torch-library sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/library.html#torch.library.Library" title="torch.library.Library"><span class="n">Library</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch-library sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/library.html#torch.library.impl" title="torch.library.impl"><span class="n">impl</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch-library sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/library.html#torch.library.impl_abstract" title="torch.library.impl_abstract"><span class="n">impl_abstract</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.library</span> <span class="kn">import</span> <a class="sphx-glr-backref-module-torch-library sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/library.html#torch.library.Library" title="torch.library.Library"><span class="n">Library</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch-library sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/library.html#torch.library.impl" title="torch.library.impl"><span class="n">impl</span></a><span class="p">,</span> <a class="sphx-glr-backref-module-torch-library sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/library.html#torch.library.impl_abstract" title="torch.library.impl_abstract"><span class="n">impl_abstract</span></a>
 
-<span class="n">m</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-library sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/library.html#torch.library.Library" title="torch.library.Library"><span class="n">Library</span></a><span class="p">(</span><span class="s2">"my_custom_library"</span><span class="p">,</span> <span class="s2">"DEF"</span><span class="p">)</span>
+<span class="n">m</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-library sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/library.html#torch.library.Library" title="torch.library.Library"><span class="n">Library</span></a><span class="p">(</span><span class="s2">"my_custom_library"</span><span class="p">,</span> <span class="s2">"DEF"</span><span class="p">)</span>
 
 <span class="n">m</span><span class="o">.</span><span class="n">define</span><span class="p">(</span><span class="s2">"custom_op(Tensor input) -&gt; Tensor"</span><span class="p">)</span>
 
 <span class="nd">@impl</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="s2">"custom_op"</span><span class="p">,</span> <span class="s2">"CompositeExplicitAutograd"</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">custom_op</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
+<span class="k">def</span> <span class="nf">custom_op</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
     <span class="nb">print</span><span class="p">(</span><span class="s2">"custom_op called!"</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 </pre></div>
 </div>
 <ul class="simple">
@@ -1706,45 +996,27 @@ <h2><a class="toc-backref" href="#id6">Custom Ops</a><a class="headerlink" href=
 tensor with the same shape as the expected output</p></li>
 </ul>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@impl_abstract</span><span class="p">(</span><span class="s2">"my_custom_library::custom_op"</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">custom_op_meta</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-    <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.empty_like.html#torch.empty_like" title="torch.empty_like"><span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
+<span class="k">def</span> <span class="nf">custom_op_meta</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.empty_like.html#torch.empty_like" title="torch.empty_like"><span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span></a><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 </pre></div>
 </div>
 <ul class="simple">
 <li><p>Call the custom op from the code you want to export using <code class="docutils literal notranslate"><span class="pre">torch.ops</span></code></p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">CustomOpExample</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.sin.html#torch.sin" title="torch.sin"><span class="n">torch</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_custom_library</span><span class="o">.</span><span class="n">custom_op</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cos.html#torch.cos" title="torch.cos"><span class="n">torch</span><span class="o">.</span><span class="n">cos</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">CustomOpExample</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.sin.html#torch.sin" title="torch.sin"><span class="n">torch</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_custom_library</span><span class="o">.</span><span class="n">custom_op</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">x</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.cos.html#torch.cos" title="torch.cos"><span class="n">torch</span><span class="o">.</span><span class="n">cos</span></a><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">x</span>
 </pre></div>
 </div>
 <ul class="simple">
 <li><p>Export the code as before</p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_custom_op_example</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">CustomOpExample</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">exported_custom_op_example</span></a><span class="o">.</span><span class="n">graph_module</span><span class="o">.</span><span class="n">print_readable</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">exported_custom_op_example</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>class GraphModule(torch.nn.Module):
-    def forward(self, arg0_1: "f32[3, 3]"):
-        # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:574 in forward, code: x = torch.sin(x)
-        sin: "f32[3, 3]" = torch.ops.aten.sin.default(arg0_1);  arg0_1 = None
-
-        # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:575 in forward, code: x = torch.ops.my_custom_library.custom_op(x)
-        custom_op: "f32[3, 3]" = torch.ops.my_custom_library.custom_op.default(sin);  sin = None
-
-        # File: /var/lib/workspace/intermediate_source/torch_export_tutorial.py:576 in forward, code: x = torch.cos(x)
-        cos: "f32[3, 3]" = torch.ops.aten.cos.default(custom_op);  custom_op = None
-        return (cos,)
-
-custom_op called!
-tensor([[0.6387, 0.5722, 1.0000],
-        [0.8776, 0.9846, 0.8223],
-        [0.9983, 1.0000, 0.9979]])
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">exported_custom_op_example</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">CustomOpExample</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
+<span class="n">exported_custom_op_example</span><span class="o">.</span><span class="n">graph_module</span><span class="o">.</span><span class="n">print_readable</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">exported_custom_op_example</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
 </pre></div>
 </div>
 <p>Note in the above outputs that the custom op is included in the exported graph.
@@ -1767,10 +1039,6 @@ <h2><a class="toc-backref" href="#id7">Decompositions</a><a class="headerlink" h
 <span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">add_</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">_schema</span><span class="o">.</span><span class="n">is_mutable</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>False
-True
-</pre></div>
-</div>
 <p>By default, the environment in which you want to run the exported graph
 should support all ~2000 of these operators.
 However, you can use the following API on the exported program
@@ -1790,35 +1058,19 @@ <h2><a class="toc-backref" href="#id7">Decompositions</a><a class="headerlink" h
 which will decompose the all ATen operators to the
 <a class="reference external" href="https://pytorch.org/docs/main/torch.compiler_ir.html#core-aten-ir">Core ATen Operator Set</a>
 which consists of only ~180 operators.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">M</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">M</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">ep</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">M</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">ep</span></a><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
+<span class="n">ep</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">M</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">ep</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
 
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">core_ir_ep</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.run_decompositions" title="torch.export.ExportedProgram.run_decompositions"><span class="n">ep</span><span class="o">.</span><span class="n">run_decompositions</span></a><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">core_ir_ep</span></a><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>graph():
-    %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
-    %arg1_1 : [num_users=1] = placeholder[target=arg1_1]
-    %arg2_1 : [num_users=1] = placeholder[target=arg2_1]
-    %t : [num_users=1] = call_function[target=torch.ops.aten.t.default](args = (%arg0_1,), kwargs = {})
-    %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%arg1_1, %arg2_1, %t), kwargs = {})
-    return (addmm,)
-graph():
-    %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
-    %arg1_1 : [num_users=1] = placeholder[target=arg1_1]
-    %arg2_1 : [num_users=1] = placeholder[target=arg2_1]
-    %permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%arg0_1, [1, 0]), kwargs = {})
-    %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%arg1_1, %arg2_1, %permute), kwargs = {})
-    return (addmm,)
+<span class="n">core_ir_ep</span> <span class="o">=</span> <span class="n">ep</span><span class="o">.</span><span class="n">run_decompositions</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">core_ir_ep</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Notice that after running <code class="docutils literal notranslate"><span class="pre">run_decompositions</span></code> the
@@ -1832,37 +1084,21 @@ <h2><a class="toc-backref" href="#id7">Decompositions</a><a class="headerlink" h
 <a class="reference external" href="https://github.com/pytorch/pytorch/blob/b460c3089367f3fadd40aa2cb3808ee370aa61e1/torch/_decomp/__init__.py#L191">get_decompositions</a>
 function, which will return a decomposition table using existing
 decomposition implementations.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">M</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">M</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">ep</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">M</span></a><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">ep</span></a><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
+<span class="n">ep</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">export</span></a><span class="p">(</span><span class="n">M</span><span class="p">(),</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">ep</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
 
 <span class="kn">from</span> <span class="nn">torch._decomp</span> <span class="kn">import</span> <span class="n">get_decompositions</span>
 <span class="n">decomp_table</span> <span class="o">=</span> <span class="n">get_decompositions</span><span class="p">([</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">default</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">transpose</span><span class="o">.</span><span class="n">int</span><span class="p">])</span>
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">core_ir_ep</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.run_decompositions" title="torch.export.ExportedProgram.run_decompositions"><span class="n">ep</span><span class="o">.</span><span class="n">run_decompositions</span></a><span class="p">(</span><span class="n">decomp_table</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">core_ir_ep</span></a><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>graph():
-    %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
-    %arg1_1 : [num_users=1] = placeholder[target=arg1_1]
-    %arg2_1 : [num_users=1] = placeholder[target=arg2_1]
-    %t : [num_users=1] = call_function[target=torch.ops.aten.t.default](args = (%arg0_1,), kwargs = {})
-    %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%arg1_1, %arg2_1, %t), kwargs = {})
-    return (addmm,)
-graph():
-    %arg0_1 : [num_users=1] = placeholder[target=arg0_1]
-    %arg1_1 : [num_users=1] = placeholder[target=arg1_1]
-    %arg2_1 : [num_users=1] = placeholder[target=arg2_1]
-    %permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%arg0_1, [1, 0]), kwargs = {})
-    %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%arg1_1, %arg2_1, %permute), kwargs = {})
-    return (addmm,)
+<span class="n">core_ir_ep</span> <span class="o">=</span> <span class="n">ep</span><span class="o">.</span><span class="n">run_decompositions</span><span class="p">(</span><span class="n">decomp_table</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">core_ir_ep</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>If there is no existing decomposition function for an ATen operator that you would
@@ -1881,15 +1117,15 @@ <h2><a class="toc-backref" href="#id8">ExportDB</a><a class="headerlink" href="#
 Examples are also tagged by category so that they can be more easily searched.</p>
 <p>For example, let’s use ExportDB to get a better understanding of how the predicate works in the <code class="docutils literal notranslate"><span class="pre">cond</span></code> operator.
 We can look at the example called <code class="docutils literal notranslate"><span class="pre">cond_predicate</span></code>, which has a <code class="docutils literal notranslate"><span class="pre">torch.cond</span></code> tag. The example code looks like:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cond_predicate</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cond_predicate</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">"""</span>
 <span class="sd">    The conditional statement (aka predicate) passed to ``cond()`` must be one of the following:</span>
 <span class="sd">    - ``torch.Tensor`` with a single element</span>
 <span class="sd">    - boolean expression</span>
 <span class="sd">    NOTE: If the `pred` is test on a dim with batch size &lt; 2, it will be specialized.</span>
 <span class="sd">    """</span>
-    <span class="n">pred</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">2</span> <span class="ow">and</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">10</span>
-    <span class="k">return</span> <span class="n">cond</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="k">lambda</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">:</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="o">.</span><span class="n">cos</span><span class="p">(),</span> <span class="k">lambda</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="p">:</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">y</span></a><span class="o">.</span><span class="n">sin</span><span class="p">(),</span> <span class="p">[</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">])</span>
+    <span class="n">pred</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">10</span>
+    <span class="k">return</span> <span class="n">cond</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">cos</span><span class="p">(),</span> <span class="k">lambda</span> <span class="n">y</span><span class="p">:</span> <span class="n">y</span><span class="o">.</span><span class="n">sin</span><span class="p">(),</span> <span class="p">[</span><span class="n">x</span><span class="p">])</span>
 </pre></div>
 </div>
 <p>More generally, ExportDB can be used as a reference when one of the following occurs:</p>
@@ -1909,34 +1145,26 @@ <h2><a class="toc-backref" href="#id9">Running the Exported Program</a><a class=
 exported artifact to backends such as Inductor through <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>,
 <a class="reference external" href="https://pytorch.org/docs/main/torch.compiler_aot_inductor.html">AOTInductor</a>,
 or <a class="reference external" href="https://pytorch.org/TensorRT/dynamo/dynamo_export.html">TensorRT</a>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">M</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">M</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">):</span>
-        <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a><span class="p">)</span>
-        <span class="k">return</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">x</span></a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">x</span>
 
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)</span>
-<span class="n">m</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><span class="n">M</span></a><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)</span>
-<a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram" title="torch.export.ExportedProgram"><span class="n">ep</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span></a><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp</span></a><span class="p">,))</span>
+<span class="n">inp</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">M</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)</span>
+<span class="n">ep</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/export.html#torch.export.export" title="torch.export.export"><span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span></a><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="p">(</span><span class="n">inp</span><span class="p">,))</span>
 
 <span class="c1"># Run it eagerly</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">res</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">ep</span><span class="o">.</span><span class="n">module</span></a><span class="p">()(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">res</span></a><span class="p">)</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">ep</span><span class="o">.</span><span class="n">module</span><span class="p">()(</span><span class="n">inp</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
 
 <span class="c1"># Run it with torch.compile</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">res</span></a> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.compile.html#torch.compile" title="torch.compile"><span class="n">torch</span><span class="o">.</span><span class="n">compile</span></a><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">ep</span><span class="o">.</span><span class="n">module</span></a><span class="p">(),</span> <span class="n">backend</span><span class="o">=</span><span class="s2">"inductor"</span><span class="p">)(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">res</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[ 1.3676, -0.4303, -0.2113],
-        [-0.5053, -0.0877,  0.5134]], device='cuda:0',
-       grad_fn=&lt;AddmmBackward0&gt;)
-tensor([[ 1.3676, -0.4303, -0.2113],
-        [-0.5053, -0.0877,  0.5134]], device='cuda:0',
-       grad_fn=&lt;CompiledFunctionBackward&gt;)
+<span class="n">res</span> <span class="o">=</span> <a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function" href="https://pytorch.org/docs/stable/generated/torch.compile.html#torch.compile" title="torch.compile"><span class="n">torch</span><span class="o">.</span><span class="n">compile</span></a><span class="p">(</span><span class="n">ep</span><span class="o">.</span><span class="n">module</span><span class="p">(),</span> <span class="n">backend</span><span class="o">=</span><span class="s2">"inductor"</span><span class="p">)(</span><span class="n">inp</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch._export</span>
@@ -1945,12 +1173,12 @@ <h2><a class="toc-backref" href="#id9">Running the Exported Program</a><a class=
 <span class="c1"># Note: these APIs are subject to change</span>
 <span class="c1"># Compile the exported program to a .so using ``AOTInductor``</span>
 <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-<span class="n">so_path</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_inductor</span><span class="o">.</span><span class="n">aot_compile</span><span class="p">(</span><a class="sphx-glr-backref-module-torch-export sphx-glr-backref-type-py-method" href="https://pytorch.org/docs/stable/export.html#torch.export.ExportedProgram.module" title="torch.export.ExportedProgram.module"><span class="n">ep</span><span class="o">.</span><span class="n">module</span></a><span class="p">(),</span> <span class="p">[</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp</span></a><span class="p">])</span>
+<span class="n">so_path</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_inductor</span><span class="o">.</span><span class="n">aot_compile</span><span class="p">(</span><span class="n">ep</span><span class="o">.</span><span class="n">module</span><span class="p">(),</span> <span class="p">[</span><span class="n">inp</span><span class="p">])</span>
 
 <span class="c1"># Load and run the .so file in Python.</span>
 <span class="c1"># To load and run it in a C++ environment, see:</span>
 <span class="c1"># https://pytorch.org/docs/main/torch.compiler_aot_inductor.html</span>
-<a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">res</span></a> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_export</span><span class="o">.</span><span class="n">aot_load</span><span class="p">(</span><span class="n">so_path</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">inp</span></a><span class="p">)</span>
+<span class="n">res</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_export</span><span class="o">.</span><span class="n">aot_load</span><span class="p">(</span><span class="n">so_path</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)(</span><span class="n">inp</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -1959,7 +1187,7 @@ <h2><a class="toc-backref" href="#id10">Conclusion</a><a class="headerlink" href
 <p>We introduced <code class="docutils literal notranslate"><span class="pre">torch.export</span></code>, the new PyTorch 2.X way to export single computation
 graphs from PyTorch programs. In particular, we demonstrate several code modifications
 and considerations (control flow ops, constraints, etc.) that need to be made in order to export a graph.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  1.776 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-torch-export-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/b865be3c401c1b4fbdb03f49916ac8e8/torch_export_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">torch_export_tutorial.py</span></code></a></p>
diff --git a/intermediate/torchvision_tutorial.html b/intermediate/torchvision_tutorial.html
index 1d551545d7..0fff088b6b 100644
--- a/intermediate/torchvision_tutorial.html
+++ b/intermediate/torchvision_tutorial.html
@@ -589,7 +589,7 @@ <h3>Writing a custom dataset for PennFudan<a class="headerlink" href="#writing-a
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">mask</span></a><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
 </pre></div>
 </div>
-<img alt="Image, Mask" class="sphx-glr-single-img" src="../_images/sphx_glr_torchvision_tutorial_001.png" srcset="../_images/sphx_glr_torchvision_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.image.AxesImage object at 0x7f071c580730&gt;
+<img alt="Image, Mask" class="sphx-glr-single-img" src="../_images/sphx_glr_torchvision_tutorial_001.png" srcset="../_images/sphx_glr_torchvision_tutorial_001.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.image.AxesImage object at 0x7f3b07a53af0&gt;
 </pre></div>
 </div>
 <p>So each image has a corresponding
@@ -715,10 +715,10 @@ <h3>1 - Finetuning from a pretrained model<a class="headerlink" href="#finetunin
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
 
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-100%|##########| 160M/160M [00:00&lt;00:00, 397MB/s]
+ 26%|##6       | 42.1M/160M [00:00&lt;00:00, 441MB/s]
+ 54%|#####4    | 87.0M/160M [00:00&lt;00:00, 458MB/s]
+ 83%|########2 | 132M/160M [00:00&lt;00:00, 463MB/s]
+100%|##########| 160M/160M [00:00&lt;00:00, 416MB/s]
 </pre></div>
 </div>
 </div>
@@ -771,8 +771,7 @@ <h3>2 - Modifying the model to add a different backbone<a class="headerlink" hre
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/mobilenet_v2-7ebf99e0.pth
 
   0%|          | 0.00/13.6M [00:00&lt;?, ?B/s]
- 82%|########1 | 11.1M/13.6M [00:00&lt;00:00, 116MB/s]
-100%|##########| 13.6M/13.6M [00:00&lt;00:00, 94.0MB/s]
+100%|##########| 13.6M/13.6M [00:00&lt;00:00, 351MB/s]
 </pre></div>
 </div>
 </div>
@@ -952,108 +951,107 @@ <h2>Testing <code class="docutils literal notranslate"><span class="pre">forward
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
 
   0%|          | 0.00/170M [00:00&lt;?, ?B/s]
-  8%|7         | 12.8M/170M [00:00&lt;00:01, 133MB/s]
- 15%|#5        | 25.5M/170M [00:00&lt;00:01, 127MB/s]
- 40%|###9      | 67.9M/170M [00:00&lt;00:00, 268MB/s]
- 65%|######5   | 111M/170M [00:00&lt;00:00, 338MB/s]
- 91%|######### | 154M/170M [00:00&lt;00:00, 378MB/s]
-100%|##########| 170M/170M [00:00&lt;00:00, 324MB/s]
+ 23%|##3       | 39.6M/170M [00:00&lt;00:00, 415MB/s]
+ 47%|####6     | 79.2M/170M [00:00&lt;00:00, 346MB/s]
+ 67%|######6   | 113M/170M [00:00&lt;00:00, 336MB/s]
+ 88%|########7 | 149M/170M [00:00&lt;00:00, 352MB/s]
+100%|##########| 170M/170M [00:00&lt;00:00, 338MB/s]
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning:
 
 Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)
 
-Epoch: [0]  [ 0/60]  eta: 0:00:27  lr: 0.000090  loss: 4.9131 (4.9131)  loss_classifier: 0.4438 (0.4438)  loss_box_reg: 0.1060 (0.1060)  loss_mask: 4.3589 (4.3589)  loss_objectness: 0.0021 (0.0021)  loss_rpn_box_reg: 0.0023 (0.0023)  time: 0.4561  data: 0.0143  max mem: 2421
+Epoch: [0]  [ 0/60]  eta: 0:00:27  lr: 0.000090  loss: 4.9131 (4.9131)  loss_classifier: 0.4438 (0.4438)  loss_box_reg: 0.1060 (0.1060)  loss_mask: 4.3589 (4.3589)  loss_objectness: 0.0021 (0.0021)  loss_rpn_box_reg: 0.0023 (0.0023)  time: 0.4656  data: 0.0142  max mem: 2421
 /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning:
 
 Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)
 
-Epoch: [0]  [10/60]  eta: 0:00:15  lr: 0.000936  loss: 1.8000 (2.7745)  loss_classifier: 0.4127 (0.3557)  loss_box_reg: 0.3088 (0.2540)  loss_mask: 0.9491 (2.1313)  loss_objectness: 0.0227 (0.0266)  loss_rpn_box_reg: 0.0056 (0.0069)  time: 0.3010  data: 0.0155  max mem: 2918
-Epoch: [0]  [20/60]  eta: 0:00:10  lr: 0.001783  loss: 0.7878 (1.7889)  loss_classifier: 0.2150 (0.2676)  loss_box_reg: 0.2016 (0.2331)  loss_mask: 0.3993 (1.2588)  loss_objectness: 0.0161 (0.0214)  loss_rpn_box_reg: 0.0076 (0.0079)  time: 0.2647  data: 0.0156  max mem: 2920
-Epoch: [0]  [30/60]  eta: 0:00:07  lr: 0.002629  loss: 0.6785 (1.4253)  loss_classifier: 0.1440 (0.2254)  loss_box_reg: 0.2312 (0.2433)  loss_mask: 0.2576 (0.9274)  loss_objectness: 0.0119 (0.0194)  loss_rpn_box_reg: 0.0101 (0.0098)  time: 0.2444  data: 0.0165  max mem: 2921
-Epoch: [0]  [40/60]  eta: 0:00:05  lr: 0.003476  loss: 0.5640 (1.2068)  loss_classifier: 0.0918 (0.1907)  loss_box_reg: 0.2431 (0.2362)  loss_mask: 0.2276 (0.7535)  loss_objectness: 0.0093 (0.0167)  loss_rpn_box_reg: 0.0117 (0.0097)  time: 0.2421  data: 0.0170  max mem: 2921
-Epoch: [0]  [50/60]  eta: 0:00:02  lr: 0.004323  loss: 0.3651 (1.0410)  loss_classifier: 0.0546 (0.1627)  loss_box_reg: 0.1496 (0.2179)  loss_mask: 0.1592 (0.6374)  loss_objectness: 0.0020 (0.0139)  loss_rpn_box_reg: 0.0072 (0.0092)  time: 0.2288  data: 0.0163  max mem: 2921
-Epoch: [0]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.3469 (0.9417)  loss_classifier: 0.0410 (0.1447)  loss_box_reg: 0.1223 (0.2055)  loss_mask: 0.1592 (0.5706)  loss_objectness: 0.0013 (0.0121)  loss_rpn_box_reg: 0.0063 (0.0088)  time: 0.2229  data: 0.0155  max mem: 2921
-Epoch: [0] Total time: 0:00:14 (0.2476 s / it)
+Epoch: [0]  [10/60]  eta: 0:00:15  lr: 0.000936  loss: 1.7988 (2.7740)  loss_classifier: 0.4161 (0.3552)  loss_box_reg: 0.3087 (0.2540)  loss_mask: 0.9491 (2.1314)  loss_objectness: 0.0227 (0.0266)  loss_rpn_box_reg: 0.0056 (0.0069)  time: 0.3027  data: 0.0155  max mem: 2918
+Epoch: [0]  [20/60]  eta: 0:00:10  lr: 0.001783  loss: 0.7890 (1.7882)  loss_classifier: 0.2149 (0.2672)  loss_box_reg: 0.2016 (0.2329)  loss_mask: 0.3993 (1.2588)  loss_objectness: 0.0162 (0.0214)  loss_rpn_box_reg: 0.0076 (0.0079)  time: 0.2652  data: 0.0155  max mem: 2920
+Epoch: [0]  [30/60]  eta: 0:00:07  lr: 0.002629  loss: 0.6767 (1.4244)  loss_classifier: 0.1435 (0.2252)  loss_box_reg: 0.2288 (0.2425)  loss_mask: 0.2597 (0.9274)  loss_objectness: 0.0123 (0.0195)  loss_rpn_box_reg: 0.0101 (0.0098)  time: 0.2443  data: 0.0164  max mem: 2921
+Epoch: [0]  [40/60]  eta: 0:00:05  lr: 0.003476  loss: 0.5594 (1.2069)  loss_classifier: 0.0942 (0.1909)  loss_box_reg: 0.2408 (0.2357)  loss_mask: 0.2277 (0.7536)  loss_objectness: 0.0071 (0.0169)  loss_rpn_box_reg: 0.0118 (0.0097)  time: 0.2398  data: 0.0168  max mem: 2921
+Epoch: [0]  [50/60]  eta: 0:00:02  lr: 0.004323  loss: 0.3676 (1.0410)  loss_classifier: 0.0578 (0.1629)  loss_box_reg: 0.1529 (0.2171)  loss_mask: 0.1593 (0.6378)  loss_objectness: 0.0030 (0.0140)  loss_rpn_box_reg: 0.0073 (0.0092)  time: 0.2282  data: 0.0161  max mem: 2921
+Epoch: [0]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.3425 (0.9415)  loss_classifier: 0.0407 (0.1449)  loss_box_reg: 0.1228 (0.2045)  loss_mask: 0.1593 (0.5710)  loss_objectness: 0.0017 (0.0123)  loss_rpn_box_reg: 0.0063 (0.0088)  time: 0.2230  data: 0.0153  max mem: 2921
+Epoch: [0] Total time: 0:00:14 (0.2475 s / it)
 creating index...
 index created!
-Test:  [ 0/50]  eta: 0:00:07  model_time: 0.1291 (0.1291)  evaluator_time: 0.0057 (0.0057)  time: 0.1485  data: 0.0132  max mem: 2921
-Test:  [49/50]  eta: 0:00:00  model_time: 0.0422 (0.0768)  evaluator_time: 0.0047 (0.0070)  time: 0.0743  data: 0.0104  max mem: 2921
-Test: Total time: 0:00:04 (0.0958 s / it)
-Averaged stats: model_time: 0.0422 (0.0768)  evaluator_time: 0.0047 (0.0070)
+Test:  [ 0/50]  eta: 0:00:07  model_time: 0.1294 (0.1294)  evaluator_time: 0.0071 (0.0071)  time: 0.1499  data: 0.0130  max mem: 2921
+Test:  [49/50]  eta: 0:00:00  model_time: 0.0424 (0.0770)  evaluator_time: 0.0048 (0.0074)  time: 0.0747  data: 0.0103  max mem: 2921
+Test: Total time: 0:00:04 (0.0962 s / it)
+Averaged stats: model_time: 0.0424 (0.0770)  evaluator_time: 0.0048 (0.0074)
 Accumulating evaluation results...
 DONE (t=0.01s).
 Accumulating evaluation results...
 DONE (t=0.01s).
 IoU metric: bbox
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.643
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.666
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.984
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.858
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.288
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.593
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.654
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.284
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.696
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.696
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.683
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.706
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.889
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.622
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.678
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.289
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.717
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.717
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.708
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.726
 IoU metric: segm
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.668
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.975
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.776
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.682
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.294
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.721
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.723
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.676
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.973
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.805
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.446
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.690
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.299
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.731
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.732
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.633
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.658
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.732
-Epoch: [1]  [ 0/60]  eta: 0:00:10  lr: 0.005000  loss: 0.2554 (0.2554)  loss_classifier: 0.0176 (0.0176)  loss_box_reg: 0.0572 (0.0572)  loss_mask: 0.1772 (0.1772)  loss_objectness: 0.0003 (0.0003)  loss_rpn_box_reg: 0.0031 (0.0031)  time: 0.1796  data: 0.0147  max mem: 2921
-Epoch: [1]  [10/60]  eta: 0:00:12  lr: 0.005000  loss: 0.3557 (0.3762)  loss_classifier: 0.0444 (0.0532)  loss_box_reg: 0.1343 (0.1466)  loss_mask: 0.1590 (0.1656)  loss_objectness: 0.0009 (0.0026)  loss_rpn_box_reg: 0.0086 (0.0083)  time: 0.2410  data: 0.0171  max mem: 2921
-Epoch: [1]  [20/60]  eta: 0:00:08  lr: 0.005000  loss: 0.3557 (0.3526)  loss_classifier: 0.0421 (0.0456)  loss_box_reg: 0.1241 (0.1217)  loss_mask: 0.1695 (0.1761)  loss_objectness: 0.0009 (0.0019)  loss_rpn_box_reg: 0.0069 (0.0072)  time: 0.2242  data: 0.0160  max mem: 2921
-Epoch: [1]  [30/60]  eta: 0:00:06  lr: 0.005000  loss: 0.3102 (0.3336)  loss_classifier: 0.0421 (0.0450)  loss_box_reg: 0.0922 (0.1158)  loss_mask: 0.1501 (0.1638)  loss_objectness: 0.0010 (0.0020)  loss_rpn_box_reg: 0.0052 (0.0071)  time: 0.2109  data: 0.0157  max mem: 2921
-Epoch: [1]  [40/60]  eta: 0:00:04  lr: 0.005000  loss: 0.2814 (0.3281)  loss_classifier: 0.0470 (0.0444)  loss_box_reg: 0.0922 (0.1093)  loss_mask: 0.1441 (0.1652)  loss_objectness: 0.0014 (0.0018)  loss_rpn_box_reg: 0.0052 (0.0073)  time: 0.2218  data: 0.0163  max mem: 2921
-Epoch: [1]  [50/60]  eta: 0:00:02  lr: 0.005000  loss: 0.2674 (0.3154)  loss_classifier: 0.0286 (0.0422)  loss_box_reg: 0.0561 (0.1018)  loss_mask: 0.1601 (0.1628)  loss_objectness: 0.0011 (0.0018)  loss_rpn_box_reg: 0.0047 (0.0068)  time: 0.2277  data: 0.0153  max mem: 2921
-Epoch: [1]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.2249 (0.3010)  loss_classifier: 0.0283 (0.0414)  loss_box_reg: 0.0512 (0.0950)  loss_mask: 0.1284 (0.1564)  loss_objectness: 0.0009 (0.0018)  loss_rpn_box_reg: 0.0037 (0.0065)  time: 0.2242  data: 0.0160  max mem: 2921
-Epoch: [1] Total time: 0:00:13 (0.2219 s / it)
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.692
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.739
+Epoch: [1]  [ 0/60]  eta: 0:00:10  lr: 0.005000  loss: 0.2538 (0.2538)  loss_classifier: 0.0204 (0.0204)  loss_box_reg: 0.0604 (0.0604)  loss_mask: 0.1699 (0.1699)  loss_objectness: 0.0001 (0.0001)  loss_rpn_box_reg: 0.0031 (0.0031)  time: 0.1800  data: 0.0149  max mem: 2921
+Epoch: [1]  [10/60]  eta: 0:00:11  lr: 0.005000  loss: 0.3333 (0.3693)  loss_classifier: 0.0447 (0.0525)  loss_box_reg: 0.1257 (0.1422)  loss_mask: 0.1603 (0.1642)  loss_objectness: 0.0016 (0.0022)  loss_rpn_box_reg: 0.0084 (0.0082)  time: 0.2395  data: 0.0172  max mem: 2921
+Epoch: [1]  [20/60]  eta: 0:00:08  lr: 0.005000  loss: 0.3333 (0.3463)  loss_classifier: 0.0365 (0.0448)  loss_box_reg: 0.1092 (0.1181)  loss_mask: 0.1679 (0.1743)  loss_objectness: 0.0016 (0.0020)  loss_rpn_box_reg: 0.0069 (0.0071)  time: 0.2214  data: 0.0159  max mem: 2921
+Epoch: [1]  [30/60]  eta: 0:00:06  lr: 0.005000  loss: 0.3062 (0.3281)  loss_classifier: 0.0355 (0.0450)  loss_box_reg: 0.0809 (0.1122)  loss_mask: 0.1473 (0.1624)  loss_objectness: 0.0010 (0.0017)  loss_rpn_box_reg: 0.0048 (0.0068)  time: 0.2082  data: 0.0155  max mem: 2921
+Epoch: [1]  [40/60]  eta: 0:00:04  lr: 0.005000  loss: 0.2747 (0.3229)  loss_classifier: 0.0461 (0.0440)  loss_box_reg: 0.0809 (0.1069)  loss_mask: 0.1425 (0.1633)  loss_objectness: 0.0009 (0.0017)  loss_rpn_box_reg: 0.0051 (0.0070)  time: 0.2188  data: 0.0162  max mem: 2921
+Epoch: [1]  [50/60]  eta: 0:00:02  lr: 0.005000  loss: 0.2658 (0.3112)  loss_classifier: 0.0307 (0.0420)  loss_box_reg: 0.0674 (0.0999)  loss_mask: 0.1544 (0.1610)  loss_objectness: 0.0009 (0.0018)  loss_rpn_box_reg: 0.0043 (0.0065)  time: 0.2254  data: 0.0152  max mem: 2921
+Epoch: [1]  [59/60]  eta: 0:00:00  lr: 0.005000  loss: 0.2286 (0.2973)  loss_classifier: 0.0351 (0.0407)  loss_box_reg: 0.0544 (0.0933)  loss_mask: 0.1298 (0.1551)  loss_objectness: 0.0009 (0.0020)  loss_rpn_box_reg: 0.0032 (0.0062)  time: 0.2254  data: 0.0158  max mem: 2921
+Epoch: [1] Total time: 0:00:13 (0.2204 s / it)
 creating index...
 index created!
-Test:  [ 0/50]  eta: 0:00:02  model_time: 0.0406 (0.0406)  evaluator_time: 0.0033 (0.0033)  time: 0.0575  data: 0.0131  max mem: 2921
-Test:  [49/50]  eta: 0:00:00  model_time: 0.0398 (0.0415)  evaluator_time: 0.0029 (0.0042)  time: 0.0560  data: 0.0104  max mem: 2921
-Test: Total time: 0:00:02 (0.0576 s / it)
-Averaged stats: model_time: 0.0398 (0.0415)  evaluator_time: 0.0029 (0.0042)
+Test:  [ 0/50]  eta: 0:00:02  model_time: 0.0411 (0.0411)  evaluator_time: 0.0039 (0.0039)  time: 0.0584  data: 0.0130  max mem: 2921
+Test:  [49/50]  eta: 0:00:00  model_time: 0.0395 (0.0410)  evaluator_time: 0.0029 (0.0041)  time: 0.0554  data: 0.0103  max mem: 2921
+Test: Total time: 0:00:02 (0.0570 s / it)
+Averaged stats: model_time: 0.0395 (0.0410)  evaluator_time: 0.0029 (0.0041)
 Accumulating evaluation results...
 DONE (t=0.01s).
 Accumulating evaluation results...
 DONE (t=0.01s).
 IoU metric: bbox
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.755
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.993
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.933
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.420
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.691
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.323
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.801
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.801
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.500
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.767
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812
-IoU metric: segm
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.729
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.990
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.892
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.401
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.585
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.747
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.732
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.985
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.937
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.433
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.676
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.746
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.312
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.773
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.773
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.778
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.778
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.433
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.758
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.789
+IoU metric: segm
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.715
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.993
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.869
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.368
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.553
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.733
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.313
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.760
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.760
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.533
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.787
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.683
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.774
 That's it!
 </pre></div>
 </div>
@@ -1092,7 +1090,7 @@ <h2>Testing <code class="docutils literal notranslate"><span class="pre">forward
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><a class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor"><span class="n">output_image</span></a><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
 </pre></div>
 </div>
-<img alt="torchvision tutorial" class="sphx-glr-single-img" src="../_images/sphx_glr_torchvision_tutorial_002.png" srcset="../_images/sphx_glr_torchvision_tutorial_002.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.image.AxesImage object at 0x7f078d82a260&gt;
+<img alt="torchvision tutorial" class="sphx-glr-single-img" src="../_images/sphx_glr_torchvision_tutorial_002.png" srcset="../_images/sphx_glr_torchvision_tutorial_002.png"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.image.AxesImage object at 0x7f3b6a7e80d0&gt;
 </pre></div>
 </div>
 <p>The results look good!</p>
@@ -1108,7 +1106,7 @@ <h2>Wrapping up<a class="headerlink" href="#wrapping-up" title="Permalink to thi
 <p>For a more complete example, which includes multi-machine / multi-GPU
 training, check <code class="docutils literal notranslate"><span class="pre">references/detection/train.py</span></code>, which is present in
 the torchvision repository.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  49.792 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  49.338 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-torchvision-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/7590258df9f28b5ae0994c3b5b035edf/torchvision_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">torchvision_tutorial.py</span></code></a></p>
diff --git a/prototype/gpu_quantization_torchao_tutorial.html b/prototype/gpu_quantization_torchao_tutorial.html
index 7177c65654..31755bfe10 100644
--- a/prototype/gpu_quantization_torchao_tutorial.html
+++ b/prototype/gpu_quantization_torchao_tutorial.html
@@ -678,7 +678,7 @@ <h2>Set up Your Environment<a class="headerlink" href="#set-up-your-environment"
     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;unable to run fp32 model: &quot;</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>base fp32 runtime of the model is 200.63ms and peak memory 8.48GB
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>base fp32 runtime of the model is 199.64ms and peak memory 8.48GB
 </pre></div>
 </div>
 <p>We can achieve an instant performance boost by converting the model to bfloat16.
@@ -694,7 +694,7 @@ <h2>Set up Your Environment<a class="headerlink" href="#set-up-your-environment"
 <span class="c1"># bf16 runtime of the block is 25.43ms and peak memory  3.17GB</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>bf16 runtime of the block is 70.56ms and peak memory  5.32GB
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>bf16 runtime of the block is 70.49ms and peak memory  5.32GB
 </pre></div>
 </div>
 <p>Just this quick change improves runtime by a factor of ~7x in the tests we have
@@ -708,102 +708,102 @@ <h2>Set up Your Environment<a class="headerlink" href="#set-up-your-environment"
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>AUTOTUNE mm(78400x1280, 1280x3840)
-  triton_mm_2 11.3526 ms 100.0%
-  triton_mm_1 11.4403 ms 99.2%
-  mm 11.7156 ms 96.9%
-  triton_mm_0 11.8470 ms 95.8%
-  triton_mm_4 11.8920 ms 95.5%
-  triton_mm_3 11.9419 ms 95.1%
-  triton_mm_8 12.0349 ms 94.3%
-  triton_mm_10 12.6655 ms 89.6%
-  triton_mm_7 13.3372 ms 85.1%
-  triton_mm_5 16.6684 ms 68.1%
-SingleProcess AUTOTUNE takes 5.3526 seconds
+  triton_mm_2 11.3563 ms 100.0%
+  triton_mm_1 11.4348 ms 99.3%
+  mm 11.7057 ms 97.0%
+  triton_mm_4 11.7720 ms 96.5%
+  triton_mm_0 11.8430 ms 95.9%
+  triton_mm_3 11.9348 ms 95.2%
+  triton_mm_8 12.1082 ms 93.8%
+  triton_mm_10 12.5455 ms 90.5%
+  triton_mm_7 13.3069 ms 85.3%
+  triton_mm_5 16.6671 ms 68.1%
+SingleProcess AUTOTUNE takes 5.3342 seconds
 AUTOTUNE bmm(6400x196x80, 6400x80x196)
-  triton_bmm_16 1.8359 ms 100.0%
-  triton_bmm_15 1.8486 ms 99.3%
-  triton_bmm_12 1.9028 ms 96.5%
-  triton_bmm_13 1.9091 ms 96.2%
-  triton_bmm_14 1.9574 ms 93.8%
-  triton_bmm_20 2.0230 ms 90.7%
-  triton_bmm_19 2.0601 ms 89.1%
-  triton_bmm_23 2.1535 ms 85.3%
-  triton_bmm_18 2.2457 ms 81.8%
-  triton_bmm_22 2.2722 ms 80.8%
-SingleProcess AUTOTUNE takes 4.0624 seconds
+  triton_bmm_16 1.8273 ms 100.0%
+  triton_bmm_15 1.8420 ms 99.2%
+  triton_bmm_13 1.9079 ms 95.8%
+  triton_bmm_12 1.9083 ms 95.8%
+  triton_bmm_14 1.9527 ms 93.6%
+  triton_bmm_20 1.9857 ms 92.0%
+  triton_bmm_19 2.0376 ms 89.7%
+  triton_bmm_23 2.1506 ms 85.0%
+  triton_bmm_18 2.2042 ms 82.9%
+  triton_bmm_21 2.2656 ms 80.7%
+SingleProcess AUTOTUNE takes 4.0607 seconds
 AUTOTUNE bmm(14x89600x80, 14x80x16)
-  triton_bmm_33 0.4705 ms 100.0%
-  triton_bmm_32 0.4767 ms 98.7%
-  bmm 0.4884 ms 96.3%
-  triton_bmm_29 0.5073 ms 92.8%
-  triton_bmm_30 0.5170 ms 91.0%
-  triton_bmm_27 0.5304 ms 88.7%
-  triton_bmm_28 0.5373 ms 87.6%
-  triton_bmm_25 0.5485 ms 85.8%
-  triton_bmm_26 0.5521 ms 85.2%
-  triton_bmm_31 0.5550 ms 84.8%
-SingleProcess AUTOTUNE takes 3.0987 seconds
+  triton_bmm_33 0.4699 ms 100.0%
+  triton_bmm_32 0.4770 ms 98.5%
+  bmm 0.4858 ms 96.7%
+  triton_bmm_29 0.5072 ms 92.6%
+  triton_bmm_30 0.5089 ms 92.3%
+  triton_bmm_27 0.5306 ms 88.6%
+  triton_bmm_28 0.5370 ms 87.5%
+  triton_bmm_25 0.5489 ms 85.6%
+  triton_bmm_26 0.5519 ms 85.1%
+  triton_bmm_24 0.5549 ms 84.7%
+SingleProcess AUTOTUNE takes 3.0938 seconds
 AUTOTUNE bmm(14x89600x80, 14x80x14)
-  triton_bmm_45 0.5457 ms 100.0%
-  triton_bmm_41 0.5816 ms 93.8%
-  triton_bmm_42 0.6095 ms 89.5%
-  triton_bmm_39 0.6402 ms 85.2%
-  triton_bmm_40 0.6869 ms 79.4%
-  triton_bmm_37 0.6942 ms 78.6%
-  triton_bmm_38 0.7172 ms 76.1%
-  triton_bmm_43 0.7334 ms 74.4%
-  triton_bmm_36 0.7350 ms 74.2%
-  triton_bmm_44 0.7639 ms 71.4%
-SingleProcess AUTOTUNE takes 3.2320 seconds
+  triton_bmm_45 0.5451 ms 100.0%
+  triton_bmm_41 0.5863 ms 93.0%
+  triton_bmm_42 0.5968 ms 91.3%
+  triton_bmm_39 0.6481 ms 84.1%
+  triton_bmm_40 0.6878 ms 79.2%
+  triton_bmm_37 0.6967 ms 78.2%
+  triton_bmm_38 0.7161 ms 76.1%
+  triton_bmm_43 0.7338 ms 74.3%
+  triton_bmm_36 0.7357 ms 74.1%
+  triton_bmm_44 0.7638 ms 71.4%
+SingleProcess AUTOTUNE takes 3.2250 seconds
 AUTOTUNE bmm(6400x196x196, 6400x196x80)
-  triton_bmm_57 1.8320 ms 100.0%
-  triton_bmm_51 1.9690 ms 93.0%
-  triton_bmm_54 1.9728 ms 92.9%
-  triton_bmm_48 2.0430 ms 89.7%
-  triton_bmm_56 2.0453 ms 89.6%
-  triton_bmm_49 2.0878 ms 87.7%
-  triton_bmm_50 2.1169 ms 86.5%
-  triton_bmm_52 2.1348 ms 85.8%
-  bmm 2.2418 ms 81.7%
-  triton_bmm_58 2.3436 ms 78.2%
-SingleProcess AUTOTUNE takes 4.1546 seconds
+  triton_bmm_57 1.8083 ms 100.0%
+  triton_bmm_54 1.9690 ms 91.8%
+  triton_bmm_51 1.9691 ms 91.8%
+  triton_bmm_56 2.0109 ms 89.9%
+  triton_bmm_48 2.0460 ms 88.4%
+  triton_bmm_49 2.0875 ms 86.6%
+  triton_bmm_50 2.1167 ms 85.4%
+  triton_bmm_52 2.1343 ms 84.7%
+  bmm 2.2412 ms 80.7%
+  triton_bmm_58 2.3435 ms 77.2%
+SingleProcess AUTOTUNE takes 4.1550 seconds
 AUTOTUNE mm(78400x1280, 1280x1280)
-  triton_mm_61 3.7660 ms 100.0%
-  triton_mm_62 3.7667 ms 100.0%
-  triton_mm_60 3.8918 ms 96.8%
-  triton_mm_63 3.9388 ms 95.6%
-  triton_mm_64 3.9490 ms 95.4%
-  triton_mm_68 3.9796 ms 94.6%
-  mm 3.9928 ms 94.3%
-  triton_mm_70 4.1273 ms 91.2%
-  triton_mm_67 4.5145 ms 83.4%
-  triton_mm_66 5.3955 ms 69.8%
-SingleProcess AUTOTUNE takes 4.5473 seconds
+  triton_mm_61 3.7334 ms 100.0%
+  triton_mm_62 3.7374 ms 99.9%
+  triton_mm_60 3.8545 ms 96.9%
+  triton_mm_63 3.8965 ms 95.8%
+  triton_mm_64 3.9136 ms 95.4%
+  triton_mm_68 3.9384 ms 94.8%
+  mm 3.9934 ms 93.5%
+  triton_mm_70 4.1300 ms 90.4%
+  triton_mm_67 4.5055 ms 82.9%
+  triton_mm_66 5.3185 ms 70.2%
+SingleProcess AUTOTUNE takes 4.5364 seconds
 AUTOTUNE mm(65536x1280, 1280x5120)
-  triton_mm_74 12.5258 ms 100.0%
-  triton_mm_73 12.7238 ms 98.4%
-  triton_mm_72 13.1657 ms 95.1%
-  mm 13.1920 ms 95.0%
-  triton_mm_76 13.2246 ms 94.7%
-  triton_mm_75 13.2952 ms 94.2%
-  triton_mm_80 13.4932 ms 92.8%
-  triton_mm_82 14.0897 ms 88.9%
-  triton_mm_79 14.9147 ms 84.0%
-  triton_mm_77 18.5459 ms 67.5%
-SingleProcess AUTOTUNE takes 5.2987 seconds
+  triton_mm_74 12.5058 ms 100.0%
+  triton_mm_73 12.6292 ms 99.0%
+  triton_mm_76 13.0039 ms 96.2%
+  triton_mm_72 13.0621 ms 95.7%
+  mm 13.1453 ms 95.1%
+  triton_mm_75 13.1844 ms 94.9%
+  triton_mm_80 13.2520 ms 94.4%
+  triton_mm_82 13.9596 ms 89.6%
+  triton_mm_79 14.8417 ms 84.3%
+  triton_mm_77 18.4300 ms 67.9%
+SingleProcess AUTOTUNE takes 5.2882 seconds
 AUTOTUNE mm(65536x5120, 5120x1280)
-  mm 12.6560 ms 100.0%
-  triton_mm_85 12.7683 ms 99.1%
-  triton_mm_87 12.7869 ms 99.0%
-  triton_mm_86 12.7999 ms 98.9%
-  triton_mm_88 12.8161 ms 98.8%
-  triton_mm_84 13.2749 ms 95.3%
-  triton_mm_92 13.3723 ms 94.6%
-  triton_mm_91 14.4787 ms 87.4%
-  triton_mm_94 15.0073 ms 84.3%
-  triton_mm_90 19.0210 ms 66.5%
-SingleProcess AUTOTUNE takes 5.0015 seconds
-bf16 compiled runtime of the block is 56.56ms and peak memory  4.38GB
+  triton_mm_85 12.6648 ms 100.0%
+  mm 12.6663 ms 100.0%
+  triton_mm_86 12.6693 ms 100.0%
+  triton_mm_87 12.7140 ms 99.6%
+  triton_mm_88 12.7156 ms 99.6%
+  triton_mm_92 13.3868 ms 94.6%
+  triton_mm_91 14.3711 ms 88.1%
+  triton_mm_94 14.9560 ms 84.7%
+  triton_mm_84 15.1951 ms 83.3%
+  triton_mm_90 19.0115 ms 66.6%
+SingleProcess AUTOTUNE takes 4.9717 seconds
+bf16 compiled runtime of the block is 55.70ms and peak memory  4.39GB
 </pre></div>
 </div>
 <p>The first time this is run, you should see a sequence of <code class="docutils literal notranslate"><span class="pre">AUTOTUNE</span></code>
@@ -863,54 +863,54 @@ <h2>Set up Your Environment<a class="headerlink" href="#set-up-your-environment"
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>AUTOTUNE int_mm(78400x1280, 1280x3840)
-  triton_mm_200 5.9585 ms 100.0%
-  triton_mm_201 6.3567 ms 93.7%
-  triton_mm_202 6.3865 ms 93.3%
-  triton_mm_194 7.0396 ms 84.6%
-  triton_mm_195 7.0889 ms 84.1%
-  triton_mm_193 7.2656 ms 82.0%
-  triton_mm_192 7.3238 ms 81.4%
-  triton_mm_196 7.8547 ms 75.9%
-  triton_mm_199 9.3244 ms 63.9%
-  triton_mm_197 13.7373 ms 43.4%
-SingleProcess AUTOTUNE takes 4.6508 seconds
+  triton_mm_200 5.9584 ms 100.0%
+  triton_mm_201 6.3571 ms 93.7%
+  triton_mm_202 6.3850 ms 93.3%
+  triton_mm_194 6.9833 ms 85.3%
+  triton_mm_192 7.2475 ms 82.2%
+  triton_mm_193 7.2500 ms 82.2%
+  triton_mm_195 7.3509 ms 81.1%
+  triton_mm_196 7.8553 ms 75.9%
+  triton_mm_199 9.2944 ms 64.1%
+  triton_mm_197 13.7887 ms 43.2%
+SingleProcess AUTOTUNE takes 4.6370 seconds
 AUTOTUNE int_mm(78400x1280, 1280x1280)
-  triton_mm_259 1.9993 ms 100.0%
-  triton_mm_260 2.1762 ms 91.9%
-  triton_mm_261 2.1892 ms 91.3%
-  triton_mm_252 2.3223 ms 86.1%
-  triton_mm_253 2.3931 ms 83.5%
-  triton_mm_251 2.4087 ms 83.0%
-  triton_mm_254 2.4433 ms 81.8%
-  triton_mm_255 2.5974 ms 77.0%
-  triton_mm_258 3.0527 ms 65.5%
-  triton_mm_256 4.4897 ms 44.5%
-SingleProcess AUTOTUNE takes 4.2690 seconds
+  triton_mm_259 2.0005 ms 100.0%
+  triton_mm_260 2.1804 ms 91.7%
+  triton_mm_261 2.1874 ms 91.5%
+  triton_mm_252 2.3162 ms 86.4%
+  triton_mm_253 2.3748 ms 84.2%
+  triton_mm_251 2.4121 ms 82.9%
+  triton_mm_254 2.4526 ms 81.6%
+  triton_mm_255 2.5976 ms 77.0%
+  triton_mm_258 3.0368 ms 65.9%
+  triton_mm_256 4.5412 ms 44.1%
+SingleProcess AUTOTUNE takes 4.2725 seconds
 AUTOTUNE int_mm(65536x1280, 1280x5120)
-  triton_mm_270 6.6328 ms 100.0%
-  triton_mm_271 7.0731 ms 93.8%
-  triton_mm_272 7.0882 ms 93.6%
-  triton_mm_264 7.6498 ms 86.7%
-  triton_mm_265 7.7615 ms 85.5%
-  triton_mm_263 7.9399 ms 83.5%
-  triton_mm_262 8.0029 ms 82.9%
-  triton_mm_266 8.6258 ms 76.9%
-  triton_mm_269 10.1545 ms 65.3%
-  triton_mm_267 15.2098 ms 43.6%
-SingleProcess AUTOTUNE takes 4.6859 seconds
+  triton_mm_270 6.6294 ms 100.0%
+  triton_mm_271 7.0671 ms 93.8%
+  triton_mm_272 7.0896 ms 93.5%
+  triton_mm_264 7.6035 ms 87.2%
+  triton_mm_265 7.7753 ms 85.3%
+  triton_mm_263 7.9491 ms 83.4%
+  triton_mm_262 8.0107 ms 82.8%
+  triton_mm_266 8.6288 ms 76.8%
+  triton_mm_269 10.1253 ms 65.5%
+  triton_mm_267 15.2579 ms 43.4%
+SingleProcess AUTOTUNE takes 4.6667 seconds
 AUTOTUNE int_mm(65536x5120, 5120x1280)
-  triton_mm_281 6.3251 ms 100.0%
-  triton_mm_282 6.4542 ms 98.0%
-  triton_mm_283 6.4747 ms 97.7%
-  triton_mm_276 6.5598 ms 96.4%
-  triton_mm_274 6.8046 ms 93.0%
-  triton_mm_277 6.8462 ms 92.4%
-  triton_mm_275 6.8892 ms 91.8%
-  triton_mm_273 7.7584 ms 81.5%
-  triton_mm_280 8.3158 ms 76.1%
-  triton_mm_278 14.5045 ms 43.6%
-SingleProcess AUTOTUNE takes 4.6013 seconds
-bf16 compiled runtime of the quantized block is 45.04ms and peak memory  4.61GB
+  triton_mm_281 6.3215 ms 100.0%
+  triton_mm_282 6.4594 ms 97.9%
+  triton_mm_283 6.4820 ms 97.5%
+  triton_mm_276 6.5656 ms 96.3%
+  triton_mm_274 6.8039 ms 92.9%
+  triton_mm_277 6.8371 ms 92.5%
+  triton_mm_275 6.8769 ms 91.9%
+  triton_mm_273 7.5087 ms 84.2%
+  triton_mm_280 8.2986 ms 76.2%
+  triton_mm_278 14.5381 ms 43.5%
+SingleProcess AUTOTUNE takes 4.6106 seconds
+bf16 compiled runtime of the quantized block is 44.96ms and peak memory  4.61GB
 </pre></div>
 </div>
 <p>With quantization, we have improved performance a bit more but memory usage increased
@@ -944,54 +944,54 @@ <h2>Set up Your Environment<a class="headerlink" href="#set-up-your-environment"
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>AUTOTUNE int_mm(78400x1280, 1280x3840, 78400x3840)
-  triton_mm_377 6.1231 ms 100.0%
-  triton_mm_384 6.2063 ms 98.7%
-  triton_mm_379 6.7273 ms 91.0%
-  triton_mm_376 6.7946 ms 90.1%
-  triton_mm_378 6.9864 ms 87.6%
-  triton_mm_385 7.0770 ms 86.5%
-  triton_mm_380 7.4593 ms 82.1%
-  triton_mm_383 8.4553 ms 72.4%
-  triton_mm_386 8.5853 ms 71.3%
-  triton_mm_381 13.9891 ms 43.8%
-SingleProcess AUTOTUNE takes 6.5138 seconds
+  triton_mm_377 6.1192 ms 100.0%
+  triton_mm_384 6.2081 ms 98.6%
+  triton_mm_379 6.7290 ms 90.9%
+  triton_mm_376 6.7761 ms 90.3%
+  triton_mm_378 6.9779 ms 87.7%
+  triton_mm_385 7.0787 ms 86.4%
+  triton_mm_380 7.4627 ms 82.0%
+  triton_mm_383 8.4477 ms 72.4%
+  triton_mm_386 8.5858 ms 71.3%
+  triton_mm_381 13.9541 ms 43.9%
+SingleProcess AUTOTUNE takes 6.5224 seconds
 AUTOTUNE int_mm(78400x1280, 1280x1280, 78400x1280)
-  triton_mm_436 2.0551 ms 100.0%
-  triton_mm_443 2.0551 ms 100.0%
-  triton_mm_435 2.2285 ms 92.2%
-  triton_mm_438 2.2868 ms 89.9%
-  triton_mm_437 2.3028 ms 89.2%
-  triton_mm_444 2.3530 ms 87.3%
-  triton_mm_439 2.4655 ms 83.4%
-  triton_mm_442 2.7680 ms 74.2%
-  triton_mm_445 2.9038 ms 70.8%
-  triton_mm_441 4.5133 ms 45.5%
-SingleProcess AUTOTUNE takes 6.0721 seconds
+  triton_mm_436 2.0552 ms 100.0%
+  triton_mm_443 2.0559 ms 100.0%
+  triton_mm_435 2.2238 ms 92.4%
+  triton_mm_438 2.2863 ms 89.9%
+  triton_mm_437 2.3029 ms 89.2%
+  triton_mm_444 2.3499 ms 87.5%
+  triton_mm_439 2.4650 ms 83.4%
+  triton_mm_442 2.7723 ms 74.1%
+  triton_mm_445 2.9047 ms 70.8%
+  triton_mm_441 4.5423 ms 45.2%
+SingleProcess AUTOTUNE takes 6.0832 seconds
 AUTOTUNE int_mm(65536x1280, 1280x5120, 65536x5120)
-  triton_mm_447 6.8120 ms 100.0%
-  triton_mm_454 6.8342 ms 99.7%
-  triton_mm_449 7.4225 ms 91.8%
-  triton_mm_446 7.5299 ms 90.5%
-  triton_mm_455 7.6827 ms 88.7%
-  triton_mm_448 7.7310 ms 88.1%
-  triton_mm_450 8.2779 ms 82.3%
-  triton_mm_453 9.1661 ms 74.3%
-  triton_mm_456 9.4833 ms 71.8%
-  triton_mm_451 15.4997 ms 43.9%
-SingleProcess AUTOTUNE takes 6.4177 seconds
+  triton_mm_447 6.8158 ms 100.0%
+  triton_mm_454 6.8352 ms 99.7%
+  triton_mm_449 7.3649 ms 92.5%
+  triton_mm_446 7.5349 ms 90.5%
+  triton_mm_455 7.6815 ms 88.7%
+  triton_mm_448 7.7256 ms 88.2%
+  triton_mm_450 8.2854 ms 82.3%
+  triton_mm_453 9.1876 ms 74.2%
+  triton_mm_456 9.4918 ms 71.8%
+  triton_mm_451 15.4714 ms 44.1%
+SingleProcess AUTOTUNE takes 6.4449 seconds
 AUTOTUNE int_mm(65536x5120, 5120x1280, 65536x1280)
-  triton_mm_465 6.3866 ms 100.0%
-  triton_mm_458 6.4628 ms 98.8%
-  triton_mm_466 6.5540 ms 97.4%
-  triton_mm_460 6.6827 ms 95.6%
-  triton_mm_459 6.6911 ms 95.4%
-  triton_mm_461 6.9435 ms 92.0%
-  triton_mm_467 6.9986 ms 91.3%
-  triton_mm_464 7.2409 ms 88.2%
-  triton_mm_457 7.4112 ms 86.2%
-  triton_mm_462 14.8194 ms 43.1%
-SingleProcess AUTOTUNE takes 6.3275 seconds
-bf16 compiled runtime of the fused quantized block is 42.36ms and peak memory  4.46GB
+  triton_mm_465 6.3819 ms 100.0%
+  triton_mm_458 6.4679 ms 98.7%
+  triton_mm_466 6.5561 ms 97.3%
+  triton_mm_460 6.6848 ms 95.5%
+  triton_mm_459 6.6976 ms 95.3%
+  triton_mm_461 6.9458 ms 91.9%
+  triton_mm_467 7.0058 ms 91.1%
+  triton_mm_464 7.2436 ms 88.1%
+  triton_mm_457 7.3977 ms 86.3%
+  triton_mm_462 14.8901 ms 42.9%
+SingleProcess AUTOTUNE takes 6.3442 seconds
+bf16 compiled runtime of the fused quantized block is 42.20ms and peak memory  4.47GB
 </pre></div>
 </div>
 <p>The fusion improves performance by another small bit (about 6% over the
@@ -1022,7 +1022,7 @@ <h2>Set up Your Environment<a class="headerlink" href="#set-up-your-environment"
 <span class="c1"># bf16 compiled runtime of the final quantized block is 18.16ms and peak memory  2.39GB</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>bf16 compiled runtime of the final quantized block is 41.74ms and peak memory  4.46GB
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>bf16 compiled runtime of the final quantized block is 41.32ms and peak memory  4.47GB
 </pre></div>
 </div>
 <p>As you can see, we’ve squeezed another small improvement from the model,
@@ -1054,84 +1054,84 @@ <h2>Set up Your Environment<a class="headerlink" href="#set-up-your-environment"
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>AUTOTUNE convolution(16x3x1024x1024, 1280x3x16x16)
-  convolution 7.2336 ms 100.0%
-  triton_convolution_747 14.9055 ms 48.5%
-  triton_convolution_745 15.4648 ms 46.8%
-  triton_convolution_750 17.2781 ms 41.9%
-  triton_convolution_744 18.6202 ms 38.8%
-  triton_convolution_749 24.8807 ms 29.1%
-  triton_convolution_748 25.3072 ms 28.6%
-  triton_convolution_746 368.6882 ms 2.0%
-SingleProcess AUTOTUNE takes 7.0899 seconds
+  convolution 7.1796 ms 100.0%
+  triton_convolution_747 14.9456 ms 48.0%
+  triton_convolution_745 15.5424 ms 46.2%
+  triton_convolution_750 17.3522 ms 41.4%
+  triton_convolution_744 18.6026 ms 38.6%
+  triton_convolution_749 24.8715 ms 28.9%
+  triton_convolution_748 25.2109 ms 28.5%
+  triton_convolution_746 367.9200 ms 2.0%
+SingleProcess AUTOTUNE takes 7.0601 seconds
 AUTOTUNE mm(65536x1280, 1280x3840)
-  triton_mm_1425 9.4828 ms 100.0%
-  triton_mm_1424 9.5640 ms 99.2%
-  mm 9.7970 ms 96.8%
-  triton_mm_1427 9.8482 ms 96.3%
-  triton_mm_1423 9.8993 ms 95.8%
-  triton_mm_1426 9.9885 ms 94.9%
-  triton_mm_1431 10.1206 ms 93.7%
-  triton_mm_1433 10.5808 ms 89.6%
-  triton_mm_1430 11.2067 ms 84.6%
-  triton_mm_1428 13.9488 ms 68.0%
-SingleProcess AUTOTUNE takes 5.0633 seconds
+  triton_mm_1425 9.4637 ms 100.0%
+  triton_mm_1424 9.5486 ms 99.1%
+  mm 9.8018 ms 96.6%
+  triton_mm_1423 9.8105 ms 96.5%
+  triton_mm_1427 9.8394 ms 96.2%
+  triton_mm_1426 9.9812 ms 94.8%
+  triton_mm_1431 10.1302 ms 93.4%
+  triton_mm_1433 10.6724 ms 88.7%
+  triton_mm_1430 11.1638 ms 84.8%
+  triton_mm_1428 14.0037 ms 67.6%
+SingleProcess AUTOTUNE takes 5.0675 seconds
 AUTOTUNE bmm(64x16384x80, 64x80x64)
-  triton_bmm_1444 0.6023 ms 100.0%
-  triton_bmm_1443 0.6179 ms 97.5%
-  triton_bmm_1440 0.6316 ms 95.4%
-  triton_bmm_1441 0.6382 ms 94.4%
-  triton_bmm_1438 0.6589 ms 91.4%
-  triton_bmm_1439 0.6623 ms 90.9%
-  triton_bmm_1436 0.6704 ms 89.8%
-  triton_bmm_1437 0.6747 ms 89.3%
-  triton_bmm_1445 0.7282 ms 82.7%
-  triton_bmm_1446 0.7294 ms 82.6%
-SingleProcess AUTOTUNE takes 3.4952 seconds
+  triton_bmm_1444 0.6020 ms 100.0%
+  triton_bmm_1443 0.6185 ms 97.3%
+  triton_bmm_1440 0.6315 ms 95.3%
+  triton_bmm_1441 0.6415 ms 93.8%
+  triton_bmm_1438 0.6597 ms 91.3%
+  triton_bmm_1439 0.6630 ms 90.8%
+  triton_bmm_1436 0.6705 ms 89.8%
+  triton_bmm_1437 0.6747 ms 89.2%
+  triton_bmm_1445 0.7285 ms 82.6%
+  triton_bmm_1446 0.7296 ms 82.5%
+SingleProcess AUTOTUNE takes 3.4790 seconds
 AUTOTUNE bmm(64x16384x80, 64x80x64)
-  triton_bmm_1456 0.7014 ms 100.0%
-  triton_bmm_1452 0.7410 ms 94.7%
-  triton_bmm_1453 0.7574 ms 92.6%
-  triton_bmm_1455 0.8460 ms 82.9%
-  triton_bmm_1450 0.8504 ms 82.5%
-  triton_bmm_1451 0.8756 ms 80.1%
-  triton_bmm_1448 0.8771 ms 80.0%
-  triton_bmm_1449 0.8778 ms 79.9%
-  triton_bmm_1454 0.8852 ms 79.2%
-  triton_bmm_1447 0.8910 ms 78.7%
-SingleProcess AUTOTUNE takes 3.5166 seconds
+  triton_bmm_1456 0.6994 ms 100.0%
+  triton_bmm_1452 0.7380 ms 94.8%
+  triton_bmm_1453 0.7520 ms 93.0%
+  triton_bmm_1455 0.8380 ms 83.5%
+  triton_bmm_1450 0.8473 ms 82.5%
+  triton_bmm_1454 0.8660 ms 80.8%
+  triton_bmm_1451 0.8734 ms 80.1%
+  triton_bmm_1448 0.8757 ms 79.9%
+  bmm 0.8767 ms 79.8%
+  triton_bmm_1449 0.8774 ms 79.7%
+SingleProcess AUTOTUNE takes 3.5089 seconds
 AUTOTUNE mm(65536x1280, 1280x1280)
-  triton_mm_1460 3.1387 ms 100.0%
-  triton_mm_1461 3.1391 ms 100.0%
-  triton_mm_1459 3.2507 ms 96.6%
-  triton_mm_1462 3.2788 ms 95.7%
-  mm 3.2864 ms 95.5%
-  triton_mm_1463 3.2976 ms 95.2%
-  triton_mm_1467 3.3253 ms 94.4%
-  triton_mm_1469 3.4802 ms 90.2%
-  triton_mm_1466 3.7579 ms 83.5%
-  triton_mm_1465 4.5079 ms 69.6%
-SingleProcess AUTOTUNE takes 4.5206 seconds
+  triton_mm_1461 3.1332 ms 100.0%
+  triton_mm_1460 3.1380 ms 99.8%
+  triton_mm_1459 3.2511 ms 96.4%
+  triton_mm_1462 3.2585 ms 96.2%
+  mm 3.2865 ms 95.3%
+  triton_mm_1463 3.2971 ms 95.0%
+  triton_mm_1467 3.3276 ms 94.2%
+  triton_mm_1469 3.4584 ms 90.6%
+  triton_mm_1466 3.7472 ms 83.6%
+  triton_mm_1465 4.4921 ms 69.7%
+SingleProcess AUTOTUNE takes 4.4936 seconds
 AUTOTUNE convolution(16x1280x64x64, 256x1280x1x1)
-  convolution 0.7790 ms 100.0%
-  triton_convolution_3727 0.7921 ms 98.3%
-  triton_convolution_3732 0.8690 ms 89.6%
-  triton_convolution_3731 1.4416 ms 54.0%
-  triton_convolution_3730 1.5993 ms 48.7%
-  triton_convolution_3728 1.9802 ms 39.3%
-  triton_convolution_3733 2.0525 ms 38.0%
-  conv1x1_via_mm 2.5197 ms 30.9%
-  triton_convolution_3729 4.5441 ms 17.1%
-SingleProcess AUTOTUNE takes 3.6567 seconds
+  convolution 0.7813 ms 100.0%
+  triton_convolution_3727 0.7990 ms 97.8%
+  triton_convolution_3732 0.8774 ms 89.0%
+  triton_convolution_3731 1.4408 ms 54.2%
+  triton_convolution_3730 1.5941 ms 49.0%
+  triton_convolution_3728 1.9737 ms 39.6%
+  triton_convolution_3733 2.0527 ms 38.1%
+  conv1x1_via_mm 2.5136 ms 31.1%
+  triton_convolution_3729 4.5326 ms 17.2%
+SingleProcess AUTOTUNE takes 3.6552 seconds
 AUTOTUNE convolution(16x256x64x64, 256x256x3x3)
-  convolution 1.4529 ms 100.0%
-  triton_convolution_3735 2.9809 ms 48.7%
-  triton_convolution_3740 3.1665 ms 45.9%
-  triton_convolution_3737 4.0455 ms 35.9%
-  triton_convolution_3738 4.3247 ms 33.6%
-  triton_convolution_3739 5.2579 ms 27.6%
-  triton_convolution_3734 6.1236 ms 23.7%
-  triton_convolution_3736 9.6210 ms 15.1%
-SingleProcess AUTOTUNE takes 3.9738 seconds
+  convolution 1.4539 ms 100.0%
+  triton_convolution_3735 2.9890 ms 48.6%
+  triton_convolution_3740 3.1716 ms 45.8%
+  triton_convolution_3737 4.0484 ms 35.9%
+  triton_convolution_3738 4.3008 ms 33.8%
+  triton_convolution_3739 5.2547 ms 27.7%
+  triton_convolution_3734 6.1150 ms 23.8%
+  triton_convolution_3736 9.5726 ms 15.2%
+SingleProcess AUTOTUNE takes 3.9534 seconds
 unable to run full model:  CUDA out of memory. Tried to allocate 8.00 GiB. GPU
 </pre></div>
 </div>
@@ -1151,7 +1151,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 <span class="c1">#</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes  32.944 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 7 minutes  11.283 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-prototype-gpu-quantization-torchao-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/0a0adb045e37300c70e9f07380224243/gpu_quantization_torchao_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">gpu_quantization_torchao_tutorial.py</span></code></a></p>
diff --git a/prototype/maskedtensor_adagrad.html b/prototype/maskedtensor_adagrad.html
index 26c2d7ada9..5677e00110 100644
--- a/prototype/maskedtensor_adagrad.html
+++ b/prototype/maskedtensor_adagrad.html
@@ -808,7 +808,7 @@ <h2>Further Reading<a class="headerlink" href="#further-reading" title="Permalin
 <a class="reference external" href="https://pytorch.org/tutorials/prototype/maskedtensor_advanced_semantics.html">MaskedTensor Advanced Semantics</a>
 to see some of the differences in design decisions between <code class="xref py py-class docutils literal notranslate"><span class="pre">MaskedTensor</span></code> and NumPy’s MaskedArray, as well
 as reduction semantics.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.018 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.017 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-prototype-maskedtensor-adagrad-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/25b0b65f5731eb063aab5b9034092772/maskedtensor_adagrad.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">maskedtensor_adagrad.py</span></code></a></p>
diff --git a/prototype/maskedtensor_overview.html b/prototype/maskedtensor_overview.html
index d26b9c1242..ad04aa7c1a 100644
--- a/prototype/maskedtensor_overview.html
+++ b/prototype/maskedtensor_overview.html
@@ -993,7 +993,7 @@ <h2>Further Reading<a class="headerlink" href="#further-reading" title="Permalin
 <p>To continue learning more, you can find our
 <a class="reference external" href="https://pytorch.org/tutorials/prototype/maskedtensor_sparsity.html">MaskedTensor Sparsity tutorial</a>
 to see how MaskedTensor enables sparsity and the different storage formats we currently support.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.031 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.033 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-prototype-maskedtensor-overview-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/5b467300752c6d9599d51103ddd06f4b/maskedtensor_overview.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">maskedtensor_overview.py</span></code></a></p>
diff --git a/prototype/numeric_suite_tutorial.html b/prototype/numeric_suite_tutorial.html
index a2db041efc..bd4c1a504e 100644
--- a/prototype/numeric_suite_tutorial.html
+++ b/prototype/numeric_suite_tutorial.html
@@ -636,9 +636,9 @@ <h3>Setup<a class="headerlink" href="#setup" title="Permalink to this heading">
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: &quot;https://download.pytorch.org/models/resnet18-f37072fd.pth&quot; to /var/lib/ci-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
 
   0%|          | 0.00/44.7M [00:00&lt;?, ?B/s]
- 37%|###6      | 16.4M/44.7M [00:00&lt;00:00, 171MB/s]
- 76%|#######5  | 33.8M/44.7M [00:00&lt;00:00, 177MB/s]
-100%|##########| 44.7M/44.7M [00:00&lt;00:00, 176MB/s]
+ 38%|###7      | 16.9M/44.7M [00:00&lt;00:00, 176MB/s]
+ 76%|#######6  | 34.1M/44.7M [00:00&lt;00:00, 179MB/s]
+100%|##########| 44.7M/44.7M [00:00&lt;00:00, 179MB/s]
 </pre></div>
 </div>
 </div>
@@ -1082,7 +1082,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 <div class="section" id="references">
 <h2>References<a class="headerlink" href="#references" title="Permalink to this heading">¶</a></h2>
 <p>[1] <a class="reference external" href="https://pytorch.org/tutorials/advanced/dynamic_quantization_tutorial.html">DYNAMIC QUANTIZATION ON AN LSTM WORD LANGUAGE MODEL</a>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  2.328 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  2.325 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-prototype-numeric-suite-tutorial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/6aefea42cfe1bb0d4cb1b244c3feba1b/numeric_suite_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">numeric_suite_tutorial.py</span></code></a></p>
diff --git a/recipes/compiling_optimizer_lr_scheduler.html b/recipes/compiling_optimizer_lr_scheduler.html
index c6b5676265..40eee78732 100644
--- a/recipes/compiling_optimizer_lr_scheduler.html
+++ b/recipes/compiling_optimizer_lr_scheduler.html
@@ -687,31 +687,31 @@ <h2>Extension: What happens with a non-tensor LR?<a class="headerlink" href="#ex
     <span class="n">fn</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>V0625 22:03:41.119000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-V0625 22:03:41.119000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-V0625 22:03:41.119000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - ___key_to_id(L[&#39;self&#39;].state) == [139670086608880,139670086608960,139670086601920,139670086603360,139670086600320,139670086599760,139670086606880,139670086609760,139670086612080,139670086602480]
-V0625 22:03:44.430000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-V0625 22:03:44.430000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-V0625 22:03:44.430000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.003333333333333333
-V0625 22:03:44.430000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - G[&#39;__optimizer_139668517417792_139670031288176_c79&#39;]() is not None
-V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.004666666666666667
-V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.003333333333333333
-V0625 22:03:46.979000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - G[&#39;__optimizer_139668517417792_139670031288176_c79&#39;]() is not None
-V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.006000000000000001
-V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.004666666666666667
-V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.003333333333333333
-V0625 22:03:49.513000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - G[&#39;__optimizer_139668517417792_139670031288176_c79&#39;]() is not None
-V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
-V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
-V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.007333333333333335
-V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.006000000000000001
-V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.004666666666666667
-V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.003333333333333333
-V0625 22:03:52.060000 139677001331328 torch/_dynamo/guards.py:1425] [__recompiles]     - G[&#39;__optimizer_139668517417792_139670031288176_c79&#39;]() is not None
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>V0625 22:40:10.944000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+V0625 22:40:10.944000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+V0625 22:40:10.944000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - ___key_to_id(L[&#39;self&#39;].state) == [139893161731440,139893161724240,139893161722720,139893161727120,139893161732960,139893161726480,139893161722640,139893161724800,139893161731680,139893161724960]
+V0625 22:40:14.236000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+V0625 22:40:14.236000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+V0625 22:40:14.236000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.003333333333333333
+V0625 22:40:14.236000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - G[&#39;__optimizer_139893146295488_139893161731840_c59&#39;]() is not None
+V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.004666666666666667
+V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.003333333333333333
+V0625 22:40:16.768000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - G[&#39;__optimizer_139893146295488_139893161731840_c59&#39;]() is not None
+V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.006000000000000001
+V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.004666666666666667
+V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.003333333333333333
+V0625 22:40:19.304000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - G[&#39;__optimizer_139893146295488_139893161731840_c59&#39;]() is not None
+V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles] Recompiling function step in /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/optim/adam.py:135
+V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     triggered by the following guard failure(s):
+V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.007333333333333335
+V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.006000000000000001
+V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.004666666666666667
+V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - L[&#39;self&#39;].param_groups[0][&#39;lr&#39;] == 0.003333333333333333
+V0625 22:40:21.833000 139900085105280 torch/_dynamo/guards.py:1425] [__recompiles]     - G[&#39;__optimizer_139893146295488_139893161731840_c59&#39;]() is not None
 </pre></div>
 </div>
 <p>With this example, we can see that we recompile the optimizer a few times
@@ -728,7 +728,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 <li><p><a class="reference external" href="https://pytorch.org/tutorials/recipes/compiling_optimizer.html">Compiled optimizer tutorial</a> - an intro into the compiled optimizer.</p></li>
 <li><p><a class="reference external" href="https://dev-discuss.pytorch.org/t/compiling-the-optimizer-with-pt2/1669">Compiling the optimizer with PT2</a> - deeper technical details on the compiled optimizer.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  16.936 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  16.831 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-recipes-compiling-optimizer-lr-scheduler-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/cba164d7ed0d3cf63f210d336236ec14/compiling_optimizer_lr_scheduler.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">compiling_optimizer_lr_scheduler.py</span></code></a></p>
diff --git a/recipes/recipes/changing_default_device.html b/recipes/recipes/changing_default_device.html
index 0be23c3572..4dc8d73e15 100644
--- a/recipes/recipes/changing_default_device.html
+++ b/recipes/recipes/changing_default_device.html
@@ -649,7 +649,7 @@
 call to the torch API (not just factory functions). If this
 is causing problems for you, please comment on
 <a class="reference external" href="https://github.com/pytorch/pytorch/issues/92701">this issue</a></p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.099 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.101 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-recipes-recipes-changing-default-device-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/716d809000230e12afb363157e62e635/changing_default_device.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">changing_default_device.py</span></code></a></p>
diff --git a/recipes/recipes/module_load_state_dict_tips.html b/recipes/recipes/module_load_state_dict_tips.html
index 64b644e0a7..1a4d9e3310 100644
--- a/recipes/recipes/module_load_state_dict_tips.html
+++ b/recipes/recipes/module_load_state_dict_tips.html
@@ -668,7 +668,7 @@ <h2>Using <code class="docutils literal notranslate"><span class="pre">torch.loa
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;loading time without mmap=</span><span class="si">{</span><span class="n">end_time</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">start_time</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loading time without mmap=0.021975040435791016
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loading time without mmap=0.022372722625732422
 </pre></div>
 </div>
 <p>The <code class="docutils literal notranslate"><span class="pre">mmap</span></code> keyword argument to <code class="docutils literal notranslate"><span class="pre">torch.load</span></code> attempts to solve the above two
@@ -683,7 +683,7 @@ <h2>Using <code class="docutils literal notranslate"><span class="pre">torch.loa
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;loading time with mmap=</span><span class="si">{</span><span class="n">end_time</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">start_time</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loading time with mmap=0.0016951560974121094
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loading time with mmap=0.0015723705291748047
 </pre></div>
 </div>
 <p>As mentioned above, one can use this argument to do per-tensor processing on a
@@ -767,7 +767,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 <code class="docutils literal notranslate"><span class="pre">torch.device()</span></code> context manager with <code class="docutils literal notranslate"><span class="pre">device=meta</span></code>, and
 <code class="docutils literal notranslate"><span class="pre">nn.Module.load_state_dict(assign=True)</span></code> as well as how these tools could
 be used to aid when loading a model from a checkpoint.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.387 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.391 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-recipes-recipes-module-load-state-dict-tips-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/edc021e5f7c55efead2a89b91cdfae27/module_load_state_dict_tips.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">module_load_state_dict_tips.py</span></code></a></p>
diff --git a/recipes/recipes/reasoning_about_shapes.html b/recipes/recipes/reasoning_about_shapes.html
index a6ef91d865..511ee5a804 100644
--- a/recipes/recipes/reasoning_about_shapes.html
+++ b/recipes/recipes/reasoning_about_shapes.html
@@ -618,7 +618,7 @@
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(..., device=&#39;meta&#39;, size=(2, 5, 9, 9), grad_fn=&lt;ConvolutionBackward0&gt;)
-Time taken: 0.00018522299978940282
+Time taken: 0.00019496499953675084
 </pre></div>
 </div>
 <p>Observe that since data is not materialized, passing arbitrarily large
@@ -634,7 +634,7 @@
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(..., device=&#39;meta&#39;, size=(1024, 5, 65535, 65535),
        grad_fn=&lt;ConvolutionBackward0&gt;)
-Time taken: 0.00011191299927304499
+Time taken: 0.00011925200124096591
 </pre></div>
 </div>
 <p>Consider an arbitrary network such as the following:</p>
@@ -697,7 +697,7 @@
 ) is torch.Size([1024, 10]).
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.015 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.016 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-recipes-recipes-reasoning-about-shapes-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/9f4fb47ef3d58524029d86df50e90a08/reasoning_about_shapes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">reasoning_about_shapes.py</span></code></a></p>
diff --git a/recipes/recipes/swap_tensors.html b/recipes/recipes/swap_tensors.html
index 73e2ce9744..3b9a4cefde 100644
--- a/recipes/recipes/swap_tensors.html
+++ b/recipes/recipes/swap_tensors.html
@@ -728,8 +728,8 @@ <h2><code class="docutils literal notranslate"><span class="pre">nn.Module.to()<
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;m.bias.dtype: </span><span class="si">{</span><a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">dtype</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Before: id(m.weight)=139960653111168, id(m.bias)=139960653111328
-After: id(m.weight)=139960653111168, id(m.bias)=139960653111328
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Before: id(m.weight)=139895005243936, id(m.bias)=139895005241616
+After: id(m.weight)=139895005243936, id(m.bias)=139895005241616
 m.weight.dtype: torch.bfloat16
 m.weight.elem.dtype: torch.float32
 m.bias.dtype: torch.bfloat16
@@ -753,8 +753,8 @@ <h2><code class="docutils literal notranslate"><span class="pre">nn.Module.to()<
 <a href="https://pytorch.org/docs/stable/future_mod.html#torch.__future__.set_swap_module_params_on_conversion" title="torch.__future__.set_swap_module_params_on_conversion" class="sphx-glr-backref-module-torch-__future__ sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">__future__</span><span class="o">.</span><span class="n">set_swap_module_params_on_conversion</span></a><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Before: id(m.weight)=139960653106848, id(m.bias)=139960652981136
-After: id(m.weight)=139960653106848, id(m.bias)=139960652981136
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Before: id(m.weight)=139895005239936, id(m.bias)=139896021365792
+After: id(m.weight)=139895005239936, id(m.bias)=139896021365792
 m.weight.dtype: torch.bfloat16
 m.weight.elem.dtype: torch.bfloat16
 m.bias.dtype: torch.bfloat16
@@ -837,7 +837,7 @@ <h2><code class="docutils literal notranslate"><span class="pre">nn.Module.load_
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  OrderedDict([(&#39;weight&#39;, MyQuantizedLinearWeight(tensor([[ 0.2430,  0.5155,  0.3337],
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 of preserving references for parameters in <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> as well as how to
 use the two new extension points that are gated by
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 <li><p><a class="reference external" href="https://pytorch.org/tutorials/recipes/compiling_optimizer.html">Compiling the Optimizers</a></p></li>
 <li><p><a class="reference external" href="https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html">Implementing High-Performance Transformers with Scaled Dot Product Attention</a></p></li>
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 <p>Alternatively, you can view the <a class="reference external" href="https://pytorch.org/docs/main/logging.html">torch._logging documentation</a> to see
 descriptions of all available logging options.</p>
 <p>For more information on torch.compile, see the <a class="reference external" href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html">torch.compile tutorial</a>.</p>
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"recipes/script_optimized.rst", "recipes/sg_execution_times.rst", "recipes/torch_compile_backend_ipex.rst", "recipes/torch_compile_caching_tutorial.rst", "recipes/torch_compile_user_defined_triton_kernel_tutorial.rst", "recipes/torch_logs.rst", "recipes/torchscript_inference.rst", "recipes/torchserve_vertexai_tutorial.rst", "recipes/xeon_run_cpu.rst", "recipes/zero_redundancy_optimizer.rst", "src/pytorch-sphinx-theme/docs/changelog.rst", "src/pytorch-sphinx-theme/docs/configuring.rst", "src/pytorch-sphinx-theme/docs/demo/api.rst", "src/pytorch-sphinx-theme/docs/demo/demo.rst", "src/pytorch-sphinx-theme/docs/demo/lists_tables.rst", "src/pytorch-sphinx-theme/docs/demo/long.rst", "src/pytorch-sphinx-theme/docs/demo/structure.rst", "src/pytorch-sphinx-theme/docs/index.rst", "src/pytorch-sphinx-theme/docs/installing.rst"], "titles": ["ONNX Live Tutorial", "TorchRL objectives: Coding a DDPG loss", "Autograd in C++ Frontend", "Using CUDA Graphs in PyTorch C++ API", "Custom C++ and CUDA Operators", "Loading a TorchScript Model in C++", "Custom C++ and CUDA Extensions", "Using the PyTorch C++ Frontend", "PyTorch Custom Operators Landing Page", "Training Transformer models using Distributed Data Parallel and Pipeline Parallelism", "Registering a Dispatched Operator in C++", "(beta) Dynamic Quantization on an LSTM Word Language Model", "Extending dispatcher for a new backend in C++", "Distributed Training with Uneven Inputs Using the Join Context Manager", "Neural Transfer Using PyTorch", "Creating Extensions Using NumPy and SciPy", "Pendulum: Writing your environment and transforms with TorchRL", "Facilitating New Backend Integration by PrivateUse1", "Python Custom Operators", "Combining Distributed DataParallel with Distributed RPC Framework", "(beta) Accelerating BERT with semi-structured (2:4) sparsity", "Computation times", "Exploring TorchRec sharding", "(beta) Static Quantization with Eager Mode in PyTorch", "(optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime", "Dynamic Parallelism in TorchScript", "Extending TorchScript with Custom C++ Classes", "Extending TorchScript with Custom C++ Operators", "Semi-Supervised Learning using USB built upon PyTorch", "Introduction to TorchScript", "Audio Data Augmentation", "Audio Datasets", "Audio Feature Augmentation", "Audio Feature Extractions", "Audio I/O", "Audio Resampling", "Automatic Differentiation with <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.autograd</span></code>", "Build the Neural Network", "Datasets &amp; DataLoaders", "Learn the Basics", "Learn the Basics", "Optimizing Model Parameters", "Quickstart", "Save and Load the Model", "Computation times", "Tensors", "Transforms", "Fast Transformer Inference with Better Transformer", "A Gentle Introduction to <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.autograd</span></code>", "Training a Classifier", "Optional: Data Parallelism", "Deep Learning with PyTorch: A 60 Minute Blitz", "Neural Networks", "Computation times", "Tensors", "Chatbot Tutorial", "Running Tutorials in Google Colab", "Writing Custom Datasets, DataLoaders and Transforms", "DCGAN Tutorial", "Fault-tolerant Distributed Training with <code class=\"docutils literal notranslate\"><span class=\"pre\">torchrun</span></code>", "Distributed Data Parallel in PyTorch - Video Tutorials", "Multi GPU training with DDP", "What is Distributed Data Parallel (DDP)", "Deep Learning with PyTorch: A 60 Minute Blitz", "Image Segmentation DeepLabV3 on Android", "Image Segmentation DeepLabV3 on iOS", "Deploying a Seq2Seq Model with TorchScript", "PyTorch Distributed Overview", "&lt;no title&gt;", "PyTorch: Tensors and autograd", "PyTorch: Defining New autograd Functions", "PyTorch: Control Flow + Weight Sharing", "&lt;no title&gt;", "PyTorch: Custom nn Modules", "PyTorch: nn", "PyTorch: optim", "Computation times", "&lt;no title&gt;", "Warm-up: numpy", "PyTorch: Tensors", "Computation times", "Adversarial Example Generation", "Finetuning Torchvision Models", "TorchMultimodal Tutorial: Finetuning FLAVA", "Autograd", "&lt;no title&gt;", "nn package", "Multi-GPU Examples", "Computation times", "Tensors", "PyTorch for Former Torch Users", "Introduction to Holistic Trace Analysis", "Trace Diff using Holistic Trace Analysis", "&lt;no title&gt;", "Learning Hybrid Frontend Syntax Through Example", "Computation times", "Hybrid Frontend Tutorials", "Hyperparameter tuning with Ray Tune", "Introduction to PyTorch - YouTube Series", "The Fundamentals of Autograd", "Model Understanding with Captum", "Introduction to PyTorch on YouTube", "Introduction to PyTorch", "Building Models with PyTorch", "Computation times", "PyTorch TensorBoard Support", "Introduction to PyTorch Tensors", "Training with PyTorch", "Knowledge Distillation Tutorial", "Advanced: Making Dynamic Decisions and the Bi-LSTM CRF", "Deep Learning with PyTorch", "Deep Learning for NLP with Pytorch", "Introduction to PyTorch", "Sequence Models and Long Short-Term Memory Networks", "Computation times", "Word Embeddings: Encoding Lexical Semantics", "What is <cite>torch.nn</cite> <em>really</em>?", "Export a PyTorch model to ONNX", "ONNX", "Introduction to ONNX", "Extending the ONNX Registry", "Computation times", "Profiling your PyTorch Module", "PyTorch Cheat Sheet", "Learning PyTorch with Examples", "Saving and Loading Models", "Computation times", "T5-Base Model for Summarization, Sentiment Classification, and Translation", "Template Tutorial", "Text classification with the torchtext library", "Preprocess custom text dataset using Torchtext", "Transfer Learning for Computer Vision Tutorial", "Language Modeling with <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Transformer</span></code> and torchtext", "Language Translation with <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Transformer</span></code> and torchtext", "Optimizing Vision Transformer Model for Deployment", "Distributed and Parallel Training Tutorials", "Welcome to PyTorch Tutorials", "Advanced Model Training with Fully Sharded Data Parallel (FSDP)", "Getting Started with Fully Sharded Data Parallel(FSDP)", "Large Scale Transformer model training with Tensor Parallel (TP)", "Hooks for autograd saved tensors", "Multi-Objective NAS with Ax", "NLP From Scratch: Classifying Names with a Character-Level RNN", "NLP From Scratch: Generating Names with a Character-Level RNN", "Fusing Convolution and Batch Norm using Custom Function", "Double Backward with Custom Functions", "Training \u201creal-world\u201d models with DDP", "Multinode Training", "Getting Started with Distributed Data Parallel", "Distributed Pipeline Parallelism Using RPC", "Writing Distributed Applications with PyTorch", "Recurrent DQN: Training recurrent policies", "(beta) Dynamic Quantization on BERT", "Model ensembling", "Deploying PyTorch in Python via a REST API with Flask", "Forced Alignment with Wav2Vec2", "Forward-mode Automatic Differentiation (Beta)", "(beta) Building a Convolution/Batch Norm fuser in FX", "(beta) Building a Simple CPU Performance Profiler with FX", "Inductor CPU backend debugging and profiling", "Jacobians, Hessians, hvp, vhp, and more: composing function transforms", "Train a Mario-playing RL Agent", "(beta) Channels Last Memory Format in PyTorch", "&lt;no title&gt;", "Single-Machine Model Parallel Best Practices", "Neural Tangent Kernels", "Getting Started - Accelerate Your Scripts with nvFuser", "How to save memory by fusing the optimizer step into the backward pass", "Parametrizations Tutorial", "Per-sample-gradients", "Customize Process Group Backends Using Cpp Extensions", "Pruning Tutorial", "(beta) Quantized Transfer Learning for Computer Vision Tutorial", "Real Time Inference on Raspberry Pi 4 (30 fps!)", "Reinforcement Learning (PPO) with TorchRL Tutorial", "Reinforcement Learning (DQN) Tutorial", "Implementing Batch RPC Processing Using Asynchronous Executions", "Implementing a Parameter Server Using Distributed RPC Framework", "Getting Started with Distributed RPC Framework", "(Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA)", "NLP From Scratch: Translation with a Sequence to Sequence Network and Attention", "Computation times", "Spatial Transformer Networks Tutorial", "Speech Recognition with Wav2Vec2", "PyTorch Profiler With TensorBoard", "Visualizing Models, Data, and Training with TensorBoard", "Text-to-speech with Tacotron2", "Whole Slide Image Classification Using PyTorch and TIAToolbox", "Introduction to <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.compile</span></code>", "torch.export Nightly Tutorial", "torch.export Tutorial", "Introduction to TorchRec", "Grokking PyTorch Intel CPU performance from first principles", "Grokking PyTorch Intel CPU performance from first principles (Part 2)", "TorchVision Object Detection Finetuning Tutorial", "(prototype) PyTorch BackendConfig Tutorial", "Profiling PyTorch RPC-Based Workloads", "(prototype) FX Graph Mode Post Training Dynamic Quantization", "(prototype) FX Graph Mode Post Training Static Quantization", "(prototype) FX Graph Mode Quantization User Guide", "(prototype) GPU Quantization with TorchAO", "(prototype) Graph Mode Dynamic Quantization on BERT", "Inductor C++ Wrapper Tutorial", "(Prototype) Convert Mobilenetv2 to Core ML", "(Prototype) Use iOS GPU in PyTorch", "(Prototype) Efficiently writing \u201csparse\u201d semantics for Adagrad with MaskedTensor", "(Prototype) MaskedTensor Advanced Semantics", "(Prototype) MaskedTensor Overview", "(Prototype) MaskedTensor Sparsity", "Getting Started with Nested Tensors", "(Beta) Convert MobileNetV2 to NNAPI", "PyTorch Numeric Suite Tutorial", "PyTorch Prototype Recipes", "(prototype) PyTorch 2 Export Post Training Quantization", "(prototype) PyTorch 2 Export Quantization-Aware Training (QAT)", "PyTorch 2 Export Quantization with X86 Backend through Inductor", "How to Write a <code class=\"docutils literal notranslate\"><span class=\"pre\">Quantizer</span></code> for PyTorch 2 Export Quantization", "(prototype) Accelerating BERT with semi-structured (2:4) sparsity", "Computation times", "Skipping Module Parameter Initialization", "Model Freezing in TorchScript", "(prototype) Tracing-based Selective Build Mobile Interpreter in Android and iOS", "torch.vmap", "PyTorch Vulkan Backend User Workflow", "Leverage Intel\u00ae Advanced Matrix Extensions", "Making Native Android Application that uses PyTorch prebuilt libraries", "(beta) Bundling inputs to PyTorch Models", "(beta) Compiling the optimizer with torch.compile", "(beta) Running the compiled optimizer with an LR Scheduler", "Direct Device-to-Device Communication with TensorPipe CUDA RPC", "Deploying with Flask", "Getting Started with Distributed Checkpoint (DCP)", "Getting Started with DeviceMesh", "Distributed Optimizer with TorchScript support", "Profiling PyTorch RPC-Based Workloads", "Fuse Modules Recipe", "(Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors", "Intel\u00ae Extension for PyTorch*", "Ease-of-use quantization for PyTorch with Intel\u00ae Neural Compressor", "Loading data in PyTorch", "(beta) Efficient mobile interpreter in Android and iOS", "Pytorch Mobile Performance Recipes", "Model Preparation for Android Recipe", "Model Preparation for iOS Recipe", "Profiling PyTorch workloads with The Instrumentation and Tracing Technology (ITT) API", "Summary of PyTorch Mobile Recipes", "Quantization Recipe", "Model Interpretability using Captum", "Automatic Mixed Precision", "SyntaxError", "Changing default device", "Defining a Neural Network in PyTorch", "Dynamic Quantization", "PyTorch Recipes", "Tips for Loading an <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Module</span></code> from a Checkpoint", "PyTorch Profiler", "Reasoning about Shapes in PyTorch", "Saving and loading models across devices in PyTorch", "Saving and loading a general checkpoint in PyTorch", "Saving and loading models for inference in PyTorch", "Saving and loading multiple models in one file using PyTorch", "Computation times", "Extension points in <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Module</span></code> for <code class=\"docutils literal notranslate\"><span class=\"pre\">load_state_dict</span></code> and tensor subclasses", "How to use TensorBoard with PyTorch", "Timer quick start", "Performance Tuning Guide", "Warmstarting model using parameters from a different model in PyTorch", "What is a state_dict in PyTorch", "Zeroing out gradients in PyTorch", "PyTorch Recipes", "Script and Optimize for Mobile Recipe", "Computation times", "Intel\u00ae Extension for PyTorch* Backend on Intel\u00ae CPUs", "Compile Time Caching in <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.compile</span></code>", "Using User-Defined Triton Kernels with <code 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269], "final": [1, 7, 11, 12, 13, 14, 16, 17, 19, 20, 23, 24, 25, 26, 27, 28, 46, 47, 48, 52, 55, 58, 64, 66, 81, 83, 94, 97, 99, 102, 103, 106, 107, 108, 113, 116, 117, 119, 120, 122, 125, 127, 131, 133, 134, 136, 137, 138, 139, 141, 142, 143, 144, 145, 149, 150, 154, 158, 159, 160, 161, 165, 167, 169, 171, 172, 174, 175, 176, 177, 178, 180, 184, 185, 191, 192, 193, 195, 200, 201, 203, 204, 205, 208, 213, 214, 215, 216, 217, 220, 221, 225, 230, 233, 238, 257], "function": [1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 15, 16, 18, 19, 20, 22, 24, 25, 26, 27, 28, 29, 38, 42, 43, 45, 46, 47, 48, 54, 55, 57, 59, 61, 65, 66, 67, 68, 71, 73, 74, 75, 83, 84, 87, 89, 91, 92, 99, 100, 102, 105, 106, 112, 115, 119, 120, 122, 125, 127, 130, 131, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 149, 150, 151, 153, 157, 158, 159, 161, 164, 167, 168, 170, 175, 176, 177, 178, 179, 180, 181, 182, 184, 185, 187, 188, 189, 190, 192, 193, 194, 195, 197, 201, 202, 203, 205, 206, 207, 208, 210, 211, 216, 217, 219, 222, 223, 225, 226, 227, 228, 230, 233, 236, 237, 238, 241, 244, 248, 250, 251, 252, 257, 259, 262, 264, 266, 270, 274, 275, 276, 279], "trainabl": [1, 7, 74, 108, 110, 172], "paramet": [1, 5, 6, 9, 12, 13, 14, 16, 17, 19, 20, 23, 24, 26, 28, 29, 36, 38, 39, 40, 43, 45, 46, 48, 49, 52, 54, 55, 57, 58, 71, 73, 74, 81, 94, 97, 99, 102, 107, 108, 110, 112, 115, 116, 122, 123, 124, 130, 131, 133, 135, 136, 137, 138, 139, 141, 142, 146, 148, 149, 150, 151, 152, 153, 154, 156, 159, 160, 161, 164, 165, 167, 168, 169, 172, 178, 179, 182, 184, 185, 187, 189, 190, 192, 193, 198, 200, 205, 212, 213, 217, 220, 227, 229, 231, 233, 236, 237, 238, 244, 246, 248, 252, 253, 254, 255, 256, 257, 258, 259, 261, 262, 267, 268, 269, 270, 272, 274, 278, 279, 287], "tutori": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 39, 42, 48, 49, 50, 51, 57, 59, 61, 62, 64, 65, 66, 67, 81, 82, 85, 87, 90, 91, 93, 97, 100, 101, 103, 105, 107, 109, 111, 112, 116, 117, 118, 119, 120, 124, 125, 126, 127, 129, 130, 133, 134, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 164, 165, 166, 167, 169, 170, 173, 176, 177, 178, 179, 180, 181, 183, 184, 185, 186, 187, 188, 191, 193, 196, 197, 198, 200, 201, 203, 204, 205, 206, 207, 208, 209, 210, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234, 236, 240, 241, 246, 247, 248, 252, 253, 254, 255, 262, 263, 264, 265, 269, 270, 275, 276, 277, 278, 279], "guid": [1, 20, 64, 65, 67, 91, 94, 136, 138, 150, 162, 173, 184, 189, 190, 192, 200, 212, 217, 230, 244, 247, 249, 253, 261, 269, 278, 282], "ground": [1, 16, 49, 81, 194], "aim": [1, 7, 83, 108, 111, 167, 175, 178, 208, 238], "focus": [1, 3, 24, 100, 108, 111, 164, 170, 180, 238], "rel": [1, 6, 7, 9, 100, 107, 131, 134, 140, 152, 160, 164, 165, 178, 180, 192, 202, 213, 238, 252, 257, 269], "straightforward": [1, 6, 7, 19, 20, 55, 66, 99, 102, 106, 108, 109, 159, 216, 252, 257, 269], "determinist": [1, 13, 16, 151, 175, 264, 265], "gradient": [1, 4, 7, 9, 12, 13, 15, 16, 18, 19, 29, 41, 47, 48, 49, 52, 55, 58, 62, 67, 69, 70, 71, 74, 78, 79, 86, 99, 102, 103, 106, 107, 108, 109, 110, 112, 115, 116, 123, 124, 129, 131, 136, 137, 138, 139, 140, 142, 145, 146, 148, 150, 156, 160, 161, 164, 167, 171, 172, 174, 176, 177, 178, 181, 185, 187, 205, 222, 231, 233, 247, 253, 261, 269, 279], "simpl": [1, 3, 4, 5, 6, 7, 10, 14, 17, 19, 20, 23, 25, 26, 27, 28, 29, 52, 55, 57, 60, 62, 73, 81, 87, 94, 97, 100, 102, 103, 106, 107, 108, 116, 119, 124, 125, 130, 135, 136, 138, 140, 141, 145, 150, 153, 159, 160, 165, 169, 171, 174, 176, 177, 178, 179, 181, 182, 184, 188, 198, 201, 215, 217, 224, 227, 228, 230, 231, 232, 237, 238, 246, 249, 252, 254, 263, 264, 265, 267, 269, 270, 274, 275, 279, 283, 284], "continu": [1, 6, 24, 25, 55, 59, 66, 94, 97, 102, 106, 108, 113, 116, 127, 130, 136, 139, 143, 146, 150, 158, 172, 174, 178, 180, 184, 192, 203, 204, 205, 207, 208, 214, 215, 216, 217, 221, 240, 249, 251, 252, 255, 259, 265, 266, 267, 268, 270, 283], "control": [1, 5, 10, 12, 16, 25, 27, 29, 36, 38, 41, 48, 66, 72, 76, 92, 94, 99, 108, 123, 127, 128, 137, 140, 141, 149, 150, 156, 168, 174, 175, 176, 188, 199, 213, 225, 244, 249, 264, 270], "It": [1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 46, 47, 48, 49, 50, 52, 55, 57, 58, 66, 81, 82, 83, 84, 91, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 111, 112, 115, 116, 125, 127, 129, 131, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, 153, 155, 156, 158, 159, 160, 161, 164, 165, 167, 168, 169, 171, 172, 175, 176, 178, 180, 182, 183, 184, 186, 187, 189, 190, 193, 194, 195, 209, 216, 217, 219, 221, 222, 225, 229, 230, 231, 232, 233, 240, 241, 242, 243, 246, 249, 250, 257, 258, 263, 265, 268, 272, 274, 276, 278, 281, 283], "consist": [1, 3, 7, 9, 16, 17, 26, 28, 29, 36, 37, 38, 41, 48, 83, 108, 110, 116, 133, 139, 146, 157, 158, 161, 165, 167, 174, 179, 180, 184, 189, 190, 193, 195, 207, 208, 215, 216, 225, 228, 249, 255, 265, 283], "parametr": [1, 2, 20, 136, 174, 181, 217], "action": [1, 23, 64, 65, 103, 107, 112, 116, 127, 137, 138, 161, 171, 174, 175, 176, 177, 178, 184, 198, 206, 213, 214, 221, 225, 255, 264, 269, 283], "pair": [1, 7, 52, 55, 58, 100, 130, 133, 143, 144, 152, 165, 169, 174, 175, 180, 184, 194, 195, 210, 215, 228, 244, 264, 283], "maxim": [1, 16, 58, 81, 91, 108, 110, 141, 161, 175, 188, 192, 210, 265, 278], "given": [1, 4, 7, 10, 12, 14, 16, 18, 20, 24, 25, 26, 27, 29, 36, 38, 41, 45, 46, 48, 54, 55, 58, 66, 81, 84, 91, 94, 100, 103, 107, 108, 111, 112, 115, 116, 125, 130, 137, 142, 143, 148, 150, 153, 156, 160, 161, 162, 169, 171, 174, 175, 177, 178, 180, 188, 189, 190, 193, 194, 208, 211, 216, 217, 233, 236, 249, 256, 257, 265, 279], "certain": [1, 5, 6, 7, 12, 13, 17, 55, 61, 66, 100, 112, 127, 135, 137, 139, 140, 144, 156, 160, 162, 174, 179, 204, 205, 208, 209, 210, 214, 247, 251, 274, 278], "what": [1, 2, 3, 4, 6, 10, 16, 18, 22, 23, 24, 25, 26, 27, 29, 36, 37, 38, 48, 50, 51, 59, 60, 61, 64, 65, 67, 81, 86, 96, 97, 100, 102, 103, 106, 107, 109, 110, 112, 113, 115, 126, 128, 136, 139, 140, 141, 143, 146, 147, 150, 151, 161, 165, 167, 171, 174, 175, 176, 179, 185, 187, 189, 194, 203, 207, 211, 213, 216, 227, 240, 248, 249, 250, 251, 253, 254, 261, 264, 269, 270, 283], "write": [1, 4, 5, 8, 10, 12, 18, 21, 25, 26, 27, 49, 55, 64, 65, 66, 67, 70, 83, 105, 109, 110, 111, 112, 116, 124, 126, 130, 131, 136, 140, 141, 145, 146, 148, 151, 152, 154, 156, 157, 159, 162, 164, 165, 168, 170, 174, 177, 178, 180, 184, 187, 188, 201, 204, 208, 212, 213, 214, 218, 222, 223, 225, 241, 242, 243, 248, 249, 250, 251, 256, 265, 277, 283], "custom": [1, 5, 7, 10, 13, 20, 21, 55, 58, 70, 71, 72, 76, 87, 100, 116, 122, 126, 136, 141, 151, 161, 174, 177, 181, 187, 188, 193, 195, 199, 204, 213, 215, 216, 217, 219, 221, 237, 238, 244, 248, 253, 262, 265, 269, 272], "its": [1, 2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 22, 24, 25, 26, 27, 29, 36, 37, 41, 46, 48, 52, 55, 57, 58, 59, 61, 66, 74, 83, 89, 91, 99, 100, 102, 103, 106, 108, 110, 112, 113, 115, 116, 117, 119, 122, 124, 125, 127, 129, 137, 138, 139, 140, 141, 142, 143, 150, 151, 152, 153, 154, 156, 158, 159, 161, 162, 164, 167, 168, 171, 174, 175, 176, 177, 178, 180, 184, 185, 188, 189, 190, 191, 192, 200, 204, 207, 208, 209, 213, 216, 217, 219, 221, 223, 224, 225, 226, 231, 232, 235, 237, 240, 244, 246, 248, 249, 251, 254, 255, 257, 258, 259, 260, 262, 265, 266, 267, 268, 279, 283], "includ": [1, 2, 3, 5, 6, 7, 10, 16, 17, 18, 26, 27, 28, 29, 41, 42, 45, 47, 54, 55, 59, 61, 64, 65, 66, 67, 75, 81, 83, 91, 94, 97, 99, 100, 102, 103, 106, 107, 108, 109, 116, 117, 119, 122, 127, 129, 139, 141, 142, 143, 145, 146, 148, 150, 151, 159, 161, 162, 170, 171, 174, 177, 180, 184, 185, 188, 189, 190, 191, 192, 193, 194, 198, 200, 204, 205, 207, 210, 215, 216, 221, 223, 225, 229, 231, 233, 236, 237, 238, 240, 242, 243, 244, 246, 247, 248, 249, 251, 252, 255, 262, 265, 274, 276, 277, 278, 281, 283], "design": [1, 6, 7, 16, 20, 28, 29, 58, 62, 81, 94, 100, 115, 116, 143, 148, 151, 174, 175, 176, 178, 179, 184, 185, 193, 195, 205, 206, 207, 211, 213, 216, 217, 222, 224, 236, 249, 251, 252, 265, 272, 274, 277, 278], "effici": [1, 6, 9, 12, 14, 17, 20, 24, 27, 29, 36, 47, 55, 57, 81, 91, 106, 108, 112, 115, 129, 134, 137, 138, 139, 141, 142, 150, 151, 160, 165, 171, 174, 178, 179, 184, 187, 191, 193, 202, 203, 208, 209, 210, 217, 218, 221, 249, 254, 272, 274, 278], "store": [1, 5, 6, 7, 19, 20, 23, 26, 27, 36, 37, 38, 42, 43, 45, 48, 54, 57, 59, 66, 74, 97, 99, 105, 106, 109, 110, 112, 115, 116, 117, 124, 140, 141, 143, 150, 151, 159, 161, 162, 168, 170, 171, 174, 175, 176, 177, 178, 180, 198, 200, 208, 213, 214, 217, 221, 233, 252, 255, 264, 265, 270, 273, 277], "trajectori": [1, 16, 151, 174], "transit": [1, 16, 66, 94, 96, 109, 151, 175, 216], "assum": [1, 2, 5, 7, 10, 12, 16, 17, 23, 25, 26, 40, 48, 49, 57, 60, 66, 81, 98, 99, 102, 108, 109, 111, 113, 116, 130, 139, 140, 142, 143, 150, 151, 154, 168, 171, 174, 177, 179, 180, 189, 190, 191, 194, 207, 208, 209, 215, 216, 241, 254, 255, 262, 264], "complet": [1, 5, 6, 7, 17, 25, 29, 40, 84, 86, 94, 97, 99, 102, 109, 110, 112, 127, 134, 136, 137, 139, 141, 145, 150, 171, 172, 173, 174, 177, 180, 187, 188, 193, 194, 200, 207, 208, 243, 246, 247, 252, 265, 270, 276], "ppo": [1, 136, 181], "compon": [1, 6, 7, 10, 12, 16, 24, 29, 58, 94, 103, 108, 112, 125, 127, 129, 134, 136, 141, 151, 157, 161, 174, 178, 182, 184, 188, 189, 190, 193, 209, 224, 276], "depend": [1, 6, 7, 9, 10, 13, 25, 26, 27, 47, 52, 56, 58, 66, 81, 91, 94, 99, 100, 105, 106, 108, 109, 113, 116, 123, 133, 134, 136, 139, 141, 144, 145, 150, 151, 152, 156, 157, 158, 160, 161, 164, 170, 173, 174, 177, 184, 188, 189, 190, 197, 198, 199, 200, 204, 207, 212, 213, 214, 221, 223, 224, 225, 227, 230, 236, 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23, 27, 29, 47, 49, 55, 58, 59, 61, 63, 66, 67, 91, 94, 103, 110, 115, 117, 122, 125, 136, 137, 138, 139, 141, 144, 150, 154, 164, 181, 184, 185, 187, 192, 193, 202, 208, 211, 212, 213, 215, 229, 233, 252, 264, 265, 270, 274, 276, 278, 279, 281], "level": [1, 2, 4, 6, 7, 18, 20, 23, 24, 27, 29, 49, 55, 59, 61, 63, 67, 74, 87, 103, 111, 117, 124, 129, 136, 137, 138, 139, 146, 148, 150, 152, 156, 157, 159, 162, 164, 179, 180, 181, 184, 187, 189, 190, 192, 193, 198, 201, 211, 212, 213, 215, 217, 226, 229, 232, 233, 238, 241, 245, 252, 279, 287], "illustr": [1, 23, 37, 49, 52, 62, 130, 131, 139, 140, 141, 153, 175, 185, 187, 194, 207, 208, 211, 232, 244, 247, 255, 262, 265], "librari": [1, 3, 4, 5, 6, 7, 8, 10, 14, 16, 18, 22, 24, 26, 27, 29, 38, 42, 47, 49, 56, 57, 63, 67, 83, 97, 100, 116, 119, 120, 126, 127, 133, 136, 141, 144, 145, 152, 154, 158, 170, 173, 174, 178, 184, 189, 190, 193, 210, 221, 223, 224, 232, 236, 237, 240, 241, 244, 245, 246, 269], "featur": [1, 5, 7, 12, 13, 14, 16, 23, 26, 27, 38, 42, 46, 55, 56, 57, 58, 64, 65, 66, 91, 92, 94, 99, 102, 103, 106, 108, 109, 115, 116, 120, 124, 127, 136, 138, 140, 151, 152, 159, 160, 161, 164, 167, 170, 173, 174, 178, 179, 185, 188, 189, 190, 191, 192, 193, 194, 201, 202, 203, 204, 208, 209, 212, 215, 217, 221, 222, 223, 224, 225, 229, 233, 236, 244, 247, 251, 252, 254, 262, 264, 265, 268, 269, 270, 273, 274], "context": [1, 2, 6, 10, 16, 19, 20, 48, 55, 66, 81, 99, 103, 115, 116, 122, 135, 139, 149, 156, 168, 174, 177, 178, 179, 180, 184, 193, 202, 215, 217, 223, 225, 229, 248, 250, 254, 255, 256, 265, 278], "bash": [1, 22, 24, 161, 175, 244], "pip3": [1, 22, 56, 137, 151, 174, 175, 184, 191, 200, 203, 204], "mujoco": [1, 151, 174], "glfw": 1, "avail": [1, 2, 3, 4, 6, 7, 12, 14, 17, 20, 22, 23, 24, 25, 26, 27, 36, 37, 38, 41, 42, 43, 45, 47, 48, 49, 54, 56, 57, 58, 59, 64, 65, 81, 89, 97, 100, 102, 103, 106, 107, 108, 112, 116, 117, 127, 129, 134, 137, 140, 150, 151, 154, 156, 161, 162, 171, 172, 173, 174, 175, 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7, 16, 19, 61, 97, 106, 112, 124, 150, 174, 176, 177, 178, 188, 248, 251, 265], "necessari": [1, 5, 6, 7, 10, 12, 14, 17, 22, 23, 27, 28, 49, 58, 59, 61, 66, 94, 97, 106, 107, 109, 116, 125, 127, 137, 138, 139, 144, 148, 161, 164, 176, 177, 178, 184, 189, 190, 193, 195, 198, 201, 207, 209, 211, 213, 214, 215, 248, 265], "return": [1, 2, 4, 5, 6, 7, 10, 12, 13, 14, 16, 17, 18, 19, 22, 23, 25, 27, 29, 37, 38, 42, 45, 50, 52, 55, 57, 58, 65, 66, 71, 81, 89, 91, 99, 100, 103, 106, 107, 108, 110, 112, 113, 115, 116, 120, 122, 123, 124, 125, 127, 130, 133, 137, 138, 139, 142, 143, 145, 148, 149, 150, 151, 152, 153, 154, 156, 159, 161, 162, 165, 168, 170, 172, 173, 174, 175, 176, 177, 178, 179, 184, 185, 187, 188, 189, 190, 192, 193, 194, 195, 198, 199, 201, 202, 203, 205, 207, 210, 211, 213, 214, 215, 216, 217, 222, 223, 225, 226, 227, 229, 230, 231, 232, 235, 236, 237, 238, 240, 241, 244, 247, 249, 251, 252, 255, 257, 264, 270, 276, 277], "replay_buff": 1, "sampl": [1, 4, 7, 10, 37, 38, 41, 42, 45, 49, 52, 54, 55, 57, 58, 61, 66, 67, 83, 86, 87, 97, 102, 103, 105, 106, 108, 123, 129, 130, 131, 133, 136, 138, 142, 144, 150, 152, 154, 160, 161, 167, 168, 172, 174, 175, 176, 178, 181, 187, 189, 190, 194, 198, 201, 209, 210, 213, 215, 222, 226, 227, 243, 247, 255, 264, 265, 283], "loss_dict": 1, "instanc": [1, 5, 6, 7, 9, 13, 14, 16, 25, 26, 27, 29, 37, 43, 50, 59, 60, 61, 64, 65, 66, 67, 86, 91, 97, 102, 103, 106, 107, 108, 109, 110, 113, 115, 116, 124, 137, 138, 140, 141, 146, 147, 148, 149, 151, 159, 161, 172, 174, 176, 177, 178, 188, 198, 199, 213, 215, 216, 219, 236, 237, 244, 248, 252, 255, 257, 263, 264, 265, 270, 276, 277, 278, 279], "written": [1, 4, 5, 6, 7, 10, 12, 16, 18, 26, 27, 29, 94, 105, 111, 116, 145, 151, 158, 165, 168, 169, 172, 179, 184, 187, 200, 205, 230, 237, 263, 274], "under": [1, 5, 6, 10, 22, 23, 27, 36, 52, 55, 56, 58, 60, 62, 67, 105, 108, 110, 122, 124, 127, 139, 140, 150, 152, 154, 160, 161, 168, 171, 178, 184, 185, 193, 194, 195, 203, 204, 206, 208, 221, 229, 231, 233, 238, 240, 243, 244, 248, 273, 283], "loss_": 1, "smth": 1, "where": [1, 3, 5, 7, 9, 10, 13, 14, 15, 16, 19, 20, 22, 24, 25, 26, 27, 28, 36, 38, 52, 55, 57, 66, 67, 70, 74, 83, 86, 87, 92, 94, 97, 99, 100, 102, 103, 105, 106, 108, 109, 110, 112, 113, 115, 123, 124, 127, 136, 137, 139, 141, 142, 143, 145, 147, 148, 149, 150, 152, 153, 154, 159, 162, 164, 165, 167, 168, 169, 173, 174, 175, 176, 177, 178, 179, 180, 185, 188, 190, 191, 193, 194, 195, 198, 200, 203, 205, 208, 210, 211, 216, 217, 221, 225, 230, 232, 233, 244, 246, 248, 249, 251, 257, 258, 262, 263, 264, 268, 269, 278, 284], "string": [1, 4, 10, 17, 26, 27, 55, 64, 65, 66, 71, 106, 117, 124, 129, 130, 133, 141, 142, 143, 151, 154, 171, 174, 180, 187, 198, 225, 226, 244, 249, 264, 277, 281], "describ": [1, 5, 6, 7, 10, 12, 16, 17, 19, 23, 24, 25, 26, 27, 45, 54, 55, 58, 64, 65, 67, 81, 102, 108, 117, 128, 135, 150, 165, 174, 175, 178, 184, 187, 189, 190, 192, 212, 213, 214, 219, 232, 249, 252, 270, 278], "addit": [1, 2, 4, 6, 9, 10, 13, 17, 20, 23, 56, 66, 81, 83, 100, 106, 108, 113, 116, 117, 120, 122, 127, 137, 139, 140, 148, 150, 152, 153, 154, 157, 159, 162, 164, 171, 176, 177, 180, 185, 188, 189, 190, 192, 201, 205, 206, 207, 208, 213, 216, 217, 223, 225, 233, 235, 236, 237, 249, 255, 258, 265, 274, 278], "kei": [1, 7, 10, 13, 16, 17, 55, 64, 91, 111, 115, 117, 122, 125, 128, 129, 130, 134, 137, 141, 151, 152, 154, 161, 173, 174, 176, 179, 180, 184, 185, 187, 189, 190, 191, 193, 201, 209, 210, 211, 217, 226, 227, 228, 237, 252, 254, 263, 266, 274, 283], "mai": [1, 4, 5, 6, 7, 8, 10, 12, 13, 14, 16, 17, 18, 20, 23, 25, 26, 27, 29, 41, 47, 55, 56, 58, 64, 65, 66, 74, 81, 94, 99, 100, 102, 103, 106, 110, 124, 125, 127, 130, 138, 139, 140, 141, 144, 145, 151, 152, 153, 154, 156, 159, 160, 165, 167, 168, 173, 174, 177, 180, 184, 187, 188, 189, 190, 192, 193, 195, 197, 198, 201, 204, 207, 209, 213, 214, 215, 216, 217, 219, 224, 225, 227, 235, 246, 249, 252, 255, 257, 258, 260, 265, 268, 270, 283, 284], "metric": [1, 20, 97, 105, 106, 108, 122, 137, 152, 184, 187, 193, 194, 217, 238, 244, 249, 263], "log": [1, 22, 41, 55, 56, 58, 59, 64, 105, 109, 110, 113, 115, 116, 138, 141, 147, 152, 173, 174, 176, 178, 184, 185, 187, 189, 193, 201, 211, 225, 228, 238, 269, 275, 278], "dure": [1, 3, 9, 10, 14, 16, 22, 23, 29, 36, 37, 41, 42, 55, 58, 66, 69, 70, 84, 94, 99, 102, 103, 106, 107, 108, 110, 115, 116, 120, 124, 125, 127, 133, 136, 137, 138, 139, 140, 143, 144, 145, 146, 148, 151, 157, 158, 159, 164, 165, 168, 172, 173, 174, 176, 178, 184, 188, 192, 193, 194, 201, 212, 214, 219, 223, 231, 233, 237, 241, 242, 243, 244, 246, 252, 255, 259, 262, 263, 270], "reason": [1, 6, 7, 10, 16, 17, 18, 20, 27, 29, 36, 58, 86, 91, 99, 102, 108, 110, 113, 116, 125, 131, 140, 144, 150, 159, 164, 172, 174, 179, 180, 200, 207, 217, 231, 241, 249, 253, 254, 259, 261, 269, 270], "independ": [1, 9, 27, 55, 66, 87, 115, 116, 120, 123, 160, 161, 165, 177, 205], "user": [1, 3, 6, 16, 20, 22, 23, 26, 28, 29, 46, 48, 49, 55, 56, 66, 84, 87, 91, 92, 94, 100, 108, 112, 120, 123, 127, 128, 129, 136, 137, 139, 143, 148, 152, 154, 157, 159, 162, 176, 178, 179, 180, 184, 187, 189, 190, 191, 192, 193, 194, 195, 198, 201, 203, 205, 206, 207, 208, 211, 212, 213, 214, 215, 216, 217, 221, 224, 229, 232, 233, 237, 238, 244, 246, 255, 269, 271, 273, 278, 283, 284], "sum": [1, 2, 4, 5, 6, 8, 9, 13, 16, 18, 22, 23, 25, 37, 48, 55, 58, 66, 71, 84, 91, 99, 100, 109, 110, 112, 115, 124, 137, 138, 142, 143, 145, 150, 160, 165, 172, 175, 176, 177, 178, 184, 188, 189, 190, 191, 198, 206, 213, 214, 227, 229, 231, 238, 249, 268, 270, 279], "done": [1, 4, 5, 6, 7, 10, 12, 16, 19, 20, 23, 24, 25, 26, 27, 29, 55, 60, 64, 65, 91, 94, 103, 106, 108, 109, 110, 116, 120, 127, 129, 137, 138, 139, 140, 143, 144, 150, 151, 153, 158, 159, 161, 162, 170, 171, 172, 173, 174, 176, 177, 178, 179, 180, 185, 187, 189, 190, 200, 201, 210, 217, 219, 225, 233, 237, 241, 252, 254, 265, 276, 277], "via": [1, 3, 4, 6, 7, 8, 9, 19, 20, 22, 24, 26, 27, 43, 60, 61, 64, 65, 81, 94, 108, 136, 137, 138, 139, 141, 150, 151, 160, 168, 173, 174, 179, 181, 185, 187, 188, 192, 193, 194, 204, 217, 229, 230, 232, 233, 236, 237, 238, 244, 254, 262, 263, 265, 269, 273, 281, 287], "loss_val": 1, "item": [1, 2, 7, 12, 13, 17, 45, 55, 71, 74, 115, 124, 125, 128, 137, 138, 142, 143, 150, 151, 152, 154, 172, 173, 174, 176, 177, 178, 185, 187, 195, 226, 230, 235, 238, 248, 258, 260, 265, 282, 284], "startswith": [1, 92, 264], "parent": [1, 16, 129, 199, 201], "As": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 16, 19, 23, 24, 25, 26, 27, 29, 48, 55, 56, 58, 64, 65, 66, 81, 94, 97, 100, 102, 103, 106, 107, 108, 115, 116, 117, 120, 124, 125, 130, 133, 137, 138, 139, 140, 141, 142, 148, 150, 151, 152, 156, 157, 158, 159, 160, 161, 164, 167, 168, 171, 172, 174, 175, 176, 177, 178, 179, 184, 187, 190, 191, 192, 193, 194, 195, 198, 200, 201, 203, 204, 208, 209, 211, 213, 216, 221, 224, 225, 229, 236, 238, 240, 244, 249, 252, 254, 265, 273, 274, 276, 278, 279], "mani": [1, 2, 5, 6, 7, 12, 16, 17, 18, 20, 22, 27, 29, 36, 37, 41, 42, 55, 57, 58, 66, 71, 75, 81, 91, 99, 100, 102, 103, 106, 108, 110, 111, 112, 116, 117, 119, 124, 127, 137, 139, 141, 142, 144, 150, 152, 153, 160, 162, 164, 165, 169, 172, 174, 176, 177, 180, 189, 190, 192, 193, 207, 210, 217, 221, 222, 237, 238, 247, 248, 249, 251, 265, 270, 278, 281, 283, 284], "expect": [1, 4, 5, 6, 7, 12, 13, 16, 24, 26, 27, 50, 52, 55, 57, 64, 65, 66, 81, 94, 97, 99, 100, 102, 103, 106, 107, 108, 112, 113, 115, 116, 125, 127, 131, 134, 141, 144, 148, 149, 151, 160, 161, 167, 168, 171, 173, 174, 175, 176, 177, 179, 187, 189, 190, 192, 194, 195, 198, 203, 204, 210, 211, 213, 215, 216, 221, 230, 237, 241, 244, 247, 248, 249, 252, 255, 258, 260, 262, 265, 279], "similar": [1, 3, 6, 10, 12, 13, 16, 17, 23, 26, 27, 45, 54, 55, 64, 65, 91, 92, 100, 102, 103, 106, 108, 109, 115, 116, 120, 124, 130, 139, 145, 149, 150, 151, 154, 164, 168, 174, 176, 177, 178, 179, 180, 184, 185, 187, 192, 194, 195, 198, 201, 205, 206, 207, 208, 209, 214, 215, 230, 235, 236, 248, 249, 252, 264, 265, 279], "structur": [1, 5, 6, 7, 10, 11, 16, 21, 22, 23, 24, 25, 26, 27, 37, 42, 43, 45, 54, 55, 58, 59, 66, 94, 99, 102, 103, 107, 108, 109, 113, 117, 123, 125, 136, 146, 153, 158, 161, 162, 164, 168, 169, 171, 174, 178, 185, 187, 188, 194, 208, 210, 212, 213, 222, 225, 252, 259, 263, 281, 283, 287], "make": [1, 4, 5, 6, 7, 10, 12, 14, 16, 22, 23, 26, 27, 37, 41, 42, 46, 48, 49, 50, 55, 56, 57, 59, 60, 61, 63, 64, 65, 66, 74, 81, 97, 99, 102, 103, 105, 106, 108, 110, 111, 112, 114, 115, 116, 120, 125, 127, 128, 129, 131, 133, 136, 137, 138, 139, 141, 142, 143, 144, 148, 150, 151, 152, 154, 157, 158, 159, 160, 161, 164, 167, 168, 170, 171, 172, 174, 175, 176, 177, 178, 180, 185, 187, 188, 189, 190, 192, 194, 198, 200, 201, 204, 205, 206, 207, 208, 209, 210, 211, 213, 216, 222, 229, 230, 231, 232, 233, 235, 236, 237, 241, 245, 246, 247, 248, 249, 252, 254, 256, 257, 263, 264, 265, 269, 270, 273, 274, 276, 283, 285], "possibl": [1, 2, 4, 5, 6, 7, 8, 10, 12, 16, 17, 20, 26, 27, 58, 66, 83, 99, 100, 103, 106, 109, 112, 120, 134, 140, 144, 145, 151, 153, 156, 158, 160, 161, 164, 172, 173, 174, 176, 177, 180, 194, 198, 201, 203, 209, 213, 214, 215, 216, 217, 219, 221, 224, 233, 237, 238, 241, 248, 252, 254, 265, 270, 274, 278, 283], "across": [1, 6, 9, 10, 13, 16, 19, 22, 24, 28, 45, 55, 58, 60, 61, 62, 91, 100, 106, 108, 117, 129, 135, 137, 138, 139, 146, 147, 148, 149, 150, 153, 161, 164, 171, 177, 178, 191, 192, 228, 231, 232, 247, 253, 261, 263, 265, 266, 268, 269, 273, 278, 279, 281], "modal": [1, 66, 247], "complex": [1, 7, 27, 29, 37, 38, 56, 73, 74, 99, 100, 103, 108, 117, 124, 125, 135, 138, 148, 165, 168, 176, 178, 185, 209, 220, 226, 252, 256, 266, 274], "multipl": [1, 4, 6, 10, 12, 13, 16, 18, 19, 20, 22, 23, 24, 27, 45, 50, 54, 55, 59, 60, 61, 62, 67, 71, 86, 87, 90, 91, 97, 99, 100, 103, 106, 107, 108, 112, 116, 123, 124, 135, 138, 139, 140, 141, 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246, 247, 262, 263, 265, 266, 272, 277, 278, 283], "being": [1, 3, 5, 6, 7, 12, 14, 16, 20, 24, 25, 27, 47, 52, 55, 58, 64, 65, 66, 84, 91, 100, 107, 108, 109, 110, 112, 115, 116, 117, 123, 127, 131, 137, 139, 141, 144, 150, 151, 168, 171, 174, 175, 177, 193, 201, 204, 206, 207, 209, 211, 215, 217, 219, 237, 249, 254, 265, 278], "run": [1, 2, 3, 5, 6, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 36, 37, 38, 41, 42, 43, 45, 46, 47, 48, 49, 52, 54, 57, 58, 62, 63, 67, 69, 70, 71, 73, 74, 75, 78, 79, 83, 84, 86, 87, 89, 91, 97, 99, 100, 102, 103, 105, 106, 107, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 124, 125, 127, 128, 130, 131, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 149, 150, 151, 152, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 182, 185, 188, 189, 191, 192, 193, 194, 197, 198, 199, 200, 202, 203, 204, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 220, 221, 222, 223, 224, 225, 226, 231, 232, 233, 235, 236, 237, 240, 241, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 254, 256, 257, 258, 259, 260, 262, 264, 265, 266, 267, 268, 269, 270, 271, 273, 274, 275, 278, 283], "elementari": [1, 2], "those": [1, 4, 5, 6, 7, 8, 12, 13, 16, 20, 36, 47, 48, 87, 97, 99, 106, 109, 115, 116, 127, 129, 130, 139, 140, 142, 150, 153, 158, 167, 170, 171, 178, 180, 185, 187, 189, 190, 193, 198, 200, 204, 206, 217, 219, 221, 222, 223, 224, 238, 241, 244, 248, 249, 264, 273, 278, 283], "keep": [1, 7, 12, 13, 16, 27, 36, 45, 48, 55, 58, 81, 91, 94, 102, 103, 106, 108, 110, 112, 113, 116, 120, 124, 125, 130, 134, 136, 137, 138, 139, 140, 142, 143, 147, 148, 151, 159, 165, 172, 178, 180, 193, 197, 198, 213, 225, 235, 249, 265, 277, 279], "didact": [1, 150], "displai": [1, 2, 6, 7, 14, 38, 49, 64, 83, 100, 105, 107, 120, 122, 131, 144, 154, 172, 180, 184, 229, 248, 249, 263, 277, 281], "each": [1, 2, 4, 6, 7, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 27, 28, 29, 36, 37, 38, 40, 41, 42, 45, 48, 49, 50, 54, 55, 57, 58, 59, 61, 62, 64, 65, 66, 67, 71, 81, 83, 84, 87, 91, 92, 94, 97, 98, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 113, 115, 116, 119, 120, 122, 124, 125, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 145, 146, 147, 148, 149, 150, 151, 153, 156, 158, 160, 161, 162, 164, 165, 167, 168, 169, 171, 172, 174, 175, 176, 177, 178, 179, 180, 182, 184, 185, 187, 189, 190, 191, 192, 193, 194, 201, 202, 205, 208, 209, 211, 213, 214, 216, 217, 225, 226, 228, 229, 232, 233, 237, 238, 244, 247, 248, 249, 251, 252, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 270, 275, 277, 278, 279, 281, 283], "popul": [1, 26, 36, 48, 55, 64, 65, 97, 106, 137, 151, 161, 174, 176, 228, 233, 264], "later": [1, 3, 4, 5, 6, 7, 13, 18, 27, 36, 45, 52, 55, 66, 81, 97, 100, 103, 108, 112, 113, 116, 125, 127, 138, 139, 142, 143, 145, 149, 150, 153, 156, 157, 158, 159, 160, 161, 165, 169, 174, 175, 178, 179, 180, 185, 187, 189, 190, 198, 205, 213, 214, 227, 228, 241, 244, 246, 248, 249, 250, 254, 259, 262, 265, 273, 274, 275], "stage": [1, 9, 16, 19, 103, 116, 202, 204, 223, 229], "start": [1, 5, 6, 7, 11, 13, 19, 20, 22, 23, 27, 28, 29, 36, 40, 48, 49, 55, 56, 58, 59, 60, 61, 65, 66, 67, 81, 97, 98, 99, 100, 103, 106, 107, 108, 111, 112, 116, 117, 127, 130, 135, 136, 137, 139, 140, 141, 142, 143, 144, 149, 150, 152, 154, 159, 160, 161, 164, 167, 168, 172, 173, 175, 176, 177, 180, 184, 185, 187, 188, 189, 192, 193, 194, 198, 200, 201, 203, 207, 211, 213, 214, 215, 216, 217, 218, 225, 229, 230, 233, 236, 241, 244, 249, 252, 253, 255, 261, 263, 265, 268, 269, 273, 274, 279, 284], "solv": [1, 7, 16, 55, 57, 108, 115, 131, 133, 164, 168, 172, 174, 176, 178, 192, 207, 249, 254, 265], "task": [1, 7, 9, 15, 16, 20, 25, 28, 41, 42, 55, 64, 65, 66, 83, 107, 108, 109, 115, 122, 127, 130, 131, 133, 134, 135, 136, 138, 151, 152, 168, 172, 174, 175, 180, 182, 187, 194, 201, 217, 221, 225, 249, 255, 264, 265, 273, 278], "strategi": [1, 6, 20, 22, 28, 58, 91, 127, 136, 143, 150, 159, 160, 164, 169, 176, 177, 217, 224, 232, 233, 238], "predict": [1, 11, 23, 24, 37, 40, 41, 42, 43, 48, 49, 55, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 99, 100, 102, 103, 107, 108, 109, 113, 115, 116, 124, 127, 130, 133, 136, 139, 141, 142, 143, 152, 153, 160, 161, 164, 175, 180, 185, 194, 197, 198, 213, 214, 217, 230, 236, 247, 251, 269, 276, 277], "henc": [1, 16, 20, 45, 48, 54, 86, 89, 91, 127, 138, 140, 148, 149, 162, 164, 165, 170, 174, 176, 178, 192, 217, 236, 237, 249], "two": [1, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 22, 23, 24, 25, 27, 28, 29, 36, 38, 41, 42, 45, 46, 47, 48, 54, 55, 56, 58, 63, 64, 65, 66, 81, 83, 92, 94, 97, 99, 100, 102, 103, 106, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 124, 130, 131, 134, 137, 138, 141, 142, 144, 145, 147, 148, 149, 150, 151, 154, 156, 158, 159, 160, 161, 162, 164, 165, 168, 169, 170, 171, 172, 174, 175, 176, 178, 179, 180, 187, 191, 192, 193, 194, 195, 199, 200, 202, 206, 208, 209, 211, 213, 214, 215, 216, 217, 219, 220, 224, 229, 232, 233, 236, 238, 242, 243, 244, 249, 251, 252, 254, 255, 259, 260, 262, 264, 265, 266, 269, 270, 276, 278, 279, 283, 284, 288], "constructor": [1, 7, 12, 13, 14, 25, 26, 27, 29, 66, 71, 73, 75, 86, 94, 103, 107, 124, 130, 137, 138, 148, 149, 170, 171, 174, 176, 178, 208, 219, 248, 249, 270], "both": [1, 2, 5, 6, 7, 9, 10, 12, 13, 14, 16, 19, 23, 24, 25, 26, 27, 28, 29, 38, 47, 55, 57, 58, 64, 65, 67, 81, 91, 94, 100, 106, 107, 108, 115, 116, 122, 124, 127, 130, 133, 137, 139, 141, 142, 144, 147, 148, 149, 150, 156, 157, 159, 160, 162, 164, 165, 171, 172, 174, 176, 177, 178, 179, 180, 189, 190, 191, 192, 193, 194, 195, 198, 200, 201, 202, 205, 208, 210, 211, 213, 215, 216, 226, 229, 232, 236, 237, 238, 241, 244, 246, 247, 248, 249, 259, 262, 264, 273, 276, 278, 281, 283], "compat": [1, 5, 6, 7, 10, 13, 20, 56, 66, 105, 106, 112, 151, 162, 179, 189, 190, 198, 203, 219, 221, 233, 240, 276], "comput": [1, 3, 4, 6, 7, 10, 13, 14, 15, 18, 19, 20, 23, 24, 25, 27, 28, 29, 41, 45, 49, 52, 54, 55, 57, 58, 59, 63, 64, 65, 66, 67, 69, 70, 71, 74, 78, 79, 81, 83, 84, 86, 87, 92, 94, 99, 100, 102, 103, 106, 107, 108, 109, 110, 111, 116, 117, 119, 123, 124, 129, 134, 135, 136, 137, 138, 139, 140, 141, 144, 145, 146, 147, 148, 150, 151, 152, 154, 156, 157, 159, 161, 164, 167, 168, 169, 171, 173, 174, 175, 177, 179, 180, 184, 187, 188, 189, 190, 192, 193, 194, 198, 200, 203, 204, 209, 210, 211, 212, 213, 214, 215, 217, 219, 222, 223, 224, 225, 227, 228, 233, 236, 241, 246, 248, 249, 251, 252, 254, 256, 264, 274, 276, 278, 283], "fit": [1, 7, 12, 13, 24, 67, 97, 115, 124, 137, 138, 139, 148, 164, 178, 248, 278, 283], "crucial": [1, 2, 14, 16, 27, 91, 112, 151, 174, 241], "convert_to_funct": 1, "extract": [1, 6, 24, 55, 58, 64, 65, 81, 105, 108, 130, 131, 136, 142, 143, 152, 156, 159, 169, 172, 174, 180, 188, 189, 190, 194, 225, 229, 230, 233, 251], "convert": [1, 6, 11, 12, 14, 23, 24, 26, 27, 37, 38, 45, 46, 49, 55, 57, 58, 61, 83, 102, 103, 106, 108, 116, 117, 119, 123, 125, 127, 129, 130, 133, 134, 136, 142, 143, 152, 154, 172, 173, 174, 176, 177, 185, 193, 197, 199, 200, 201, 204, 205, 206, 208, 209, 212, 215, 216, 226, 230, 233, 235, 237, 241, 242, 243, 245, 246, 247, 252, 257, 262, 265, 269, 270], "strictli": [1, 106, 174], "speak": [1, 10, 48, 106, 140, 150, 164, 265], "perfectli": [1, 16, 71, 124], "encourag": [1, 7, 23, 103, 154, 175, 180, 187], "usag": [1, 3, 4, 5, 13, 15, 17, 25, 27, 66, 91, 99, 122, 130, 136, 138, 140, 150, 151, 159, 160, 174, 176, 178, 179, 184, 193, 200, 201, 204, 209, 210, 211, 215, 224, 237, 244, 248, 263, 265, 269, 276, 278, 283], "doe": [1, 2, 4, 6, 7, 10, 15, 16, 17, 18, 23, 26, 27, 29, 36, 46, 48, 52, 66, 78, 79, 81, 87, 89, 94, 99, 100, 102, 103, 105, 106, 107, 108, 110, 112, 115, 116, 117, 120, 124, 125, 127, 131, 137, 138, 145, 148, 149, 150, 151, 154, 157, 160, 161, 162, 164, 167, 168, 173, 174, 175, 177, 178, 179, 180, 184, 185, 188, 189, 190, 192, 194, 198, 199, 200, 206, 207, 208, 213, 215, 219, 222, 225, 233, 241, 243, 244, 246, 248, 249, 252, 254, 257, 259, 262, 264, 265, 278, 283], "often": [1, 5, 6, 7, 12, 16, 20, 45, 55, 81, 97, 99, 100, 102, 106, 108, 110, 112, 115, 116, 124, 125, 127, 139, 140, 141, 143, 161, 168, 193, 209, 217, 220, 227, 233, 248, 264, 265, 283], "same": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 17, 20, 23, 24, 25, 26, 27, 29, 36, 37, 43, 45, 48, 49, 55, 57, 58, 59, 61, 64, 65, 66, 71, 79, 81, 86, 89, 94, 97, 99, 106, 107, 108, 110, 112, 113, 115, 116, 117, 120, 122, 124, 125, 133, 134, 137, 138, 139, 140, 142, 144, 147, 148, 150, 152, 153, 154, 156, 159, 160, 161, 162, 164, 167, 168, 169, 170, 171, 173, 174, 175, 176, 177, 178, 179, 180, 182, 184, 185, 187, 188, 189, 190, 192, 193, 195, 197, 198, 201, 202, 205, 206, 207, 208, 209, 210, 213, 214, 215, 216, 217, 225, 230, 231, 235, 240, 241, 246, 248, 249, 252, 255, 259, 262, 265, 268, 270, 273, 276, 278, 279, 281, 283], "usual": [1, 6, 7, 10, 18, 23, 27, 48, 64, 65, 66, 100, 102, 103, 105, 106, 110, 111, 113, 115, 116, 127, 131, 139, 140, 143, 144, 145, 148, 150, 151, 159, 162, 167, 171, 172, 174, 211, 222, 248, 254, 255, 265, 278, 281], "former": [1, 6, 87, 142, 143, 180], "lag": [1, 174], "absolut": [1, 7, 9, 12, 91, 110, 171, 175, 225, 264], "dilut": 1, "averag": [1, 3, 23, 55, 58, 67, 91, 97, 100, 102, 107, 108, 116, 127, 129, 138, 142, 143, 150, 152, 169, 175, 178, 180, 184, 192, 193, 198, 213, 214, 249, 265], "associ": [1, 4, 6, 7, 10, 12, 37, 38, 56, 91, 100, 145, 156, 171, 179, 187, 206, 217, 219, 251, 262, 265], "One": [1, 2, 5, 6, 7, 9, 12, 13, 17, 25, 27, 55, 57, 66, 81, 87, 91, 99, 103, 106, 108, 109, 110, 112, 137, 138, 139, 140, 143, 148, 150, 152, 153, 157, 164, 167, 168, 182, 185, 188, 193, 194, 207, 211, 216, 222, 226, 238, 241, 249, 256, 262, 264, 265, 283, 284], "advantag": [1, 3, 7, 16, 20, 27, 55, 66, 94, 106, 109, 116, 119, 135, 137, 140, 150, 151, 168, 174, 188, 193, 198, 201, 208, 217, 226, 237, 240, 244, 252, 265, 277], "match": [1, 5, 6, 12, 16, 20, 23, 24, 26, 64, 65, 66, 74, 83, 84, 102, 106, 108, 116, 117, 120, 124, 125, 127, 149, 152, 153, 157, 159, 162, 164, 167, 169, 174, 177, 188, 189, 190, 198, 201, 206, 208, 211, 213, 217, 236, 237, 248, 251, 256, 266, 278], "exactli": [1, 6, 9, 10, 12, 14, 20, 29, 36, 48, 57, 58, 66, 86, 89, 102, 112, 115, 116, 117, 124, 151, 159, 168, 190, 201, 217], "configur": [1, 5, 6, 7, 16, 22, 23, 24, 26, 27, 28, 47, 55, 56, 66, 91, 100, 107, 127, 137, 139, 146, 148, 157, 159, 164, 167, 172, 174, 177, 184, 187, 192, 199, 200, 215, 216, 225, 229, 236, 237, 238, 243, 269, 273, 274, 278, 287], "pessimist": [1, 174], "bound": [1, 27, 55, 125, 141, 159, 174, 175, 184, 189, 190, 192, 194, 200, 248, 249, 255, 259, 265, 278], "pai": [1, 12, 50, 55, 66, 129, 265], "attent": [1, 9, 12, 47, 50, 55, 103, 129, 133, 134, 136, 139, 142, 181, 182, 200, 201, 209, 270, 274], "create_target_param": 1, "keyword": [1, 6, 171, 174, 187, 254, 262], "argument": [1, 2, 5, 6, 7, 10, 16, 18, 25, 26, 27, 36, 42, 45, 48, 49, 54, 57, 61, 66, 75, 84, 91, 99, 100, 103, 105, 106, 108, 110, 113, 115, 122, 124, 125, 129, 137, 138, 142, 143, 147, 148, 150, 151, 153, 159, 160, 169, 170, 171, 174, 176, 177, 178, 179, 184, 187, 188, 189, 190, 195, 204, 207, 210, 215, 222, 223, 225, 226, 229, 240, 241, 248, 249, 254, 255, 257, 262, 263, 265, 266, 274, 278, 283, 284], "below": [1, 2, 5, 7, 12, 13, 14, 16, 19, 22, 23, 24, 27, 28, 38, 45, 48, 50, 54, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 87, 91, 94, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 127, 128, 130, 133, 134, 139, 141, 144, 146, 147, 148, 149, 150, 151, 152, 156, 159, 162, 164, 167, 170, 172, 174, 175, 176, 177, 178, 179, 184, 185, 187, 188, 189, 190, 191, 192, 193, 194, 200, 201, 202, 204, 206, 207, 208, 209, 211, 219, 220, 221, 228, 229, 230, 231, 233, 235, 236, 238, 240, 241, 243, 244, 246, 248, 249, 252, 254, 255, 265, 270, 272, 273, 278, 279, 283], "tell": [1, 18, 22, 27, 47, 75, 97, 99, 102, 103, 107, 115, 116, 124, 141, 142, 151, 153, 156, 167, 175, 176, 178, 180, 191, 203, 204, 226, 249, 255, 283], "fals": [1, 2, 4, 7, 12, 13, 16, 23, 24, 27, 47, 48, 61, 65, 69, 70, 91, 92, 99, 112, 123, 124, 125, 129, 130, 131, 137, 138, 139, 144, 149, 152, 156, 159, 162, 165, 167, 168, 172, 173, 176, 177, 185, 187, 189, 190, 192, 193, 195, 198, 201, 202, 206, 207, 210, 213, 214, 216, 217, 223, 225, 227, 235, 236, 237, 238, 241, 246, 248, 262, 264, 265, 266, 270, 272, 278, 279, 281, 282, 283, 284], "target_actor_network_param": 1, "attribut": [1, 7, 13, 16, 26, 29, 36, 48, 52, 59, 66, 84, 87, 91, 94, 103, 115, 116, 120, 130, 140, 149, 151, 156, 162, 168, 171, 189, 190, 192, 198, 201, 209, 210, 212, 215, 220, 224, 248, 268, 269, 283], "access": [1, 6, 7, 9, 12, 16, 20, 23, 27, 37, 38, 56, 66, 74, 81, 86, 87, 97, 99, 102, 103, 106, 107, 108, 113, 116, 124, 125, 133, 134, 136, 137, 140, 146, 150, 157, 168, 173, 175, 177, 187, 189, 190, 193, 201, 203, 205, 206, 208, 210, 217, 225, 226, 232, 235, 258, 260, 264, 268, 270, 273, 281], "detach": [1, 2, 7, 13, 36, 99, 106, 112, 120, 152, 165, 169, 201], "q": [1, 9, 48, 55, 66, 136, 139, 161, 179, 187, 213, 217], "base": [1, 6, 7, 9, 11, 12, 13, 16, 19, 20, 22, 24, 27, 28, 29, 38, 47, 49, 55, 63, 64, 65, 66, 81, 83, 84, 86, 94, 97, 99, 100, 103, 107, 108, 110, 117, 122, 125, 126, 129, 130, 134, 135, 136, 137, 138, 141, 142, 151, 152, 161, 170, 171, 175, 177, 180, 184, 185, 187, 190, 193, 194, 197, 198, 201, 202, 207, 211, 212, 213, 214, 215, 216, 217, 229, 236, 241, 243, 248, 252, 258, 262, 264, 265, 269, 283], "empir": [1, 165, 174], "bootstrap": [1, 151], "td": 1, "varianc": [1, 7, 23, 105, 174], "bia": [1, 2, 6, 7, 23, 37, 48, 52, 74, 103, 110, 116, 124, 125, 138, 144, 149, 151, 160, 171, 174, 178, 179, 189, 195, 201, 209, 214, 216, 217, 235, 241, 262], "mean": [1, 2, 6, 7, 9, 12, 14, 16, 23, 27, 29, 41, 47, 52, 55, 57, 58, 64, 65, 66, 74, 81, 84, 89, 91, 94, 99, 102, 103, 106, 108, 109, 110, 112, 115, 122, 124, 125, 129, 130, 137, 139, 141, 142, 144, 150, 152, 154, 162, 164, 168, 172, 173, 174, 175, 177, 178, 180, 184, 185, 187, 189, 190, 198, 201, 203, 205, 206, 207, 209, 213, 214, 216, 221, 222, 223, 224, 227, 231, 246, 249, 252, 254, 255, 259, 264, 265, 270, 278], "obtain": [1, 11, 28, 36, 41, 55, 66, 74, 97, 116, 124, 141, 144, 150, 152, 160, 169, 174, 175, 177, 184, 187, 194, 255], "reward": [1, 5, 16, 151, 161, 174, 175, 176, 178], "noth": [1, 27, 47, 81, 102, 116, 127, 145, 164, 178, 184, 195, 276], "els": [1, 5, 6, 10, 13, 22, 23, 27, 29, 37, 47, 52, 55, 64, 65, 97, 99, 115, 120, 123, 130, 137, 149, 150, 152, 165, 170, 172, 176, 177, 178, 185, 187, 188, 198, 201, 202, 213, 214, 217, 225, 226, 229, 232, 233, 235, 240, 248, 264, 270, 276, 279, 283, 284], "mont": 1, "carlo": 1, "whole": [1, 3, 29, 49, 52, 66, 103, 116, 125, 127, 136, 150, 171, 172, 174, 180, 198, 199, 201, 206, 216, 222, 225, 252], "sequenc": [1, 6, 7, 14, 25, 27, 45, 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238, 239, 251, 252, 265, 269, 272], "10m": 1, "element": [1, 6, 7, 14, 20, 23, 42, 45, 54, 55, 64, 65, 84, 99, 100, 103, 106, 110, 112, 113, 115, 130, 136, 140, 150, 159, 165, 168, 174, 187, 189, 190, 194, 206, 207, 208, 209, 217, 241, 249, 251, 265, 283, 284, 287], "anoth": [1, 4, 5, 6, 9, 10, 13, 16, 24, 25, 27, 29, 36, 45, 48, 54, 55, 58, 65, 66, 69, 81, 89, 94, 102, 103, 106, 107, 109, 113, 116, 120, 122, 124, 125, 127, 136, 143, 144, 145, 147, 150, 151, 156, 157, 168, 171, 174, 176, 177, 178, 179, 180, 188, 190, 192, 194, 195, 198, 200, 204, 211, 213, 216, 223, 230, 246, 247, 249, 255, 265, 266, 270, 278, 283, 284], "ratio": [1, 91, 108, 129, 159, 174, 184, 194, 211], "interact": [1, 6, 7, 16, 55, 66, 100, 105, 106, 141, 145, 161, 174, 177, 185, 188, 189, 190, 213, 283], "nutshel": [1, 207], "cautiou": 1, "deal": [1, 6, 49, 55, 57, 58, 66, 102, 108, 116, 123, 124, 125, 137, 160, 174, 238, 267], "lead": [1, 4, 6, 7, 16, 18, 55, 58, 81, 87, 91, 97, 108, 116, 134, 145, 151, 153, 164, 172, 207, 222, 262, 265, 278, 283], "bias": [1, 37, 48, 103, 108, 125, 179, 262, 267, 268], "comparison": [1, 11, 20, 25, 92, 100, 106, 107, 108, 109, 138, 152, 159, 160, 192, 197, 200, 205, 207, 209, 211, 217, 221, 240, 264], "scale": [1, 7, 23, 37, 46, 57, 62, 81, 103, 105, 108, 117, 125, 135, 136, 137, 138, 150, 151, 161, 174, 175, 181, 182, 193, 195, 200, 201, 216, 238, 248, 252, 265, 274, 276, 277], "help": [1, 6, 10, 12, 17, 20, 22, 23, 26, 27, 36, 37, 48, 55, 58, 64, 65, 66, 74, 91, 97, 100, 108, 109, 112, 113, 115, 116, 125, 127, 135, 136, 137, 138, 141, 147, 148, 149, 151, 152, 153, 159, 160, 165, 167, 170, 174, 175, 176, 177, 178, 179, 180, 187, 192, 193, 200, 201, 206, 207, 208, 209, 213, 217, 222, 227, 229, 231, 232, 233, 238, 240, 241, 244, 245, 247, 248, 249, 251, 258, 260, 263, 265, 266, 269, 274, 275, 277, 278], "signal": [1, 48, 100, 102, 148, 174, 176, 211, 252, 255, 264], "magnitud": [1, 20, 47, 99, 171, 217, 248], "truncat": [1, 106, 122, 127, 159, 174, 217], "A": 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193, 198, 201, 205, 207, 209, 222, 224, 231, 237, 247, 254, 255, 264, 277, 278], "specifi": [1, 4, 5, 6, 7, 10, 11, 13, 18, 20, 23, 24, 26, 27, 28, 38, 42, 57, 58, 65, 84, 91, 97, 99, 100, 102, 103, 106, 107, 112, 123, 124, 127, 130, 137, 138, 139, 141, 145, 149, 151, 152, 153, 156, 170, 171, 174, 177, 178, 184, 187, 189, 190, 195, 205, 206, 207, 208, 209, 212, 213, 214, 216, 217, 221, 222, 223, 225, 226, 229, 238, 240, 246, 254, 255, 257, 259, 260, 263, 265, 266, 270, 273, 276, 277, 278, 281, 283], "scenario": [1, 5, 7, 16, 117, 119, 120, 125, 131, 140, 175, 178, 215, 236, 262, 266, 278], "tensordictsequenti": [1, 151], "valueoper": 1, "automat": [1, 2, 4, 6, 7, 10, 12, 17, 26, 29, 37, 39, 40, 42, 45, 48, 50, 51, 52, 54, 59, 63, 67, 84, 105, 107, 109, 116, 124, 128, 136, 137, 138, 139, 140, 141, 145, 152, 154, 157, 159, 162, 164, 174, 175, 178, 181, 182, 184, 187, 192, 193, 198, 201, 224, 226, 229, 231, 233, 236, 237, 238, 244, 250, 253, 254, 255, 261, 264, 265, 269, 270, 274, 283], "out_kei": [1, 16, 151, 174], "state_action_valu": 1, "state_valu": 1, "built": [1, 4, 5, 6, 7, 8, 9, 10, 21, 26, 27, 36, 52, 65, 99, 100, 102, 103, 110, 120, 127, 129, 130, 136, 150, 151, 152, 174, 179, 185, 189, 190, 193, 194, 211, 221, 233, 238, 240, 247, 249, 251, 268, 277], "present": [1, 7, 10, 22, 26, 41, 91, 92, 108, 120, 149, 152, 168, 170, 171, 172, 175, 176, 178, 187, 188, 191, 194, 209, 265, 279], "origin": [1, 6, 7, 9, 10, 14, 16, 20, 23, 24, 27, 36, 55, 58, 64, 65, 66, 81, 97, 100, 106, 108, 113, 117, 127, 129, 133, 134, 139, 140, 142, 143, 152, 165, 168, 169, 171, 172, 175, 187, 188, 189, 190, 192, 193, 195, 197, 198, 199, 200, 202, 208, 213, 216, 217, 235, 237, 238, 244, 246, 252, 270, 277], "paper": [1, 6, 14, 19, 20, 58, 64, 65, 81, 127, 128, 133, 134, 139, 152, 174, 175, 177, 179, 180, 182, 209, 217], "found": [1, 5, 6, 7, 12, 19, 26, 27, 29, 47, 55, 56, 66, 87, 100, 108, 127, 129, 130, 133, 141, 148, 149, 150, 151, 152, 157, 158, 162, 177, 178, 179, 187, 189, 190, 197, 198, 199, 201, 209, 223, 237, 240, 244, 265, 283], "ddpgmlpactor": 1, "ddpgmlpqnet": 1, "sinc": [1, 3, 5, 6, 9, 10, 11, 12, 13, 15, 16, 19, 23, 24, 25, 27, 49, 55, 57, 58, 59, 66, 71, 81, 86, 94, 99, 100, 102, 108, 109, 110, 112, 113, 115, 116, 117, 124, 127, 129, 130, 131, 133, 134, 139, 141, 142, 143, 145, 146, 150, 151, 154, 157, 158, 165, 167, 168, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 185, 187, 188, 191, 192, 194, 195, 198, 199, 200, 201, 203, 204, 205, 206, 207, 213, 214, 215, 216, 221, 224, 227, 229, 231, 241, 244, 249, 254, 255, 256, 268, 279, 283], "lazi": [1, 65], "materi": [1, 138, 139, 160, 205, 219, 225, 252, 256, 262], "achiev": [1, 3, 7, 16, 20, 23, 28, 36, 49, 55, 62, 91, 97, 108, 120, 134, 136, 140, 144, 150, 151, 152, 159, 164, 167, 168, 171, 172, 174, 175, 184, 192, 195, 198, 200, 201, 214, 215, 217, 220, 229, 236, 240, 265, 269, 274, 278], "oper": [1, 5, 7, 15, 16, 17, 20, 21, 22, 23, 24, 25, 29, 36, 37, 42, 47, 48, 50, 52, 55, 57, 66, 67, 69, 70, 71, 79, 84, 89, 91, 92, 94, 98, 102, 103, 106, 108, 109, 110, 115, 116, 117, 119, 122, 123, 124, 130, 133, 136, 138, 139, 140, 142, 145, 150, 151, 152, 153, 156, 157, 159, 160, 162, 164, 165, 169, 170, 173, 174, 176, 180, 184, 185, 187, 188, 189, 190, 192, 198, 199, 200, 201, 203, 204, 205, 206, 210, 212, 213, 214, 215, 217, 220, 221, 222, 223, 231, 233, 236, 237, 240, 244, 246, 247, 248, 249, 251, 252, 255, 256, 264, 268, 269, 270, 273, 278, 279], "practic": [1, 6, 7, 13, 24, 27, 52, 55, 57, 58, 64, 65, 66, 99, 100, 102, 106, 108, 110, 116, 124, 128, 131, 136, 139, 140, 142, 146, 149, 151, 152, 159, 168, 171, 174, 181, 189, 190, 191, 193, 205, 206, 209, 211, 249, 250, 254, 265, 278], "fake": [1, 7, 20, 23, 29, 52, 58, 86, 167, 172, 209, 213, 214, 216, 217, 246], "spec": [1, 120, 151, 174, 178, 195, 216, 249], "ornsteinuhlenbeckprocesswrapp": 1, "suggest": [1, 5, 14, 58, 96, 108, 143, 152, 159, 160, 161, 162, 165, 172, 184, 187, 189, 190, 191, 252], "ou": 1, "nois": [1, 7, 14, 58, 81, 211, 264], "reach": [1, 12, 20, 58, 66, 67, 108, 148, 150, 152, 161, 162, 164, 174, 178, 189, 190, 217, 238, 249], "minimum": [1, 91, 174, 178, 189, 190, 193, 207], "iter": [1, 3, 5, 7, 13, 14, 16, 28, 36, 37, 41, 42, 47, 48, 49, 52, 58, 59, 61, 66, 83, 94, 97, 99, 100, 102, 103, 109, 115, 116, 117, 125, 127, 130, 133, 141, 146, 148, 149, 151, 157, 159, 172, 174, 175, 176, 177, 178, 184, 185, 188, 194, 198, 210, 213, 214, 228, 235, 241, 244, 248, 252, 265, 268, 279], "reset": [1, 23, 41, 102, 106, 116, 131, 151, 174, 175, 176, 178, 184, 188, 189, 190, 198, 202, 211, 213, 214, 238, 275], "tight": [1, 117, 119, 120], "per": [1, 6, 7, 10, 13, 23, 108, 115, 136, 137, 142, 143, 144, 148, 150, 151, 152, 160, 162, 167, 171, 178, 179, 180, 181, 184, 185, 187, 191, 192, 193, 194, 201, 205, 210, 222, 224, 229, 231, 233, 235, 238, 241, 249, 254, 264, 273, 278, 279], "sync": [1, 12, 13, 19, 61, 136, 137, 138, 161, 204, 210, 277], "natur": [1, 6, 7, 20, 22, 27, 28, 29, 50, 81, 83, 103, 108, 116, 119, 130, 134, 141, 142, 150, 151, 152, 177, 187, 207, 209, 213, 216, 217, 224, 283], "resourc": [1, 59, 64, 65, 81, 97, 117, 134, 138, 148, 150, 167, 174, 184, 187, 192, 233, 241, 249, 265, 272, 278], "alloc": [1, 7, 22, 25, 26, 27, 45, 54, 61, 65, 106, 144, 150, 167, 184, 191, 192, 209, 219, 231, 241, 254, 255, 279], "gpu": [1, 3, 5, 9, 14, 20, 22, 23, 24, 37, 42, 45, 47, 48, 52, 54, 55, 56, 58, 59, 60, 62, 63, 66, 67, 70, 79, 81, 85, 88, 89, 90, 91, 92, 98, 102, 107, 108, 110, 117, 124, 128, 131, 135, 136, 137, 138, 139, 140, 146, 147, 148, 149, 150, 151, 152, 153, 159, 162, 164, 165, 167, 169, 172, 174, 175, 177, 178, 179, 187, 188, 191, 193, 194, 201, 202, 212, 217, 218, 223, 224, 227, 231, 233, 241, 248, 249, 252, 255, 268, 269, 270, 274, 277], "worker": [1, 7, 9, 13, 19, 57, 58, 102, 129, 135, 137, 138, 149, 150, 162, 174, 177, 178, 184, 229, 233, 265], "syncdatacollector": [1, 174], "process": [1, 4, 5, 6, 7, 13, 14, 16, 17, 19, 20, 22, 24, 26, 27, 28, 29, 38, 41, 42, 46, 47, 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255, 264, 284], "maximum": [1, 13, 55, 66, 91, 103, 113, 127, 141, 143, 151, 152, 159, 174, 179, 180, 189, 190, 201, 210, 211, 230, 265], "non": [1, 2, 3, 6, 10, 13, 16, 23, 26, 37, 55, 57, 59, 60, 62, 66, 91, 94, 103, 106, 108, 109, 111, 115, 116, 125, 127, 134, 137, 141, 144, 145, 149, 150, 151, 152, 154, 156, 160, 162, 165, 171, 172, 176, 179, 180, 184, 188, 189, 192, 198, 200, 201, 205, 215, 219, 231, 246, 249, 262, 266, 270, 278, 284, 286], "termin": [1, 16, 27, 59, 66, 97, 174, 175, 177, 178, 187, 204, 223, 230, 243], "max_frames_per_traj": [1, 174], "effect": [1, 6, 7, 10, 11, 13, 18, 27, 61, 81, 91, 100, 103, 106, 108, 115, 116, 120, 139, 142, 143, 153, 167, 169, 171, 175, 179, 180, 187, 192, 193, 207, 215, 216, 222, 248, 252, 265, 281], "regist": [1, 8, 18, 26, 41, 48, 52, 86, 103, 120, 122, 125, 136, 137, 139, 148, 156, 167, 168, 174, 189, 190, 193, 224, 225, 233, 237, 244, 248, 256, 267], "new": [1, 2, 4, 5, 6, 7, 8, 10, 14, 16, 18, 26, 27, 28, 29, 35, 36, 40, 45, 47, 48, 50, 54, 55, 56, 58, 61, 66, 68, 81, 83, 87, 89, 91, 94, 105, 106, 107, 108, 110, 113, 115, 120, 122, 123, 125, 127, 131, 136, 137, 138, 140, 142, 151, 152, 156, 157, 159, 161, 164, 167, 168, 169, 170, 171, 172, 174, 175, 176, 179, 180, 184, 187, 189, 190, 194, 195, 202, 203, 204, 205, 209, 211, 212, 213, 214, 215, 216, 221, 222, 223, 224, 225, 230, 233, 240, 241, 242, 243, 244, 252, 255, 257, 262, 265, 266, 269, 277, 283], "stepcount": [1, 151, 174], "infer": [1, 3, 7, 8, 10, 11, 12, 23, 24, 25, 26, 27, 43, 45, 54, 55, 66, 100, 102, 103, 107, 108, 112, 115, 116, 117, 127, 136, 140, 141, 144, 157, 159, 174, 176, 187, 188, 193, 194, 201, 203, 204, 209, 210, 212, 213, 214, 215, 217, 220, 223, 224, 226, 231, 238, 241, 242, 243, 244, 246, 251, 252, 253, 258, 260, 261, 267, 269], "manual": [1, 4, 6, 10, 14, 18, 26, 27, 38, 52, 55, 59, 75, 78, 79, 106, 108, 116, 124, 125, 145, 151, 153, 168, 169, 172, 198, 199, 201, 209, 214, 224, 229, 232, 244, 248, 257, 265, 278, 282, 283], "around": [1, 6, 7, 13, 27, 28, 38, 42, 55, 61, 64, 65, 83, 100, 102, 103, 106, 108, 131, 136, 143, 153, 159, 160, 165, 169, 172, 180, 184, 185, 189, 190, 198, 205, 206, 210, 213, 236, 252, 256, 283, 286], "1m": [1, 151, 174], "outer": [1, 7, 137, 160, 264], "loop": [1, 3, 5, 6, 9, 10, 13, 19, 23, 25, 29, 40, 42, 49, 52, 55, 57, 58, 65, 66, 71, 81, 83, 94, 99, 102, 105, 108, 116, 124, 133, 138, 141, 142, 152, 153, 159, 160, 161, 167, 169, 177, 178, 185, 193, 199, 201, 217, 236, 249, 255, 264, 265, 268], "equal": [1, 5, 12, 99, 106, 108, 129, 130, 140, 152, 162, 171, 174, 175, 187, 189, 190, 192, 208, 233, 278], "length": [1, 9, 14, 16, 47, 55, 58, 66, 99, 100, 103, 115, 116, 127, 129, 130, 139, 143, 152, 174, 179, 180, 191, 201, 207, 209, 212, 217, 222, 225, 233, 269, 284], "sub": [1, 7, 24, 29, 99, 122, 139, 164, 174, 178, 184, 192, 193, 201, 232, 264, 278, 283], "shape": [1, 4, 5, 18, 20, 27, 36, 45, 47, 48, 54, 55, 58, 64, 65, 66, 74, 84, 94, 99, 102, 108, 109, 112, 115, 122, 124, 137, 139, 142, 143, 152, 153, 156, 160, 165, 169, 174, 184, 187, 194, 200, 201, 203, 208, 209, 213, 217, 219, 226, 230, 236, 244, 249, 252, 253, 255, 261, 265, 269, 273, 274], "env_per_collector": 1, "traj_len": 1, "assess": 1, "mode": [1, 5, 14, 24, 43, 47, 48, 55, 58, 61, 66, 81, 87, 91, 94, 96, 100, 105, 125, 129, 130, 136, 137, 144, 145, 149, 151, 154, 157, 159, 161, 162, 165, 172, 176, 179, 180, 181, 185, 187, 188, 190, 193, 195, 203, 204, 210, 211, 212, 214, 215, 216, 218, 233, 236, 238, 249, 258, 259, 260, 265, 278], "dedic": [1, 12, 61, 66, 106, 125, 149, 177, 178, 193, 215, 225, 241, 246, 247, 248, 279, 284], "frequenc": [1, 9, 92, 141, 241], "everi": [1, 2, 7, 10, 12, 16, 17, 20, 22, 23, 28, 36, 37, 38, 42, 48, 49, 52, 55, 57, 58, 66, 67, 99, 102, 103, 105, 106, 107, 115, 131, 139, 140, 142, 143, 144, 150, 151, 156, 162, 165, 167, 168, 169, 171, 172, 174, 175, 176, 178, 180, 184, 185, 209, 213, 214, 217, 225, 227, 241, 250, 257, 263, 265, 279], "10": [1, 3, 5, 7, 9, 10, 13, 20, 23, 27, 37, 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149, 160, 168, 169, 182, 205, 207, 209, 215, 237, 265], "circular": 1, "compos": [1, 4, 6, 7, 8, 12, 18, 20, 23, 25, 29, 64, 65, 67, 87, 102, 103, 110, 120, 123, 127, 129, 136, 138, 150, 154, 156, 157, 165, 169, 172, 173, 174, 177, 179, 181, 185, 187, 198, 213, 214, 216, 221, 226, 230, 232, 237, 238, 251, 255, 272], "pick": [1, 7, 23, 49, 109, 142, 175, 180, 233, 258, 264, 269], "tensor": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 23, 24, 25, 26, 27, 37, 38, 39, 40, 41, 42, 43, 46, 47, 48, 49, 50, 51, 52, 53, 55, 63, 64, 65, 66, 67, 68, 70, 71, 73, 74, 75, 77, 80, 81, 83, 85, 86, 88, 90, 94, 98, 99, 100, 101, 103, 105, 107, 108, 110, 113, 115, 116, 117, 120, 122, 125, 129, 130, 133, 136, 137, 138, 143, 144, 145, 148, 149, 150, 151, 152, 153, 154, 156, 159, 160, 162, 164, 165, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 180, 181, 184, 185, 187, 188, 189, 190, 191, 192, 194, 198, 200, 201, 202, 203, 204, 205, 206, 207, 210, 211, 212, 213, 215, 217, 218, 219, 220, 222, 223, 225, 226, 229, 230, 232, 233, 236, 237, 246, 247, 248, 249, 251, 252, 253, 254, 255, 256, 257, 261, 264, 267, 268, 269, 273, 274, 276], "physic": [1, 16, 115, 193, 254, 265, 278], "memori": [1, 5, 6, 7, 10, 12, 19, 20, 22, 23, 25, 26, 27, 45, 54, 55, 57, 61, 89, 97, 103, 106, 107, 108, 111, 114, 116, 117, 123, 125, 129, 136, 137, 138, 139, 146, 150, 151, 159, 160, 161, 165, 171, 177, 179, 181, 184, 187, 191, 194, 200, 204, 205, 208, 209, 210, 215, 217, 224, 229, 235, 237, 246, 248, 249, 252, 254, 256, 269, 273, 279], "map": [1, 7, 14, 37, 55, 58, 64, 66, 83, 100, 102, 103, 108, 113, 115, 120, 123, 125, 127, 129, 130, 134, 142, 151, 153, 154, 160, 167, 168, 169, 174, 175, 178, 187, 189, 190, 192, 194, 198, 213, 216, 217, 222, 226, 229, 230, 231, 254, 264, 265, 267], "arrai": [1, 7, 36, 37, 48, 49, 52, 57, 64, 65, 67, 78, 79, 94, 99, 100, 106, 115, 116, 122, 124, 142, 143, 154, 161, 172, 173, 180, 187, 208, 247], "desir": [1, 6, 10, 14, 16, 19, 55, 58, 81, 91, 108, 123, 125, 130, 150, 157, 171, 184, 187, 200, 206, 219, 244, 248, 251, 278, 284], "hyperparamet": [1, 7, 16, 20, 58, 116, 125, 126, 129, 136, 141, 205, 209, 267], "temporari": [1, 6, 141, 193, 197], "disk": [1, 5, 27, 29, 38, 102, 105, 117, 125, 151, 203, 204, 210, 225, 241, 249, 254, 255, 263, 273], "along": [1, 5, 6, 7, 9, 10, 16, 20, 25, 27, 36, 37, 45, 48, 54, 55, 59, 60, 61, 62, 66, 92, 98, 99, 100, 102, 103, 105, 106, 107, 108, 112, 113, 116, 123, 130, 131, 134, 137, 138, 142, 143, 146, 147, 151, 167, 171, 175, 177, 185, 187, 191, 207, 217, 219], "dimens": [1, 6, 7, 9, 14, 16, 22, 24, 25, 37, 45, 52, 54, 55, 86, 87, 100, 102, 103, 105, 112, 113, 115, 116, 123, 129, 136, 139, 142, 151, 153, 162, 169, 173, 174, 176, 189, 190, 191, 206, 209, 213, 214, 222, 230, 241, 248, 252, 265], "feed": [1, 7, 14, 23, 49, 52, 55, 66, 102, 103, 107, 108, 111, 113, 117, 125, 139, 142, 143, 148, 149, 161, 175, 180, 185, 192, 211, 231, 251, 257, 268], "adapt": [1, 2, 16, 20, 28, 97, 102, 117, 141, 217, 249], "divid": [1, 9, 10, 12, 13, 14, 16, 17, 23, 94, 108, 110, 142, 149, 150, 164, 168, 178, 192, 193, 278], "yield": [1, 7, 13, 14, 20, 23, 25, 41, 43, 106, 125, 129, 130, 133, 139, 140, 176, 178, 200, 208, 217, 236, 238, 258, 259, 260, 265], "regard": [1, 7, 13, 16, 27, 66, 125, 159, 160, 174, 184, 283], "200": [1, 7, 11, 103, 151, 162, 178, 200, 210, 236], "random_crop_len": 1, "25": [1, 9, 26, 27, 81, 99, 108, 122, 131, 138, 172, 177, 187, 200, 236, 249, 251, 255], "balanc": [1, 7, 22, 148, 184, 244], "choic": [1, 5, 7, 10, 97, 141, 143, 227, 265], "heterogen": [1, 212], "figur": [1, 10, 14, 16, 26, 55, 81, 91, 102, 108, 136, 139, 141, 151, 159, 164, 176, 184, 185, 187, 192, 198, 211, 213, 284, 286], "dataflow": 1, "8": [1, 3, 6, 7, 9, 20, 23, 27, 64, 65, 71, 97, 103, 113, 122, 124, 130, 134, 137, 139, 144, 148, 149, 152, 157, 159, 162, 172, 173, 184, 187, 189, 192, 200, 201, 203, 204, 206, 215, 217, 223, 224, 225, 229, 231, 232, 233, 236, 238, 241, 242, 243, 246, 248, 255, 265, 277, 278, 279, 283, 284, 287], "1000": [1, 2, 23, 48, 105, 107, 131, 137, 138, 140, 142, 149, 159, 174, 185, 192, 193, 203, 215, 217, 229, 230, 244, 249, 264], "known": [1, 4, 5, 6, 14, 20, 47, 99, 127, 139, 150, 152, 156, 158, 171, 175, 180, 201, 204, 216, 217, 224, 249, 252, 264], "utd": 1, "64": [1, 6, 7, 17, 20, 22, 23, 27, 38, 42, 48, 97, 106, 113, 116, 129, 134, 137, 138, 139, 149, 151, 153, 169, 173, 176, 177, 189, 191, 217, 222, 224, 225, 236, 238, 249, 251, 255], "reproduc": [1, 14, 58, 81, 102, 106, 108, 152, 159, 174, 178, 188, 201, 236, 249, 265], "realiz": [1, 20, 217], "sever": [1, 2, 3, 12, 13, 17, 19, 23, 26, 29, 36, 42, 46, 50, 52, 58, 59, 60, 81, 108, 110, 127, 137, 142, 147, 150, 161, 164, 167, 168, 170, 172, 174, 176, 177, 184, 185, 188, 189, 190, 192, 193, 207, 216, 225, 236, 237, 241, 249, 264, 270, 273, 278], "qnet": 1, "ve": [1, 6, 7, 9, 10, 20, 25, 26, 29, 50, 52, 56, 84, 86, 99, 100, 102, 103, 106, 107, 116, 127, 136, 140, 151, 158, 160, 167, 169, 177, 185, 192, 200, 205, 207, 208, 217, 230, 231, 249, 252, 277], "_must_": 1, "off": [1, 9, 10, 19, 23, 25, 28, 36, 48, 55, 103, 106, 125, 127, 134, 138, 141, 142, 159, 167, 172, 174, 176, 177, 178, 187, 203, 205, 230, 252, 258, 269, 276], "dictat": [1, 16], "introduc": [1, 13, 17, 19, 20, 25, 26, 27, 28, 37, 40, 47, 60, 66, 83, 86, 87, 97, 100, 108, 110, 122, 124, 133, 134, 136, 137, 148, 159, 162, 164, 167, 176, 178, 179, 180, 187, 188, 189, 190, 191, 193, 197, 198, 201, 202, 204, 205, 207, 208, 211, 213, 215, 216, 217, 220, 221, 222, 224, 226, 229, 233, 237, 240, 241, 262, 265, 275, 278], "outdat": 1, "trick": [1, 7, 12, 55, 106, 145, 150, 160], "multi": [1, 6, 7, 16, 22, 52, 55, 58, 59, 60, 62, 67, 85, 88, 99, 100, 103, 106, 110, 127, 136, 137, 139, 146, 147, 148, 149, 151, 152, 164, 174, 176, 179, 181, 184, 193, 194, 201, 209, 231, 232, 265, 278], "altern": [1, 7, 12, 27, 54, 55, 113, 127, 131, 138, 153, 158, 162, 170, 172, 174, 180, 184, 187, 188, 198, 200, 208, 215, 226, 262, 264, 265, 275], "hack": [1, 152, 201, 205, 262], "find": [1, 2, 4, 5, 6, 7, 10, 12, 20, 26, 27, 36, 38, 45, 54, 81, 91, 92, 94, 97, 102, 103, 106, 109, 112, 116, 136, 138, 142, 150, 152, 156, 157, 159, 164, 168, 172, 175, 178, 180, 184, 189, 190, 199, 205, 206, 207, 208, 211, 213, 216, 217, 221, 225, 230, 238, 240, 241, 244, 247, 249, 263, 270], "despit": [1, 81, 148], "fact": [1, 3, 6, 7, 10, 14, 16, 23, 26, 27, 36, 45, 66, 81, 89, 99, 100, 102, 103, 106, 108, 110, 115, 127, 140, 168, 174, 175, 185], "part": [1, 3, 6, 7, 10, 12, 15, 16, 17, 19, 24, 27, 41, 47, 48, 50, 55, 58, 64, 65, 66, 81, 84, 97, 99, 100, 103, 109, 111, 115, 116, 127, 130, 133, 136, 137, 139, 140, 143, 145, 146, 149, 150, 151, 152, 154, 158, 159, 164, 167, 168, 174, 180, 189, 190, 198, 199, 200, 202, 204, 205, 207, 212, 214, 221, 225, 226, 230, 233, 238, 240, 241, 244, 247, 248, 252, 262, 264, 270, 273, 274, 278, 281, 282, 283], "made": [1, 7, 12, 17, 23, 29, 58, 97, 100, 106, 108, 115, 127, 134, 137, 140, 150, 151, 180, 185, 187, 189, 190, 191, 193, 208, 212, 216, 242, 243, 246, 247, 252, 265, 283], "thank": [1, 11, 23, 55, 116, 134, 141, 142, 143, 150, 152, 180, 192, 193, 201, 203, 204, 211, 221, 240], "hardupd": 1, "softupd": [1, 151], "appropri": [1, 7, 9, 10, 47, 55, 66, 100, 116, 141, 142, 148, 168, 172, 177, 185, 191, 198, 213, 214, 215, 230, 236, 237, 248], "locat": [1, 4, 7, 35, 38, 45, 54, 86, 89, 91, 100, 102, 115, 122, 127, 150, 172, 174, 184, 189, 190, 192, 194, 204, 221, 240, 241, 244, 255, 265, 273, 277], "adam": [1, 7, 13, 15, 41, 52, 58, 75, 107, 110, 123, 124, 150, 167, 175, 176, 178, 227, 228, 231, 279], "pretti": [1, 19, 25, 49, 106, 122, 124, 127, 160, 178, 198, 222], "plot": [1, 7, 58, 81, 91, 99, 127, 141, 151, 164, 175, 176, 185, 187, 211], "mention": [1, 6, 7, 12, 13, 17, 19, 58, 66, 81, 94, 106, 108, 109, 125, 138, 139, 150, 151, 175, 177, 184, 187, 201, 213, 244, 254, 262, 278], "greater": [1, 13, 55, 58, 100, 124, 140, 192, 204, 223, 278], "concret": [1, 6, 7, 10, 13, 99, 164, 174, 205, 216], "takeawai": [1, 128], "further": [1, 5, 6, 7, 10, 23, 25, 27, 52, 81, 97, 103, 108, 116, 127, 130, 133, 139, 142, 157, 159, 164, 171, 174, 187, 188, 193, 200, 209, 211, 212, 213, 214, 215, 221, 233, 237, 240, 246, 254, 265, 270, 272], "dispatch": [1, 6, 17, 136, 170, 193, 224, 225, 236, 248], "distpatch": 1, "iql": 1, "flexibl": [1, 2, 6, 7, 27, 28, 29, 55, 66, 94, 99, 116, 117, 125, 129, 136, 137, 146, 215, 249, 257, 259, 277], "runnable_code_remov": [1, 9, 11, 14, 15, 16, 18, 20, 24, 26, 27, 28, 29, 36, 38, 40, 41, 42, 43, 45, 46, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 73, 74, 75, 78, 79, 81, 83, 86, 87, 89, 94, 97, 99, 100, 102, 105, 106, 107, 108, 109, 110, 112, 113, 116, 117, 119, 120, 122, 124, 125, 127, 128, 129, 131, 133, 134, 140, 141, 142, 143, 144, 151, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 167, 168, 169, 171, 174, 175, 179, 180, 182, 184, 188, 190, 194, 197, 200, 205, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 265, 266, 267, 268, 274, 275], "minut": [1, 9, 11, 14, 15, 16, 18, 20, 24, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 123, 125, 127, 128, 129, 130, 131, 133, 134, 140, 141, 142, 143, 144, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 173, 174, 175, 179, 180, 182, 184, 185, 188, 190, 194, 197, 200, 205, 206, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275], "001": [1, 11, 14, 16, 20, 21, 42, 44, 45, 49, 53, 54, 57, 58, 66, 80, 81, 88, 94, 95, 102, 104, 105, 106, 107, 109, 110, 112, 113, 114, 115, 120, 121, 125, 126, 131, 134, 140, 142, 143, 148, 156, 159, 160, 161, 162, 167, 168, 174, 175, 176, 179, 181, 182, 185, 188, 190, 194, 200, 205, 207, 211, 218, 228, 237, 261, 271, 272], "second": [1, 5, 6, 7, 9, 10, 11, 14, 15, 16, 18, 20, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 81, 82, 83, 84, 86, 87, 89, 91, 92, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 125, 127, 128, 129, 130, 131, 133, 134, 136, 138, 140, 141, 142, 143, 144, 145, 149, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 171, 174, 175, 179, 180, 182, 183, 184, 185, 186, 188, 189, 190, 191, 192, 194, 197, 200, 201, 205, 206, 207, 208, 209, 211, 216, 220, 222, 228, 229, 235, 238, 241, 242, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275, 278, 283], "coding_ddpg": [1, 21], "jupyt": [1, 9, 11, 14, 15, 16, 18, 20, 24, 27, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 91, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 125, 127, 128, 129, 130, 131, 133, 134, 140, 141, 142, 143, 144, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 174, 175, 179, 180, 182, 184, 185, 187, 188, 190, 194, 197, 200, 205, 206, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275], "notebook": [1, 9, 11, 14, 15, 16, 18, 20, 22, 24, 27, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 56, 57, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 91, 92, 94, 97, 98, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 116, 117, 119, 120, 122, 125, 127, 128, 129, 130, 131, 133, 134, 140, 141, 142, 143, 144, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 174, 175, 179, 180, 182, 184, 185, 187, 188, 190, 194, 197, 200, 205, 206, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275, 277], "ipynb": [1, 9, 11, 14, 15, 16, 18, 20, 24, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 125, 127, 128, 129, 130, 131, 133, 134, 140, 141, 142, 143, 144, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 174, 175, 179, 180, 182, 184, 188, 190, 194, 197, 200, 205, 206, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275], "galleri": [1, 9, 11, 14, 15, 16, 18, 20, 24, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 125, 127, 128, 129, 130, 131, 133, 134, 140, 141, 142, 143, 144, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 174, 175, 179, 180, 182, 184, 188, 190, 194, 197, 200, 205, 206, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275], "sphinx": [1, 9, 11, 14, 15, 16, 18, 20, 24, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 125, 127, 128, 129, 130, 131, 133, 134, 140, 141, 142, 143, 144, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 174, 175, 179, 180, 182, 184, 188, 190, 194, 197, 200, 205, 206, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275, 281, 282, 283, 284, 288], "build": [2, 7, 9, 16, 23, 24, 29, 36, 38, 39, 40, 41, 42, 43, 45, 46, 52, 55, 67, 71, 74, 75, 83, 84, 86, 90, 97, 98, 99, 100, 101, 102, 105, 106, 107, 109, 115, 116, 122, 124, 127, 128, 129, 133, 135, 136, 142, 143, 149, 150, 151, 152, 154, 156, 167, 169, 174, 175, 176, 177, 178, 181, 190, 191, 197, 198, 201, 203, 204, 209, 210, 212, 213, 215, 222, 237, 241, 242, 243, 249, 251, 258, 260, 264, 265, 274, 282, 287], "highli": [2, 6, 7, 12, 22, 55, 66, 97, 180, 191, 193, 222, 265], "dynam": [2, 5, 6, 7, 10, 14, 16, 17, 21, 23, 24, 26, 27, 29, 36, 48, 59, 71, 99, 108, 111, 114, 119, 124, 125, 134, 136, 162, 171, 174, 195, 198, 199, 200, 212, 213, 214, 215, 218, 225, 237, 238, 240, 248, 253, 257, 261, 265, 269, 274], "explor": [2, 7, 10, 16, 25, 27, 50, 55, 81, 120, 136, 141, 145, 151, 159, 161, 167, 174, 179, 180, 247, 258, 259, 269, 273, 274, 278], "note": [2, 4, 5, 6, 7, 9, 10, 11, 14, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 36, 48, 49, 50, 52, 55, 56, 58, 81, 94, 99, 100, 102, 103, 106, 107, 110, 111, 112, 113, 115, 116, 120, 122, 125, 127, 130, 131, 137, 138, 139, 140, 144, 145, 147, 148, 149, 150, 152, 153, 156, 157, 159, 160, 162, 164, 167, 169, 170, 171, 172, 176, 177, 178, 184, 185, 187, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 199, 201, 203, 204, 205, 206, 207, 208, 209, 213, 214, 215, 217, 219, 220, 221, 222, 224, 229, 231, 232, 233, 240, 255, 257, 264, 267, 270, 273, 277, 278, 279, 281, 283], "differenti": [2, 6, 7, 16, 22, 29, 39, 40, 45, 51, 52, 63, 84, 99, 124, 136, 151, 169, 175, 181, 182, 207], "requires_grad": [2, 4, 7, 10, 36, 48, 52, 69, 70, 84, 99, 103, 106, 112, 116, 120, 123, 124, 131, 145, 172, 217, 254, 268], "track": [2, 6, 9, 10, 16, 37, 41, 48, 58, 91, 99, 103, 105, 106, 110, 112, 116, 123, 124, 137, 142, 143, 147, 151, 157, 172, 178, 180, 184, 225, 255, 263, 268, 277], "auto": [2, 3, 6, 7, 10, 12, 14, 26, 61, 65, 137, 138, 159, 170, 202, 203, 204, 223, 225, 237, 238, 249, 264, 278, 283, 284], "std": [2, 5, 6, 7, 14, 17, 23, 26, 27, 57, 64, 65, 102, 108, 170, 172, 173, 178, 187, 198, 202, 203, 205, 213, 214, 221, 225, 237, 240, 249, 276], "cout": [2, 5, 7, 26, 27, 203, 276], "endl": [2, 7, 26, 27, 203, 225], "cpufloattyp": [2, 5, 7, 27, 225], "y": [2, 6, 9, 22, 24, 26, 27, 36, 45, 46, 48, 54, 58, 69, 70, 71, 73, 74, 75, 78, 79, 81, 84, 91, 94, 99, 103, 106, 108, 109, 112, 113, 116, 123, 124, 127, 130, 137, 140, 152, 170, 184, 187, 188, 189, 191, 201, 202, 207, 211, 225, 229, 264, 284], "wa": [2, 3, 4, 5, 6, 13, 18, 20, 24, 26, 27, 29, 47, 49, 57, 58, 64, 65, 66, 81, 84, 87, 99, 100, 102, 103, 106, 107, 108, 109, 110, 112, 116, 120, 125, 127, 130, 138, 139, 141, 148, 150, 161, 165, 167, 168, 169, 171, 173, 174, 175, 178, 179, 180, 185, 192, 193, 200, 204, 207, 208, 214, 217, 225, 241, 244, 248, 249, 252, 255, 257, 277, 278, 283], "result": [2, 4, 5, 6, 7, 9, 10, 11, 12, 14, 16, 20, 23, 24, 25, 27, 28, 29, 36, 41, 43, 45, 48, 49, 57, 64, 65, 66, 70, 71, 84, 86, 91, 92, 97, 99, 100, 102, 106, 107, 108, 112, 116, 119, 120, 124, 125, 128, 130, 134, 137, 139, 140, 143, 148, 150, 151, 152, 156, 157, 159, 160, 161, 164, 165, 169, 170, 171, 172, 173, 175, 176, 177, 178, 179, 185, 187, 188, 189, 190, 191, 192, 193, 194, 195, 198, 200, 204, 206, 207, 208, 209, 211, 213, 214, 215, 216, 219, 220, 221, 223, 225, 226, 227, 229, 235, 236, 238, 240, 241, 246, 251, 252, 254, 255, 258, 259, 260, 262, 264, 265, 269, 270, 276, 278, 279, 281], "grad_fn": [2, 5, 29, 36, 37, 48, 52, 84, 99, 103, 112, 115, 140, 145, 189, 201], "addbackward1": 2, "z": [2, 6, 9, 27, 48, 58, 84, 94, 106, 112, 207, 225, 284], "27": [2, 9, 57, 159, 178, 192, 200, 236, 246, 249], "mulbackward1": 2, "meanbackward0": [2, 103], "requires_grad_": [2, 36, 84, 112], "flag": [2, 6, 16, 27, 48, 81, 84, 99, 112, 152, 165, 168, 180, 190, 192, 201, 212, 214, 221, 254, 264, 278], "place": [2, 6, 7, 13, 16, 22, 26, 27, 45, 48, 50, 54, 58, 84, 94, 100, 103, 105, 110, 112, 116, 120, 127, 130, 133, 137, 141, 148, 150, 153, 164, 167, 169, 172, 174, 180, 187, 188, 191, 198, 205, 213, 214, 215, 222, 225, 229, 230, 231, 254, 262, 264, 265, 270, 283, 284, 285], "randn": [2, 4, 6, 7, 14, 27, 71, 84, 112, 123, 124, 148, 149, 153, 165, 176, 178, 189, 202, 213, 214, 215, 222, 225, 229, 249, 279], "b": [2, 4, 6, 7, 9, 22, 25, 27, 36, 48, 69, 70, 71, 84, 89, 92, 99, 103, 106, 110, 113, 115, 122, 123, 124, 140, 142, 143, 159, 160, 162, 173, 175, 188, 190, 210, 226, 249, 255, 284], "sumbackward0": 2, "backprop": [2, 48, 84, 112, 116, 142, 161], "scalar": [2, 6, 16, 17, 27, 36, 48, 55, 58, 66, 69, 84, 99, 102, 106, 112, 124, 185, 213, 223], "backward": [2, 3, 4, 7, 10, 13, 14, 15, 17, 18, 19, 23, 29, 36, 41, 48, 50, 52, 58, 69, 70, 71, 78, 79, 84, 99, 100, 102, 107, 112, 113, 115, 116, 124, 131, 136, 138, 139, 140, 143, 146, 148, 149, 150, 156, 160, 164, 165, 169, 172, 176, 177, 178, 181, 184, 185, 189, 190, 214, 227, 228, 229, 231, 233, 237, 238, 248, 255, 265, 268, 272, 279], "equival": [2, 5, 6, 13, 26, 27, 36, 48, 99, 110, 124, 152, 156, 169, 175, 177, 187, 189, 190, 201, 202, 205, 207, 209, 214, 215, 216, 217, 265, 275, 276], "print": [2, 5, 6, 7, 13, 16, 22, 23, 25, 26, 27, 29, 37, 42, 47, 48, 49, 50, 55, 57, 58, 59, 64, 65, 71, 81, 84, 86, 94, 97, 99, 102, 103, 106, 108, 110, 115, 116, 119, 123, 124, 125, 130, 134, 137, 138, 142, 143, 144, 147, 148, 149, 150, 151, 152, 153, 154, 157, 159, 165, 168, 170, 172, 173, 176, 177, 178, 180, 185, 189, 190, 191, 192, 193, 195, 198, 201, 204, 205, 206, 207, 210, 213, 214, 217, 223, 225, 226, 227, 229, 231, 232, 235, 236, 237, 238, 241, 246, 248, 249, 251, 255, 256, 264, 265, 267, 270, 275, 276, 279, 283], "dx": [2, 70, 84, 110, 124, 145], "grad": [2, 4, 7, 14, 18, 36, 48, 52, 69, 84, 99, 112, 116, 124, 138, 140, 145, 148, 150, 156, 160, 165, 167, 176, 177, 178, 205, 222, 228, 233, 248, 268], "5000": [2, 154, 230, 249], "got": [2, 17, 23, 61, 100, 106, 109, 110, 112, 116, 127, 148, 170, 177, 204, 213, 216, 230, 283], "matrix": [2, 6, 7, 14, 20, 27, 36, 45, 48, 54, 91, 99, 102, 103, 106, 109, 110, 112, 115, 116, 122, 123, 139, 142, 143, 160, 165, 168, 179, 180, 187, 189, 190, 192, 193, 208, 209, 215, 217, 222, 236, 237, 238, 244, 251, 265, 269], "explan": [2, 58, 185, 254], "arriv": [2, 7, 17, 27, 47, 127, 137, 148, 176, 225, 265], "valu": [2, 4, 5, 6, 7, 9, 11, 14, 16, 22, 23, 24, 25, 26, 27, 29, 36, 37, 41, 45, 46, 48, 52, 54, 55, 56, 61, 64, 65, 66, 69, 81, 89, 91, 94, 97, 99, 100, 102, 103, 106, 107, 108, 110, 112, 113, 115, 116, 117, 124, 127, 129, 139, 140, 141, 142, 147, 148, 150, 152, 153, 154, 158, 161, 164, 165, 170, 175, 176, 177, 178, 179, 180, 184, 187, 188, 189, 190, 191, 198, 199, 201, 203, 205, 206, 207, 208, 209, 211, 212, 213, 214, 216, 220, 221, 225, 226, 228, 230, 233, 240, 248, 249, 252, 255, 262, 263, 273, 278, 279, 281], "section": [2, 4, 5, 6, 7, 9, 10, 13, 17, 22, 23, 25, 27, 37, 38, 40, 41, 42, 43, 45, 48, 49, 52, 56, 57, 58, 81, 98, 100, 106, 107, 108, 109, 110, 113, 115, 116, 120, 127, 130, 133, 140, 144, 150, 154, 159, 161, 165, 172, 175, 176, 178, 184, 187, 189, 192, 193, 194, 195, 198, 204, 206, 207, 216, 219, 224, 228, 230, 237, 244, 248, 249, 254, 265, 281, 283, 284, 285, 287], "jacobian": [2, 48, 99, 136, 156, 181, 222], "product": [2, 3, 5, 7, 45, 47, 48, 54, 66, 94, 99, 110, 117, 127, 136, 137, 150, 154, 156, 180, 181, 191, 192, 193, 201, 215, 216, 222, 226, 252, 269, 274, 277], "norm": [2, 16, 20, 23, 58, 103, 108, 123, 136, 139, 168, 171, 181, 187, 198, 217], "1021": 2, "4020": 2, "314": 2, "6695": 2, "613": [2, 236], "4944": [2, 225], "v": [2, 9, 23, 36, 48, 58, 59, 61, 99, 112, 113, 115, 120, 139, 152, 156, 165, 175, 177, 184, 199, 216, 225, 226, 284], "0001": [2, 23, 37, 97, 159, 238], "kfloat": [2, 3, 4, 65, 202, 204, 223], "102": 2, "4000": [2, 102, 264], "1024": [2, 6, 22, 25, 47, 91, 108, 215, 225, 227, 249], "0000": [2, 27, 37, 103, 189, 206, 217, 225, 284], "stop": [2, 5, 6, 27, 36, 64, 65, 84, 86, 97, 99, 112, 123, 143, 150, 162, 167, 174, 176, 180, 184, 255], "histori": [2, 36, 45, 52, 54, 99, 106, 112, 123, 127, 171, 172], "nogradguard": [2, 276], "block": [2, 4, 6, 7, 10, 12, 14, 20, 23, 26, 27, 36, 37, 52, 55, 83, 84, 91, 99, 112, 129, 130, 138, 139, 149, 150, 151, 159, 172, 176, 177, 178, 179, 184, 187, 200, 217, 224, 225, 229, 257, 265, 287], "pow": [2, 6, 205], "no_grad": [2, 23, 36, 47, 48, 64, 65, 84, 99, 110, 112, 113, 116, 123, 124, 137, 138, 152, 161, 172, 173, 177, 185, 190, 193, 198, 201, 210, 213, 214, 215, 219, 233, 237, 265, 272, 276], "Or": [2, 25, 27, 167, 178, 195, 214, 223, 225, 283], "eq": [2, 23, 27, 55, 138, 177, 189, 198, 213, 214, 238, 255, 283], "bool": [2, 13, 16, 17, 27, 152, 170, 187, 195, 201, 206, 217, 225, 270, 278, 281], "is_leaf": 2, "detach_": [2, 178], "register_hook": 2, "retain_grad": 2, "doc": [2, 5, 7, 8, 66, 99, 106, 107, 122, 147, 150, 157, 158, 176, 178, 187, 190, 197, 209, 222, 244, 248, 263, 281, 282, 283, 288], "calcul": [2, 14, 20, 36, 41, 48, 49, 55, 58, 62, 66, 81, 91, 94, 97, 99, 100, 102, 107, 108, 116, 123, 142, 143, 152, 161, 175, 176, 178, 179, 180, 187, 193, 198, 207, 213, 216, 217, 232, 238, 268], "penalti": [2, 168, 173, 248], "h": [2, 5, 6, 7, 9, 10, 12, 26, 27, 29, 55, 139, 152, 159, 162, 170, 194, 201, 204, 225, 230, 237, 240, 243, 264, 276, 278], "model": [2, 3, 6, 10, 13, 16, 19, 21, 26, 27, 28, 36, 38, 39, 40, 45, 46, 47, 48, 49, 52, 54, 58, 59, 60, 62, 71, 73, 74, 75, 83, 86, 96, 97, 98, 99, 101, 106, 109, 110, 111, 112, 114, 116, 118, 119, 120, 122, 123, 124, 126, 130, 133, 135, 136, 138, 141, 142, 143, 144, 147, 150, 154, 156, 157, 159, 160, 167, 168, 169, 174, 175, 176, 177, 178, 179, 181, 188, 189, 190, 192, 193, 199, 200, 202, 209, 212, 215, 216, 217, 218, 221, 222, 224, 229, 230, 231, 232, 233, 236, 240, 245, 246, 248, 253, 254, 256, 258, 261, 262, 263, 268, 269, 272, 274, 278, 279], "linear": [2, 6, 7, 11, 13, 19, 20, 23, 29, 45, 48, 50, 52, 54, 66, 74, 81, 86, 102, 106, 111, 115, 122, 123, 124, 125, 129, 131, 133, 138, 139, 142, 143, 148, 149, 152, 153, 159, 160, 164, 165, 168, 172, 176, 177, 178, 180, 185, 189, 193, 195, 197, 198, 200, 201, 205, 209, 211, 213, 214, 215, 216, 217, 219, 224, 226, 227, 228, 229, 231, 232, 235, 236, 237, 238, 241, 244, 246, 248, 251, 254, 256, 262, 263, 267, 270, 279], "loss": [2, 3, 6, 7, 9, 11, 13, 16, 19, 20, 21, 23, 36, 42, 45, 48, 54, 71, 73, 74, 75, 78, 79, 81, 83, 86, 97, 99, 102, 105, 110, 115, 116, 124, 125, 133, 136, 137, 138, 142, 149, 150, 161, 162, 164, 169, 172, 175, 177, 178, 180, 184, 185, 198, 204, 213, 214, 217, 233, 237, 238, 252, 258, 263, 272, 279], "target": [2, 3, 5, 7, 16, 19, 22, 23, 26, 27, 41, 49, 52, 55, 61, 66, 81, 86, 100, 106, 108, 110, 113, 115, 116, 127, 130, 133, 138, 142, 143, 149, 150, 151, 153, 159, 167, 169, 170, 173, 175, 176, 177, 178, 180, 185, 187, 188, 189, 190, 194, 195, 198, 204, 213, 214, 215, 216, 221, 223, 225, 237, 238, 240, 243, 244, 249, 251, 252, 272, 276], "mseloss": [2, 14, 41, 52, 71, 123, 124, 148, 149, 164, 176, 231, 279], "grad_output": [2, 10, 12, 84, 86], "ones_lik": [2, 106], "create_graph": [2, 145], "gradient_penalti": 2, "dim": [2, 5, 6, 13, 25, 37, 102, 103, 106, 115, 123, 136, 138, 149, 171, 173, 176, 177, 178, 185, 187, 189, 190, 206, 207, 223, 236, 238, 251, 276], "combined_loss": 2, "1042": 2, "0638": 2, "0103": [2, 103], "0723": 2, "2543": 2, "1222": 2, "0071": 2, "0814": 2, "1683": 2, "1052": 2, "0355": [2, 103], "document": [2, 5, 6, 7, 18, 24, 36, 52, 58, 66, 67, 87, 91, 94, 97, 99, 100, 103, 105, 106, 107, 112, 125, 127, 131, 136, 148, 150, 151, 154, 156, 158, 159, 172, 177, 178, 179, 184, 187, 188, 189, 190, 192, 193, 194, 195, 207, 213, 215, 222, 223, 226, 230, 231, 235, 237, 238, 246, 265, 270, 273, 274, 275, 276, 277, 281, 283, 284, 288], "link": [2, 5, 6, 7, 12, 14, 26, 27, 40, 42, 58, 64, 65, 91, 98, 116, 117, 120, 128, 130, 133, 150, 154, 156, 207, 221, 223, 225, 237, 281, 282, 287], "subclass": [2, 6, 7, 16, 20, 29, 37, 38, 73, 87, 99, 102, 103, 116, 124, 136, 141, 151, 171, 177, 185, 194, 207, 209, 217, 236, 247, 248, 253, 261, 269, 274, 276], "encod": [2, 9, 16, 45, 46, 47, 52, 54, 83, 84, 103, 111, 114, 127, 133, 137, 141, 151, 168, 174, 178, 187, 200, 201, 211, 216, 217, 225, 248, 270], "method": [2, 5, 6, 7, 9, 10, 12, 13, 14, 16, 19, 20, 23, 25, 27, 29, 36, 37, 43, 45, 49, 52, 55, 57, 61, 64, 65, 66, 71, 81, 87, 92, 94, 99, 100, 102, 103, 105, 106, 108, 110, 112, 116, 124, 125, 127, 129, 135, 136, 141, 145, 148, 151, 152, 154, 156, 157, 158, 159, 160, 161, 164, 168, 169, 170, 171, 172, 174, 175, 176, 177, 185, 187, 188, 189, 190, 192, 198, 199, 205, 213, 214, 216, 217, 225, 226, 230, 238, 241, 242, 243, 246, 247, 248, 251, 259, 263, 264, 265, 278, 283], "forward": [2, 3, 5, 10, 12, 13, 14, 16, 17, 19, 23, 26, 27, 29, 36, 37, 42, 48, 50, 52, 55, 58, 62, 64, 65, 66, 69, 70, 71, 78, 79, 83, 84, 94, 99, 102, 103, 106, 108, 109, 110, 111, 113, 115, 116, 124, 125, 131, 136, 137, 138, 139, 140, 142, 144, 145, 146, 148, 149, 150, 153, 154, 157, 164, 165, 167, 168, 169, 171, 172, 175, 176, 177, 178, 180, 181, 184, 185, 187, 188, 189, 193, 195, 198, 199, 201, 203, 204, 210, 211, 213, 214, 215, 221, 223, 225, 226, 228, 229, 230, 231, 232, 235, 236, 237, 238, 244, 246, 248, 251, 252, 255, 256, 265, 270, 276, 279], "detail": [2, 4, 6, 7, 10, 12, 14, 17, 18, 19, 24, 26, 27, 29, 38, 41, 48, 49, 50, 58, 59, 64, 65, 66, 81, 91, 92, 94, 103, 106, 107, 127, 130, 134, 138, 139, 141, 143, 144, 146, 148, 157, 159, 164, 165, 167, 172, 178, 179, 184, 185, 188, 189, 190, 193, 195, 201, 204, 205, 206, 207, 208, 210, 214, 215, 222, 224, 225, 228, 230, 235, 236, 237, 242, 243, 244, 246, 247, 248, 249, 252, 254, 264, 265, 270, 277], "namespac": [2, 4, 7, 10, 12, 26, 27, 37, 116, 120, 152, 170, 195, 201, 209, 225, 276], "inherit": [2, 13, 17, 26, 42, 57, 66, 94, 102, 106, 107, 110, 158, 161, 164, 174, 187, 194, 207, 209, 211, 215, 233], "linearfunct": 2, "public": [2, 10, 12, 17, 170, 225, 284], "static": [2, 10, 12, 16, 25, 64, 65, 66, 99, 119, 136, 152, 156, 157, 170, 189, 190, 197, 199, 200, 201, 212, 213, 214, 215, 216, 225, 238, 265, 281], "option": [2, 4, 6, 7, 10, 12, 13, 21, 27, 40, 49, 51, 53, 57, 59, 66, 91, 97, 98, 99, 100, 103, 106, 108, 122, 135, 136, 141, 146, 149, 151, 153, 159, 162, 164, 171, 173, 187, 189, 190, 198, 199, 200, 201, 210, 213, 214, 215, 216, 221, 226, 229, 233, 235, 245, 248, 249, 255, 265, 269, 270, 272, 273, 275, 283, 287], "autogradcontext": [2, 10, 12], "ctx": [2, 4, 6, 10, 12, 22, 145, 229], "save_for_backward": [2, 4, 6, 145], "mm": [2, 6, 65, 123, 152, 201, 202, 204, 210, 213, 223, 224, 240, 243], "unsqueez": [2, 16, 52, 64, 65, 74, 86, 106, 123, 124, 154, 165, 169, 173, 176, 178, 185, 221, 223], "expand_a": [2, 23, 198, 213, 214], "tensor_list": [2, 10, 12, 150], "get_saved_vari": 2, "grad_input": [2, 145], "grad_weight": 2, "grad_bia": 2, "Then": [2, 14, 17, 20, 24, 26, 28, 29, 49, 50, 58, 64, 65, 81, 94, 109, 110, 113, 115, 116, 128, 136, 138, 148, 149, 164, 167, 170, 171, 174, 175, 176, 178, 180, 184, 189, 190, 204, 211, 216, 217, 229, 232, 240, 242, 243, 246, 262], "5314": 2, "2807": 2, "4864": 2, "7608": 2, "9101": [2, 189], "0073": [2, 103], "mulconst": [2, 86], "object": [2, 4, 5, 6, 7, 9, 12, 13, 16, 21, 23, 26, 27, 36, 37, 38, 41, 42, 48, 55, 58, 66, 83, 99, 100, 102, 103, 106, 108, 112, 115, 116, 123, 124, 125, 130, 131, 136, 140, 144, 150, 153, 156, 158, 170, 176, 177, 178, 179, 181, 184, 185, 187, 189, 190, 193, 197, 198, 209, 213, 214, 216, 233, 237, 238, 241, 243, 249, 259, 262, 264, 265, 267, 269], "stash": 2, "saved_data": 2, "were": [2, 3, 6, 7, 11, 14, 20, 22, 27, 36, 58, 66, 92, 94, 99, 103, 106, 108, 110, 112, 115, 116, 127, 128, 141, 147, 148, 153, 162, 168, 174, 175, 177, 179, 180, 189, 190, 192, 205, 217, 221, 222, 244, 249, 252, 254, 255, 262, 278], "todoubl": 2, "On": [2, 5, 6, 7, 10, 23, 25, 26, 27, 106, 116, 129, 131, 137, 148, 150, 152, 162, 168, 171, 176, 177, 188, 193, 194, 217, 220, 225, 236, 244, 248, 265, 278], "easiest": [2, 6, 11, 27, 40, 98, 116, 136, 154, 160, 172, 174, 246, 265], "tabl": [2, 19, 25, 115, 122, 129, 136, 137, 150, 152, 159, 177, 178, 184, 189, 190, 191, 236, 249, 255, 287], "set_data": 2, "output_nr": 2, "after": [2, 3, 6, 7, 9, 11, 12, 14, 16, 17, 18, 20, 22, 23, 24, 25, 27, 28, 29, 36, 37, 38, 45, 48, 50, 52, 55, 58, 59, 64, 65, 66, 84, 87, 97, 99, 100, 103, 108, 109, 110, 113, 115, 116, 117, 123, 125, 127, 129, 130, 136, 137, 139, 142, 143, 144, 147, 148, 149, 150, 151, 152, 153, 159, 161, 162, 164, 167, 168, 170, 171, 172, 173, 174, 175, 177, 178, 180, 182, 184, 185, 189, 190, 192, 194, 195, 197, 198, 199, 201, 203, 204, 211, 213, 214, 215, 216, 217, 219, 220, 221, 223, 225, 230, 231, 241, 242, 243, 246, 248, 249, 252, 254, 255, 259, 262, 263, 265, 270, 274, 277, 279], "bug": [2, 6, 12, 27, 105, 120, 159, 202], "report": [2, 12, 20, 27, 58, 81, 97, 99, 102, 103, 107, 136, 152, 159, 176, 178, 179, 202, 217, 255], "fix": [2, 16, 20, 24, 27, 28, 36, 55, 56, 57, 58, 108, 120, 127, 140, 172, 176, 189, 190, 200, 217, 244, 264, 265, 283], "soon": [2, 6, 58, 64, 65, 137, 162, 167, 214, 237], "overview": [2, 6, 7, 13, 59, 61, 102, 127, 134, 135, 136, 142, 143, 148, 149, 150, 157, 170, 174, 176, 177, 178, 180, 184, 205, 206, 208, 212, 218, 224, 227, 245, 277], "alwai": [2, 3, 5, 7, 11, 16, 19, 22, 23, 26, 27, 46, 58, 102, 103, 107, 113, 115, 116, 120, 127, 139, 140, 144, 150, 151, 152, 154, 173, 174, 175, 176, 178, 189, 194, 201, 203, 204, 205, 211, 221, 224, 240, 270, 283], "problem": [2, 5, 7, 13, 16, 17, 22, 26, 27, 55, 57, 58, 109, 111, 115, 116, 124, 129, 131, 141, 151, 157, 159, 160, 164, 168, 172, 174, 176, 178, 184, 188, 192, 205, 207, 224, 249, 250, 254, 265, 278, 283], "question": [2, 5, 6, 7, 10, 12, 20, 26, 27, 55, 83, 100, 137, 150, 152, 158, 180, 199, 206, 216, 217, 224, 249], "forum": [2, 5, 6, 7, 26, 27, 49, 87, 123, 157, 158, 199, 224], "view": [3, 12, 13, 16, 17, 19, 23, 29, 56, 59, 61, 62, 67, 91, 100, 103, 105, 106, 112, 115, 116, 117, 123, 124, 125, 138, 139, 141, 142, 146, 147, 148, 149, 150, 157, 159, 164, 165, 170, 176, 177, 178, 179, 180, 185, 189, 190, 198, 199, 209, 213, 214, 223, 228, 231, 232, 244, 247, 256, 263, 275, 281], "prerequisit": [3, 4, 9, 18, 59, 61, 62, 111, 120, 128, 139, 146, 147, 148, 149, 150, 151, 170, 176, 177, 178, 187, 213, 231, 232], "frontend": [3, 4, 12, 93, 95, 123, 136, 193, 202, 203, 209, 215, 237, 238, 272], "semant": [3, 4, 7, 26, 55, 64, 65, 74, 106, 111, 113, 114, 124, 150, 152, 207, 208, 209, 212, 218, 222, 283], "11": [3, 6, 7, 9, 13, 20, 22, 27, 65, 103, 122, 137, 138, 156, 173, 178, 187, 188, 189, 191, 206, 210, 221, 225, 232, 236, 243, 245, 249, 255, 276, 283, 287], "nvidia": [3, 6, 20, 56, 106, 144, 150, 152, 162, 188, 217, 232, 248, 265, 269, 277], "toolkit": [3, 27, 111, 157, 161, 263], "releas": [3, 5, 7, 12, 20, 24, 27, 47, 56, 117, 120, 122, 125, 137, 138, 140, 154, 157, 167, 177, 179, 184, 215, 217, 221, 225, 229, 236, 237, 238, 255, 265, 283], "greatli": [3, 7, 55, 99, 116, 175], "cpu": [3, 4, 6, 7, 10, 12, 13, 16, 17, 22, 23, 24, 27, 37, 42, 45, 47, 48, 49, 54, 58, 66, 79, 81, 89, 91, 92, 97, 106, 108, 110, 116, 117, 120, 122, 123, 131, 136, 138, 139, 148, 149, 150, 152, 162, 165, 168, 170, 172, 173, 174, 176, 177, 178, 179, 180, 181, 184, 187, 191, 194, 198, 201, 202, 203, 204, 210, 213, 214, 215, 219, 223, 229, 233, 236, 241, 244, 248, 249, 250, 252, 254, 255, 262, 269, 270, 278], "overhead": [3, 6, 7, 12, 20, 62, 91, 122, 137, 138, 139, 148, 160, 162, 164, 173, 176, 178, 179, 184, 188, 192, 193, 200, 202, 209, 215, 217, 249, 255, 265, 278], "increas": [3, 6, 7, 22, 23, 24, 28, 41, 42, 49, 81, 91, 92, 97, 100, 108, 116, 137, 138, 139, 141, 143, 146, 149, 157, 167, 173, 184, 198, 200, 209, 210, 213, 226, 236, 247, 248, 249, 252, 264, 265, 268, 269, 278], "mostli": [3, 12, 23, 94, 99, 108, 130, 142, 178, 180, 195, 213, 214, 215], "util": [3, 4, 6, 9, 12, 17, 23, 26, 27, 28, 29, 37, 38, 42, 49, 55, 57, 59, 61, 66, 91, 102, 105, 107, 116, 122, 124, 125, 129, 130, 131, 133, 136, 137, 138, 139, 149, 150, 152, 153, 156, 157, 159, 160, 162, 168, 170, 172, 173, 174, 177, 179, 180, 184, 185, 187, 188, 192, 193, 194, 198, 201, 204, 209, 210, 213, 214, 215, 216, 217, 219, 221, 223, 225, 226, 227, 233, 235, 237, 238, 240, 242, 243, 244, 247, 248, 257, 263, 264, 270, 272, 273, 274, 278], "deploy": [3, 5, 29, 47, 66, 108, 125, 126, 141, 193, 202, 215, 221, 237, 245, 246, 252, 269, 270, 277], "appear": [3, 13, 16, 26, 29, 115, 244, 252, 283], "heart": [3, 55, 127, 236, 284], "veri": [3, 4, 5, 6, 7, 10, 16, 17, 22, 23, 25, 26, 27, 28, 29, 45, 50, 52, 54, 55, 64, 65, 66, 71, 81, 83, 84, 94, 100, 103, 110, 112, 116, 124, 127, 129, 131, 138, 139, 140, 142, 149, 150, 164, 167, 168, 172, 175, 176, 178, 179, 180, 182, 184, 185, 192, 194, 198, 205, 207, 211, 214, 244, 252, 255, 264, 265, 284, 285], "time": [3, 4, 5, 6, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 23, 25, 27, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 92, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 124, 125, 127, 128, 129, 130, 131, 133, 134, 136, 137, 138, 140, 141, 142, 143, 144, 145, 148, 149, 150, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 172, 174, 175, 176, 177, 178, 179, 180, 182, 184, 185, 187, 188, 189, 190, 192, 193, 194, 197, 198, 200, 201, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 217, 220, 221, 222, 223, 228, 229, 231, 236, 244, 246, 247, 248, 249, 250, 251, 252, 254, 256, 257, 258, 259, 260, 262, 263, 265, 266, 267, 268, 269, 274, 275, 276, 277, 281, 283], "compil": [3, 5, 7, 8, 10, 12, 18, 20, 25, 26, 29, 66, 94, 109, 120, 136, 150, 158, 181, 189, 190, 191, 200, 202, 209, 215, 221, 224, 233, 237, 241, 249, 255, 264, 265, 269, 271, 272, 276, 281], "boost": [3, 108, 110, 159, 160, 192, 200, 215, 224, 233, 237, 238, 265], "demonstr": [3, 4, 9, 11, 16, 18, 19, 20, 24, 25, 26, 29, 47, 48, 56, 63, 83, 91, 94, 100, 102, 107, 120, 127, 135, 136, 137, 138, 139, 140, 142, 144, 145, 148, 149, 152, 153, 156, 157, 158, 159, 165, 170, 174, 176, 177, 178, 179, 184, 187, 189, 190, 193, 195, 200, 201, 202, 203, 204, 207, 209, 211, 214, 217, 219, 220, 221, 228, 231, 232, 235, 236, 238, 240, 242, 243, 246, 248, 249, 252, 254, 255, 260, 268, 270, 273, 274, 275, 278, 279, 283, 284, 285], "mnist": [3, 7, 38, 49, 52, 81, 86, 102, 105, 107, 134, 135, 136, 138, 141, 144, 150, 153, 169, 177, 182, 185, 238, 251, 269], "libtorch": [3, 7, 26, 27, 203, 221, 223, 225, 237, 240, 243, 269, 276], "counterpart": [3, 20, 120, 149, 159, 211, 217, 232, 237, 265, 270], "syntax": [3, 27, 29, 54, 66, 93, 95, 96, 125, 136, 249, 259], "depict": [3, 105], "chunk": [3, 6, 9, 61, 150, 167], "batch": [3, 6, 7, 19, 20, 23, 24, 25, 38, 41, 42, 43, 47, 50, 52, 55, 57, 58, 59, 61, 62, 66, 81, 86, 87, 91, 97, 99, 100, 102, 103, 105, 106, 107, 108, 113, 116, 123, 125, 127, 131, 133, 135, 136, 137, 138, 139, 140, 141, 142, 146, 149, 150, 151, 152, 153, 154, 161, 162, 164, 165, 167, 169, 172, 173, 174, 175, 177, 178, 179, 181, 182, 184, 185, 187, 188, 189, 191, 193, 194, 197, 198, 200, 201, 207, 209, 212, 214, 217, 221, 222, 230, 238, 241, 248, 249, 258, 259, 260], "data_load": [3, 7, 23, 198, 213, 214, 215], "zero_grad": [3, 7, 23, 41, 52, 71, 99, 115, 116, 124, 137, 138, 148, 150, 167, 172, 176, 178, 185, 214, 231, 237, 238, 268, 272], "nll_loss": [3, 138, 150, 177, 238], "updat": [3, 7, 12, 13, 14, 15, 16, 19, 20, 23, 25, 27, 41, 47, 48, 49, 55, 58, 71, 75, 91, 99, 108, 110, 115, 116, 123, 124, 125, 137, 138, 141, 144, 151, 152, 154, 162, 167, 174, 175, 177, 178, 184, 198, 201, 205, 213, 214, 215, 216, 217, 221, 224, 227, 231, 233, 235, 238, 240, 246, 248, 258, 262, 267, 279], "captur": [3, 5, 7, 26, 27, 29, 58, 66, 99, 119, 138, 156, 187, 188, 189, 190, 202, 210, 213, 214, 216, 249], "But": [3, 7, 10, 12, 24, 47, 49, 50, 58, 81, 86, 100, 106, 112, 115, 130, 140, 162, 167, 168, 169, 175, 189, 190, 192, 194, 198, 201, 205, 208, 216, 222, 235, 238, 241, 246, 249, 270, 283], "slightli": [3, 6, 16, 27, 137, 150, 151, 173, 180, 189, 190, 208, 249, 265], "prealloc": 3, "reus": [3, 12, 71, 86, 116, 124, 145, 152, 156, 168, 175, 192, 193, 201, 203, 260, 265, 269, 278], "tensoropt": [3, 202], "floatcuda": 3, "dtype": [3, 4, 10, 12, 16, 17, 106, 112, 115, 145, 152, 165, 189, 190, 191, 195, 201, 202, 206, 208, 213, 215, 216, 223, 226, 235, 237, 241, 246, 248, 262, 270, 272], "longcuda": 3, "klong": 3, "zero": [3, 7, 13, 20, 23, 36, 41, 46, 52, 55, 71, 99, 100, 102, 103, 106, 107, 115, 116, 123, 124, 137, 138, 142, 143, 149, 150, 151, 165, 168, 170, 171, 172, 176, 178, 180, 185, 201, 205, 207, 208, 210, 216, 217, 226, 238, 241, 248, 253, 255, 261, 264, 265, 269, 270, 279], "ktrainbatchs": 3, "28": [3, 7, 9, 20, 37, 38, 103, 105, 116, 153, 169, 185, 192, 217, 221, 225, 236, 238, 241, 249, 251, 264], "copy_": [3, 45, 54, 122, 159, 176, 254, 262], "training_step": 3, "void": [3, 4, 6, 7, 17, 26, 27, 65, 159, 170, 202, 204, 225, 249, 255, 264], "cudagraph": 3, "cudastream": 3, "capturestream": 3, "getstreamfrompool": 3, "setcurrentcudastream": 3, "capture_begin": 3, "capture_end": 3, "warm": [3, 25, 77, 80, 115, 122, 184, 188, 192, 193, 209, 220, 236, 249, 255, 265], "side": [3, 24, 57, 58, 91, 115, 153, 162, 167, 169, 170, 175, 176, 184, 204, 244, 281], "prepar": [3, 13, 20, 23, 29, 49, 57, 58, 64, 65, 74, 75, 105, 113, 115, 124, 125, 130, 136, 149, 152, 153, 167, 170, 174, 176, 201, 211, 212, 215, 216, 217, 221, 226, 229, 235, 240, 245, 246, 255, 257, 269], "cach": [3, 48, 152, 159, 184, 192, 193, 200, 201, 265, 269, 278], "cubla": [3, 249], "cudnn": [3, 6, 86, 144, 151, 162, 165, 248], "warmupstream": 3, "int": [3, 4, 5, 6, 7, 11, 13, 22, 23, 26, 27, 57, 59, 61, 64, 65, 137, 138, 150, 152, 159, 170, 176, 177, 178, 184, 188, 189, 190, 194, 197, 201, 204, 223, 225, 226, 230, 232, 237, 241, 270, 276, 278, 281], "num_warmup_it": 3, "success": [3, 7, 16, 27, 81, 112, 115, 141, 159, 180, 204, 221, 223, 244], "replai": [3, 16, 29, 84, 161], "spin": [3, 66, 102, 192], "ordinari": [3, 210], "epoch": [3, 7, 9, 11, 19, 23, 38, 41, 42, 58, 59, 61, 97, 102, 105, 108, 110, 115, 116, 125, 129, 131, 133, 137, 138, 141, 150, 162, 167, 172, 174, 178, 185, 194, 214, 238, 258, 263], "59584": 3, "60000": [3, 150], "3921": 3, "2051": 3, "accuraci": [3, 11, 20, 23, 24, 28, 42, 49, 102, 107, 108, 116, 134, 135, 136, 137, 138, 141, 165, 171, 172, 173, 177, 182, 185, 187, 193, 198, 201, 210, 211, 214, 215, 217, 235, 236, 245, 246, 248, 263, 268, 269], "938": [3, 7, 162], "1826": 3, "1273": 3, "960": 3, "1796": [3, 37], "1012": [3, 162], "968": 3, "1603": 3, "0869": 3, "973": 3, "2315": 3, "0736": 3, "978": 3, "0511": [3, 201], "0704": 3, "977": [3, 162, 236], "7": [3, 6, 7, 20, 22, 23, 26, 27, 37, 40, 48, 57, 64, 65, 89, 94, 103, 115, 122, 130, 131, 137, 138, 149, 150, 161, 162, 164, 172, 173, 187, 188, 189, 191, 193, 200, 204, 206, 215, 219, 221, 223, 224, 225, 227, 228, 232, 235, 236, 238, 240, 242, 243, 245, 246, 248, 265, 270, 287], "0802": 3, "0654": 3, "979": 3, "0774": 3, "0604": 3, "980": [3, 192], "0669": 3, "0544": [3, 103], "984": [3, 236], "0219": 3, "0517": [3, 103], "983": 3, "real": [3, 7, 16, 24, 36, 42, 58, 60, 64, 99, 108, 109, 110, 111, 115, 136, 138, 142, 143, 150, 151, 164, 167, 170, 175, 180, 188, 192, 207, 209, 213, 216, 235, 236, 249, 252], "0m44": 3, "287": [3, 193, 283], "018": 3, "0m1": 3, "116": [3, 201], "produc": [3, 5, 6, 7, 13, 26, 27, 29, 66, 74, 108, 124, 127, 129, 141, 151, 153, 156, 158, 160, 162, 164, 174, 175, 180, 187, 189, 190, 195, 198, 199, 201, 213, 214, 215, 223, 231, 248, 252, 265, 283], "4092": 3, "2037": 3, "2039": 3, "1274": 3, "961": 3, "1779": 3, "1017": 3, "1559": 3, "0871": 3, "972": 3, "2240": 3, "0735": [3, 103, 217], "0520": 3, "0710": [3, 103], 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91, 94, 106, 120, 137, 138, 139, 148, 167, 168, 172, 177, 188, 192, 198, 200, 207, 216, 217, 219, 228, 233, 238, 252, 278, 282, 283], "heavi": [3, 7, 55, 108, 112, 241], "impact": [3, 14, 20, 41, 97, 108, 151, 176, 179, 200, 217, 226, 247, 270, 278, 279], "smaller": [3, 11, 41, 87, 103, 108, 115, 116, 138, 139, 172, 174, 176, 187, 220, 225, 241, 249, 252, 254], "nevertheless": [3, 6, 23, 27, 162, 174], "richard": 4, "zou": 4, "integr": [4, 7, 12, 16, 18, 27, 40, 86, 97, 122, 136, 141, 154, 185, 192, 193, 195, 215, 216, 223, 236, 244, 247, 262, 267, 274, 278], "opcheck": [4, 18], "basic": [4, 10, 13, 17, 18, 19, 27, 28, 36, 37, 38, 41, 42, 43, 45, 46, 55, 60, 62, 63, 79, 83, 96, 98, 99, 102, 103, 106, 108, 111, 115, 116, 117, 119, 120, 124, 127, 129, 136, 139, 142, 144, 159, 174, 179, 206, 209, 231, 249, 263, 269, 270, 273, 284], "program": [4, 5, 6, 22, 25, 26, 27, 29, 66, 67, 103, 109, 111, 112, 115, 136, 139, 142, 158, 177, 188, 189, 193, 213, 214, 215, 216, 231, 249, 265, 276, 278], 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265, 269, 271], "meta": [4, 18, 22, 139, 141, 153, 169, 174, 189, 190, 191, 192, 193, 216, 219, 256, 262, 269, 287], "abstract": [4, 6, 13, 16, 38, 57, 67, 97, 106, 107, 111, 115, 116, 123, 124, 127, 139, 141, 150, 157, 170, 174, 232, 284], "metadata": [4, 12, 18, 20, 26, 174, 185, 189, 190, 217, 254, 281], "register_fak": [4, 18], "though": [4, 7, 12, 27, 52, 81, 99, 106, 108, 109, 127, 131, 144, 145, 153, 154, 158, 169, 174, 185, 189, 190, 200, 205, 237, 249, 264], "load": [4, 6, 20, 22, 23, 24, 25, 27, 28, 36, 37, 39, 40, 41, 45, 46, 47, 48, 52, 57, 58, 61, 81, 83, 97, 102, 106, 107, 109, 116, 117, 123, 126, 127, 130, 136, 138, 142, 154, 159, 162, 167, 174, 184, 185, 187, 190, 194, 197, 198, 200, 201, 202, 203, 210, 211, 217, 221, 223, 225, 230, 237, 238, 240, 242, 243, 248, 253, 261, 262, 264, 269], "next": [4, 5, 6, 7, 10, 11, 13, 14, 16, 17, 19, 20, 23, 24, 26, 27, 38, 47, 48, 55, 57, 59, 61, 62, 64, 65, 66, 83, 86, 89, 91, 94, 99, 102, 103, 106, 108, 109, 113, 115, 116, 117, 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150, 171, 176, 184, 185, 200, 201, 213, 215, 225, 226, 249, 254, 265, 268], "modul": [4, 6, 9, 11, 14, 15, 16, 19, 22, 23, 25, 26, 27, 28, 37, 42, 43, 46, 47, 49, 50, 52, 55, 59, 61, 64, 65, 67, 71, 72, 74, 76, 86, 87, 99, 100, 102, 106, 107, 110, 115, 119, 120, 123, 125, 126, 129, 130, 131, 133, 136, 137, 138, 139, 140, 142, 144, 148, 150, 153, 157, 158, 159, 162, 165, 167, 169, 170, 172, 173, 174, 176, 177, 178, 179, 182, 185, 187, 188, 189, 190, 191, 192, 193, 195, 197, 198, 199, 200, 201, 202, 203, 204, 210, 212, 213, 214, 215, 216, 217, 220, 223, 225, 226, 229, 231, 232, 236, 237, 238, 240, 241, 244, 245, 246, 249, 251, 252, 253, 255, 257, 259, 260, 261, 265, 267, 269, 270, 276, 278, 282, 283, 284], "share": [4, 5, 6, 7, 12, 13, 22, 26, 27, 45, 54, 61, 72, 76, 86, 89, 97, 108, 112, 120, 123, 127, 136, 137, 140, 148, 150, 151, 161, 174, 176, 177, 178, 189, 190, 192, 211, 225, 237, 249, 254, 264, 273, 278], "load_librari": [4, 26, 27], "register_autograd": [4, 18], "prefer": [4, 5, 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164, 165, 167, 171, 176, 178, 184, 189, 193, 203, 214, 217, 226, 238, 241, 249, 255, 264, 279, 287], "14": [4, 5, 7, 26, 27, 28, 102, 115, 138, 159, 187, 192, 225, 236, 237, 238, 246, 249, 255, 278, 287], "123": [4, 57, 178, 236, 284], "reference_muladd": 4, "extend": [4, 5, 6, 7, 10, 23, 28, 58, 64, 65, 97, 117, 118, 119, 127, 136, 138, 143, 149, 151, 173, 177, 178, 187, 191, 193, 195, 202, 215, 225, 237, 238, 265], "arg": [4, 22, 23, 25, 59, 61, 112, 123, 125, 137, 138, 148, 149, 150, 152, 158, 172, 175, 176, 177, 178, 187, 188, 189, 190, 201, 202, 210, 214, 216, 227, 229, 231, 233, 237, 279, 287], "assert_clos": [4, 120], "check": [4, 5, 6, 7, 9, 10, 15, 16, 17, 23, 24, 26, 27, 29, 37, 40, 41, 42, 45, 47, 48, 49, 50, 55, 56, 58, 61, 64, 65, 81, 83, 94, 99, 102, 103, 106, 107, 108, 109, 112, 116, 117, 120, 122, 123, 129, 130, 136, 137, 141, 148, 150, 151, 154, 156, 159, 162, 169, 171, 173, 174, 177, 187, 188, 189, 190, 192, 194, 204, 208, 209, 214, 216, 223, 225, 230, 231, 236, 237, 240, 241, 244, 248, 255, 270, 272, 276], "undefin": [4, 150, 164, 205, 206, 207], "behavior": [4, 10, 12, 13, 14, 16, 26, 29, 41, 55, 57, 103, 113, 116, 120, 151, 158, 159, 162, 164, 167, 189, 190, 206, 207, 213, 214, 219, 241, 254, 265, 278], "myadd_out": 4, "myadd_out_cpu": 4, "is_contigu": [4, 6, 162, 264], "caus": [4, 14, 17, 25, 26, 81, 91, 99, 106, 116, 140, 159, 173, 176, 180, 184, 187, 190, 192, 193, 205, 211, 244, 246, 250, 265, 270, 278, 281], "incompat": [4, 5, 189, 190, 213], "went": [4, 58, 103, 106, 159, 213], "fairli": [4, 6, 108, 116, 127, 150, 167, 175], "primari": [5, 6, 7, 17, 157, 191, 192, 193, 202, 209, 223, 264, 265, 278], "languag": [5, 6, 7, 9, 21, 25, 27, 28, 29, 49, 55, 64, 65, 66, 86, 87, 103, 109, 111, 113, 126, 130, 134, 136, 139, 141, 142, 143, 152, 178, 180, 189, 190, 197, 202, 211, 224, 236, 252, 264, 265, 270, 274, 276, 282, 283, 284], "suitabl": [5, 10, 46, 146, 150, 154, 187, 215, 241, 264], "eas": [5, 6, 94, 106, 107, 137, 193, 237, 254, 269], "situat": [5, 17, 27, 29, 99, 106, 144, 145, 148, 150, 194, 200, 211], "properti": [5, 6, 7, 13, 16, 25, 27, 29, 36, 45, 54, 99, 102, 106, 112, 115, 141, 168, 180, 189, 190, 192, 193, 206, 225, 237, 248, 249, 262, 267, 276, 281], "unfavor": 5, "environ": [5, 6, 7, 13, 21, 22, 26, 29, 40, 47, 61, 91, 94, 98, 100, 105, 117, 125, 128, 129, 136, 137, 138, 139, 147, 148, 149, 150, 159, 164, 170, 175, 176, 177, 178, 188, 189, 190, 191, 192, 221, 223, 224, 225, 229, 230, 231, 232, 233, 236, 240, 241, 244, 247, 249, 264, 265, 270, 273, 275, 276, 278, 279], "latter": [5, 6, 7, 66, 141, 176, 214], "land": [5, 27, 127, 136, 161, 207, 237, 279], "latenc": [5, 7, 20, 136, 139, 141, 147, 159, 173, 188, 192, 193, 203, 210, 217, 236, 241, 273, 278], "strict": [5, 125, 187, 206, 237, 266], "java": [5, 64, 193, 221, 240, 241], "rust": 5, "paragraph": [5, 7, 27, 284, 287], "outlin": [5, 6, 7, 27, 245], "pure": [5, 6, 7, 12, 27, 136, 142, 145, 153, 169, 194, 202, 215], "journei": [5, 7, 58, 152], "enabl": 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7, 11, 14, 20, 23, 24, 28, 41, 43, 81, 99, 100, 105, 108, 117, 119, 125, 133, 160, 165, 175, 177, 185, 188, 189, 190, 194, 197, 214, 217, 238, 258, 259, 260], "onc": [5, 6, 7, 10, 12, 13, 16, 19, 20, 24, 25, 26, 27, 29, 36, 38, 41, 57, 58, 62, 66, 91, 100, 102, 105, 106, 107, 108, 109, 113, 117, 127, 146, 151, 154, 162, 167, 168, 171, 173, 174, 175, 177, 180, 184, 185, 193, 200, 201, 204, 209, 211, 217, 230, 237, 241, 244, 248, 249, 257, 265, 277], "record": [5, 7, 10, 23, 24, 27, 29, 36, 48, 55, 66, 116, 125, 136, 137, 138, 142, 144, 145, 167, 174, 175, 177, 178, 190, 221, 238, 252, 255, 258, 270], "limit": [5, 7, 11, 12, 13, 17, 20, 23, 55, 67, 81, 134, 138, 139, 151, 152, 153, 161, 162, 164, 169, 180, 188, 189, 190, 193, 198, 200, 201, 203, 204, 210, 213, 217, 241, 246, 249, 252, 262, 282], "explicit": [5, 7, 25, 27, 66, 154, 162, 178, 206, 216, 226, 244, 248, 283], "pars": [5, 6, 27, 55, 57, 115, 130, 137, 138, 141, 226, 249, 283], "subject": [5, 13, 16, 27, 47, 120, 127, 138, 156, 180, 189, 190, 203, 204, 209, 214, 221, 222, 223, 224, 229, 233, 240, 249], "constraint": [5, 7, 14, 20, 22, 66, 94, 110, 136, 139, 141, 168, 174, 187, 213, 214, 216, 217, 248, 249, 262, 270], "impos": [5, 241, 250, 262], "guidanc": [5, 10, 192, 193, 211, 248, 278], "offici": [5, 91, 120, 127, 129, 150, 175, 188, 190, 197, 215, 235, 237, 270], "jit": [5, 7, 10, 17, 23, 25, 26, 29, 64, 65, 66, 94, 125, 152, 157, 162, 188, 193, 198, 201, 203, 204, 210, 213, 214, 220, 221, 223, 224, 225, 226, 233, 235, 237, 240, 241, 242, 243, 249, 255, 265, 270, 274, 276], "scriptmodul": [5, 26, 27, 29, 94, 220, 240, 270, 276], "embed": [5, 9, 19, 25, 27, 55, 66, 83, 103, 109, 111, 113, 114, 123, 125, 129, 133, 136, 137, 139, 152, 177, 178, 180, 185, 191, 197, 204, 211, 258, 283], "resnet18": [5, 48, 162, 172, 173, 184, 187, 198, 211, 213, 214, 215, 276], "normal": [5, 7, 10, 13, 14, 23, 24, 25, 41, 43, 46, 55, 57, 58, 64, 65, 66, 71, 89, 94, 100, 102, 103, 107, 108, 110, 115, 124, 125, 131, 138, 142, 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273], "form": [5, 7, 11, 14, 17, 20, 27, 46, 52, 55, 58, 66, 67, 109, 116, 117, 123, 127, 130, 136, 140, 143, 154, 159, 180, 187, 190, 200, 209, 217, 219, 230, 231, 244, 252, 265, 278, 283], "accordingli": [5, 12, 22, 26, 151, 164, 167, 176, 187, 204, 224, 281], "sai": [5, 6, 7, 18, 28, 48, 57, 106, 110, 112, 115, 127, 129, 140, 153, 160, 164, 167, 171, 184, 200, 216, 240, 252, 284], "mymodul": [5, 7, 189, 219, 229], "__init__": [5, 6, 7, 13, 16, 23, 25, 26, 37, 42, 57, 59, 71, 102, 103, 110, 115, 124, 125, 138, 148, 149, 150, 153, 165, 171, 176, 177, 178, 185, 187, 189, 193, 195, 198, 199, 210, 213, 214, 215, 219, 226, 229, 231, 232, 233, 235, 236, 238, 241, 244, 246, 251, 254, 270, 283], "self": [5, 6, 7, 10, 12, 13, 16, 17, 23, 25, 26, 28, 29, 37, 38, 55, 59, 61, 66, 71, 103, 110, 115, 116, 122, 124, 125, 136, 138, 139, 148, 149, 150, 153, 159, 161, 165, 168, 176, 177, 178, 184, 185, 187, 189, 190, 193, 195, 198, 199, 201, 210, 213, 214, 215, 219, 226, 229, 231, 232, 233, 235, 236, 238, 240, 241, 244, 246, 251, 255, 262, 270], "n": [5, 6, 7, 9, 14, 20, 23, 26, 27, 36, 37, 48, 65, 67, 78, 79, 91, 94, 99, 103, 106, 108, 116, 123, 124, 129, 134, 137, 138, 148, 150, 152, 160, 161, 165, 171, 176, 177, 178, 180, 185, 194, 198, 200, 201, 206, 213, 214, 216, 217, 222, 225, 230, 237, 265, 276], "super": [5, 6, 7, 13, 22, 23, 24, 25, 26, 29, 37, 65, 71, 103, 115, 124, 125, 138, 148, 149, 153, 161, 165, 176, 177, 178, 185, 187, 189, 193, 195, 210, 213, 214, 215, 219, 225, 226, 229, 231, 232, 235, 236, 238, 240, 241, 244, 251, 252], "mv": [5, 123], "my_modul": 5, "sm": [5, 184], "exclud": [5, 10, 48, 255], "doesn": [5, 8, 10, 12, 15, 18, 20, 29, 64, 67, 110, 112, 115, 116, 127, 140, 160, 162, 171, 187, 188, 192, 195, 199, 200, 205, 211, 214, 216, 217, 222, 225, 227, 228, 246, 264, 265, 275, 278, 283], "yet": [5, 7, 12, 13, 22, 27, 56, 81, 99, 102, 106, 119, 120, 127, 150, 177, 180, 191, 195, 201, 209, 214, 215, 233, 237, 242, 243], "could": [5, 6, 7, 10, 12, 13, 27, 58, 66, 97, 99, 103, 108, 109, 110, 112, 113, 115, 117, 122, 137, 138, 139, 140, 142, 143, 150, 154, 162, 164, 167, 174, 175, 176, 177, 178, 180, 184, 185, 187, 192, 193, 194, 195, 205, 207, 213, 215, 216, 222, 231, 232, 233, 237, 238, 254, 278], "ignor": [5, 23, 57, 108, 113, 115, 125, 170, 187, 194, 195, 198, 203, 206, 207, 209, 213, 214, 235, 248, 255, 266], "readi": [5, 7, 11, 12, 19, 26, 27, 47, 55, 64, 65, 100, 102, 107, 109, 110, 115, 116, 136, 137, 149, 150, 165, 170, 174, 176, 177, 178, 191, 194, 203, 210, 213, 214, 215, 225, 230, 241, 242, 243, 246, 255, 270], "hand": [5, 6, 7, 10, 16, 20, 22, 27, 81, 106, 109, 115, 116, 143, 150, 154, 169, 188, 193, 206, 217, 252], "shown": [5, 7, 10, 20, 23, 24, 25, 58, 64, 65, 99, 106, 116, 127, 130, 139, 141, 152, 159, 161, 172, 175, 176, 178, 179, 184, 187, 188, 192, 193, 199, 204, 206, 207, 208, 211, 214, 216, 217, 230, 231, 236, 237, 244, 246, 252, 270, 275, 277, 278, 279, 281, 283], "filenam": [5, 7, 122, 130, 187, 248], 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203, 204, 208, 213, 214, 215, 216, 221, 225, 236, 240, 267, 270], "begin": [5, 6, 7, 9, 13, 14, 20, 23, 26, 27, 29, 36, 48, 55, 56, 58, 61, 64, 65, 81, 94, 99, 102, 113, 115, 120, 127, 129, 130, 137, 139, 152, 167, 172, 175, 177, 184, 185, 207, 217, 241, 246, 248, 249, 251, 255, 258, 259, 260, 266, 267, 268, 283], "iostream": [5, 6, 7, 26, 27, 237], "argc": [5, 26, 27, 237, 276], "char": [5, 26, 27, 65, 159, 225, 237, 276], "cerr": [5, 26, 27, 237, 276], "app": [5, 27, 117, 134, 136, 154, 210, 221, 237, 240, 245, 246, 269, 270], "try": [5, 7, 14, 16, 17, 20, 23, 25, 26, 27, 29, 41, 45, 47, 49, 52, 54, 55, 58, 59, 64, 65, 67, 81, 99, 106, 107, 108, 109, 110, 111, 112, 116, 117, 122, 130, 141, 142, 143, 151, 157, 159, 161, 164, 165, 167, 170, 171, 174, 175, 180, 184, 188, 189, 190, 192, 198, 200, 203, 206, 213, 217, 225, 230, 233, 236, 237, 240, 248, 249, 252, 259, 262, 263, 276, 278, 283, 284], "deseri": [5, 7, 27, 125, 198, 213, 214, 259, 276], "catch": [5, 10, 13, 26, 64, 225, 237, 240, 276], "c10": [5, 10, 12, 17, 26, 170, 202, 204, 225, 236, 237, 249, 264, 276], "ok": [5, 115, 176, 283], "relev": [5, 7, 16, 59, 103, 109, 111, 115, 127, 128, 137, 139, 171, 187, 258, 260, 265, 267, 273], "accept": [5, 6, 24, 46, 97, 99, 108, 113, 129, 130, 139, 141, 156, 160, 165, 169, 174, 177, 184, 187, 195, 216, 219, 222, 229, 236, 255, 265, 270], "proce": [5, 13, 29, 108, 110, 159, 172, 180, 252, 265], "examin": [5, 13, 26, 29, 64, 65, 91, 99, 100, 105, 107, 108, 158], "moment": [5, 7, 13, 189, 195, 208, 223, 241], "cmakelist": [5, 7, 26, 27, 225, 237, 276], "txt": [5, 6, 7, 26, 27, 55, 130, 142, 143, 152, 159, 173, 180, 201, 225, 237, 276], "cmake_minimum_requir": [5, 7, 26, 27, 225, 237, 276], "fatal_error": [5, 7, 26, 27, 225, 237, 276], "custom_op": [5, 18, 189, 276], "find_packag": [5, 7, 26, 27, 237, 276], "add_execut": [5, 7, 26, 27, 237, 276], "target_link_librari": [5, 7, 26, 27, 225, 237, 276], "set_properti": [5, 7, 237, 276], "cxx_standard": [5, 7, 237, 276], "last": [5, 7, 13, 16, 23, 27, 37, 48, 55, 58, 59, 65, 66, 81, 92, 94, 97, 102, 103, 106, 107, 110, 113, 117, 127, 131, 136, 139, 140, 142, 143, 150, 151, 159, 164, 167, 172, 174, 175, 176, 178, 179, 180, 181, 185, 192, 194, 204, 205, 208, 235, 237, 240, 246, 248, 258, 265, 269, 270], "thing": [5, 6, 7, 10, 17, 18, 25, 26, 27, 29, 36, 48, 49, 52, 55, 64, 65, 94, 97, 99, 102, 106, 107, 108, 109, 110, 112, 113, 115, 116, 127, 130, 136, 139, 140, 141, 145, 146, 147, 150, 151, 154, 158, 159, 162, 168, 173, 174, 182, 193, 198, 200, 211, 213, 225, 230, 249, 259, 268, 283], "grab": [5, 7, 8, 58, 105, 173, 178], "latest": [5, 7, 12, 16, 24, 97, 100, 119, 124, 125, 136, 152, 172, 173, 174, 177, 180, 182, 187, 188, 225, 258, 277, 281], "stabl": [5, 24, 30, 31, 32, 33, 34, 109, 127, 155, 173, 183, 184, 186, 197, 238, 241, 248, 269, 281], "page": [5, 7, 12, 26, 27, 56, 60, 67, 116, 122, 124, 136, 142, 154, 178, 184, 191, 215, 221, 224, 225, 226, 234, 237, 240, 265, 285, 287], "websit": [5, 7, 100, 175, 244, 247], "unzip": [5, 7, 23, 56, 187, 194, 197, 198, 213, 214, 225], "archiv": [5, 6, 7, 29, 162, 277], "lib": [5, 6, 7, 12, 22, 26, 27, 48, 120, 159, 203, 221, 225, 237, 264, 278], "against": [5, 18, 26, 27, 41, 42, 49, 66, 90, 103, 105, 117, 150, 162, 174, 229, 237, 249, 252], "window": [5, 6, 7, 24, 49, 57, 103, 115, 117, 148, 177, 184, 194, 223, 230, 244, 283], "debug": [5, 7, 10, 23, 29, 64, 65, 66, 67, 86, 109, 136, 140, 181, 189, 190, 202, 211, 212, 249, 264, 273], "abi": [5, 6, 7, 26, 27, 221, 223, 225, 237], "laid": 5, "within": [5, 6, 16, 22, 25, 27, 67, 94, 99, 100, 105, 115, 116, 117, 122, 123, 139, 145, 152, 157, 159, 168, 171, 175, 177, 187, 192, 193, 201, 202, 208, 209, 211, 215, 223, 225, 230, 232, 236, 249, 256, 265, 273, 278, 281, 283], "mkdir": [5, 7, 27, 184, 187, 197, 210, 225], "dcmake_prefix_path": [5, 7, 26, 27, 237, 276], "config": [5, 7, 12, 20, 24, 97, 138, 141, 152, 173, 192, 193, 195, 200, 201, 202, 213, 215, 217, 237, 238, 262, 273, 274], "someth": 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58, 59, 64, 102, 116, 151, 162, 171, 175, 178, 193, 194, 198, 201, 213, 215, 217, 220, 236, 238, 241, 246, 265], "cmakefil": [5, 7, 26, 27], "dir": [5, 7, 26, 27, 91, 105, 141, 221, 225, 241, 264], "o": [5, 6, 7, 9, 26, 27, 108, 120, 152, 165, 167, 187, 217, 249, 283], "100": [5, 7, 11, 23, 25, 26, 27, 45, 48, 54, 103, 110, 138, 148, 153, 159, 162, 164, 173, 174, 175, 178, 185, 187, 189, 192, 193, 198, 203, 213, 214, 217, 232, 236, 238, 249, 264, 277], "suppli": [5, 7, 112, 162, 173, 283], "binari": [5, 7, 24, 26, 27, 55, 58, 106, 117, 150, 162, 188, 204, 206, 212, 215, 221, 225, 229, 235, 237, 240, 241], "1d": [5, 74, 103, 124, 222, 265], "4d": [5, 52, 86, 162, 216], "path_to_model": 5, "successfulli": [5, 7, 26, 56, 64, 65, 66, 117, 134, 141, 150, 159, 177, 207, 210, 223, 236, 243, 245, 251, 257, 258, 259, 260, 266, 267, 268, 276], "coupl": [5, 16, 40, 55, 98, 106, 107, 115, 137, 139, 145, 151, 153, 167, 185, 199, 220, 265], "awai": [5, 6, 7, 27, 52, 66, 103, 105, 109, 110, 112, 127, 164, 174, 175, 176, 208, 252, 283], "ivalu": [5, 27, 64, 170, 223, 225, 237, 241, 276], "push_back": [5, 26, 27, 237, 276], "totensor": [5, 23, 27, 57, 64, 65, 102, 130, 138, 150, 154, 172, 173, 177, 185, 187, 198, 203, 204, 213, 214, 221, 223, 230, 237, 238, 241, 272, 276], "slice": [5, 6, 45, 54, 89, 113, 116, 142, 165, 171, 209, 223, 264], "eras": [5, 29], "org": [5, 7, 30, 31, 32, 33, 34, 39, 47, 48, 50, 51, 55, 64, 65, 82, 85, 93, 101, 103, 105, 107, 111, 118, 127, 137, 142, 143, 152, 155, 156, 157, 158, 167, 172, 173, 180, 183, 184, 186, 188, 190, 197, 200, 203, 204, 206, 208, 210, 212, 220, 221, 222, 223, 225, 230, 238, 240, 241, 242, 244, 248, 252, 253, 254, 276, 281, 283], "cppdoc": [5, 7], "pariti": 5, "manipul": [5, 45, 46, 66, 102, 105, 115, 124, 158, 167, 198, 201, 230], "five": [5, 11, 71, 124, 127], "ideal": [5, 7, 16, 38, 64, 65, 99, 102, 106, 108, 164, 180, 193, 213, 224], "variabl": [5, 6, 7, 9, 10, 24, 26, 27, 36, 47, 55, 66, 84, 91, 97, 99, 106, 109, 110, 112, 116, 128, 142, 147, 150, 159, 176, 179, 189, 190, 192, 200, 207, 209, 222, 223, 224, 225, 236, 240, 244, 260, 264, 270, 273, 275, 278], "kcuda": [5, 7, 202], "live": [5, 7, 12, 17, 106, 136, 140, 149, 177, 178, 208, 233, 283], "hopefulli": [5, 7, 56, 57, 81, 94, 110, 125, 266], "equip": [5, 6, 145, 151, 205, 278], "concept": [5, 7, 13, 26, 40, 41, 61, 98, 111, 112, 116, 124, 128, 136, 141, 161, 176, 179, 180, 202, 213, 215, 216, 255, 264, 278], "close": [5, 7, 11, 16, 20, 23, 27, 58, 99, 103, 106, 108, 109, 137, 141, 152, 167, 185, 197, 201, 217, 263, 265, 270], "Of": [5, 16, 27, 108, 112, 116, 140, 148, 150, 185, 206, 208, 244], "cours": [5, 7, 16, 20, 23, 27, 59, 99, 100, 106, 108, 111, 112, 115, 116, 140, 148, 150, 185, 217, 230, 244], "did": [5, 7, 10, 18, 23, 27, 29, 49, 58, 66, 99, 102, 103, 106, 117, 127, 150, 156, 168, 174, 177, 180, 192, 198, 249, 283], "cover": [5, 6, 8, 16, 17, 19, 22, 26, 29, 38, 52, 64, 65, 106, 111, 120, 127, 128, 134, 136, 137, 141, 150, 170, 174, 177, 178, 185, 188, 189, 190, 191, 207, 209, 213, 216, 229, 236, 237, 248, 263, 264, 270, 278], "insid": [5, 6, 7, 12, 19, 22, 26, 27, 37, 41, 50, 120, 139, 184, 194, 211, 217, 222, 224, 241, 283], "shortli": [5, 176], "html": [5, 30, 31, 32, 33, 34, 39, 50, 51, 82, 85, 93, 101, 111, 118, 127, 128, 133, 137, 152, 155, 157, 158, 172, 183, 186, 187, 190, 197, 203, 204, 206, 208, 220, 221, 248, 252, 253, 254, 283], "peter": 6, "goldsborough": 6, "plethora": 6, "relat": [6, 13, 16, 58, 66, 99, 100, 106, 107, 112, 115, 127, 136, 139, 159, 168, 189, 190, 198, 249, 265, 283], "algebra": [6, 16, 45, 54, 102, 106, 110, 236], "wrangl": 6, "novel": [6, 100], "research": [6, 7, 20, 23, 27, 29, 55, 58, 66, 81, 83, 94, 100, 106, 110, 128, 129, 150, 152, 165, 169, 171, 187, 197, 217, 233], "power": [6, 7, 27, 48, 55, 63, 71, 81, 83, 97, 99, 102, 108, 110, 116, 124, 136, 138, 141, 150, 159, 161, 165, 170, 171, 173, 180, 187, 200, 203, 208, 210, 245, 249, 274, 278, 283], "spare": [6, 7], "deriv": [6, 7, 12, 18, 20, 27, 29, 36, 41, 45, 48, 54, 84, 86, 99, 106, 110, 112, 113, 115, 123, 124, 140, 145, 156, 160, 211, 217, 265], "express": [6, 8, 18, 27, 99, 102, 103, 106, 109, 119, 128, 139, 152, 189, 190, 207, 213, 214, 216, 226, 283], "better": [6, 7, 16, 20, 23, 25, 38, 41, 42, 49, 58, 62, 87, 99, 102, 103, 105, 108, 115, 116, 122, 127, 129, 136, 141, 142, 143, 154, 159, 160, 161, 167, 169, 172, 174, 175, 180, 184, 185, 187, 189, 190, 192, 193, 198, 200, 205, 206, 211, 213, 214, 215, 216, 217, 224, 241, 249, 265, 272, 277, 278], "frequent": [6, 26, 27, 36, 83, 91, 115, 124, 138, 191, 192, 193, 207, 237], "expens": [6, 20, 45, 108, 122, 178, 193, 200, 217, 255, 264], "plausibl": 6, "address": [6, 14, 22, 26, 28, 91, 103, 136, 150, 176, 177, 190, 192, 213, 238, 262, 278, 284], "nativ": [6, 7, 10, 17, 18, 24, 27, 47, 61, 97, 119, 120, 134, 136, 137, 139, 151, 152, 178, 192, 193, 195, 200, 205, 208, 213, 233, 236, 237, 241, 255, 264, 265, 269], "intend": [6, 7, 10, 14, 27, 99, 108, 138, 139, 159, 162, 179, 188, 189, 190, 205, 213, 229, 251, 265], "much": [6, 7, 10, 12, 14, 16, 20, 23, 27, 29, 41, 58, 62, 81, 99, 100, 108, 109, 110, 115, 116, 125, 134, 137, 140, 141, 157, 160, 161, 167, 173, 174, 177, 178, 180, 184, 185, 188, 192, 200, 205, 208, 216, 217, 235, 244, 245, 246, 248, 249, 252, 254, 263, 265, 266, 269, 278, 281, 283], "boilerpl": [6, 7, 23, 49, 110, 185, 205], "degre": [6, 41, 70, 103, 124, 141, 180, 184, 208], "organ": [6, 10, 16, 27, 57, 102, 125, 142, 164, 249, 258, 277, 278, 284], "tackl": [6, 122], "decid": [6, 7, 10, 12, 20, 22, 58, 67, 97, 99, 175, 191, 208, 212, 217, 268], "contribut": [6, 58, 67, 71, 91, 100, 106, 124, 152, 162, 184, 192], "upstream": [6, 237, 265], "rest": [6, 7, 16, 49, 131, 136, 151, 158, 172, 174, 176, 177, 181, 207, 214, 235, 252, 262, 283, 284], "chase": [6, 127], "someon": [6, 180], "fire": [6, 148], "dai": [6, 47, 115, 130, 139, 262], "head": [6, 8, 9, 20, 25, 40, 47, 83, 92, 103, 122, 127, 139, 148, 172, 179, 194, 209, 217], 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58, 64, 65, 91, 100, 137, 144, 152, 158, 159, 160, 162, 167, 174, 179, 184, 188, 193, 220, 231, 232, 236, 238, 241, 248, 249, 252, 274, 283], "lstmcell": 6, "cell": [6, 25, 27, 29, 56, 66, 83, 89, 99, 100, 102, 103, 106, 107, 122, 175, 179, 187, 200, 252, 265, 284], "plain": [6, 7, 27, 55, 116, 142, 143, 180, 194, 262, 264], "input_featur": 6, "state_s": 6, "candid": [6, 159, 265], "reset_paramet": 6, "stdv": 6, "math": [6, 12, 71, 94, 99, 112, 123, 124, 165, 173, 252, 265, 287], "sqrt": [6, 94, 116, 205, 219], "uniform_": [6, 7, 178, 219], "old_h": 6, "old_cel": 6, "cat": [6, 24, 25, 45, 49, 54, 100, 102, 109, 112, 123, 149, 154, 159, 178, 180, 185, 194, 216, 247], "gate_weight": 6, "f": [6, 9, 10, 13, 14, 18, 22, 23, 26, 36, 37, 47, 48, 52, 59, 61, 71, 99, 103, 110, 115, 116, 123, 124, 125, 127, 130, 137, 138, 139, 140, 148, 149, 150, 152, 153, 159, 160, 170, 172, 173, 176, 177, 178, 185, 187, 189, 190, 198, 201, 202, 203, 204, 213, 214, 217, 221, 222, 227, 229, 230, 231, 232, 233, 236, 238, 244, 249, 251, 264, 276, 277, 279, 283], "split": [6, 9, 10, 22, 23, 24, 25, 50, 55, 58, 87, 94, 97, 102, 105, 107, 115, 127, 136, 142, 143, 148, 149, 152, 164, 174, 177, 178, 180, 194, 198, 201, 213, 214, 229, 244, 264], "input_g": 6, "sigmoid": [6, 7, 58, 103, 123, 195, 216, 265], "output_g": 6, "tanh": [6, 7, 58, 103, 110, 123, 174, 265], "candidate_cel": 6, "new_cel": 6, "hidden": [6, 9, 25, 55, 66, 100, 103, 108, 109, 113, 116, 139, 141, 142, 143, 151, 157, 178, 179, 180, 213, 247, 252, 281], "new_h": 6, "batch_siz": [6, 7, 9, 16, 23, 24, 38, 55, 57, 58, 61, 97, 123, 127, 130, 137, 138, 139, 149, 150, 152, 172, 175, 176, 177, 184, 185, 187, 191, 192, 193, 198, 201, 213, 214, 217, 237, 238, 248, 272], "rnn": [6, 25, 29, 50, 55, 66, 103, 123, 136, 149, 151, 168, 177, 180, 181, 215, 265], "new_c": 6, "intel": [6, 136, 150, 159, 162, 215, 223, 269], "mkl": [6, 159, 255, 278], "nnpack": 6, "why": [6, 7, 10, 13, 16, 18, 29, 49, 58, 94, 100, 108, 110, 115, 125, 128, 145, 148, 159, 167, 180, 188, 189, 190, 206, 208, 249, 283], "room": [6, 108, 164, 236, 283], "obviou": [6, 127, 249], "knowledg": [6, 55, 58, 81, 110, 111, 126, 128, 136, 159, 161, 283], "execut": [6, 7, 9, 10, 13, 17, 19, 21, 24, 25, 26, 27, 29, 36, 37, 44, 47, 48, 50, 53, 56, 66, 76, 80, 84, 86, 88, 91, 95, 99, 102, 104, 106, 109, 114, 120, 121, 126, 135, 136, 140, 141, 149, 150, 151, 158, 159, 162, 164, 170, 174, 175, 177, 178, 179, 181, 188, 189, 190, 192, 193, 198, 199, 201, 202, 203, 210, 218, 220, 221, 223, 224, 225, 229, 236, 241, 242, 243, 244, 248, 252, 261, 264, 270, 271, 276, 278], "involv": [6, 10, 11, 17, 20, 23, 27, 29, 40, 56, 66, 94, 99, 100, 106, 109, 112, 117, 125, 135, 140, 142, 147, 154, 161, 167, 178, 180, 189, 190, 198, 200, 209, 217, 256, 259, 265, 274], "launch": [6, 7, 25, 59, 67, 129, 141, 147, 148, 164, 176, 177, 178, 179, 184, 192, 223, 236, 238, 249, 255, 265, 278], "amount": [6, 23, 29, 81, 91, 125, 139, 148, 171, 188, 200, 241, 255, 259, 265], "becom": [6, 7, 13, 25, 28, 58, 81, 83, 86, 94, 100, 108, 139, 145, 160, 162, 184, 185, 192, 202, 209, 213, 227, 233, 236, 248, 270, 278], "furthermor": [6, 23, 108, 153, 160, 185, 192, 202, 216, 242, 243, 249], "interpret": [6, 7, 27, 29, 62, 91, 94, 100, 115, 136, 142, 143, 152, 180, 188, 189, 190, 202, 212, 233, 249, 253, 261, 264, 269, 278, 283], "slow": [6, 7, 10, 41, 47, 138, 192, 246, 255, 265], "down": [6, 10, 12, 13, 23, 37, 38, 47, 56, 91, 97, 99, 103, 105, 110, 116, 138, 151, 159, 160, 161, 177, 182, 185, 192, 205, 264, 265, 281], "therefor": [6, 7, 11, 17, 23, 55, 57, 66, 108, 120, 125, 127, 129, 135, 148, 149, 162, 165, 170, 171, 177, 178, 189, 190, 192, 207, 208, 216, 223, 241, 248, 257, 278, 283], "rewrit": [6, 25, 50, 66, 119, 168, 189, 190, 216, 222, 223, 270], "group": [6, 13, 22, 23, 28, 55, 67, 92, 103, 106, 122, 127, 135, 136, 137, 138, 143, 144, 146, 148, 149, 150, 159, 184, 191, 194, 231, 232, 233, 249, 278, 279, 283, 284], "profit": 6, "fewer": [6, 13, 144, 160], "visibl": [6, 26, 27, 49, 97, 105, 187, 198], "global": [6, 7, 29, 55, 62, 66, 97, 139, 153, 167, 177, 182, 189, 190, 198, 217, 227, 229, 233, 250, 262, 264, 265, 270], "aten": [6, 10, 12, 17, 27, 47, 122, 159, 184, 189, 190, 193, 198, 201, 202, 204, 213, 214, 215, 236, 237, 244, 255, 264], "translat": [6, 27, 29, 55, 66, 117, 119, 126, 130, 136, 142, 165, 181, 203, 207, 230, 265, 270], "benefit": [6, 7, 11, 20, 22, 47, 48, 94, 97, 134, 137, 156, 162, 167, 172, 179, 192, 200, 213, 217, 233, 236, 237, 248, 252, 265, 277, 278], "massiv": [6, 29, 49, 112, 115, 237], "parallel": [6, 7, 13, 19, 21, 22, 49, 51, 53, 55, 57, 61, 81, 87, 97, 102, 106, 125, 136, 141, 146, 150, 152, 165, 174, 176, 177, 178, 181, 191, 192, 231, 232, 233, 248, 255, 257, 278, 279], "lltm_cpp": 6, "conveni": [6, 10, 16, 26, 27, 49, 52, 55, 100, 112, 116, 140, 142, 143, 153, 160, 170, 174, 206, 208, 230, 237, 248, 249, 264, 270], "wrapper": [6, 7, 10, 61, 116, 125, 137, 138, 145, 151, 161, 174, 187, 212, 215, 257], "include_dir": [6, 12, 170], "include_path": 6, "manag": [6, 26, 37, 48, 55, 67, 99, 116, 120, 122, 127, 135, 139, 146, 147, 148, 164, 168, 179, 184, 193, 231, 232, 233, 248, 249, 250, 254, 255, 256, 265, 277, 278], "And": [6, 7, 12, 18, 26, 27, 28, 29, 58, 100, 112, 115, 116, 117, 127, 145, 159, 160, 162, 165, 173, 184, 185, 188, 189, 190, 192, 193, 211, 215, 216, 230, 249, 264], "overal": [6, 23, 47, 55, 103, 137, 138, 150, 164, 175, 187, 213, 216, 236, 246, 249, 265], "d_sigmoid": 6, "bit": [6, 17, 27, 29, 74, 102, 106, 122, 124, 127, 151, 173, 174, 180, 200, 205, 213, 215, 224, 238, 246, 249, 252], "pybind11": [6, 10, 26, 27, 170, 249], "datatyp": [6, 27, 45, 54, 79, 102, 106, 122, 237, 248, 252, 265], "Its": [6, 108, 110, 209, 283], "inspect": [6, 27, 86, 99, 108, 120, 137, 141, 158, 179, 182, 188, 189, 190, 198, 201, 233, 249], "notic": [6, 10, 16, 25, 26, 27, 29, 36, 47, 48, 49, 58, 66, 81, 94, 99, 102, 106, 108, 110, 125, 145, 150, 159, 161, 164, 168, 169, 172, 174, 184, 188, 189, 190, 192, 193, 204, 205, 207, 211, 259, 268, 276], "dispos": 6, "nvcc": 6, "workaround": [6, 27, 87, 94, 145, 156], "logic": [6, 7, 13, 20, 27, 41, 46, 94, 109, 138, 141, 147, 149, 171, 177, 178, 187, 193, 199, 217, 219, 231, 233, 278], "sigmoidalphablendforwardcuda": 6, "port": [6, 19, 150, 177, 230, 273], "entir": [6, 7, 16, 19, 22, 23, 29, 52, 55, 59, 66, 86, 108, 110, 113, 116, 131, 136, 137, 138, 142, 144, 149, 157, 164, 167, 169, 171, 172, 174, 178, 180, 192, 198, 205, 206, 207, 210, 213, 214, 225, 231, 248, 254, 256, 265, 269, 283], "lltm_forward": 6, "addmm": [6, 7, 122, 159, 189, 204, 213, 223, 224, 236, 255], "transpos": [6, 7, 14, 45, 54, 55, 58, 123, 159, 172, 185, 189, 209, 223], "alpha": [6, 7, 17, 161, 187, 205, 284], "respect": [6, 8, 18, 19, 36, 38, 40, 41, 42, 48, 55, 58, 69, 70, 99, 100, 110, 112, 124, 128, 139, 140, 149, 151, 156, 159, 160, 169, 174, 178, 180, 184, 189, 190, 192, 193, 214, 229, 262, 265, 267, 278], "ultim": [6, 23, 55, 58, 66, 94, 205, 224], "plop": [6, 27], "autograd": [6, 8, 14, 17, 18, 19, 25, 29, 37, 38, 40, 41, 42, 43, 45, 46, 47, 51, 52, 53, 63, 65, 68, 74, 85, 86, 88, 90, 98, 100, 101, 102, 103, 105, 106, 107, 111, 112, 116, 123, 136, 142, 143, 145, 148, 149, 159, 160, 165, 169, 175, 176, 177, 180, 181, 193, 216, 222, 225, 229, 230, 233, 244, 248, 265, 274, 276], "nice": [6, 55, 89, 116, 167, 169], "dig": [6, 100, 110, 115, 179], "deeper": [6, 13, 14, 42, 100, 106, 108, 110, 158, 159, 179, 193, 228, 276], "interest": [6, 7, 12, 16, 20, 24, 27, 29, 49, 55, 57, 64, 65, 97, 99, 100, 108, 110, 117, 119, 120, 127, 131, 137, 141, 160, 167, 168, 172, 174, 182, 189, 190, 192, 194, 217, 222, 249, 252, 264, 267, 268, 283], "alex": 6, "grave": 6, "thesi": 6, "d_tanh": 6, "relu": [6, 7, 14, 23, 27, 52, 58, 103, 110, 115, 116, 123, 125, 138, 148, 149, 153, 159, 165, 172, 173, 176, 177, 178, 185, 189, 190, 193, 197, 198, 216, 231, 232, 235, 236, 237, 238, 241, 248, 251, 265, 270], "exp": [6, 71, 109, 110, 124, 145, 207], "d_elu": 6, "mask": [6, 20, 64, 83, 100, 103, 122, 133, 136, 151, 168, 171, 179, 187, 194, 200, 205, 206, 208, 209, 212, 217, 237], "type_a": 6, "lltm_backward": 6, "grad_h": 6, "grad_cel": 6, "d_output_g": 6, "d_tanh_new_cel": 6, "d_new_cel": 6, "d_old_cel": 6, "d_candidate_cel": 6, "d_input_g": 6, "d_gate": 6, "d_weight": 6, "d_bia": 6, "keepdim": [6, 23, 138, 177, 198, 213, 214, 238], "d_x": 6, "d_old_h": 6, "d_input": 6, "span": [6, 20, 83, 109, 148, 164, 184, 217, 244, 284], "four": [6, 16, 22, 26, 94, 102, 103, 106, 120, 124, 129, 134, 137, 149, 150, 164, 185, 241, 246, 277, 283, 284], "pybind11_modul": [6, 170], "torch_extension_nam": [6, 170], "macro": [6, 7, 10, 17, 27], "maintain": [6, 12, 16, 26, 36, 37, 38, 48, 55, 81, 103, 108, 113, 120, 136, 146, 150, 161, 175, 178, 192, 193, 210, 248, 279], "mismatch": [6, 108], "nasti": [6, 262], "hard": [6, 7, 10, 17, 25, 38, 110, 141, 171, 249, 273], "At": [6, 7, 10, 16, 17, 20, 24, 27, 48, 52, 55, 56, 94, 97, 113, 116, 124, 130, 137, 138, 139, 151, 156, 161, 172, 174, 175, 176, 179, 180, 184, 205, 208, 213, 217, 223, 241, 255, 262, 265, 282], "point": [6, 7, 10, 12, 13, 16, 20, 22, 23, 24, 26, 27, 48, 52, 55, 56, 57, 58, 59, 64, 66, 91, 94, 99, 102, 103, 106, 108, 109, 111, 112, 113, 115, 116, 123, 124, 136, 138, 139, 140, 141, 145, 146, 148, 158, 161, 164, 165, 172, 174, 176, 180, 185, 187, 189, 190, 197, 198, 200, 201, 205, 207, 208, 210, 213, 216, 217, 225, 238, 241, 246, 252, 253, 261, 263, 265, 269, 281, 282, 283], "bdist_egg": 6, "egg_info": [6, 27], "egg": [6, 27], "pkg": [6, 27, 277], "dependency_link": [6, 27], "top_level": [6, 27], "manifest": [6, 27, 207, 210], "bdist": 6, "x86_64": [6, 22, 27, 221, 225], "install_lib": 6, "temp": [6, 23, 27, 152, 198, 201, 213, 214, 273], "gcc": [6, 27, 120, 159], "miniconda": [6, 22, 27], "compiler_compat": [6, 27], "wl": [6, 26, 27], "sysroot": [6, 27], "wsign": [6, 27], "dndebug": [6, 27], "fwrapv": [6, 27], "o3": [6, 27, 249], "wall": [6, 27, 109, 158, 249], "wstrict": [6, 27], "prototyp": [6, 12, 13, 17, 27, 38, 189, 190, 202, 209, 210, 216, 218, 222, 223, 229], "fpic": [6, 27, 120], "site": [6, 8, 22, 26, 27, 56, 58, 64, 65, 158, 180, 203, 230, 242, 243, 245, 255, 264, 277, 283], "csrc": [6, 26, 27, 170, 204, 240], "th": [6, 16, 27, 57, 110, 115, 150, 161], "thc": [6, 27], "7m": [6, 27, 48], "dtorch_api_include_extension_h": [6, 27], "dtorch_extension_nam": [6, 27], "d_glibcxx_use_cxx11_abi": [6, 27], "cc1plu": [6, 27], "warn": [6, 23, 27, 47, 57, 152, 159, 187, 189, 198, 201, 203, 205, 206, 207, 208, 213, 214, 233, 249, 255, 264, 270], "valid": [6, 9, 24, 27, 28, 41, 55, 97, 103, 105, 107, 108, 119, 122, 125, 127, 129, 131, 137, 138, 141, 144, 145, 170, 172, 187, 194, 198, 206, 207, 216, 217, 221, 240, 249], "objc": [6, 27], "l": [6, 9, 14, 16, 27, 48, 57, 58, 99, 110, 120, 123, 174, 175, 189, 210, 249], "rpath": [6, 27], "cpython": [6, 27, 264], "37m": [6, 27], "stub": [6, 172, 226, 241, 284], "loader": [6, 7, 28, 42, 49, 87, 107, 116, 174, 177, 194, 240, 249], "byte": [6, 152, 154, 190, 197, 225], "37": [6, 9, 20, 122, 153, 173, 178, 193, 217, 236, 255], "pyc": 6, "native_lib": 6, "zip_saf": 6, "analyz": [6, 23, 27, 66, 91, 136, 167, 174, 188, 190, 201, 264], "__pycache__": 6, "__file__": [6, 170, 249], "dist": [6, 13, 22, 123, 137, 138, 148, 150, 170, 177, 191, 231, 232, 279], "py3": 6, "remov": [6, 20, 23, 55, 58, 59, 65, 66, 92, 116, 122, 123, 128, 130, 140, 148, 152, 157, 159, 167, 173, 179, 180, 187, 188, 189, 190, 198, 199, 200, 201, 205, 206, 209, 210, 213, 214, 217, 221, 233, 246, 249, 264, 270, 281], "everyth": [6, 18, 48, 97, 106, 108, 110, 120, 141, 142, 145, 151, 154, 172, 173, 174, 175, 176, 180, 185, 203, 204, 229, 230, 241, 264], "finish": [6, 7, 19, 27, 50, 64, 102, 148, 149, 150, 164, 176, 178, 185, 204, 215, 221, 229, 235, 241, 244, 251, 265], "ubuntu": [6, 7, 184, 225, 278], "16": [6, 9, 20, 23, 27, 48, 97, 102, 103, 108, 125, 148, 151, 162, 172, 173, 178, 185, 187, 189, 193, 194, 200, 203, 210, 217, 221, 225, 231, 236, 249, 287], "04": [6, 9, 133, 184, 236, 249], "recent": [6, 55, 83, 113, 122, 127, 129, 139, 150, 152, 159, 165, 168, 172, 175, 184, 225, 278], "maco": [6, 7, 117, 150, 187], "clang": [6, 221, 223], "worst": [6, 152], "resolv": [6, 27, 108, 157, 162, 193, 207], "symbol": [6, 29, 129, 133, 157, 189, 190, 198, 216, 249, 264, 283], "linker": [6, 27, 221], "pycapsul": [6, 27], "builtin": [6, 170, 249, 264], "arg0": 6, "arg1": [6, 177], "arg2": [6, 177], "arg3": 6, "arg4": 6, "citizen": [6, 27, 205, 207], "lltmfunction": 6, "staticmethod": [6, 145, 176, 187], "benchmark": [6, 17, 20, 28, 38, 47, 122, 136, 141, 152, 153, 159, 160, 173, 179, 188, 192, 193, 200, 203, 209, 215, 217, 237, 238, 255, 261, 264, 265, 269, 278], "measur": [6, 14, 25, 41, 91, 99, 100, 102, 103, 108, 115, 138, 152, 153, 158, 160, 164, 174, 175, 179, 182, 188, 192, 193, 200, 211, 215, 220, 229, 236, 241, 248, 249, 255, 264, 269], "durat": [6, 92, 170, 175, 184, 193, 283], "32": [6, 22, 23, 25, 61, 97, 102, 103, 106, 108, 113, 122, 138, 152, 165, 169, 173, 177, 178, 184, 187, 189, 192, 193, 214, 236, 238, 246, 249, 251], "128": [6, 7, 26, 57, 58, 61, 97, 108, 115, 138, 139, 149, 150, 152, 153, 164, 173, 176, 177, 178, 201, 216, 224, 229, 237, 249, 251, 264, 272], "rang": [6, 7, 13, 14, 16, 22, 23, 25, 46, 47, 49, 57, 58, 59, 66, 71, 81, 91, 97, 100, 108, 115, 116, 117, 119, 120, 124, 130, 137, 138, 141, 149, 150, 152, 153, 172, 173, 176, 178, 180, 184, 185, 189, 190, 192, 193, 194, 195, 200, 201, 202, 210, 214, 217, 226, 227, 229, 232, 236, 238, 244, 247, 248, 252, 255, 265, 274, 278, 279], "100000": [6, 64, 65, 152, 249], "3f": [6, 23, 214, 217], "wrote": [6, 27, 154, 188, 194, 283], "post": [6, 7, 13, 24, 55, 64, 65, 108, 136, 137, 138, 141, 152, 154, 162, 164, 182, 192, 193, 199, 201, 212, 214, 216, 218, 238, 247], "my": [6, 25, 47, 56, 109, 115, 214, 220, 283], "machin": [6, 7, 22, 24, 25, 29, 40, 42, 46, 49, 55, 56, 57, 59, 60, 61, 62, 64, 65, 66, 67, 81, 97, 98, 99, 100, 117, 119, 130, 133, 136, 137, 138, 141, 146, 147, 148, 149, 150, 158, 169, 173, 177, 178, 179, 180, 181, 192, 194, 201, 210, 214, 220, 227, 236, 244, 263, 265, 273, 277, 278], "506": [6, 115], "480": [6, 255], "444": 6, "694": 6, "349": [6, 102], "335": [6, 162, 178, 279], "443": [6, 178, 255], "523": 6, "speedup": [6, 20, 25, 47, 49, 124, 136, 153, 159, 164, 169, 193, 197, 198, 200, 209, 217, 236, 241, 265], "30": [6, 7, 9, 23, 50, 91, 110, 129, 136, 137, 162, 171, 176, 178, 198, 213, 217, 249, 255], "albeit": [6, 16, 246], "major": [6, 12, 13, 23, 103, 106, 115, 131, 159, 167, 179, 188, 192, 193, 208, 233, 236, 279], "particularli": [6, 15, 20, 59, 116, 168, 180, 217, 265], "engin": [6, 7, 16, 24, 36, 48, 97, 99, 119, 138, 173, 178, 187, 190, 194, 203, 222, 224, 237, 246, 281], "wonder": [6, 110, 167], "correspondingli": 6, "big": [6, 47, 58, 103, 109, 115, 124, 143, 153, 167, 174, 180, 187, 210], "win": [6, 129, 167, 201], "No": [6, 7, 59, 66, 159, 195, 221, 228], "cuda_devic": 6, "creation": [6, 7, 12, 106, 208, 219, 225, 254], "assert": [6, 13, 22, 23, 26, 120, 148, 153, 159, 165, 177, 179, 185, 210, 216, 217, 225, 226, 227], "is_avail": [6, 7, 14, 37, 47, 97, 106, 123, 170, 172, 177], "synchron": [6, 13, 19, 61, 62, 91, 148, 150, 164, 174, 176, 177, 184, 188, 192, 193, 229, 244, 248, 249, 279], "1e6": [6, 23, 152, 198, 201, 213, 214, 227, 246, 279], "1e5": 6, "again": [6, 7, 11, 25, 29, 49, 56, 66, 86, 99, 100, 103, 105, 106, 108, 109, 115, 116, 120, 127, 130, 134, 140, 144, 150, 151, 167, 176, 178, 180, 184, 187, 188, 192, 200, 213, 216, 241, 249, 259, 283], "187": [6, 249], "719": 6, "410": [6, 162], "815": 6, "149": 6, "802": [6, 159], "393": [6, 193], "458": [6, 159], "That": [6, 7, 20, 27, 48, 49, 50, 55, 99, 100, 102, 106, 110, 112, 113, 115, 116, 117, 120, 130, 139, 142, 149, 156, 158, 160, 162, 164, 165, 167, 174, 179, 184, 194, 205, 206, 208, 217, 241, 242, 252, 264, 269, 283], "great": [6, 55, 66, 117, 124, 125, 127, 207, 213, 249, 267, 283], "pull": [6, 9, 25, 100, 106, 107, 158, 189, 190, 230], "dive": [6, 7, 13, 27, 42, 148, 159, 172, 264], "elabor": [6, 7, 139, 159, 176], "fly": [6, 16, 27, 57, 109, 129, 174, 246], "background": [6, 7, 27, 37, 64, 65, 81, 105, 127, 173, 185, 187, 194, 273, 283], "tmp": [6, 27, 159, 187, 202, 235, 241, 246, 273], "torch_extens": 6, "emit": [6, 7, 109], "ninja": 6, "verbos": [6, 27, 147, 187, 193, 224, 225, 284], "complic": [6, 16, 109, 110, 115, 116, 124, 141, 193, 213, 222, 226, 232, 248, 249, 270], "techniqu": [6, 11, 19, 20, 23, 25, 55, 66, 67, 108, 109, 115, 119, 136, 139, 144, 145, 146, 158, 164, 168, 171, 172, 178, 187, 193, 200, 205, 209, 217, 220, 221, 246, 252, 265, 278], "fine": [6, 7, 20, 23, 55, 57, 99, 109, 127, 135, 140, 149, 150, 159, 172, 173, 184, 201, 204, 205, 217, 248, 249, 264, 265, 278], "system": [6, 7, 10, 12, 14, 16, 17, 26, 27, 29, 61, 84, 108, 136, 141, 150, 168, 173, 174, 176, 191, 192, 193, 223, 224, 225, 230, 251, 265, 273, 278], "increment": [6, 13, 66, 94, 112, 116, 150, 175], 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209, 216, 244, 265], "zeros_lik": [6, 106, 176, 233], "dim3": 6, "at_dispatch_floating_typ": 6, "lltm_forward_cuda": 6, "lltm_cuda_forward_kernel": 6, "indic": [6, 7, 12, 13, 16, 27, 37, 55, 66, 69, 70, 91, 99, 100, 102, 103, 105, 109, 110, 115, 120, 122, 123, 124, 129, 130, 133, 141, 142, 151, 152, 159, 171, 174, 175, 177, 184, 185, 187, 192, 193, 201, 205, 207, 208, 211, 216, 224, 247, 249, 276, 281, 283], "runtim": [6, 10, 16, 21, 22, 25, 27, 29, 45, 56, 66, 91, 94, 98, 99, 102, 106, 119, 122, 136, 139, 144, 158, 167, 184, 188, 192, 193, 197, 200, 201, 213, 223, 224, 227, 236, 248, 249, 252, 255, 257, 268, 269], "back": [6, 7, 10, 12, 16, 17, 23, 24, 26, 27, 36, 49, 52, 57, 64, 65, 66, 81, 89, 97, 99, 106, 108, 112, 116, 117, 122, 127, 140, 142, 154, 162, 164, 167, 174, 176, 177, 178, 180, 187, 192, 204, 205, 209, 210, 230, 252, 262, 265, 283], "determin": [6, 7, 10, 13, 20, 23, 45, 54, 100, 102, 106, 107, 108, 109, 112, 115, 117, 139, 141, 153, 167, 168, 169, 171, 188, 198, 209, 217, 249, 252, 255, 256, 265], "conceptu": [6, 7, 36, 48, 55, 66, 124, 193, 264], "scalartyp": 6, "messag": [6, 55, 99, 120, 150, 152, 187, 189, 190, 201, 224, 225, 240, 243, 270, 278], "alia": [6, 12, 106, 189, 190], "instanti": [6, 7, 12, 16, 25, 26, 29, 38, 43, 57, 58, 71, 73, 94, 102, 108, 116, 117, 124, 127, 133, 141, 170, 171, 175, 187, 191, 219, 252, 254], "retriev": [6, 7, 16, 19, 25, 36, 38, 55, 140, 141, 174, 176, 177, 193, 226, 244, 278], "at_dispatch_all_typ": 6, "sens": [6, 10, 14, 16, 108, 115, 116, 127, 153, 185, 283], "routin": [6, 7, 27], "convolut": [6, 7, 10, 14, 15, 24, 58, 66, 100, 102, 106, 108, 116, 125, 131, 134, 136, 162, 165, 171, 172, 181, 182, 192, 193, 198, 215, 216, 223, 224, 236, 237, 241, 244, 248, 251, 255, 256, 267, 268, 270], "harder": [6, 108, 200, 201], "ourselv": [6, 7, 41, 55, 84, 144, 174], "grid": [6, 105, 107, 116, 131, 172, 182, 185, 202], "fill": [6, 7, 16, 89, 102, 115, 116, 142, 151, 192, 206, 207, 225, 241], "matric": [6, 14, 20, 27, 29, 45, 54, 99, 112, 160, 168, 217, 224], "2048": [6, 22, 37, 108], "heard": 6, "introductori": [6, 87], "ever": [6, 7, 27, 140, 189, 190, 254], "__restrict__": 6, "size_t": 6, "column": [6, 9, 22, 27, 99, 106, 110, 112, 122, 139, 142, 159, 160, 165, 184, 187, 206, 207, 208, 255, 284], "index": [6, 7, 17, 38, 45, 46, 49, 54, 55, 57, 64, 65, 66, 92, 106, 109, 110, 112, 113, 115, 116, 122, 129, 130, 133, 134, 138, 140, 141, 142, 150, 154, 168, 176, 180, 184, 187, 188, 192, 200, 209, 222, 230, 278, 281, 287], "gates_row": 6, "primarili": [6, 91, 177, 188, 248, 265], "imagin": [6, 99, 106, 109, 115, 145, 150, 167, 168, 180, 249, 262], "giant": [6, 180], "million": [6, 129, 131, 134, 137, 151, 192], "serial": [6, 7, 12, 27, 29, 42, 43, 66, 116, 125, 136, 189, 190, 192, 198, 213, 214, 244, 249, 258, 259], "faster": [6, 7, 10, 11, 14, 23, 55, 62, 64, 65, 81, 103, 106, 108, 116, 125, 137, 147, 151, 153, 160, 162, 169, 176, 180, 188, 193, 194, 198, 220, 235, 241, 245, 246, 248, 249, 252, 264, 265, 266, 269, 278], "right": [6, 7, 10, 12, 14, 16, 24, 27, 36, 40, 45, 48, 54, 55, 58, 70, 91, 99, 103, 105, 108, 110, 112, 115, 124, 127, 150, 152, 161, 165, 167, 172, 174, 175, 176, 179, 180, 184, 187, 194, 201, 211, 213, 222, 236, 244, 252, 270, 283], "inde": [6, 16, 18, 64, 65, 108, 144, 160, 174, 179, 188, 207, 208, 249, 265], "agnost": [6, 66, 123, 250], "ineffici": [6, 91, 192, 209], "readabl": [6, 29, 38, 57, 100, 106, 109, 123, 143, 154, 184, 230, 249, 264], "especi": [6, 8, 20, 23, 55, 58, 66, 81, 100, 106, 127, 137, 148, 158, 165, 167, 189, 190, 193, 200, 206, 215, 217, 238, 241, 246], "dimension": [6, 37, 45, 52, 54, 55, 58, 66, 67, 78, 79, 99, 103, 105, 106, 108, 111, 112, 113, 115, 124, 127, 139, 162, 171, 179, 180, 185, 187, 208, 224, 232, 241], "stride": [6, 7, 23, 58, 100, 103, 127, 138, 144, 149, 159, 162, 187, 193, 195, 208, 235, 254], "row": [6, 22, 27, 57, 81, 99, 103, 106, 110, 112, 113, 115, 116, 130, 139, 141, 142, 160, 172, 176, 192, 193, 206, 208, 222, 225, 244, 284], "arithmet": [6, 23, 45, 102, 106, 201, 252], "fortun": [6, 7, 12, 17, 27, 97, 150, 151, 249], "expos": [6, 7, 10, 26, 27, 107, 120, 127, 136, 154, 178, 197, 213, 214, 223, 225, 230, 265], "foo": [6, 25, 26, 177, 198, 213, 219, 226, 264, 283, 284], "12": [6, 9, 27, 47, 64, 65, 102, 122, 137, 138, 159, 164, 176, 185, 189, 195, 200, 206, 216, 217, 221, 225, 236, 238, 240, 243, 245, 249, 277, 283, 287], "foo_a": 6, "packed_accessor64": 6, "packed_accessor32": 6, "pack": [6, 55, 129, 159, 174, 176, 178, 225, 229, 241, 270], "integ": [6, 7, 10, 46, 66, 102, 106, 110, 112, 115, 127, 129, 141, 171, 189, 190, 194, 200, 208, 213, 214, 215, 224, 246, 252, 255, 286], "fundament": [6, 55, 98, 101, 103, 112, 115, 124, 136, 161, 206, 214, 231, 264], "packedtensoraccessor32": 6, "restrictptrtrait": 6, "decompos": [6, 12, 20, 138, 164, 189, 190, 213, 217], "packedaccessor32": 6, "variant": [6, 55, 66, 102, 103, 105, 107, 139, 144, 173, 179], "int32_t": 6, "packedaccessor64": 6, "slower": [6, 20, 62, 148, 164, 173, 175, 188, 192, 194, 200, 202, 209, 217], "host": [6, 9, 22, 40, 60, 91, 98, 109, 137, 138, 139, 148, 149, 150, 164, 177, 178, 184, 232, 233, 255, 265, 273], "reshap": [6, 7, 14, 23, 103, 106, 115, 116, 123, 198, 204, 206, 209, 213, 214, 223, 238], "pattern": [6, 20, 25, 103, 105, 115, 139, 150, 157, 159, 168, 177, 193, 198, 199, 201, 205, 207, 213, 217, 219, 232, 237], "lltm_cuda_backward_kernel": 6, "lltm_backward_cuda": 6, "d_gate_weight": 6, "hassl": [6, 7], "entail": 6, "simpler": [6, 86, 116, 139, 144, 156, 168, 213, 222, 249], "fastest": [6, 164, 179], "129": [6, 122, 203], "431": 6, "304": [6, 55, 193], "641": [6, 162], "faq": [6, 26, 27], "sit": [7, 47, 117, 119, 120, 164, 168, 282, 284, 286], "atop": 7, "substanti": [7, 141], "codebas": [7, 12, 16], "foundat": [7, 174, 187], "underli": [7, 10, 16, 27, 45, 54, 67, 81, 89, 106, 108, 125, 141, 153, 159, 164, 169, 177, 198, 207, 208, 209, 232, 233, 257, 265], "popular": [7, 74, 81, 83, 108, 124, 141, 151, 152, 193, 200, 237, 238, 282], "stochast": [7, 9, 41, 52, 58, 71, 99, 102, 107, 116, 124, 129, 150, 174, 175], "descent": [7, 9, 41, 48, 52, 71, 99, 102, 107, 116, 123, 124, 129, 150, 200, 268], "digit": [7, 52, 102, 116, 136, 137, 138, 187], "whirlwind": 7, "wet": 7, "appetit": 7, "watch": [7, 107, 127, 146, 150], "lightn": [7, 141], "talk": [7, 10, 55, 58, 61, 102, 112, 129, 150, 174, 177], "cppcon": 7, "2018": [7, 133, 152], "quick": [7, 20, 37, 54, 64, 65, 99, 108, 110, 113, 115, 134, 136, 137, 142, 148, 153, 160, 169, 200, 217, 230, 249, 252, 253, 261, 269, 276], "humor": 7, "sweep": [7, 179], "philosophi": [7, 127], "ecosystem": [7, 120], "descript": [7, 56, 137, 138, 159, 174, 176, 177, 178, 179, 187, 197, 249, 265, 275, 277, 284], "embark": 7, "excit": [7, 26, 27, 158, 167], "team": [7, 120, 129, 141, 152, 175, 187], "job": [7, 50, 58, 59, 60, 67, 91, 108, 141, 146, 147, 148, 150, 241], "reinforc": [7, 16, 107, 136, 161, 176, 177, 181], "game": [7, 49, 58, 87, 161], "tractabl": [7, 109], "multithread": [7, 48, 62, 122, 233, 244, 249, 278], "lock": [7, 16, 29, 62, 149, 150, 176, 177, 193, 233, 282], "gil": [7, 62, 148, 233], "multiprocess": [7, 13, 16, 38, 42, 57, 59, 61, 62, 123, 137, 138, 148, 149, 150, 174, 177, 178, 229, 231, 233, 278, 279], "scalabl": [7, 141, 205, 224, 236, 237, 265], "shortcom": [7, 207], "neuroevolut": 7, "owner": [7, 176, 177, 178], "anyth": [7, 15, 18, 49, 78, 79, 102, 103, 106, 109, 112, 113, 115, 116, 124, 154, 173, 197, 198, 200, 244, 262, 263, 283, 284], "serv": [7, 63, 67, 94, 102, 108, 113, 124, 136, 142, 148, 154, 158, 161, 164, 170, 177, 178, 192, 193, 207, 229, 230, 248, 249, 264, 265, 273, 277], "web": [7, 117, 230, 269, 283], "server": [7, 19, 29, 134, 135, 136, 142, 148, 164, 170, 178, 193, 195, 201, 210, 230, 231, 233, 237, 246, 269, 273, 278], "3d": [7, 67, 83, 103, 105, 112, 113, 187, 213, 216, 265], "graphic": [7, 179, 223], "photo": [7, 100], "softwar": [7, 152, 164, 170, 184, 192, 223, 230, 283], "remain": [7, 9, 97, 99, 108, 134, 150, 157, 167, 171, 180, 195, 200, 207, 209, 211, 215, 226, 265], "forth": [7, 116, 164, 192, 284], "retain": [7, 45, 54, 84, 89, 106, 125], "intuit": [7, 58, 81, 86, 102, 106, 110, 120, 125, 159, 164, 180, 187, 206, 244, 259, 262], "tradit": [7, 47, 58, 108, 110, 119, 160, 177], "compet": [7, 127, 129, 134, 141, 192, 193], "complement": 7, "alik": 7, "love": [7, 127], "simplic": [7, 81, 137, 144, 174, 175, 232, 240, 249], "core": [7, 10, 12, 13, 47, 50, 66, 84, 109, 110, 111, 120, 124, 125, 129, 136, 139, 150, 151, 159, 162, 173, 184, 189, 190, 193, 210, 212, 213, 221, 240, 244, 248, 264, 278], "principl": [7, 10, 113, 115, 136, 141], "curiou": [7, 153, 167, 169, 228], "tri": [7, 22, 58, 99, 109, 115, 127, 175, 223, 248], "experienc": [7, 94], "ask": [7, 20, 26, 27, 66, 100, 102, 103, 115, 143, 151, 174, 217, 226, 249], "rememb": [7, 49, 58, 65, 81, 84, 113, 116, 122, 125, 154, 160, 161, 167, 180, 249, 257, 258, 259, 260], "dot": [7, 16, 36, 58, 113, 115, 136, 160, 180, 181, 192, 193, 222, 249, 274], "colon": [7, 187, 283], "minim": [7, 12, 14, 20, 27, 41, 58, 69, 70, 73, 74, 75, 78, 79, 81, 91, 99, 110, 115, 116, 124, 136, 137, 141, 147, 152, 159, 175, 188, 198, 201, 205, 215, 217, 221, 226, 252, 265, 268], "verifi": [7, 24, 64, 65, 94, 99, 106, 120, 128, 130, 134, 145, 153, 156, 157, 160, 162, 171, 173, 192, 193, 194, 223, 236, 237, 244, 276], "zip": [7, 22, 55, 56, 58, 120, 125, 130, 152, 153, 165, 176, 178, 185, 187, 194, 197, 201, 217, 225, 233, 241], "too": [7, 12, 16, 23, 49, 70, 74, 91, 97, 102, 103, 108, 109, 115, 116, 122, 124, 139, 148, 154, 164, 167, 171, 175, 176, 178, 180, 205, 246, 259, 264, 281, 283, 284], "cu90": 7, "url": [7, 23, 133, 184, 188, 200, 225, 240, 263, 281], "wget": [7, 22, 83, 194, 197, 200, 225], "nightli": [7, 22, 83, 137, 152, 156, 188, 191, 194, 200, 203, 204, 212, 213, 215, 221, 222, 225, 238], "dep": 7, "tini": [7, 138, 164, 167], "three": [7, 12, 14, 16, 17, 38, 41, 57, 58, 70, 81, 91, 94, 99, 100, 105, 106, 109, 116, 120, 124, 125, 127, 133, 136, 139, 143, 150, 162, 168, 170, 174, 178, 179, 180, 182, 184, 185, 191, 192, 193, 200, 208, 215, 244, 246, 255, 276, 283, 284], "ey": [7, 24, 27, 115, 165, 225, 283], "fledg": 7, "visual": [7, 16, 29, 48, 58, 81, 83, 91, 92, 102, 107, 130, 136, 137, 140, 141, 145, 158, 174, 184, 187, 192, 193, 202, 207, 255, 263, 269], "studio": [7, 64, 221, 240, 242, 245], "qmake": 7, "makefil": 7, "comfort": 7, "box": [7, 10, 12, 13, 18, 20, 46, 81, 127, 136, 141, 145, 150, 173, 176, 184, 192, 193, 194, 215, 217, 222, 224, 237, 243, 244, 247, 283], "cmake_prefix_path": [7, 26, 27], "invok": [7, 10, 26, 27, 29, 108, 148, 149, 156, 158, 159, 170, 177, 178, 193, 202, 204, 207, 215, 220, 224, 244, 262, 265, 272, 278], "agre": [7, 27, 152, 279], "break": [7, 10, 12, 13, 18, 23, 37, 38, 91, 99, 103, 125, 127, 130, 154, 167, 176, 178, 188, 205, 259, 283], "unexpect": [7, 57, 207, 211, 216, 265], "pwd": [7, 225, 244], "fa350df05ecf": 7, "home": [7, 45, 54, 127, 205, 210, 221, 223, 226, 234, 240], "enter": [7, 55, 66, 137, 148, 150], "ran": [7, 115, 174, 177, 179, 189, 190, 221, 249], "me": [7, 20, 47, 55, 167, 180, 217, 283, 284], "besid": [7, 27, 102, 115, 168, 176, 238, 247, 265, 283], "encapsul": [7, 41, 52, 103, 107, 167, 176], "buffer": [7, 16, 49, 52, 65, 84, 123, 125, 137, 144, 148, 153, 156, 161, 165, 167, 168, 169, 171, 175, 189, 190, 193, 204, 219, 223, 225, 241, 254, 258, 267, 268], "nest": [7, 16, 37, 48, 102, 106, 123, 157, 174, 176, 178, 179, 212, 218, 283, 284], "similarli": [7, 11, 14, 26, 57, 66, 89, 92, 100, 106, 127, 130, 140, 141, 149, 159, 160, 167, 195, 208, 231], "w": [7, 9, 36, 41, 48, 52, 81, 84, 113, 115, 137, 152, 162, 178, 184, 194, 200, 201, 203, 240], "struct": [7, 17, 26, 225, 270], "register_paramet": [7, 219], "reflect": [7, 10, 27, 45, 54, 102, 106, 174, 211], "magic": [7, 110, 143, 150, 160], "behind": [7, 10, 20, 27, 50, 91, 108, 110, 116, 124, 144, 175, 195, 206, 208, 212, 217, 219, 283], "scene": [7, 27, 64, 65, 116, 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"recipes/script_optimized.rst", "recipes/sg_execution_times.rst", "recipes/torch_compile_backend_ipex.rst", "recipes/torch_compile_caching_tutorial.rst", "recipes/torch_compile_user_defined_triton_kernel_tutorial.rst", "recipes/torch_logs.rst", "recipes/torchscript_inference.rst", "recipes/torchserve_vertexai_tutorial.rst", "recipes/xeon_run_cpu.rst", "recipes/zero_redundancy_optimizer.rst", "src/pytorch-sphinx-theme/docs/changelog.rst", "src/pytorch-sphinx-theme/docs/configuring.rst", "src/pytorch-sphinx-theme/docs/demo/api.rst", "src/pytorch-sphinx-theme/docs/demo/demo.rst", "src/pytorch-sphinx-theme/docs/demo/lists_tables.rst", "src/pytorch-sphinx-theme/docs/demo/long.rst", "src/pytorch-sphinx-theme/docs/demo/structure.rst", "src/pytorch-sphinx-theme/docs/index.rst", "src/pytorch-sphinx-theme/docs/installing.rst"], "titles": ["ONNX Live Tutorial", "TorchRL objectives: Coding a DDPG loss", "Autograd in C++ Frontend", "Using CUDA Graphs in PyTorch C++ API", "Custom C++ and CUDA Operators", "Loading a TorchScript Model in C++", "Custom C++ and CUDA Extensions", "Using the PyTorch C++ Frontend", "PyTorch Custom Operators Landing Page", "Training Transformer models using Distributed Data Parallel and Pipeline Parallelism", "Registering a Dispatched Operator in C++", "(beta) Dynamic Quantization on an LSTM Word Language Model", "Extending dispatcher for a new backend in C++", "Distributed Training with Uneven Inputs Using the Join Context Manager", "Neural Transfer Using PyTorch", "Creating Extensions Using NumPy and SciPy", "Pendulum: Writing your environment and transforms with TorchRL", "Facilitating New Backend Integration by PrivateUse1", "Python Custom Operators", "Combining Distributed DataParallel with Distributed RPC Framework", "(beta) Accelerating BERT with semi-structured (2:4) sparsity", "Computation times", "Exploring TorchRec sharding", "(beta) Static Quantization with Eager Mode in PyTorch", "(optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime", "Dynamic Parallelism in TorchScript", "Extending TorchScript with Custom C++ Classes", "Extending TorchScript with Custom C++ Operators", "Semi-Supervised Learning using USB built upon PyTorch", "Introduction to TorchScript", "Audio Data Augmentation", "Audio Datasets", "Audio Feature Augmentation", "Audio Feature Extractions", "Audio I/O", "Audio Resampling", "Automatic Differentiation with <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.autograd</span></code>", "Build the Neural Network", "Datasets &amp; DataLoaders", "Learn the Basics", "Learn the Basics", "Optimizing Model Parameters", "Quickstart", "Save and Load the Model", "Computation times", "Tensors", "Transforms", "Fast Transformer Inference with Better Transformer", "A Gentle Introduction to <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.autograd</span></code>", "Training a Classifier", "Optional: Data Parallelism", "Deep Learning with PyTorch: A 60 Minute Blitz", "Neural Networks", "Computation times", "Tensors", "Chatbot Tutorial", "Running Tutorials in Google Colab", "Writing Custom Datasets, DataLoaders and Transforms", "DCGAN Tutorial", "Fault-tolerant Distributed Training with <code class=\"docutils literal notranslate\"><span class=\"pre\">torchrun</span></code>", "Distributed Data Parallel in PyTorch - Video Tutorials", "Multi GPU training with DDP", "What is Distributed Data Parallel (DDP)", "Deep Learning with PyTorch: A 60 Minute Blitz", "Image Segmentation DeepLabV3 on Android", "Image Segmentation DeepLabV3 on iOS", "Deploying a Seq2Seq Model with TorchScript", "PyTorch Distributed Overview", "&lt;no title&gt;", "PyTorch: Tensors and autograd", "PyTorch: Defining New autograd Functions", "PyTorch: Control Flow + Weight Sharing", "&lt;no title&gt;", "PyTorch: Custom nn Modules", "PyTorch: nn", "PyTorch: optim", "Computation times", "&lt;no title&gt;", "Warm-up: numpy", "PyTorch: Tensors", "Computation times", "Adversarial Example Generation", "Finetuning Torchvision Models", "TorchMultimodal Tutorial: Finetuning FLAVA", "Autograd", "&lt;no title&gt;", "nn package", "Multi-GPU Examples", "Computation times", "Tensors", "PyTorch for Former Torch Users", "Introduction to Holistic Trace Analysis", "Trace Diff using Holistic Trace Analysis", "&lt;no title&gt;", "Learning Hybrid Frontend Syntax Through Example", "Computation times", "Hybrid Frontend Tutorials", "Hyperparameter tuning with Ray Tune", "Introduction to PyTorch - YouTube Series", "The Fundamentals of Autograd", "Model Understanding with Captum", "Introduction to PyTorch on YouTube", "Introduction to PyTorch", "Building Models with PyTorch", "Computation times", "PyTorch TensorBoard Support", "Introduction to PyTorch Tensors", "Training with PyTorch", "Knowledge Distillation Tutorial", "Advanced: Making Dynamic Decisions and the Bi-LSTM CRF", "Deep Learning with PyTorch", "Deep Learning for NLP with Pytorch", "Introduction to PyTorch", "Sequence Models and Long Short-Term Memory Networks", "Computation times", "Word Embeddings: Encoding Lexical Semantics", "What is <cite>torch.nn</cite> <em>really</em>?", "Export a PyTorch model to ONNX", "ONNX", "Introduction to ONNX", "Extending the ONNX Registry", "Computation times", "Profiling your PyTorch Module", "PyTorch Cheat Sheet", "Learning PyTorch with Examples", "Saving and Loading Models", "Computation times", "T5-Base Model for Summarization, Sentiment Classification, and Translation", "Template Tutorial", "Text classification with the torchtext library", "Preprocess custom text dataset using Torchtext", "Transfer Learning for Computer Vision Tutorial", "Language Modeling with <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Transformer</span></code> and torchtext", "Language Translation with <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Transformer</span></code> and torchtext", "Optimizing Vision Transformer Model for Deployment", "Distributed and Parallel Training Tutorials", "Welcome to PyTorch Tutorials", "Advanced Model Training with Fully Sharded Data Parallel (FSDP)", "Getting Started with Fully Sharded Data Parallel(FSDP)", "Large Scale Transformer model training with Tensor Parallel (TP)", "Hooks for autograd saved tensors", "Multi-Objective NAS with Ax", "NLP From Scratch: Classifying Names with a Character-Level RNN", "NLP From Scratch: Generating Names with a Character-Level RNN", "Fusing Convolution and Batch Norm using Custom Function", "Double Backward with Custom Functions", "Training \u201creal-world\u201d models with DDP", "Multinode Training", "Getting Started with Distributed Data Parallel", "Distributed Pipeline Parallelism Using RPC", "Writing Distributed Applications with PyTorch", "Recurrent DQN: Training recurrent policies", "(beta) Dynamic Quantization on BERT", "Model ensembling", "Deploying PyTorch in Python via a REST API with Flask", "Forced Alignment with Wav2Vec2", "Forward-mode Automatic Differentiation (Beta)", "(beta) Building a Convolution/Batch Norm fuser in FX", "(beta) Building a Simple CPU Performance Profiler with FX", "Inductor CPU backend debugging and profiling", "Jacobians, Hessians, hvp, vhp, and more: composing function transforms", "Train a Mario-playing RL Agent", "(beta) Channels Last Memory Format in PyTorch", "&lt;no title&gt;", "Single-Machine Model Parallel Best Practices", "Neural Tangent Kernels", "Getting Started - Accelerate Your Scripts with nvFuser", "How to save memory by fusing the optimizer step into the backward pass", "Parametrizations Tutorial", "Per-sample-gradients", "Customize Process Group Backends Using Cpp Extensions", "Pruning Tutorial", "(beta) Quantized Transfer Learning for Computer Vision Tutorial", "Real Time Inference on Raspberry Pi 4 (30 fps!)", "Reinforcement Learning (PPO) with TorchRL Tutorial", "Reinforcement Learning (DQN) Tutorial", "Implementing Batch RPC Processing Using Asynchronous Executions", "Implementing a Parameter Server Using Distributed RPC Framework", "Getting Started with Distributed RPC Framework", "(Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA)", "NLP From Scratch: Translation with a Sequence to Sequence Network and Attention", "Computation times", "Spatial Transformer Networks Tutorial", "Speech Recognition with Wav2Vec2", "PyTorch Profiler With TensorBoard", "Visualizing Models, Data, and Training with TensorBoard", "Text-to-speech with Tacotron2", "Whole Slide Image Classification Using PyTorch and TIAToolbox", "Introduction to <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.compile</span></code>", "torch.export Nightly Tutorial", "torch.export Tutorial", "Introduction to TorchRec", "Grokking PyTorch Intel CPU performance from first principles", "Grokking PyTorch Intel CPU performance from first principles (Part 2)", "TorchVision Object Detection Finetuning Tutorial", "(prototype) PyTorch BackendConfig Tutorial", "Profiling PyTorch RPC-Based Workloads", "(prototype) FX Graph Mode Post Training Dynamic Quantization", "(prototype) FX Graph Mode Post Training Static Quantization", "(prototype) FX Graph Mode Quantization User Guide", "(prototype) GPU Quantization with TorchAO", "(prototype) Graph Mode Dynamic Quantization on BERT", "Inductor C++ Wrapper Tutorial", "(Prototype) Convert Mobilenetv2 to Core ML", "(Prototype) Use iOS GPU in PyTorch", "(Prototype) Efficiently writing \u201csparse\u201d semantics for Adagrad with MaskedTensor", "(Prototype) MaskedTensor Advanced Semantics", "(Prototype) MaskedTensor Overview", "(Prototype) MaskedTensor Sparsity", "Getting Started with Nested Tensors", "(Beta) Convert MobileNetV2 to NNAPI", "PyTorch Numeric Suite Tutorial", "PyTorch Prototype Recipes", "(prototype) PyTorch 2 Export Post Training Quantization", "(prototype) PyTorch 2 Export Quantization-Aware Training (QAT)", "PyTorch 2 Export Quantization with X86 Backend through Inductor", "How to Write a <code class=\"docutils literal notranslate\"><span class=\"pre\">Quantizer</span></code> for PyTorch 2 Export Quantization", "(prototype) Accelerating BERT with semi-structured (2:4) sparsity", "Computation times", "Skipping Module Parameter Initialization", "Model Freezing in TorchScript", "(prototype) Tracing-based Selective Build Mobile Interpreter in Android and iOS", "torch.vmap", "PyTorch Vulkan Backend User Workflow", "Leverage Intel\u00ae Advanced Matrix Extensions", "Making Native Android Application that uses PyTorch prebuilt libraries", "(beta) Bundling inputs to PyTorch Models", "(beta) Compiling the optimizer with torch.compile", "(beta) Running the compiled optimizer with an LR Scheduler", "Direct Device-to-Device Communication with TensorPipe CUDA RPC", "Deploying with Flask", "Getting Started with Distributed Checkpoint (DCP)", "Getting Started with DeviceMesh", "Distributed Optimizer with TorchScript support", "Profiling PyTorch RPC-Based Workloads", "Fuse Modules Recipe", "(Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors", "Intel\u00ae Extension for PyTorch*", "Ease-of-use quantization for PyTorch with Intel\u00ae Neural Compressor", "Loading data in PyTorch", "(beta) Efficient mobile interpreter in Android and iOS", "Pytorch Mobile Performance Recipes", "Model Preparation for Android Recipe", "Model Preparation for iOS Recipe", "Profiling PyTorch workloads with The Instrumentation and Tracing Technology (ITT) API", "Summary of PyTorch Mobile Recipes", "Quantization Recipe", "Model Interpretability using Captum", "Automatic Mixed Precision", "SyntaxError", "Changing default device", "Defining a Neural Network in PyTorch", "Dynamic Quantization", "PyTorch Recipes", "Tips for Loading an <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Module</span></code> from a Checkpoint", "PyTorch Profiler", "Reasoning about Shapes in PyTorch", "Saving and loading models across devices in PyTorch", "Saving and loading a general checkpoint in PyTorch", "Saving and loading models for inference in PyTorch", "Saving and loading multiple models in one file using PyTorch", "Computation times", "Extension points in <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Module</span></code> for <code class=\"docutils literal notranslate\"><span class=\"pre\">load_state_dict</span></code> and tensor subclasses", "How to use TensorBoard with PyTorch", "Timer quick start", "Performance Tuning Guide", "Warmstarting model using parameters from a different model in PyTorch", "What is a state_dict in PyTorch", "Zeroing out gradients in PyTorch", "PyTorch Recipes", "Script and Optimize for Mobile Recipe", "Computation times", "Intel\u00ae Extension for PyTorch* Backend on Intel\u00ae CPUs", "Compile Time Caching in <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.compile</span></code>", "Using User-Defined Triton Kernels with <code 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269], "final": [1, 7, 11, 12, 13, 14, 16, 17, 19, 20, 23, 24, 25, 26, 27, 28, 46, 47, 48, 52, 55, 58, 64, 66, 81, 83, 94, 97, 99, 102, 103, 106, 107, 108, 113, 116, 117, 119, 120, 122, 125, 127, 131, 133, 134, 136, 137, 138, 139, 141, 142, 143, 144, 145, 149, 150, 154, 158, 159, 160, 161, 165, 167, 169, 171, 172, 174, 175, 176, 177, 178, 180, 184, 185, 191, 192, 193, 195, 200, 201, 203, 204, 205, 208, 213, 214, 215, 216, 217, 220, 221, 225, 230, 233, 238, 257], "function": [1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 15, 16, 18, 19, 20, 22, 24, 25, 26, 27, 28, 29, 38, 42, 43, 45, 46, 47, 48, 54, 55, 57, 59, 61, 65, 66, 67, 68, 71, 73, 74, 75, 83, 84, 87, 89, 91, 92, 99, 100, 102, 105, 106, 112, 115, 119, 120, 122, 125, 127, 130, 131, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 149, 150, 151, 153, 157, 158, 159, 161, 164, 167, 168, 170, 175, 176, 177, 178, 179, 180, 181, 182, 184, 185, 187, 188, 189, 190, 192, 193, 194, 195, 197, 201, 202, 203, 205, 206, 207, 208, 210, 211, 216, 217, 219, 222, 223, 225, 226, 227, 228, 230, 233, 236, 237, 238, 241, 244, 248, 250, 251, 252, 257, 259, 262, 264, 266, 270, 274, 275, 276, 279], "trainabl": [1, 7, 74, 108, 110, 172], "paramet": [1, 5, 6, 9, 12, 13, 14, 16, 17, 19, 20, 23, 24, 26, 28, 29, 36, 38, 39, 40, 43, 45, 46, 48, 49, 52, 54, 55, 57, 58, 71, 73, 74, 81, 94, 97, 99, 102, 107, 108, 110, 112, 115, 116, 122, 123, 124, 130, 131, 133, 135, 136, 137, 138, 139, 141, 142, 146, 148, 149, 150, 151, 152, 153, 154, 156, 159, 160, 161, 164, 165, 167, 168, 169, 172, 178, 179, 182, 184, 185, 187, 189, 190, 192, 193, 198, 200, 205, 212, 213, 217, 220, 227, 229, 231, 233, 236, 237, 238, 244, 246, 248, 252, 253, 254, 255, 256, 257, 258, 259, 261, 262, 267, 268, 269, 270, 272, 274, 278, 279, 287], "tutori": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 39, 42, 48, 49, 50, 51, 57, 59, 61, 62, 64, 65, 66, 67, 81, 82, 85, 87, 90, 91, 93, 97, 100, 101, 103, 105, 107, 109, 111, 112, 116, 117, 118, 119, 120, 124, 125, 126, 127, 129, 130, 133, 134, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 164, 165, 166, 167, 169, 170, 173, 176, 177, 178, 179, 180, 181, 183, 184, 185, 186, 187, 188, 191, 193, 196, 197, 198, 200, 201, 203, 204, 205, 206, 207, 208, 209, 210, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234, 236, 240, 241, 246, 247, 248, 252, 253, 254, 255, 262, 263, 264, 265, 269, 270, 275, 276, 277, 278, 279], "guid": [1, 20, 64, 65, 67, 91, 94, 136, 138, 150, 162, 173, 184, 189, 190, 192, 200, 212, 217, 230, 244, 247, 249, 253, 261, 269, 278, 282], "ground": [1, 16, 49, 81, 194], "aim": [1, 7, 83, 108, 111, 167, 175, 178, 208, 238], "focus": [1, 3, 24, 100, 108, 111, 164, 170, 180, 238], "rel": [1, 6, 7, 9, 100, 107, 131, 134, 140, 152, 160, 164, 165, 178, 180, 192, 202, 213, 238, 252, 257, 269], "straightforward": [1, 6, 7, 19, 20, 55, 66, 99, 102, 106, 108, 109, 159, 216, 252, 257, 269], "determinist": [1, 13, 16, 151, 175, 264, 265], "gradient": [1, 4, 7, 9, 12, 13, 15, 16, 18, 19, 29, 41, 47, 48, 49, 52, 55, 58, 62, 67, 69, 70, 71, 74, 78, 79, 86, 99, 102, 103, 106, 107, 108, 109, 110, 112, 115, 116, 123, 124, 129, 131, 136, 137, 138, 139, 140, 142, 145, 146, 148, 150, 156, 160, 161, 164, 167, 171, 172, 174, 176, 177, 178, 181, 185, 187, 205, 222, 231, 233, 247, 253, 261, 269, 279], "simpl": [1, 3, 4, 5, 6, 7, 10, 14, 17, 19, 20, 23, 25, 26, 27, 28, 29, 52, 55, 57, 60, 62, 73, 81, 87, 94, 97, 100, 102, 103, 106, 107, 108, 116, 119, 124, 125, 130, 135, 136, 138, 140, 141, 145, 150, 153, 159, 160, 165, 169, 171, 174, 176, 177, 178, 179, 181, 182, 184, 188, 198, 201, 215, 217, 224, 227, 228, 230, 231, 232, 237, 238, 246, 249, 252, 254, 263, 264, 265, 267, 269, 270, 274, 275, 279, 283, 284], "continu": [1, 6, 24, 25, 55, 59, 66, 94, 97, 102, 106, 108, 113, 116, 127, 130, 136, 139, 143, 146, 150, 158, 172, 174, 178, 180, 184, 192, 203, 204, 205, 207, 208, 214, 215, 216, 217, 221, 240, 249, 251, 252, 255, 259, 265, 266, 267, 268, 270, 283], "control": [1, 5, 10, 12, 16, 25, 27, 29, 36, 38, 41, 48, 66, 72, 76, 92, 94, 99, 108, 123, 127, 128, 137, 140, 141, 149, 150, 156, 168, 174, 175, 176, 188, 199, 213, 225, 244, 249, 264, 270], "It": [1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 46, 47, 48, 49, 50, 52, 55, 57, 58, 66, 81, 82, 83, 84, 91, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 111, 112, 115, 116, 125, 127, 129, 131, 137, 138, 139, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, 153, 155, 156, 158, 159, 160, 161, 164, 165, 167, 168, 169, 171, 172, 175, 176, 178, 180, 182, 183, 184, 186, 187, 189, 190, 193, 194, 195, 209, 216, 217, 219, 221, 222, 225, 229, 230, 231, 232, 233, 240, 241, 242, 243, 246, 249, 250, 257, 258, 263, 265, 268, 272, 274, 276, 278, 281, 283], "consist": [1, 3, 7, 9, 16, 17, 26, 28, 29, 36, 37, 38, 41, 48, 83, 108, 110, 116, 133, 139, 146, 157, 158, 161, 165, 167, 174, 179, 180, 184, 189, 190, 193, 195, 207, 208, 215, 216, 225, 228, 249, 255, 265, 283], "parametr": [1, 2, 20, 136, 174, 181, 217], "action": [1, 23, 64, 65, 103, 107, 112, 116, 127, 137, 138, 161, 171, 174, 175, 176, 177, 178, 184, 198, 206, 213, 214, 221, 225, 255, 264, 269, 283], "pair": [1, 7, 52, 55, 58, 100, 130, 133, 143, 144, 152, 165, 169, 174, 175, 180, 184, 194, 195, 210, 215, 228, 244, 264, 283], "maxim": [1, 16, 58, 81, 91, 108, 110, 141, 161, 175, 188, 192, 210, 265, 278], "given": [1, 4, 7, 10, 12, 14, 16, 18, 20, 24, 25, 26, 27, 29, 36, 38, 41, 45, 46, 48, 54, 55, 58, 66, 81, 84, 91, 94, 100, 103, 107, 108, 111, 112, 115, 116, 125, 130, 137, 142, 143, 148, 150, 153, 156, 160, 161, 162, 169, 171, 174, 175, 177, 178, 180, 188, 189, 190, 193, 194, 208, 211, 216, 217, 233, 236, 249, 256, 257, 265, 279], "certain": [1, 5, 6, 7, 12, 13, 17, 55, 61, 66, 100, 112, 127, 135, 137, 139, 140, 144, 156, 160, 162, 174, 179, 204, 205, 208, 209, 210, 214, 247, 251, 274, 278], "what": [1, 2, 3, 4, 6, 10, 16, 18, 22, 23, 24, 25, 26, 27, 29, 36, 37, 38, 48, 50, 51, 59, 60, 61, 64, 65, 67, 81, 86, 96, 97, 100, 102, 103, 106, 107, 109, 110, 112, 113, 115, 126, 128, 136, 139, 140, 141, 143, 146, 147, 150, 151, 161, 165, 167, 171, 174, 175, 176, 179, 185, 187, 189, 194, 203, 207, 211, 213, 216, 227, 240, 248, 249, 250, 251, 253, 254, 261, 264, 269, 270, 283], "write": [1, 4, 5, 8, 10, 12, 18, 21, 25, 26, 27, 49, 55, 64, 65, 66, 67, 70, 83, 105, 109, 110, 111, 112, 116, 124, 126, 130, 131, 136, 140, 141, 145, 146, 148, 151, 152, 154, 156, 157, 159, 162, 164, 165, 168, 170, 174, 177, 178, 180, 184, 187, 188, 201, 204, 208, 212, 213, 214, 218, 222, 223, 225, 241, 242, 243, 248, 249, 250, 251, 256, 265, 277, 283], "custom": [1, 5, 7, 10, 13, 20, 21, 55, 58, 70, 71, 72, 76, 87, 100, 116, 122, 126, 136, 141, 151, 161, 174, 177, 181, 187, 188, 193, 195, 199, 204, 213, 215, 216, 217, 219, 221, 237, 238, 244, 248, 253, 262, 265, 269, 272], "its": [1, 2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 22, 24, 25, 26, 27, 29, 36, 37, 41, 46, 48, 52, 55, 57, 58, 59, 61, 66, 74, 83, 89, 91, 99, 100, 102, 103, 106, 108, 110, 112, 113, 115, 116, 117, 119, 122, 124, 125, 127, 129, 137, 138, 139, 140, 141, 142, 143, 150, 151, 152, 153, 154, 156, 158, 159, 161, 162, 164, 167, 168, 171, 174, 175, 176, 177, 178, 180, 184, 185, 188, 189, 190, 191, 192, 200, 204, 207, 208, 209, 213, 216, 217, 219, 221, 223, 224, 225, 226, 231, 232, 235, 237, 240, 244, 246, 248, 249, 251, 254, 255, 257, 258, 259, 260, 262, 265, 266, 267, 268, 279, 283], "includ": [1, 2, 3, 5, 6, 7, 10, 16, 17, 18, 26, 27, 28, 29, 41, 42, 45, 47, 54, 55, 59, 61, 64, 65, 66, 67, 75, 81, 83, 91, 94, 97, 99, 100, 102, 103, 106, 107, 108, 109, 116, 117, 119, 122, 127, 129, 139, 141, 142, 143, 145, 146, 148, 150, 151, 159, 161, 162, 170, 171, 174, 177, 180, 184, 185, 188, 189, 190, 191, 192, 193, 194, 198, 200, 204, 205, 207, 210, 215, 216, 221, 223, 225, 229, 231, 233, 236, 237, 238, 240, 242, 243, 244, 246, 247, 248, 249, 251, 252, 255, 262, 265, 274, 276, 277, 278, 281, 283], "design": [1, 6, 7, 16, 20, 28, 29, 58, 62, 81, 94, 100, 115, 116, 143, 148, 151, 174, 175, 176, 178, 179, 184, 185, 193, 195, 205, 206, 207, 211, 213, 216, 217, 222, 224, 236, 249, 251, 252, 265, 272, 274, 277, 278], "effici": [1, 6, 9, 12, 14, 17, 20, 24, 27, 29, 36, 47, 55, 57, 81, 91, 106, 108, 112, 115, 129, 134, 137, 138, 139, 141, 142, 150, 151, 160, 165, 171, 174, 178, 179, 184, 187, 191, 193, 202, 203, 208, 209, 210, 217, 218, 221, 249, 254, 272, 274, 278], "store": [1, 5, 6, 7, 19, 20, 23, 26, 27, 36, 37, 38, 42, 43, 45, 48, 54, 57, 59, 66, 74, 97, 99, 105, 106, 109, 110, 112, 115, 116, 117, 124, 140, 141, 143, 150, 151, 159, 161, 162, 168, 170, 171, 174, 175, 176, 177, 178, 180, 198, 200, 208, 213, 214, 217, 221, 233, 252, 255, 264, 265, 270, 273, 277], "trajectori": [1, 16, 151, 174], "transit": [1, 16, 66, 94, 96, 109, 151, 175, 216], "assum": [1, 2, 5, 7, 10, 12, 16, 17, 23, 25, 26, 40, 48, 49, 57, 60, 66, 81, 98, 99, 102, 108, 109, 111, 113, 116, 130, 139, 140, 142, 143, 150, 151, 154, 168, 171, 174, 177, 179, 180, 189, 190, 191, 194, 207, 208, 209, 215, 216, 241, 254, 255, 262, 264], "complet": [1, 5, 6, 7, 17, 25, 29, 40, 84, 86, 94, 97, 99, 102, 109, 110, 112, 127, 134, 136, 137, 139, 141, 145, 150, 171, 172, 173, 174, 177, 180, 187, 188, 193, 194, 200, 207, 208, 243, 246, 247, 252, 265, 270, 276], "ppo": [1, 136, 181], "compon": [1, 6, 7, 10, 12, 16, 24, 29, 58, 94, 103, 108, 112, 125, 127, 129, 134, 136, 141, 151, 157, 161, 174, 178, 182, 184, 188, 189, 190, 193, 209, 224, 276], "depend": [1, 6, 7, 9, 10, 13, 25, 26, 27, 47, 52, 56, 58, 66, 81, 91, 94, 99, 100, 105, 106, 108, 109, 113, 116, 123, 133, 134, 136, 139, 141, 144, 145, 150, 151, 152, 156, 157, 158, 160, 161, 164, 170, 173, 174, 177, 184, 188, 189, 190, 197, 198, 199, 200, 204, 207, 212, 213, 214, 221, 223, 224, 225, 227, 230, 236, 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23, 27, 29, 47, 49, 55, 58, 59, 61, 63, 66, 67, 91, 94, 103, 110, 115, 117, 122, 125, 136, 137, 138, 139, 141, 144, 150, 154, 164, 181, 184, 185, 187, 192, 193, 202, 208, 211, 212, 213, 215, 229, 233, 252, 264, 265, 270, 274, 276, 278, 279, 281], "level": [1, 2, 4, 6, 7, 18, 20, 23, 24, 27, 29, 49, 55, 59, 61, 63, 67, 74, 87, 103, 111, 117, 124, 129, 136, 137, 138, 139, 146, 148, 150, 152, 156, 157, 159, 162, 164, 179, 180, 181, 184, 187, 189, 190, 192, 193, 198, 201, 211, 212, 213, 215, 217, 226, 229, 232, 233, 238, 241, 245, 252, 279, 287], "illustr": [1, 23, 37, 49, 52, 62, 130, 131, 139, 140, 141, 153, 175, 185, 187, 194, 207, 208, 211, 232, 244, 247, 255, 262, 265], "librari": [1, 3, 4, 5, 6, 7, 8, 10, 14, 16, 18, 22, 24, 26, 27, 29, 38, 42, 47, 49, 56, 57, 63, 67, 83, 97, 100, 116, 119, 120, 126, 127, 133, 136, 141, 144, 145, 152, 154, 158, 170, 173, 174, 178, 184, 189, 190, 193, 210, 221, 223, 224, 232, 236, 237, 240, 241, 244, 245, 246, 269], "featur": [1, 5, 7, 12, 13, 14, 16, 23, 26, 27, 38, 42, 46, 55, 56, 57, 58, 64, 65, 66, 91, 92, 94, 99, 102, 103, 106, 108, 109, 115, 116, 120, 124, 127, 136, 138, 140, 151, 152, 159, 160, 161, 164, 167, 170, 173, 174, 178, 179, 185, 188, 189, 190, 191, 192, 193, 194, 201, 202, 203, 204, 208, 209, 212, 215, 217, 221, 222, 223, 224, 225, 229, 233, 236, 244, 247, 251, 252, 254, 262, 264, 265, 268, 269, 270, 273, 274], "context": [1, 2, 6, 10, 16, 19, 20, 48, 55, 66, 81, 99, 103, 115, 116, 122, 135, 139, 149, 156, 168, 174, 177, 178, 179, 180, 184, 193, 202, 215, 217, 223, 225, 229, 248, 250, 254, 255, 256, 265, 278], "bash": [1, 22, 24, 161, 175, 244], "pip3": [1, 22, 56, 137, 151, 174, 175, 184, 191, 200, 203, 204], "mujoco": [1, 151, 174], "glfw": 1, "avail": [1, 2, 3, 4, 6, 7, 12, 14, 17, 20, 22, 23, 24, 25, 26, 27, 36, 37, 38, 41, 42, 43, 45, 47, 48, 49, 54, 56, 57, 58, 59, 64, 65, 81, 89, 97, 100, 102, 103, 106, 107, 108, 112, 116, 117, 127, 129, 134, 137, 140, 150, 151, 154, 156, 161, 162, 171, 172, 173, 174, 175, 178, 179, 180, 184, 187, 191, 192, 193, 194, 197, 198, 203, 204, 212, 213, 214, 215, 217, 222, 229, 230, 231, 237, 240, 241, 242, 243, 244, 245, 246, 247, 249, 250, 251, 257, 258, 259, 260, 265, 266, 267, 268, 275, 276, 281], "seri": [1, 7, 17, 27, 58, 59, 60, 61, 62, 91, 101, 103, 106, 107, 135, 136, 142, 143, 146, 147, 154, 158, 171, 174, 207, 236, 278], "reusabl": [1, 7, 29], "swappabl": 1, "signatur": [1, 6, 10, 12, 16, 17, 27, 120, 150, 168, 177, 189, 190, 270], "characterist": [1, 16, 48, 158, 160, 161, 173, 179], "copi": [1, 6, 7, 14, 22, 26, 27, 40, 45, 49, 50, 56, 61, 64, 81, 91, 98, 99, 107, 108, 122, 123, 125, 128, 136, 138, 140, 144, 148, 150, 151, 152, 153, 157, 158, 161, 164, 168, 172, 177, 184, 187, 197, 198, 199, 204, 210, 214, 215, 221, 223, 225, 229, 230, 235, 236, 254, 257, 265, 277, 284], "loss_modul": 1, "whatev": [1, 10, 26, 27, 102, 106, 107, 110, 112, 116, 125, 211, 244, 257], "convent": [1, 16, 58, 66, 116, 125, 151, 187, 233, 249, 258, 259], "receiv": [1, 5, 7, 16, 19, 61, 97, 106, 112, 124, 150, 174, 176, 177, 178, 188, 248, 251, 265], "necessari": [1, 5, 6, 7, 10, 12, 14, 17, 22, 23, 27, 28, 49, 58, 59, 61, 66, 94, 97, 106, 107, 109, 116, 125, 127, 137, 138, 139, 144, 148, 161, 164, 176, 177, 178, 184, 189, 190, 193, 195, 198, 201, 207, 209, 211, 213, 214, 215, 248, 265], "return": [1, 2, 4, 5, 6, 7, 10, 12, 13, 14, 16, 17, 18, 19, 22, 23, 25, 27, 29, 37, 38, 42, 45, 50, 52, 55, 57, 58, 65, 66, 71, 81, 89, 91, 99, 100, 103, 106, 107, 108, 110, 112, 113, 115, 116, 120, 122, 123, 124, 125, 127, 130, 133, 137, 138, 139, 142, 143, 145, 148, 149, 150, 151, 152, 153, 154, 156, 159, 161, 162, 165, 168, 170, 172, 173, 174, 175, 176, 177, 178, 179, 184, 185, 187, 188, 189, 190, 192, 193, 194, 195, 198, 199, 201, 202, 203, 205, 207, 210, 211, 213, 214, 215, 216, 217, 222, 223, 225, 226, 227, 229, 230, 231, 232, 235, 236, 237, 238, 240, 241, 244, 247, 249, 251, 252, 255, 257, 264, 270, 276, 277], "replay_buff": 1, "sampl": [1, 4, 7, 10, 37, 38, 41, 42, 45, 49, 52, 54, 55, 57, 58, 61, 66, 67, 83, 86, 87, 97, 102, 103, 105, 106, 108, 123, 129, 130, 131, 133, 136, 138, 142, 144, 150, 152, 154, 160, 161, 167, 168, 172, 174, 175, 176, 178, 181, 187, 189, 190, 194, 198, 201, 209, 210, 213, 215, 222, 226, 227, 243, 247, 255, 264, 265, 283], "loss_dict": 1, "instanc": [1, 5, 6, 7, 9, 13, 14, 16, 25, 26, 27, 29, 37, 43, 50, 59, 60, 61, 64, 65, 66, 67, 86, 91, 97, 102, 103, 106, 107, 108, 109, 110, 113, 115, 116, 124, 137, 138, 140, 141, 146, 147, 148, 149, 151, 159, 161, 172, 174, 176, 177, 178, 188, 198, 199, 213, 215, 216, 219, 236, 237, 244, 248, 252, 255, 257, 263, 264, 265, 270, 276, 277, 278, 279], "written": [1, 4, 5, 6, 7, 10, 12, 16, 18, 26, 27, 29, 94, 105, 111, 116, 145, 151, 158, 165, 168, 169, 172, 179, 184, 187, 200, 205, 230, 237, 263, 274], "under": [1, 5, 6, 10, 22, 23, 27, 36, 52, 55, 56, 58, 60, 62, 67, 105, 108, 110, 122, 124, 127, 139, 140, 150, 152, 154, 160, 161, 168, 171, 178, 184, 185, 193, 194, 195, 203, 204, 206, 208, 221, 229, 231, 233, 238, 240, 243, 244, 248, 273, 283], "loss_": 1, "smth": 1, "where": [1, 3, 5, 7, 9, 10, 13, 14, 15, 16, 19, 20, 22, 24, 25, 26, 27, 28, 36, 38, 52, 55, 57, 66, 67, 70, 74, 83, 86, 87, 92, 94, 97, 99, 100, 102, 103, 105, 106, 108, 109, 110, 112, 113, 115, 123, 124, 127, 136, 137, 139, 141, 142, 143, 145, 147, 148, 149, 150, 152, 153, 154, 159, 162, 164, 165, 167, 168, 169, 173, 174, 175, 176, 177, 178, 179, 180, 185, 188, 190, 191, 193, 194, 195, 198, 200, 203, 205, 208, 210, 211, 216, 217, 221, 225, 230, 232, 233, 244, 246, 248, 249, 251, 257, 258, 262, 263, 264, 268, 269, 278, 284], "string": [1, 4, 10, 17, 26, 27, 55, 64, 65, 66, 71, 106, 117, 124, 129, 130, 133, 141, 142, 143, 151, 154, 171, 174, 180, 187, 198, 225, 226, 244, 249, 264, 277, 281], "describ": [1, 5, 6, 7, 10, 12, 16, 17, 19, 23, 24, 25, 26, 27, 45, 54, 55, 58, 64, 65, 67, 81, 102, 108, 117, 128, 135, 150, 165, 174, 175, 178, 184, 187, 189, 190, 192, 212, 213, 214, 219, 232, 249, 252, 270, 278], "addit": [1, 2, 4, 6, 9, 10, 13, 17, 20, 23, 56, 66, 81, 83, 100, 106, 108, 113, 116, 117, 120, 122, 127, 137, 139, 140, 148, 150, 152, 153, 154, 157, 159, 162, 164, 171, 176, 177, 180, 185, 188, 189, 190, 192, 201, 205, 206, 207, 208, 213, 216, 217, 223, 225, 233, 235, 236, 237, 249, 255, 258, 265, 274, 278], "kei": [1, 7, 10, 13, 16, 17, 55, 64, 91, 111, 115, 117, 122, 125, 128, 129, 130, 134, 137, 141, 151, 152, 154, 161, 173, 174, 176, 179, 180, 184, 185, 187, 189, 190, 191, 193, 201, 209, 210, 211, 217, 226, 227, 228, 237, 252, 254, 263, 266, 274, 283], "mai": [1, 4, 5, 6, 7, 8, 10, 12, 13, 14, 16, 17, 18, 20, 23, 25, 26, 27, 29, 41, 47, 55, 56, 58, 64, 65, 66, 74, 81, 94, 99, 100, 102, 103, 106, 110, 124, 125, 127, 130, 138, 139, 140, 141, 144, 145, 151, 152, 153, 154, 156, 159, 160, 165, 167, 168, 173, 174, 177, 180, 184, 187, 188, 189, 190, 192, 193, 195, 197, 198, 201, 204, 207, 209, 213, 214, 215, 216, 217, 219, 224, 225, 227, 235, 246, 249, 252, 255, 257, 258, 260, 265, 268, 270, 283, 284], "metric": [1, 20, 97, 105, 106, 108, 122, 137, 152, 184, 187, 193, 194, 217, 238, 244, 249, 263], "log": [1, 22, 41, 55, 56, 58, 59, 64, 105, 109, 110, 113, 115, 116, 138, 141, 147, 152, 173, 174, 176, 178, 184, 185, 187, 189, 193, 201, 211, 225, 228, 238, 269, 275, 278], "dure": [1, 3, 9, 10, 14, 16, 22, 23, 29, 36, 37, 41, 42, 55, 58, 66, 69, 70, 84, 94, 99, 102, 103, 106, 107, 108, 110, 115, 116, 120, 124, 125, 127, 133, 136, 137, 138, 139, 140, 143, 144, 145, 146, 148, 151, 157, 158, 159, 164, 165, 168, 172, 173, 174, 176, 178, 184, 188, 192, 193, 194, 201, 212, 214, 219, 223, 231, 233, 237, 241, 242, 243, 244, 246, 252, 255, 259, 262, 263, 270], "reason": [1, 6, 7, 10, 16, 17, 18, 20, 27, 29, 36, 58, 86, 91, 99, 102, 108, 110, 113, 116, 125, 131, 140, 144, 150, 159, 164, 172, 174, 179, 180, 200, 207, 217, 231, 241, 249, 253, 254, 259, 261, 269, 270], "independ": [1, 9, 27, 55, 66, 87, 115, 116, 120, 123, 160, 161, 165, 177, 205], "user": [1, 3, 6, 16, 20, 22, 23, 26, 28, 29, 46, 48, 49, 55, 56, 66, 84, 87, 91, 92, 94, 100, 108, 112, 120, 123, 127, 128, 129, 136, 137, 139, 143, 148, 152, 154, 157, 159, 162, 176, 178, 179, 180, 184, 187, 189, 190, 191, 192, 193, 194, 195, 198, 201, 203, 205, 206, 207, 208, 211, 212, 213, 214, 215, 216, 217, 221, 224, 229, 232, 233, 237, 238, 244, 246, 255, 269, 271, 273, 278, 283, 284], "sum": [1, 2, 4, 5, 6, 8, 9, 13, 16, 18, 22, 23, 25, 37, 48, 55, 58, 66, 71, 84, 91, 99, 100, 109, 110, 112, 115, 124, 137, 138, 142, 143, 145, 150, 160, 165, 172, 175, 176, 177, 178, 184, 188, 189, 190, 191, 198, 206, 213, 214, 227, 229, 231, 238, 249, 268, 270, 279], "done": [1, 4, 5, 6, 7, 10, 12, 16, 19, 20, 23, 24, 25, 26, 27, 29, 55, 60, 64, 65, 91, 94, 103, 106, 108, 109, 110, 116, 120, 127, 129, 137, 138, 139, 140, 143, 144, 150, 151, 153, 158, 159, 161, 162, 170, 171, 172, 173, 174, 176, 177, 178, 179, 180, 185, 187, 189, 190, 200, 201, 210, 217, 219, 225, 233, 237, 241, 252, 254, 265, 276, 277], "via": [1, 3, 4, 6, 7, 8, 9, 19, 20, 22, 24, 26, 27, 43, 60, 61, 64, 65, 81, 94, 108, 136, 137, 138, 139, 141, 150, 151, 160, 168, 173, 174, 179, 181, 185, 187, 188, 192, 193, 194, 204, 217, 229, 230, 232, 233, 236, 237, 238, 244, 254, 262, 263, 265, 269, 273, 281, 287], "loss_val": 1, "item": [1, 2, 7, 12, 13, 17, 45, 55, 71, 74, 115, 124, 125, 128, 137, 138, 142, 143, 150, 151, 152, 154, 172, 173, 174, 176, 177, 178, 185, 187, 195, 226, 230, 235, 238, 248, 258, 260, 265, 282, 284], "startswith": [1, 92, 264], "parent": [1, 16, 129, 199, 201], "As": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 16, 19, 23, 24, 25, 26, 27, 29, 48, 55, 56, 58, 64, 65, 66, 81, 94, 97, 100, 102, 103, 106, 107, 108, 115, 116, 117, 120, 124, 125, 130, 133, 137, 138, 139, 140, 141, 142, 148, 150, 151, 152, 156, 157, 158, 159, 160, 161, 164, 167, 168, 171, 172, 174, 175, 176, 177, 178, 179, 184, 187, 190, 191, 192, 193, 194, 195, 198, 200, 201, 203, 204, 208, 209, 211, 213, 216, 221, 224, 225, 229, 236, 238, 240, 244, 249, 252, 254, 265, 273, 274, 276, 278, 279], "mani": [1, 2, 5, 6, 7, 12, 16, 17, 18, 20, 22, 27, 29, 36, 37, 41, 42, 55, 57, 58, 66, 71, 75, 81, 91, 99, 100, 102, 103, 106, 108, 110, 111, 112, 116, 117, 119, 124, 127, 137, 139, 141, 142, 144, 150, 152, 153, 160, 162, 164, 165, 169, 172, 174, 176, 177, 180, 189, 190, 192, 193, 207, 210, 217, 221, 222, 237, 238, 247, 248, 249, 251, 265, 270, 278, 281, 283, 284], "expect": [1, 4, 5, 6, 7, 12, 13, 16, 24, 26, 27, 50, 52, 55, 57, 64, 65, 66, 81, 94, 97, 99, 100, 102, 103, 106, 107, 108, 112, 113, 115, 116, 125, 127, 131, 134, 141, 144, 148, 149, 151, 160, 161, 167, 168, 171, 173, 174, 175, 176, 177, 179, 187, 189, 190, 192, 194, 195, 198, 203, 204, 210, 211, 213, 215, 216, 221, 230, 237, 241, 244, 247, 248, 249, 252, 255, 258, 260, 262, 265, 279], "similar": [1, 3, 6, 10, 12, 13, 16, 17, 23, 26, 27, 45, 54, 55, 64, 65, 91, 92, 100, 102, 103, 106, 108, 109, 115, 116, 120, 124, 130, 139, 145, 149, 150, 151, 154, 164, 168, 174, 176, 177, 178, 179, 180, 184, 185, 187, 192, 194, 195, 198, 201, 205, 206, 207, 208, 209, 214, 215, 230, 235, 236, 248, 249, 252, 264, 265, 279], "structur": [1, 5, 6, 7, 10, 11, 16, 21, 22, 23, 24, 25, 26, 27, 37, 42, 43, 45, 54, 55, 58, 59, 66, 94, 99, 102, 103, 107, 108, 109, 113, 117, 123, 125, 136, 146, 153, 158, 161, 162, 164, 168, 169, 171, 174, 178, 185, 187, 188, 194, 208, 210, 212, 213, 222, 225, 252, 259, 263, 281, 283, 287], "make": [1, 4, 5, 6, 7, 10, 12, 14, 16, 22, 23, 26, 27, 37, 41, 42, 46, 48, 49, 50, 55, 56, 57, 59, 60, 61, 63, 64, 65, 66, 74, 81, 97, 99, 102, 103, 105, 106, 108, 110, 111, 112, 114, 115, 116, 120, 125, 127, 128, 129, 131, 133, 136, 137, 138, 139, 141, 142, 143, 144, 148, 150, 151, 152, 154, 157, 158, 159, 160, 161, 164, 167, 168, 170, 171, 172, 174, 175, 176, 177, 178, 180, 185, 187, 188, 189, 190, 192, 194, 198, 200, 201, 204, 205, 206, 207, 208, 209, 210, 211, 213, 216, 222, 229, 230, 231, 232, 233, 235, 236, 237, 241, 245, 246, 247, 248, 249, 252, 254, 256, 257, 263, 264, 265, 269, 270, 273, 274, 276, 283, 285], "possibl": [1, 2, 4, 5, 6, 7, 8, 10, 12, 16, 17, 20, 26, 27, 58, 66, 83, 99, 100, 103, 106, 109, 112, 120, 134, 140, 144, 145, 151, 153, 156, 158, 160, 161, 164, 172, 173, 174, 176, 177, 180, 194, 198, 201, 203, 209, 213, 214, 215, 216, 217, 219, 221, 224, 233, 237, 238, 241, 248, 252, 254, 265, 270, 274, 278, 283], "across": [1, 6, 9, 10, 13, 16, 19, 22, 24, 28, 45, 55, 58, 60, 61, 62, 91, 100, 106, 108, 117, 129, 135, 137, 138, 139, 146, 147, 148, 149, 150, 153, 161, 164, 171, 177, 178, 191, 192, 228, 231, 232, 247, 253, 261, 263, 265, 266, 268, 269, 273, 278, 279, 281], "modal": [1, 66, 247], "complex": [1, 7, 27, 29, 37, 38, 56, 73, 74, 99, 100, 103, 108, 117, 124, 125, 135, 138, 148, 165, 168, 176, 178, 185, 209, 220, 226, 252, 256, 266, 274], "multipl": [1, 4, 6, 10, 12, 13, 16, 18, 19, 20, 22, 23, 24, 27, 45, 50, 54, 55, 59, 60, 61, 62, 67, 71, 86, 87, 90, 91, 97, 99, 100, 103, 106, 107, 108, 112, 116, 123, 124, 135, 138, 139, 140, 141, 142, 143, 148, 149, 150, 153, 154, 159, 161, 164, 173, 174, 176, 177, 178, 180, 184, 185, 187, 189, 190, 191, 192, 193, 198, 200, 209, 215, 216, 217, 224, 230, 231, 236, 248, 249, 253, 255, 258, 259, 261, 264, 265, 266, 269, 278, 283, 284], "entri": [1, 5, 13, 16, 27, 59, 83, 112, 115, 122, 123, 125, 129, 146, 151, 159, 171, 174, 176, 184, 189, 190, 207, 208, 209, 211, 229, 267, 273], "word": [1, 7, 9, 12, 13, 16, 21, 47, 49, 55, 66, 81, 87, 91, 100, 103, 108, 109, 111, 113, 114, 125, 129, 130, 133, 136, 142, 143, 150, 152, 167, 168, 171, 178, 180, 192, 197, 206, 208, 209, 211, 215, 252, 283], "oblivi": [1, 174], "type": [1, 4, 5, 6, 7, 10, 12, 16, 22, 23, 24, 25, 26, 27, 45, 47, 54, 55, 56, 66, 81, 89, 91, 94, 98, 100, 102, 105, 107, 112, 116, 117, 120, 127, 135, 137, 138, 139, 141, 149, 152, 153, 154, 159, 169, 170, 171, 174, 176, 177, 178, 179, 184, 187, 188, 189, 191, 193, 195, 201, 203, 205, 210, 213, 215, 216, 219, 221, 224, 225, 226, 229, 230, 231, 233, 237, 238, 240, 241, 244, 246, 247, 262, 263, 265, 266, 272, 277, 278, 283], "being": [1, 3, 5, 6, 7, 12, 14, 16, 20, 24, 25, 27, 47, 52, 55, 58, 64, 65, 66, 84, 91, 100, 107, 108, 109, 110, 112, 115, 116, 117, 123, 127, 131, 137, 139, 141, 144, 150, 151, 168, 171, 174, 175, 177, 193, 201, 204, 206, 207, 209, 211, 215, 217, 219, 237, 249, 254, 265, 278], "run": [1, 2, 3, 5, 6, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 36, 37, 38, 41, 42, 43, 45, 46, 47, 48, 49, 52, 54, 57, 58, 62, 63, 67, 69, 70, 71, 73, 74, 75, 78, 79, 83, 84, 86, 87, 89, 91, 97, 99, 100, 102, 103, 105, 106, 107, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 124, 125, 127, 128, 130, 131, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 149, 150, 151, 152, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 182, 185, 188, 189, 191, 192, 193, 194, 197, 198, 199, 200, 202, 203, 204, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 220, 221, 222, 223, 224, 225, 226, 231, 232, 233, 235, 236, 237, 240, 241, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 254, 256, 257, 258, 259, 260, 262, 264, 265, 266, 267, 268, 269, 270, 271, 273, 274, 275, 278, 283], "elementari": [1, 2], "those": [1, 4, 5, 6, 7, 8, 12, 13, 16, 20, 36, 47, 48, 87, 97, 99, 106, 109, 115, 116, 127, 129, 130, 139, 140, 142, 150, 153, 158, 167, 170, 171, 178, 180, 185, 187, 189, 190, 193, 198, 200, 204, 206, 217, 219, 221, 222, 223, 224, 238, 241, 244, 248, 249, 264, 273, 278, 283], "keep": [1, 7, 12, 13, 16, 27, 36, 45, 48, 55, 58, 81, 91, 94, 102, 103, 106, 108, 110, 112, 113, 116, 120, 124, 125, 130, 134, 136, 137, 138, 139, 140, 142, 143, 147, 148, 151, 159, 165, 172, 178, 180, 193, 197, 198, 213, 225, 235, 249, 265, 277, 279], "didact": [1, 150], "displai": [1, 2, 6, 7, 14, 38, 49, 64, 83, 100, 105, 107, 120, 122, 131, 144, 154, 172, 180, 184, 229, 248, 249, 263, 277, 281], "each": [1, 2, 4, 6, 7, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 27, 28, 29, 36, 37, 38, 40, 41, 42, 45, 48, 49, 50, 54, 55, 57, 58, 59, 61, 62, 64, 65, 66, 67, 71, 81, 83, 84, 87, 91, 92, 94, 97, 98, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 113, 115, 116, 119, 120, 122, 124, 125, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 145, 146, 147, 148, 149, 150, 151, 153, 156, 158, 160, 161, 162, 164, 165, 167, 168, 169, 171, 172, 174, 175, 176, 177, 178, 179, 180, 182, 184, 185, 187, 189, 190, 191, 192, 193, 194, 201, 202, 205, 208, 209, 211, 213, 214, 216, 217, 225, 226, 228, 229, 232, 233, 237, 238, 244, 247, 248, 249, 251, 252, 254, 255, 256, 260, 262, 263, 264, 265, 267, 268, 270, 275, 277, 278, 279, 281, 283], "popul": [1, 26, 36, 48, 55, 64, 65, 97, 106, 137, 151, 161, 174, 176, 228, 233, 264], "later": [1, 3, 4, 5, 6, 7, 13, 18, 27, 36, 45, 52, 55, 66, 81, 97, 100, 103, 108, 112, 113, 116, 125, 127, 138, 139, 142, 143, 145, 149, 150, 153, 156, 157, 158, 159, 160, 161, 165, 169, 174, 175, 178, 179, 180, 185, 187, 189, 190, 198, 205, 213, 214, 227, 228, 241, 244, 246, 248, 249, 250, 254, 259, 262, 265, 273, 274, 275], "stage": [1, 9, 16, 19, 103, 116, 202, 204, 223, 229], "start": [1, 5, 6, 7, 11, 13, 19, 20, 22, 23, 27, 28, 29, 36, 40, 48, 49, 55, 56, 58, 59, 60, 61, 65, 66, 67, 81, 97, 98, 99, 100, 103, 106, 107, 108, 111, 112, 116, 117, 127, 130, 135, 136, 137, 139, 140, 141, 142, 143, 144, 149, 150, 152, 154, 159, 160, 161, 164, 167, 168, 172, 173, 175, 176, 177, 180, 184, 185, 187, 188, 189, 192, 193, 194, 198, 200, 201, 203, 207, 211, 213, 214, 215, 216, 217, 218, 225, 229, 230, 233, 236, 241, 244, 249, 252, 253, 255, 261, 263, 265, 268, 269, 273, 274, 279, 284], "solv": [1, 7, 16, 55, 57, 108, 115, 131, 133, 164, 168, 172, 174, 176, 178, 192, 207, 249, 254, 265], "task": [1, 7, 9, 15, 16, 20, 25, 28, 41, 42, 55, 64, 65, 66, 83, 107, 108, 109, 115, 122, 127, 130, 131, 133, 134, 135, 136, 138, 151, 152, 168, 172, 174, 175, 180, 182, 187, 194, 201, 217, 221, 225, 249, 255, 264, 265, 273, 278], "strategi": [1, 6, 20, 22, 28, 58, 91, 127, 136, 143, 150, 159, 160, 164, 169, 176, 177, 217, 224, 232, 233, 238], "predict": [1, 11, 23, 24, 37, 40, 41, 42, 43, 48, 49, 55, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 99, 100, 102, 103, 107, 108, 109, 113, 115, 116, 124, 127, 130, 133, 136, 139, 141, 142, 143, 152, 153, 160, 161, 164, 175, 180, 185, 194, 197, 198, 213, 214, 217, 230, 236, 247, 251, 269, 276, 277], "henc": [1, 16, 20, 45, 48, 54, 86, 89, 91, 127, 138, 140, 148, 149, 162, 164, 165, 170, 174, 176, 178, 192, 217, 236, 237, 249], "two": [1, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 22, 23, 24, 25, 27, 28, 29, 36, 38, 41, 42, 45, 46, 47, 48, 54, 55, 56, 58, 63, 64, 65, 66, 81, 83, 92, 94, 97, 99, 100, 102, 103, 106, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 124, 130, 131, 134, 137, 138, 141, 142, 144, 145, 147, 148, 149, 150, 151, 154, 156, 158, 159, 160, 161, 162, 164, 165, 168, 169, 170, 171, 172, 174, 175, 176, 178, 179, 180, 187, 191, 192, 193, 194, 195, 199, 200, 202, 206, 208, 209, 211, 213, 214, 215, 216, 217, 219, 220, 224, 229, 232, 233, 236, 238, 242, 243, 244, 249, 251, 252, 254, 255, 259, 260, 262, 264, 265, 266, 269, 270, 276, 278, 279, 283, 284, 288], "constructor": [1, 7, 12, 13, 14, 25, 26, 27, 29, 66, 71, 73, 75, 86, 94, 103, 107, 124, 130, 137, 138, 148, 149, 170, 171, 174, 176, 178, 208, 219, 248, 249, 270], "both": [1, 2, 5, 6, 7, 9, 10, 12, 13, 14, 16, 19, 23, 24, 25, 26, 27, 28, 29, 38, 47, 55, 57, 58, 64, 65, 67, 81, 91, 94, 100, 106, 107, 108, 115, 116, 122, 124, 127, 130, 133, 137, 139, 141, 142, 144, 147, 148, 149, 150, 156, 157, 159, 160, 162, 164, 165, 171, 172, 174, 176, 177, 178, 179, 180, 189, 190, 191, 192, 193, 194, 195, 198, 200, 201, 202, 205, 208, 210, 211, 213, 215, 216, 226, 229, 232, 236, 237, 238, 241, 244, 246, 247, 248, 249, 259, 262, 264, 273, 276, 278, 281, 283], "compat": [1, 5, 6, 7, 10, 13, 20, 56, 66, 105, 106, 112, 151, 162, 179, 189, 190, 198, 203, 219, 221, 233, 240, 276], "comput": [1, 3, 4, 6, 7, 10, 13, 14, 15, 18, 19, 20, 23, 24, 25, 27, 28, 29, 41, 45, 49, 52, 54, 55, 57, 58, 59, 63, 64, 65, 66, 67, 69, 70, 71, 74, 78, 79, 81, 83, 84, 86, 87, 92, 94, 99, 100, 102, 103, 106, 107, 108, 109, 110, 111, 116, 117, 119, 123, 124, 129, 134, 135, 136, 137, 138, 139, 140, 141, 144, 145, 146, 147, 148, 150, 151, 152, 154, 156, 157, 159, 161, 164, 167, 168, 169, 171, 173, 174, 175, 177, 179, 180, 184, 187, 188, 189, 190, 192, 193, 194, 198, 200, 203, 204, 209, 210, 211, 212, 213, 214, 215, 217, 219, 222, 223, 224, 225, 227, 228, 233, 236, 241, 246, 248, 249, 251, 252, 254, 256, 264, 274, 276, 278, 283], "fit": [1, 7, 12, 13, 24, 67, 97, 115, 124, 137, 138, 139, 148, 164, 178, 248, 278, 283], "crucial": [1, 2, 14, 16, 27, 91, 112, 151, 174, 241], "convert_to_funct": 1, "extract": [1, 6, 24, 55, 58, 64, 65, 81, 105, 108, 130, 131, 136, 142, 143, 152, 156, 159, 169, 172, 174, 180, 188, 189, 190, 194, 225, 229, 230, 233, 251], "convert": [1, 6, 11, 12, 14, 23, 24, 26, 27, 37, 38, 45, 46, 49, 55, 57, 58, 61, 83, 102, 103, 106, 108, 116, 117, 119, 123, 125, 127, 129, 130, 133, 134, 136, 142, 143, 152, 154, 172, 173, 174, 176, 177, 185, 193, 197, 199, 200, 201, 204, 205, 206, 208, 209, 212, 215, 216, 226, 230, 233, 235, 237, 241, 242, 243, 245, 246, 247, 252, 257, 262, 265, 269, 270], "strictli": [1, 106, 174], "speak": [1, 10, 48, 106, 140, 150, 164, 265], "perfectli": [1, 16, 71, 124], "encourag": [1, 7, 23, 103, 154, 175, 180, 187], "usag": [1, 3, 4, 5, 13, 15, 17, 25, 27, 66, 91, 99, 122, 130, 136, 138, 140, 150, 151, 159, 160, 174, 176, 178, 179, 184, 193, 200, 201, 204, 209, 210, 211, 215, 224, 237, 244, 248, 263, 265, 269, 276, 278, 283], "doe": [1, 2, 4, 6, 7, 10, 15, 16, 17, 18, 23, 26, 27, 29, 36, 46, 48, 52, 66, 78, 79, 81, 87, 89, 94, 99, 100, 102, 103, 105, 106, 107, 108, 110, 112, 115, 116, 117, 120, 124, 125, 127, 131, 137, 138, 145, 148, 149, 150, 151, 154, 157, 160, 161, 162, 164, 167, 168, 173, 174, 175, 177, 178, 179, 180, 184, 185, 188, 189, 190, 192, 194, 198, 199, 200, 206, 207, 208, 213, 215, 219, 222, 225, 233, 241, 243, 244, 246, 248, 249, 252, 254, 257, 259, 262, 264, 265, 278, 283], "often": [1, 5, 6, 7, 12, 16, 20, 45, 55, 81, 97, 99, 100, 102, 106, 108, 110, 112, 115, 116, 124, 125, 127, 139, 140, 141, 143, 161, 168, 193, 209, 217, 220, 227, 233, 248, 264, 265, 283], "same": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 17, 20, 23, 24, 25, 26, 27, 29, 36, 37, 43, 45, 48, 49, 55, 57, 58, 59, 61, 64, 65, 66, 71, 79, 81, 86, 89, 94, 97, 99, 106, 107, 108, 110, 112, 113, 115, 116, 117, 120, 122, 124, 125, 133, 134, 137, 138, 139, 140, 142, 144, 147, 148, 150, 152, 153, 154, 156, 159, 160, 161, 162, 164, 167, 168, 169, 170, 171, 173, 174, 175, 176, 177, 178, 179, 180, 182, 184, 185, 187, 188, 189, 190, 192, 193, 195, 197, 198, 201, 202, 205, 206, 207, 208, 209, 210, 213, 214, 215, 216, 217, 225, 230, 231, 235, 240, 241, 246, 248, 249, 252, 255, 259, 262, 265, 268, 270, 273, 276, 278, 279, 281, 283], "usual": [1, 6, 7, 10, 18, 23, 27, 48, 64, 65, 66, 100, 102, 103, 105, 106, 110, 111, 113, 115, 116, 127, 131, 139, 140, 143, 144, 145, 148, 150, 151, 159, 162, 167, 171, 172, 174, 211, 222, 248, 254, 255, 265, 278, 281], "former": [1, 6, 87, 142, 143, 180], "lag": [1, 174], "absolut": [1, 7, 9, 12, 91, 110, 171, 175, 225, 264], "dilut": 1, "averag": [1, 3, 23, 55, 58, 67, 91, 97, 100, 102, 107, 108, 116, 127, 129, 138, 142, 143, 150, 152, 169, 175, 178, 180, 184, 192, 193, 198, 213, 214, 249, 265], "associ": [1, 4, 6, 7, 10, 12, 37, 38, 56, 91, 100, 145, 156, 171, 179, 187, 206, 217, 219, 251, 262, 265], "One": [1, 2, 5, 6, 7, 9, 12, 13, 17, 25, 27, 55, 57, 66, 81, 87, 91, 99, 103, 106, 108, 109, 110, 112, 137, 138, 139, 140, 143, 148, 150, 152, 153, 157, 164, 167, 168, 182, 185, 188, 193, 194, 207, 211, 216, 222, 226, 238, 241, 249, 256, 262, 264, 265, 283, 284], "advantag": [1, 3, 7, 16, 20, 27, 55, 66, 94, 106, 109, 116, 119, 135, 137, 140, 150, 151, 168, 174, 188, 193, 198, 201, 208, 217, 226, 237, 240, 244, 252, 265, 277], "match": [1, 5, 6, 12, 16, 20, 23, 24, 26, 64, 65, 66, 74, 83, 84, 102, 106, 108, 116, 117, 120, 124, 125, 127, 149, 152, 153, 157, 159, 162, 164, 167, 169, 174, 177, 188, 189, 190, 198, 201, 206, 208, 211, 213, 217, 236, 237, 248, 251, 256, 266, 278], "exactli": [1, 6, 9, 10, 12, 14, 20, 29, 36, 48, 57, 58, 66, 86, 89, 102, 112, 115, 116, 117, 124, 151, 159, 168, 190, 201, 217], "configur": [1, 5, 6, 7, 16, 22, 23, 24, 26, 27, 28, 47, 55, 56, 66, 91, 100, 107, 127, 137, 139, 146, 148, 157, 159, 164, 167, 172, 174, 177, 184, 187, 192, 199, 200, 215, 216, 225, 229, 236, 237, 238, 243, 269, 273, 274, 278, 287], "pessimist": [1, 174], "bound": [1, 27, 55, 125, 141, 159, 174, 175, 184, 189, 190, 192, 194, 200, 248, 249, 255, 259, 265, 278], "pai": [1, 12, 50, 55, 66, 129, 265], "attent": [1, 9, 12, 47, 50, 55, 103, 129, 133, 134, 136, 139, 142, 181, 182, 200, 201, 209, 270, 274], "create_target_param": 1, "keyword": [1, 6, 171, 174, 187, 254, 262], "argument": [1, 2, 5, 6, 7, 10, 16, 18, 25, 26, 27, 36, 42, 45, 48, 49, 54, 57, 61, 66, 75, 84, 91, 99, 100, 103, 105, 106, 108, 110, 113, 115, 122, 124, 125, 129, 137, 138, 142, 143, 147, 148, 150, 151, 153, 159, 160, 169, 170, 171, 174, 176, 177, 178, 179, 184, 187, 188, 189, 190, 195, 204, 207, 210, 215, 222, 223, 225, 226, 229, 240, 241, 248, 249, 254, 255, 257, 262, 263, 265, 266, 274, 278, 283, 284], "below": [1, 2, 5, 7, 12, 13, 14, 16, 19, 22, 23, 24, 27, 28, 38, 45, 48, 50, 54, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 87, 91, 94, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 127, 128, 130, 133, 134, 139, 141, 144, 146, 147, 148, 149, 150, 151, 152, 156, 159, 162, 164, 167, 170, 172, 174, 175, 176, 177, 178, 179, 184, 185, 187, 188, 189, 190, 191, 192, 193, 194, 200, 201, 202, 204, 206, 207, 208, 209, 211, 219, 220, 221, 228, 229, 230, 231, 233, 235, 236, 238, 240, 241, 243, 244, 246, 248, 249, 252, 254, 255, 265, 270, 272, 273, 278, 279, 283], "tell": [1, 18, 22, 27, 47, 75, 97, 99, 102, 103, 107, 115, 116, 124, 141, 142, 151, 153, 156, 167, 175, 176, 178, 180, 191, 203, 204, 226, 249, 255, 283], "fals": [1, 2, 4, 7, 12, 13, 16, 23, 24, 27, 47, 48, 61, 65, 69, 70, 91, 92, 99, 112, 123, 124, 125, 129, 130, 131, 137, 138, 139, 144, 149, 152, 156, 159, 162, 165, 167, 168, 172, 173, 176, 177, 185, 187, 189, 190, 192, 193, 195, 198, 201, 202, 206, 207, 210, 213, 214, 216, 217, 223, 225, 227, 235, 236, 237, 238, 241, 246, 248, 262, 264, 265, 266, 270, 272, 278, 279, 281, 282, 283, 284], "target_actor_network_param": 1, "attribut": [1, 7, 13, 16, 26, 29, 36, 48, 52, 59, 66, 84, 87, 91, 94, 103, 115, 116, 120, 130, 140, 149, 151, 156, 162, 168, 171, 189, 190, 192, 198, 201, 209, 210, 212, 215, 220, 224, 248, 268, 269, 283], "access": [1, 6, 7, 9, 12, 16, 20, 23, 27, 37, 38, 56, 66, 74, 81, 86, 87, 97, 99, 102, 103, 106, 107, 108, 113, 116, 124, 125, 133, 134, 136, 137, 140, 146, 150, 157, 168, 173, 175, 177, 187, 189, 190, 193, 201, 203, 205, 206, 208, 210, 217, 225, 226, 232, 235, 258, 260, 264, 268, 270, 273, 281], "detach": [1, 2, 7, 13, 36, 99, 106, 112, 120, 152, 165, 169, 201], "q": [1, 9, 48, 55, 66, 136, 139, 161, 179, 187, 213, 217], "base": [1, 6, 7, 9, 11, 12, 13, 16, 19, 20, 22, 24, 27, 28, 29, 38, 47, 49, 55, 63, 64, 65, 66, 81, 83, 84, 86, 94, 97, 99, 100, 103, 107, 108, 110, 117, 122, 125, 126, 129, 130, 134, 135, 136, 137, 138, 141, 142, 151, 152, 161, 170, 171, 175, 177, 180, 184, 185, 187, 190, 193, 194, 197, 198, 201, 202, 207, 211, 212, 213, 214, 215, 216, 217, 229, 236, 241, 243, 248, 252, 258, 262, 264, 265, 269, 283], "empir": [1, 165, 174], "bootstrap": [1, 151], "td": 1, "varianc": [1, 7, 23, 105, 174], "bia": [1, 2, 6, 7, 23, 37, 48, 52, 74, 103, 110, 116, 124, 125, 138, 144, 149, 151, 160, 171, 174, 178, 179, 189, 195, 201, 209, 214, 216, 217, 235, 241, 262], "mean": [1, 2, 6, 7, 9, 12, 14, 16, 23, 27, 29, 41, 47, 52, 55, 57, 58, 64, 65, 66, 74, 81, 84, 89, 91, 94, 99, 102, 103, 106, 108, 109, 110, 112, 115, 122, 124, 125, 129, 130, 137, 139, 141, 142, 144, 150, 152, 154, 162, 164, 168, 172, 173, 174, 175, 177, 178, 180, 184, 185, 187, 189, 190, 198, 201, 203, 205, 206, 207, 209, 213, 214, 216, 221, 222, 223, 224, 227, 231, 246, 249, 252, 254, 255, 259, 264, 265, 270, 278], "obtain": [1, 11, 28, 36, 41, 55, 66, 74, 97, 116, 124, 141, 144, 150, 152, 160, 169, 174, 175, 177, 184, 187, 194, 255], "reward": [1, 5, 16, 151, 161, 174, 175, 176, 178], "noth": [1, 27, 47, 81, 102, 116, 127, 145, 164, 178, 184, 195, 276], "els": [1, 5, 6, 10, 13, 22, 23, 27, 29, 37, 47, 52, 55, 64, 65, 97, 99, 115, 120, 123, 130, 137, 149, 150, 152, 165, 170, 172, 176, 177, 178, 185, 187, 188, 198, 201, 202, 213, 214, 217, 225, 226, 229, 232, 233, 235, 240, 248, 264, 270, 276, 279, 283, 284], "mont": 1, "carlo": 1, "whole": [1, 3, 29, 49, 52, 66, 103, 116, 125, 127, 136, 150, 171, 172, 174, 180, 198, 199, 201, 206, 216, 222, 225, 252], "sequenc": [1, 6, 7, 14, 25, 27, 45, 54, 55, 66, 73, 74, 103, 109, 111, 114, 115, 124, 125, 127, 129, 130, 133, 136, 142, 151, 152, 168, 174, 179, 181, 189, 190, 191, 198, 200, 201, 209, 211, 222, 227, 228, 235, 241, 248, 252, 255, 260, 265, 270, 283], "upcom": 1, "intermedi": [1, 29, 82, 99, 103, 106, 120, 125, 136, 146, 149, 156, 159, 160, 161, 164, 167, 181, 216, 217, 249, 256, 276], "lambda": [1, 6, 26, 116, 130, 173, 176, 189, 217, 248, 249, 264, 283], "compromis": 1, "easi": [1, 2, 6, 7, 11, 27, 28, 38, 49, 50, 57, 64, 65, 66, 74, 89, 99, 100, 106, 108, 110, 137, 139, 141, 150, 157, 160, 165, 168, 174, 184, 192, 199, 202, 211, 217, 222, 226, 237, 249, 264, 277], "valueestim": 1, "enum": 1, "pointer": [1, 6, 7, 8, 26, 27, 177], "defin": [1, 2, 6, 14, 16, 17, 18, 19, 20, 22, 24, 25, 27, 29, 36, 41, 42, 43, 46, 48, 50, 58, 64, 65, 68, 71, 73, 74, 75, 81, 86, 87, 94, 97, 99, 103, 105, 107, 109, 110, 115, 116, 117, 125, 127, 130, 133, 136, 137, 138, 140, 142, 146, 150, 151, 153, 154, 157, 158, 161, 165, 167, 169, 175, 176, 177, 179, 182, 184, 185, 188, 189, 190, 199, 203, 205, 211, 215, 216, 217, 225, 226, 227, 233, 236, 237, 238, 246, 252, 253, 254, 255, 261, 262, 269, 271, 279, 281], "default": [1, 3, 4, 6, 7, 10, 11, 12, 13, 17, 23, 24, 25, 28, 36, 41, 45, 48, 50, 55, 57, 58, 61, 66, 69, 70, 83, 84, 86, 91, 102, 103, 105, 106, 110, 112, 120, 124, 129, 130, 131, 133, 134, 136, 137, 138, 139, 149, 150, 151, 153, 156, 157, 159, 162, 164, 167, 170, 172, 173, 176, 177, 178, 184, 185, 187, 188, 189, 190, 193, 195, 197, 198, 202, 204, 208, 210, 211, 213, 214, 215, 216, 219, 223, 224, 225, 230, 231, 233, 236, 237, 241, 244, 246, 249, 252, 253, 254, 255, 261, 263, 264, 265, 268, 270, 272, 273, 278, 279, 281, 282, 283], "simplest": [1, 7, 27, 36, 52, 99, 106, 110, 151, 174, 180, 230, 265], "chang": [1, 2, 6, 7, 12, 13, 14, 16, 23, 25, 26, 27, 28, 36, 45, 48, 54, 56, 57, 58, 59, 61, 64, 65, 84, 86, 87, 89, 91, 92, 94, 97, 98, 99, 100, 102, 107, 108, 109, 111, 112, 113, 117, 120, 125, 130, 136, 138, 139, 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238, 239, 251, 252, 265, 269, 272], "10m": 1, "element": [1, 6, 7, 14, 20, 23, 42, 45, 54, 55, 64, 65, 84, 99, 100, 103, 106, 110, 112, 113, 115, 130, 136, 140, 150, 159, 165, 168, 174, 187, 189, 190, 194, 206, 207, 208, 209, 217, 241, 249, 251, 265, 283, 284, 287], "anoth": [1, 4, 5, 6, 9, 10, 13, 16, 24, 25, 27, 29, 36, 45, 48, 54, 55, 58, 65, 66, 69, 81, 89, 94, 102, 103, 106, 107, 109, 113, 116, 120, 122, 124, 125, 127, 136, 143, 144, 145, 147, 150, 151, 156, 157, 168, 171, 174, 176, 177, 178, 179, 180, 188, 190, 192, 194, 195, 198, 200, 204, 211, 213, 216, 223, 230, 246, 247, 249, 255, 265, 266, 270, 278, 283, 284], "ratio": [1, 91, 108, 129, 159, 174, 184, 194, 211], "interact": [1, 6, 7, 16, 55, 66, 100, 105, 106, 141, 145, 161, 174, 177, 185, 188, 189, 190, 213, 283], "nutshel": [1, 207], "cautiou": 1, "deal": [1, 6, 49, 55, 57, 58, 66, 102, 108, 116, 123, 124, 125, 137, 160, 174, 238, 267], "lead": [1, 4, 6, 7, 16, 18, 55, 58, 81, 87, 91, 97, 108, 116, 134, 145, 151, 153, 164, 172, 207, 222, 262, 265, 278, 283], "bias": [1, 37, 48, 103, 108, 125, 179, 262, 267, 268], "comparison": [1, 11, 20, 25, 92, 100, 106, 107, 108, 109, 138, 152, 159, 160, 192, 197, 200, 205, 207, 209, 211, 217, 221, 240, 264], "scale": [1, 7, 23, 37, 46, 57, 62, 81, 103, 105, 108, 117, 125, 135, 136, 137, 138, 150, 151, 161, 174, 175, 181, 182, 193, 195, 200, 201, 216, 238, 248, 252, 265, 274, 276, 277], "help": [1, 6, 10, 12, 17, 20, 22, 23, 26, 27, 36, 37, 48, 55, 58, 64, 65, 66, 74, 91, 97, 100, 108, 109, 112, 113, 115, 116, 125, 127, 135, 136, 137, 138, 141, 147, 148, 149, 151, 152, 153, 159, 160, 165, 167, 170, 174, 175, 176, 177, 178, 179, 180, 187, 192, 193, 200, 201, 206, 207, 208, 209, 213, 217, 222, 227, 229, 231, 232, 233, 238, 240, 241, 244, 245, 247, 248, 249, 251, 258, 260, 263, 265, 266, 269, 274, 275, 277, 278], "signal": [1, 48, 100, 102, 148, 174, 176, 211, 252, 255, 264], "magnitud": [1, 20, 47, 99, 171, 217, 248], "truncat": [1, 106, 122, 127, 159, 174, 217], "A": 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193, 198, 201, 205, 207, 209, 222, 224, 231, 237, 247, 254, 255, 264, 277, 278], "specifi": [1, 4, 5, 6, 7, 10, 11, 13, 18, 20, 23, 24, 26, 27, 28, 38, 42, 57, 58, 65, 84, 91, 97, 99, 100, 102, 103, 106, 107, 112, 123, 124, 127, 130, 137, 138, 139, 141, 145, 149, 151, 152, 153, 156, 170, 171, 174, 177, 178, 184, 187, 189, 190, 195, 205, 206, 207, 208, 209, 212, 213, 214, 216, 217, 221, 222, 223, 225, 226, 229, 238, 240, 246, 254, 255, 257, 259, 260, 263, 265, 266, 270, 273, 276, 277, 278, 281, 283], "scenario": [1, 5, 7, 16, 117, 119, 120, 125, 131, 140, 175, 178, 215, 236, 262, 266, 278], "tensordictsequenti": [1, 151], "valueoper": 1, "automat": [1, 2, 4, 6, 7, 10, 12, 17, 26, 29, 37, 39, 40, 42, 45, 48, 50, 51, 52, 54, 59, 63, 67, 84, 105, 107, 109, 116, 124, 128, 136, 137, 138, 139, 140, 141, 145, 152, 154, 157, 159, 162, 164, 174, 175, 178, 181, 182, 184, 187, 192, 193, 198, 201, 224, 226, 229, 231, 233, 236, 237, 238, 244, 250, 253, 254, 255, 261, 264, 265, 269, 270, 274, 283], "out_kei": [1, 16, 151, 174], "state_action_valu": 1, "state_valu": 1, "built": [1, 4, 5, 6, 7, 8, 9, 10, 21, 26, 27, 36, 52, 65, 99, 100, 102, 103, 110, 120, 127, 129, 130, 136, 150, 151, 152, 174, 179, 185, 189, 190, 193, 194, 211, 221, 233, 238, 240, 247, 249, 251, 268, 277], "present": [1, 7, 10, 22, 26, 41, 91, 92, 108, 120, 149, 152, 168, 170, 171, 172, 175, 176, 178, 187, 188, 191, 194, 209, 265, 279], "origin": [1, 6, 7, 9, 10, 14, 16, 20, 23, 24, 27, 36, 55, 58, 64, 65, 66, 81, 97, 100, 106, 108, 113, 117, 127, 129, 133, 134, 139, 140, 142, 143, 152, 165, 168, 169, 171, 172, 175, 187, 188, 189, 190, 192, 193, 195, 197, 198, 199, 200, 202, 208, 213, 216, 217, 235, 237, 238, 244, 246, 252, 270, 277], "paper": [1, 6, 14, 19, 20, 58, 64, 65, 81, 127, 128, 133, 134, 139, 152, 174, 175, 177, 179, 180, 182, 209, 217], "found": [1, 5, 6, 7, 12, 19, 26, 27, 29, 47, 55, 56, 66, 87, 100, 108, 127, 129, 130, 133, 141, 148, 149, 150, 151, 152, 157, 158, 162, 177, 178, 179, 187, 189, 190, 197, 198, 199, 201, 209, 223, 237, 240, 244, 265, 283], "ddpgmlpactor": 1, "ddpgmlpqnet": 1, "sinc": [1, 3, 5, 6, 9, 10, 11, 12, 13, 15, 16, 19, 23, 24, 25, 27, 49, 55, 57, 58, 59, 66, 71, 81, 86, 94, 99, 100, 102, 108, 109, 110, 112, 113, 115, 116, 117, 124, 127, 129, 130, 131, 133, 134, 139, 141, 142, 143, 145, 146, 150, 151, 154, 157, 158, 165, 167, 168, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 185, 187, 188, 191, 192, 194, 195, 198, 199, 200, 201, 203, 204, 205, 206, 207, 213, 214, 215, 216, 221, 224, 227, 229, 231, 241, 244, 249, 254, 255, 256, 268, 279, 283], "lazi": [1, 65], "materi": [1, 138, 139, 160, 205, 219, 225, 252, 256, 262], "achiev": [1, 3, 7, 16, 20, 23, 28, 36, 49, 55, 62, 91, 97, 108, 120, 134, 136, 140, 144, 150, 151, 152, 159, 164, 167, 168, 171, 172, 174, 175, 184, 192, 195, 198, 200, 201, 214, 215, 217, 220, 229, 236, 240, 265, 269, 274, 278], "oper": [1, 5, 7, 15, 16, 17, 20, 21, 22, 23, 24, 25, 29, 36, 37, 42, 47, 48, 50, 52, 55, 57, 66, 67, 69, 70, 71, 79, 84, 89, 91, 92, 94, 98, 102, 103, 106, 108, 109, 110, 115, 116, 117, 119, 122, 123, 124, 130, 133, 136, 138, 139, 140, 142, 145, 150, 151, 152, 153, 156, 157, 159, 160, 162, 164, 165, 169, 170, 173, 174, 176, 180, 184, 185, 187, 188, 189, 190, 192, 198, 199, 200, 201, 203, 204, 205, 206, 210, 212, 213, 214, 215, 217, 220, 221, 222, 223, 231, 233, 236, 237, 240, 244, 246, 247, 248, 249, 251, 252, 255, 256, 264, 268, 269, 270, 273, 278, 279], "practic": [1, 6, 7, 13, 24, 27, 52, 55, 57, 58, 64, 65, 66, 99, 100, 102, 106, 108, 110, 116, 124, 128, 131, 136, 139, 140, 142, 146, 149, 151, 152, 159, 168, 171, 174, 181, 189, 190, 191, 193, 205, 206, 209, 211, 249, 250, 254, 265, 278], "fake": [1, 7, 20, 23, 29, 52, 58, 86, 167, 172, 209, 213, 214, 216, 217, 246], "spec": [1, 120, 151, 174, 178, 195, 216, 249], "ornsteinuhlenbeckprocesswrapp": 1, "suggest": [1, 5, 14, 58, 96, 108, 143, 152, 159, 160, 161, 162, 165, 172, 184, 187, 189, 190, 191, 252], "ou": 1, "nois": [1, 7, 14, 58, 81, 211, 264], "reach": [1, 12, 20, 58, 66, 67, 108, 148, 150, 152, 161, 162, 164, 174, 178, 189, 190, 217, 238, 249], "minimum": [1, 91, 174, 178, 189, 190, 193, 207], "iter": [1, 3, 5, 7, 13, 14, 16, 28, 36, 37, 41, 42, 47, 48, 49, 52, 58, 59, 61, 66, 83, 94, 97, 99, 100, 102, 103, 109, 115, 116, 117, 125, 127, 130, 133, 141, 146, 148, 149, 151, 157, 159, 172, 174, 175, 176, 177, 178, 184, 185, 188, 194, 198, 210, 213, 214, 228, 235, 241, 244, 248, 252, 265, 268, 279], "reset": [1, 23, 41, 102, 106, 116, 131, 151, 174, 175, 176, 178, 184, 188, 189, 190, 198, 202, 211, 213, 214, 238, 275], "tight": [1, 117, 119, 120], "per": [1, 6, 7, 10, 13, 23, 108, 115, 136, 137, 142, 143, 144, 148, 150, 151, 152, 160, 162, 167, 171, 178, 179, 180, 181, 184, 185, 187, 191, 192, 193, 194, 201, 205, 210, 222, 224, 229, 231, 233, 235, 238, 241, 249, 254, 264, 273, 278, 279], "sync": [1, 12, 13, 19, 61, 136, 137, 138, 161, 204, 210, 277], "natur": [1, 6, 7, 20, 22, 27, 28, 29, 50, 81, 83, 103, 108, 116, 119, 130, 134, 141, 142, 150, 151, 152, 177, 187, 207, 209, 213, 216, 217, 224, 283], "resourc": [1, 59, 64, 65, 81, 97, 117, 134, 138, 148, 150, 167, 174, 184, 187, 192, 233, 241, 249, 265, 272, 278], "alloc": [1, 7, 22, 25, 26, 27, 45, 54, 61, 65, 106, 144, 150, 167, 184, 191, 192, 209, 219, 231, 241, 254, 255, 279], "gpu": [1, 3, 5, 9, 14, 20, 22, 23, 24, 37, 42, 45, 47, 48, 52, 54, 55, 56, 58, 59, 60, 62, 63, 66, 67, 70, 79, 81, 85, 88, 89, 90, 91, 92, 98, 102, 107, 108, 110, 117, 124, 128, 131, 135, 136, 137, 138, 139, 140, 146, 147, 148, 149, 150, 151, 152, 153, 159, 162, 164, 165, 167, 169, 172, 174, 175, 177, 178, 179, 187, 188, 191, 193, 194, 201, 202, 212, 217, 218, 223, 224, 227, 231, 233, 241, 248, 249, 252, 255, 268, 269, 270, 274, 277], "worker": [1, 7, 9, 13, 19, 57, 58, 102, 129, 135, 137, 138, 149, 150, 162, 174, 177, 178, 184, 229, 233, 265], "syncdatacollector": [1, 174], "process": [1, 4, 5, 6, 7, 13, 14, 16, 17, 19, 20, 22, 24, 26, 27, 28, 29, 38, 41, 42, 46, 47, 52, 55, 56, 57, 58, 62, 66, 67, 81, 91, 94, 99, 100, 102, 103, 107, 108, 115, 116, 117, 123, 125, 127, 128, 130, 134, 135, 136, 137, 138, 140, 141, 142, 143, 146, 147, 150, 151, 152, 158, 159, 161, 162, 164, 169, 173, 175, 177, 178, 179, 180, 184, 187, 189, 190, 191, 192, 193, 198, 200, 201, 203, 204, 211, 212, 217, 220, 221, 224, 225, 229, 231, 232, 238, 246, 249, 251, 254, 255, 264, 265, 266, 268, 269, 273, 275, 278, 279, 282, 283], "offer": [1, 4, 8, 13, 16, 18, 22, 42, 46, 47, 48, 59, 67, 99, 100, 110, 137, 139, 153, 156, 159, 160, 213, 231, 233, 247, 249, 255, 264, 265, 278], "multiasyncdatacollector": [1, 174], "rollout": [1, 174], "asynchron": [1, 25, 135, 136, 141, 149, 164, 170, 174, 178, 255], "manner": [1, 6, 10, 16, 23, 36, 99, 116, 174, 187, 233], "therebi": [1, 202, 205, 209], "decoupl": [1, 38, 168, 213], "factori": [1, 7, 106, 112, 129, 206, 207, 250, 254], "empti": [1, 4, 6, 7, 10, 16, 23, 25, 27, 106, 120, 143, 159, 173, 180, 184, 187, 189, 190, 192, 201, 219, 223, 255, 264, 284], "maximum": [1, 13, 55, 66, 91, 103, 113, 127, 141, 143, 151, 152, 159, 174, 179, 180, 189, 190, 201, 210, 211, 230, 265], "non": [1, 2, 3, 6, 10, 13, 16, 23, 26, 37, 55, 57, 59, 60, 62, 66, 91, 94, 103, 106, 108, 109, 111, 115, 116, 125, 127, 134, 137, 141, 144, 145, 149, 150, 151, 152, 154, 156, 160, 162, 165, 171, 172, 176, 179, 180, 184, 188, 189, 192, 198, 200, 201, 205, 215, 219, 231, 246, 249, 262, 266, 270, 278, 284, 286], "termin": [1, 16, 27, 59, 66, 97, 174, 175, 177, 178, 187, 204, 223, 230, 243], "max_frames_per_traj": [1, 174], "effect": [1, 6, 7, 10, 11, 13, 18, 27, 61, 81, 91, 100, 103, 106, 108, 115, 116, 120, 139, 142, 143, 153, 167, 169, 171, 175, 179, 180, 187, 192, 193, 207, 215, 216, 222, 248, 252, 265, 281], "regist": [1, 8, 18, 26, 41, 48, 52, 86, 103, 120, 122, 125, 136, 137, 139, 148, 156, 167, 168, 174, 189, 190, 193, 224, 225, 233, 237, 244, 248, 256, 267], "new": [1, 2, 4, 5, 6, 7, 8, 10, 14, 16, 18, 26, 27, 28, 29, 35, 36, 40, 45, 47, 48, 50, 54, 55, 56, 58, 61, 66, 68, 81, 83, 87, 89, 91, 94, 105, 106, 107, 108, 110, 113, 115, 120, 122, 123, 125, 127, 131, 136, 137, 138, 140, 142, 151, 152, 156, 157, 159, 161, 164, 167, 168, 169, 170, 171, 172, 174, 175, 176, 179, 180, 184, 187, 189, 190, 194, 195, 202, 203, 204, 205, 209, 211, 212, 213, 214, 215, 216, 221, 222, 223, 224, 225, 230, 233, 240, 241, 242, 243, 244, 252, 255, 257, 262, 265, 266, 269, 277, 283], "stepcount": [1, 151, 174], "infer": [1, 3, 7, 8, 10, 11, 12, 23, 24, 25, 26, 27, 43, 45, 54, 55, 66, 100, 102, 103, 107, 108, 112, 115, 116, 117, 127, 136, 140, 141, 144, 157, 159, 174, 176, 187, 188, 193, 194, 201, 203, 204, 209, 210, 212, 213, 214, 215, 217, 220, 223, 224, 226, 231, 238, 241, 242, 243, 244, 246, 251, 252, 253, 258, 260, 261, 267, 269], "manual": [1, 4, 6, 10, 14, 18, 26, 27, 38, 52, 55, 59, 75, 78, 79, 106, 108, 116, 124, 125, 145, 151, 153, 168, 169, 172, 198, 199, 201, 209, 214, 224, 229, 232, 244, 248, 257, 265, 278, 282, 283], "around": [1, 6, 7, 13, 27, 28, 38, 42, 55, 61, 64, 65, 83, 100, 102, 103, 106, 108, 131, 136, 143, 153, 159, 160, 165, 169, 172, 180, 184, 185, 189, 190, 198, 205, 206, 210, 213, 236, 252, 256, 283, 286], "1m": [1, 151, 174], "outer": [1, 7, 137, 160, 264], "loop": [1, 3, 5, 6, 9, 10, 13, 19, 23, 25, 29, 40, 42, 49, 52, 55, 57, 58, 65, 66, 71, 81, 83, 94, 99, 102, 105, 108, 116, 124, 133, 138, 141, 142, 152, 153, 159, 160, 161, 167, 169, 177, 178, 185, 193, 199, 201, 217, 236, 249, 255, 264, 265, 268], "equal": [1, 5, 12, 99, 106, 108, 129, 130, 140, 152, 162, 171, 174, 175, 187, 189, 190, 192, 208, 233, 278], "length": [1, 9, 14, 16, 47, 55, 58, 66, 99, 100, 103, 115, 116, 127, 129, 130, 139, 143, 152, 174, 179, 180, 191, 201, 207, 209, 212, 217, 222, 225, 233, 269, 284], "sub": [1, 7, 24, 29, 99, 122, 139, 164, 174, 178, 184, 192, 193, 201, 232, 264, 278, 283], "shape": [1, 4, 5, 18, 20, 27, 36, 45, 47, 48, 54, 55, 58, 64, 65, 66, 74, 84, 94, 99, 102, 108, 109, 112, 115, 122, 124, 137, 139, 142, 143, 152, 153, 156, 160, 165, 169, 174, 184, 187, 194, 200, 201, 203, 208, 209, 213, 217, 219, 226, 230, 236, 244, 249, 252, 253, 255, 261, 265, 269, 273, 274], "env_per_collector": 1, "traj_len": 1, "assess": 1, "mode": [1, 5, 14, 24, 43, 47, 48, 55, 58, 61, 66, 81, 87, 91, 94, 96, 100, 105, 125, 129, 130, 136, 137, 144, 145, 149, 151, 154, 157, 159, 161, 162, 165, 172, 176, 179, 180, 181, 185, 187, 188, 190, 193, 195, 203, 204, 210, 211, 212, 214, 215, 216, 218, 233, 236, 238, 249, 258, 259, 260, 265, 278], "dedic": [1, 12, 61, 66, 106, 125, 149, 177, 178, 193, 215, 225, 241, 246, 247, 248, 279, 284], "frequenc": [1, 9, 92, 141, 241], "everi": [1, 2, 7, 10, 12, 16, 17, 20, 22, 23, 28, 36, 37, 38, 42, 48, 49, 52, 55, 57, 58, 66, 67, 99, 102, 103, 105, 106, 107, 115, 131, 139, 140, 142, 143, 144, 150, 151, 156, 162, 165, 167, 168, 169, 171, 172, 174, 175, 176, 178, 180, 184, 185, 209, 213, 214, 217, 225, 227, 241, 250, 257, 263, 265, 279], "10": [1, 3, 5, 7, 9, 10, 13, 20, 23, 27, 37, 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149, 160, 168, 169, 182, 205, 207, 209, 215, 237, 265], "circular": 1, "compos": [1, 4, 6, 7, 8, 12, 18, 20, 23, 25, 29, 64, 65, 67, 87, 102, 103, 110, 120, 123, 127, 129, 136, 138, 150, 154, 156, 157, 165, 169, 172, 173, 174, 177, 179, 181, 185, 187, 198, 213, 214, 216, 221, 226, 230, 232, 237, 238, 251, 255, 272], "pick": [1, 7, 23, 49, 109, 142, 175, 180, 233, 258, 264, 269], "tensor": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 23, 24, 25, 26, 27, 37, 38, 39, 40, 41, 42, 43, 46, 47, 48, 49, 50, 51, 52, 53, 55, 63, 64, 65, 66, 67, 68, 70, 71, 73, 74, 75, 77, 80, 81, 83, 85, 86, 88, 90, 94, 98, 99, 100, 101, 103, 105, 107, 108, 110, 113, 115, 116, 117, 120, 122, 125, 129, 130, 133, 136, 137, 138, 143, 144, 145, 148, 149, 150, 151, 152, 153, 154, 156, 159, 160, 162, 164, 165, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 180, 181, 184, 185, 187, 188, 189, 190, 191, 192, 194, 198, 200, 201, 202, 203, 204, 205, 206, 207, 210, 211, 212, 213, 215, 217, 218, 219, 220, 222, 223, 225, 226, 229, 230, 232, 233, 236, 237, 246, 247, 248, 249, 251, 252, 253, 254, 255, 256, 257, 261, 264, 267, 268, 269, 273, 274, 276], "physic": [1, 16, 115, 193, 254, 265, 278], "memori": [1, 5, 6, 7, 10, 12, 19, 20, 22, 23, 25, 26, 27, 45, 54, 55, 57, 61, 89, 97, 103, 106, 107, 108, 111, 114, 116, 117, 123, 125, 129, 136, 137, 138, 139, 146, 150, 151, 159, 160, 161, 165, 171, 177, 179, 181, 184, 187, 191, 194, 200, 204, 205, 208, 209, 210, 215, 217, 224, 229, 235, 237, 246, 248, 249, 252, 254, 256, 269, 273, 279], "map": [1, 7, 14, 37, 55, 58, 64, 66, 83, 100, 102, 103, 108, 113, 115, 120, 123, 125, 127, 129, 130, 134, 142, 151, 153, 154, 160, 167, 168, 169, 174, 175, 178, 187, 189, 190, 192, 194, 198, 213, 216, 217, 222, 226, 229, 230, 231, 254, 264, 265, 267], "arrai": [1, 7, 36, 37, 48, 49, 52, 57, 64, 65, 67, 78, 79, 94, 99, 100, 106, 115, 116, 122, 124, 142, 143, 154, 161, 172, 173, 180, 187, 208, 247], "desir": [1, 6, 10, 14, 16, 19, 55, 58, 81, 91, 108, 123, 125, 130, 150, 157, 171, 184, 187, 200, 206, 219, 244, 248, 251, 278, 284], "hyperparamet": [1, 7, 16, 20, 58, 116, 125, 126, 129, 136, 141, 205, 209, 267], "temporari": [1, 6, 141, 193, 197], "disk": [1, 5, 27, 29, 38, 102, 105, 117, 125, 151, 203, 204, 210, 225, 241, 249, 254, 255, 263, 273], "along": [1, 5, 6, 7, 9, 10, 16, 20, 25, 27, 36, 37, 45, 48, 54, 55, 59, 60, 61, 62, 66, 92, 98, 99, 100, 102, 103, 105, 106, 107, 108, 112, 113, 116, 123, 130, 131, 134, 137, 138, 142, 143, 146, 147, 151, 167, 171, 175, 177, 185, 187, 191, 207, 217, 219], "dimens": [1, 6, 7, 9, 14, 16, 22, 24, 25, 37, 45, 52, 54, 55, 86, 87, 100, 102, 103, 105, 112, 113, 115, 116, 123, 129, 136, 139, 142, 151, 153, 162, 169, 173, 174, 176, 189, 190, 191, 206, 209, 213, 214, 222, 230, 241, 248, 252, 265], "feed": [1, 7, 14, 23, 49, 52, 55, 66, 102, 103, 107, 108, 111, 113, 117, 125, 139, 142, 143, 148, 149, 161, 175, 180, 185, 192, 211, 231, 251, 257, 268], "adapt": [1, 2, 16, 20, 28, 97, 102, 117, 141, 217, 249], "divid": [1, 9, 10, 12, 13, 14, 16, 17, 23, 94, 108, 110, 142, 149, 150, 164, 168, 178, 192, 193, 278], "yield": [1, 7, 13, 14, 20, 23, 25, 41, 43, 106, 125, 129, 130, 133, 139, 140, 176, 178, 200, 208, 217, 236, 238, 258, 259, 260, 265], "regard": [1, 7, 13, 16, 27, 66, 125, 159, 160, 174, 184, 283], "200": [1, 7, 11, 103, 151, 162, 178, 200, 210, 236], "random_crop_len": 1, "25": [1, 9, 26, 27, 81, 99, 108, 122, 131, 138, 172, 177, 187, 200, 236, 249, 251, 255], "balanc": [1, 7, 22, 148, 184, 244], "choic": [1, 5, 7, 10, 97, 141, 143, 227, 265], "heterogen": [1, 212], "figur": [1, 10, 14, 16, 26, 55, 81, 91, 102, 108, 136, 139, 141, 151, 159, 164, 176, 184, 185, 187, 192, 198, 211, 213, 284, 286], "dataflow": 1, "8": [1, 3, 6, 7, 9, 20, 23, 27, 64, 65, 71, 97, 103, 113, 122, 124, 130, 134, 137, 139, 144, 148, 149, 152, 157, 159, 162, 172, 173, 184, 187, 189, 192, 200, 201, 203, 204, 206, 215, 217, 223, 224, 225, 229, 231, 232, 233, 236, 238, 241, 242, 243, 246, 248, 255, 265, 277, 278, 279, 283, 284, 287], "1000": [1, 2, 23, 48, 105, 107, 131, 137, 138, 140, 142, 149, 159, 174, 185, 192, 193, 203, 215, 217, 229, 230, 244, 249, 264], "known": [1, 4, 5, 6, 14, 20, 47, 99, 127, 139, 150, 152, 156, 158, 171, 175, 180, 201, 204, 216, 217, 224, 249, 252, 264], "utd": 1, "64": [1, 6, 7, 17, 20, 22, 23, 27, 38, 42, 48, 97, 106, 113, 116, 129, 134, 137, 138, 139, 149, 151, 153, 169, 173, 176, 177, 189, 191, 217, 222, 224, 225, 236, 238, 249, 251, 255], "reproduc": [1, 14, 58, 81, 102, 106, 108, 152, 159, 174, 178, 188, 201, 236, 249, 265], "realiz": [1, 20, 217], "sever": [1, 2, 3, 12, 13, 17, 19, 23, 26, 29, 36, 42, 46, 50, 52, 58, 59, 60, 81, 108, 110, 127, 137, 142, 147, 150, 161, 164, 167, 168, 170, 172, 174, 176, 177, 184, 185, 188, 189, 190, 192, 193, 207, 216, 225, 236, 237, 241, 249, 264, 270, 273, 278], "qnet": 1, "ve": [1, 6, 7, 9, 10, 20, 25, 26, 29, 50, 52, 56, 84, 86, 99, 100, 102, 103, 106, 107, 116, 127, 136, 140, 151, 158, 160, 167, 169, 177, 185, 192, 200, 205, 207, 208, 217, 230, 231, 249, 252, 277], "_must_": 1, "off": [1, 9, 10, 19, 23, 25, 28, 36, 48, 55, 103, 106, 125, 127, 134, 138, 141, 142, 159, 167, 172, 174, 176, 177, 178, 187, 203, 205, 230, 252, 258, 269, 276], "dictat": [1, 16], "introduc": [1, 13, 17, 19, 20, 25, 26, 27, 28, 37, 40, 47, 60, 66, 83, 86, 87, 97, 100, 108, 110, 122, 124, 133, 134, 136, 137, 148, 159, 162, 164, 167, 176, 178, 179, 180, 187, 188, 189, 190, 191, 193, 197, 198, 201, 202, 204, 205, 207, 208, 211, 213, 215, 216, 217, 220, 221, 222, 224, 226, 229, 233, 237, 240, 241, 262, 265, 275, 278], "outdat": 1, "trick": [1, 7, 12, 55, 106, 145, 150, 160], "multi": [1, 6, 7, 16, 22, 52, 55, 58, 59, 60, 62, 67, 85, 88, 99, 100, 103, 106, 110, 127, 136, 137, 139, 146, 147, 148, 149, 151, 152, 164, 174, 176, 179, 181, 184, 193, 194, 201, 209, 231, 232, 265, 278], "altern": [1, 7, 12, 27, 54, 55, 113, 127, 131, 138, 153, 158, 162, 170, 172, 174, 180, 184, 187, 188, 198, 200, 208, 215, 226, 262, 264, 265, 275], "hack": [1, 152, 201, 205, 262], "find": [1, 2, 4, 5, 6, 7, 10, 12, 20, 26, 27, 36, 38, 45, 54, 81, 91, 92, 94, 97, 102, 103, 106, 109, 112, 116, 136, 138, 142, 150, 152, 156, 157, 159, 164, 168, 172, 175, 178, 180, 184, 189, 190, 199, 205, 206, 207, 208, 211, 213, 216, 217, 221, 225, 230, 238, 240, 241, 244, 247, 249, 263, 270], "despit": [1, 81, 148], "fact": [1, 3, 6, 7, 10, 14, 16, 23, 26, 27, 36, 45, 66, 81, 89, 99, 100, 102, 103, 106, 108, 110, 115, 127, 140, 168, 174, 175, 185], "part": [1, 3, 6, 7, 10, 12, 15, 16, 17, 19, 24, 27, 41, 47, 48, 50, 55, 58, 64, 65, 66, 81, 84, 97, 99, 100, 103, 109, 111, 115, 116, 127, 130, 133, 136, 137, 139, 140, 143, 145, 146, 149, 150, 151, 152, 154, 158, 159, 164, 167, 168, 174, 180, 189, 190, 198, 199, 200, 202, 204, 205, 207, 212, 214, 221, 225, 226, 230, 233, 238, 240, 241, 244, 247, 248, 252, 262, 264, 270, 273, 274, 278, 281, 282, 283], "made": [1, 7, 12, 17, 23, 29, 58, 97, 100, 106, 108, 115, 127, 134, 137, 140, 150, 151, 180, 185, 187, 189, 190, 191, 193, 208, 212, 216, 242, 243, 246, 247, 252, 265, 283], "thank": [1, 11, 23, 55, 116, 134, 141, 142, 143, 150, 152, 180, 192, 193, 201, 203, 204, 211, 221, 240], "hardupd": 1, "softupd": [1, 151], "appropri": [1, 7, 9, 10, 47, 55, 66, 100, 116, 141, 142, 148, 168, 172, 177, 185, 191, 198, 213, 214, 215, 230, 236, 237, 248], "locat": [1, 4, 7, 35, 38, 45, 54, 86, 89, 91, 100, 102, 115, 122, 127, 150, 172, 174, 184, 189, 190, 192, 194, 204, 221, 240, 241, 244, 255, 265, 273, 277], "adam": [1, 7, 13, 15, 41, 52, 58, 75, 107, 110, 123, 124, 150, 167, 175, 176, 178, 227, 228, 231, 279], "pretti": [1, 19, 25, 49, 106, 122, 124, 127, 160, 178, 198, 222], "plot": [1, 7, 58, 81, 91, 99, 127, 141, 151, 164, 175, 176, 185, 187, 211], "mention": [1, 6, 7, 12, 13, 17, 19, 58, 66, 81, 94, 106, 108, 109, 125, 138, 139, 150, 151, 175, 177, 184, 187, 201, 213, 244, 254, 262, 278], "greater": [1, 13, 55, 58, 100, 124, 140, 192, 204, 223, 278], "concret": [1, 6, 7, 10, 13, 99, 164, 174, 205, 216], "takeawai": [1, 128], "further": [1, 5, 6, 7, 10, 23, 25, 27, 52, 81, 97, 103, 108, 116, 127, 130, 133, 139, 142, 157, 159, 164, 171, 174, 187, 188, 193, 200, 209, 211, 212, 213, 214, 215, 221, 233, 237, 240, 246, 254, 265, 270, 272], "dispatch": [1, 6, 17, 136, 170, 193, 224, 225, 236, 248], "distpatch": 1, "iql": 1, "flexibl": [1, 2, 6, 7, 27, 28, 29, 55, 66, 94, 99, 116, 117, 125, 129, 136, 137, 146, 215, 249, 257, 259, 277], "runnable_code_remov": [1, 9, 11, 14, 15, 16, 18, 20, 24, 26, 27, 28, 29, 36, 38, 40, 41, 42, 43, 45, 46, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 73, 74, 75, 78, 79, 81, 83, 86, 87, 89, 94, 97, 99, 100, 102, 105, 106, 107, 108, 109, 110, 112, 113, 116, 117, 119, 120, 122, 124, 125, 127, 128, 129, 131, 133, 134, 140, 141, 142, 143, 144, 151, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 167, 168, 169, 171, 174, 175, 179, 180, 182, 184, 188, 190, 194, 197, 200, 205, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 265, 266, 267, 268, 274, 275], "minut": [1, 9, 11, 14, 15, 16, 18, 20, 24, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 123, 125, 127, 128, 129, 130, 131, 133, 134, 140, 141, 142, 143, 144, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 173, 174, 175, 179, 180, 182, 184, 185, 188, 190, 194, 197, 200, 205, 206, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275], "001": [1, 11, 14, 16, 20, 21, 42, 44, 45, 49, 53, 54, 57, 58, 66, 80, 81, 88, 94, 95, 102, 104, 105, 106, 107, 109, 110, 112, 113, 114, 115, 116, 125, 126, 131, 134, 140, 142, 143, 148, 156, 160, 161, 162, 167, 168, 174, 175, 176, 179, 181, 182, 185, 188, 194, 200, 205, 207, 211, 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78, 79, 81, 83, 84, 86, 87, 89, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 125, 127, 128, 129, 130, 131, 133, 134, 140, 141, 142, 143, 144, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 174, 175, 179, 180, 182, 184, 188, 190, 194, 197, 200, 205, 206, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275], "sphinx": [1, 9, 11, 14, 15, 16, 18, 20, 24, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 54, 55, 57, 58, 66, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 122, 125, 127, 128, 129, 130, 131, 133, 134, 140, 141, 142, 143, 144, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 174, 175, 179, 180, 182, 184, 188, 190, 194, 197, 200, 205, 206, 207, 208, 209, 211, 220, 222, 228, 247, 248, 249, 250, 251, 252, 254, 255, 256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 274, 275, 281, 282, 283, 284, 288], "build": [2, 7, 9, 16, 23, 24, 29, 36, 38, 39, 40, 41, 42, 43, 45, 46, 52, 55, 67, 71, 74, 75, 83, 84, 86, 90, 97, 98, 99, 100, 101, 102, 105, 106, 107, 109, 115, 116, 122, 124, 127, 128, 129, 133, 135, 136, 142, 143, 149, 150, 151, 152, 154, 156, 167, 169, 174, 175, 176, 177, 178, 181, 190, 191, 197, 198, 201, 203, 204, 209, 210, 212, 213, 215, 222, 237, 241, 242, 243, 249, 251, 258, 260, 264, 265, 274, 282, 287], "highli": [2, 6, 7, 12, 22, 55, 66, 97, 180, 191, 193, 222, 265], "dynam": [2, 5, 6, 7, 10, 14, 16, 17, 21, 23, 24, 26, 27, 29, 36, 48, 59, 71, 99, 108, 111, 114, 119, 124, 125, 134, 136, 162, 171, 174, 195, 198, 199, 200, 212, 213, 214, 215, 218, 225, 237, 238, 240, 248, 253, 257, 261, 265, 269, 274], "explor": [2, 7, 10, 16, 25, 27, 50, 55, 81, 120, 136, 141, 145, 151, 159, 161, 167, 174, 179, 180, 247, 258, 259, 269, 273, 274, 278], "note": [2, 4, 5, 6, 7, 9, 10, 11, 14, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 36, 48, 49, 50, 52, 55, 56, 58, 81, 94, 99, 100, 102, 103, 106, 107, 110, 111, 112, 113, 115, 116, 120, 122, 125, 127, 130, 131, 137, 138, 139, 140, 144, 145, 147, 148, 149, 150, 152, 153, 156, 157, 159, 160, 162, 164, 167, 169, 170, 171, 172, 176, 177, 178, 184, 185, 187, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 199, 201, 203, 204, 205, 206, 207, 208, 209, 213, 214, 215, 217, 219, 220, 221, 222, 224, 229, 231, 232, 233, 240, 255, 257, 264, 267, 270, 273, 277, 278, 279, 281, 283], "differenti": [2, 6, 7, 16, 22, 29, 39, 40, 45, 51, 52, 63, 84, 99, 124, 136, 151, 169, 175, 181, 182, 207], "requires_grad": [2, 4, 7, 10, 36, 48, 52, 69, 70, 84, 99, 103, 106, 112, 116, 120, 123, 124, 131, 145, 172, 217, 254, 268], "track": [2, 6, 9, 10, 16, 37, 41, 48, 58, 91, 99, 103, 105, 106, 110, 112, 116, 123, 124, 137, 142, 143, 147, 151, 157, 172, 178, 180, 184, 225, 255, 263, 268, 277], "auto": [2, 3, 6, 7, 10, 12, 14, 26, 61, 65, 137, 138, 159, 170, 202, 203, 204, 223, 225, 237, 238, 249, 264, 278, 283, 284], "std": [2, 5, 6, 7, 14, 17, 23, 26, 27, 57, 64, 65, 102, 108, 170, 172, 173, 178, 187, 198, 202, 203, 205, 213, 214, 221, 225, 237, 240, 249, 276], "cout": [2, 5, 7, 26, 27, 203, 276], "endl": [2, 7, 26, 27, 203, 225], "cpufloattyp": [2, 5, 7, 27, 225], "y": [2, 6, 9, 22, 24, 26, 27, 36, 45, 46, 48, 54, 58, 69, 70, 71, 73, 74, 75, 78, 79, 81, 84, 91, 94, 99, 103, 106, 108, 109, 112, 113, 116, 123, 124, 127, 130, 137, 140, 152, 170, 184, 187, 188, 189, 191, 201, 202, 207, 211, 225, 229, 264, 284], "wa": [2, 3, 4, 5, 6, 13, 18, 20, 24, 26, 27, 29, 47, 49, 57, 58, 64, 65, 66, 81, 84, 87, 99, 100, 102, 103, 106, 107, 108, 109, 110, 112, 116, 120, 125, 127, 130, 138, 139, 141, 148, 150, 161, 165, 167, 168, 169, 171, 173, 174, 175, 178, 179, 180, 185, 192, 193, 200, 204, 207, 208, 214, 217, 225, 241, 244, 248, 249, 252, 255, 257, 277, 278, 283], "result": [2, 4, 5, 6, 7, 9, 10, 11, 12, 14, 16, 20, 23, 24, 25, 27, 28, 29, 36, 41, 43, 45, 48, 49, 57, 64, 65, 66, 70, 71, 84, 86, 91, 92, 97, 99, 100, 102, 106, 107, 108, 112, 116, 119, 120, 124, 125, 128, 130, 134, 137, 139, 140, 143, 148, 150, 151, 152, 156, 157, 159, 160, 161, 164, 165, 169, 170, 171, 172, 173, 175, 176, 177, 178, 179, 185, 187, 188, 189, 190, 191, 192, 193, 194, 195, 198, 200, 204, 206, 207, 208, 209, 211, 213, 214, 215, 216, 219, 220, 221, 223, 225, 226, 227, 229, 235, 236, 238, 240, 241, 246, 251, 252, 254, 255, 258, 259, 260, 262, 264, 265, 269, 270, 276, 278, 279, 281], "grad_fn": [2, 5, 29, 36, 37, 48, 52, 84, 99, 103, 112, 115, 140, 145, 189, 201], "addbackward1": 2, "z": [2, 6, 9, 27, 48, 58, 84, 94, 106, 112, 207, 225, 284], "27": [2, 9, 57, 115, 159, 178, 192, 200, 236, 246, 249], "mulbackward1": 2, "meanbackward0": [2, 103], "requires_grad_": [2, 36, 84, 112], "flag": [2, 6, 16, 27, 48, 81, 84, 99, 112, 152, 165, 168, 180, 190, 192, 201, 212, 214, 221, 254, 264, 278], "place": [2, 6, 7, 13, 16, 22, 26, 27, 45, 48, 50, 54, 58, 84, 94, 100, 103, 105, 110, 112, 116, 120, 127, 130, 133, 137, 141, 148, 150, 153, 164, 167, 169, 172, 174, 180, 187, 188, 191, 198, 205, 213, 214, 215, 222, 225, 229, 230, 231, 254, 262, 264, 265, 270, 283, 284, 285], "randn": [2, 4, 6, 7, 14, 27, 71, 84, 112, 123, 124, 148, 149, 153, 165, 176, 178, 189, 202, 213, 214, 215, 222, 225, 229, 249, 279], "b": [2, 4, 6, 7, 9, 22, 25, 27, 36, 48, 69, 70, 71, 84, 89, 92, 99, 103, 106, 110, 113, 115, 122, 123, 124, 140, 142, 143, 159, 160, 162, 173, 175, 188, 190, 210, 226, 249, 255, 284], "sumbackward0": 2, "backprop": [2, 48, 84, 112, 116, 142, 161], "scalar": [2, 6, 16, 17, 27, 36, 48, 55, 58, 66, 69, 84, 99, 102, 106, 112, 124, 185, 213, 223], "backward": [2, 3, 4, 7, 10, 13, 14, 15, 17, 18, 19, 23, 29, 36, 41, 48, 50, 52, 58, 69, 70, 71, 78, 79, 84, 99, 100, 102, 107, 112, 113, 115, 116, 124, 131, 136, 138, 139, 140, 143, 146, 148, 149, 150, 156, 160, 164, 165, 169, 172, 176, 177, 178, 181, 184, 185, 189, 190, 214, 227, 228, 229, 231, 233, 237, 238, 248, 255, 265, 268, 272, 279], "equival": [2, 5, 6, 13, 26, 27, 36, 48, 99, 110, 124, 152, 156, 169, 175, 177, 187, 189, 190, 201, 202, 205, 207, 209, 214, 215, 216, 217, 265, 275, 276], "print": [2, 5, 6, 7, 13, 16, 22, 23, 25, 26, 27, 29, 37, 42, 47, 48, 49, 50, 55, 57, 58, 59, 64, 65, 71, 81, 84, 86, 94, 97, 99, 102, 103, 106, 108, 110, 115, 116, 119, 123, 124, 125, 130, 134, 137, 138, 142, 143, 144, 147, 148, 149, 150, 151, 152, 153, 154, 157, 159, 165, 168, 170, 172, 173, 176, 177, 178, 180, 185, 189, 190, 191, 192, 193, 195, 198, 201, 204, 205, 206, 207, 210, 213, 214, 217, 223, 225, 226, 227, 229, 231, 232, 235, 236, 237, 238, 241, 246, 248, 249, 251, 255, 256, 264, 265, 267, 270, 275, 276, 279, 283], "dx": [2, 70, 84, 110, 124, 145], "grad": [2, 4, 7, 14, 18, 36, 48, 52, 69, 84, 99, 112, 116, 124, 138, 140, 145, 148, 150, 156, 160, 165, 167, 176, 177, 178, 205, 222, 228, 233, 248, 268], "5000": [2, 154, 230, 249], "got": [2, 17, 23, 61, 100, 106, 109, 110, 112, 116, 127, 148, 170, 177, 204, 213, 216, 230, 283], "matrix": [2, 6, 7, 14, 20, 27, 36, 45, 48, 54, 91, 99, 102, 103, 106, 109, 110, 112, 115, 116, 122, 123, 139, 142, 143, 160, 165, 168, 179, 180, 187, 189, 190, 192, 193, 208, 209, 215, 217, 222, 236, 237, 238, 244, 251, 265, 269], "explan": [2, 58, 185, 254], "arriv": [2, 7, 17, 27, 47, 127, 137, 148, 176, 225, 265], "valu": [2, 4, 5, 6, 7, 9, 11, 14, 16, 22, 23, 24, 25, 26, 27, 29, 36, 37, 41, 45, 46, 48, 52, 54, 55, 56, 61, 64, 65, 66, 69, 81, 89, 91, 94, 97, 99, 100, 102, 103, 106, 107, 108, 110, 112, 113, 115, 116, 117, 124, 127, 129, 139, 140, 141, 142, 147, 148, 150, 152, 153, 154, 158, 161, 164, 165, 170, 175, 176, 177, 178, 179, 180, 184, 187, 188, 189, 190, 191, 198, 199, 201, 203, 205, 206, 207, 208, 209, 211, 212, 213, 214, 216, 220, 221, 225, 226, 228, 230, 233, 240, 248, 249, 252, 255, 262, 263, 273, 278, 279, 281], "section": [2, 4, 5, 6, 7, 9, 10, 13, 17, 22, 23, 25, 27, 37, 38, 40, 41, 42, 43, 45, 48, 49, 52, 56, 57, 58, 81, 98, 100, 106, 107, 108, 109, 110, 113, 115, 116, 120, 127, 130, 133, 140, 144, 150, 154, 159, 161, 165, 172, 175, 176, 178, 184, 187, 189, 192, 193, 194, 195, 198, 204, 206, 207, 216, 219, 224, 228, 230, 237, 244, 248, 249, 254, 265, 281, 283, 284, 285, 287], "jacobian": [2, 48, 99, 136, 156, 181, 222], "product": [2, 3, 5, 7, 45, 47, 48, 54, 66, 94, 99, 110, 117, 127, 136, 137, 150, 154, 156, 180, 181, 191, 192, 193, 201, 215, 216, 222, 226, 252, 269, 274, 277], "norm": [2, 16, 20, 23, 58, 103, 108, 123, 136, 139, 168, 171, 181, 187, 198, 217], "1021": 2, "4020": 2, "314": 2, "6695": 2, "613": [2, 236], "4944": [2, 225], "v": [2, 9, 23, 36, 48, 58, 59, 61, 99, 112, 113, 115, 120, 139, 152, 156, 165, 175, 177, 184, 199, 216, 225, 226, 284], "0001": [2, 23, 37, 97, 159, 238], "kfloat": [2, 3, 4, 65, 202, 204, 223], "102": 2, "4000": [2, 102, 264], "1024": [2, 6, 22, 25, 47, 91, 108, 215, 225, 227, 249], "0000": [2, 27, 37, 103, 189, 206, 217, 225, 284], "stop": [2, 5, 6, 27, 36, 64, 65, 84, 86, 97, 99, 112, 123, 143, 150, 162, 167, 174, 176, 180, 184, 255], "histori": [2, 36, 45, 52, 54, 99, 106, 112, 123, 127, 171, 172], "nogradguard": [2, 276], "block": [2, 4, 6, 7, 10, 12, 14, 20, 23, 26, 27, 36, 37, 52, 55, 83, 84, 91, 99, 112, 129, 130, 138, 139, 149, 150, 151, 159, 172, 176, 177, 178, 179, 184, 187, 200, 217, 224, 225, 229, 257, 265, 287], "pow": [2, 6, 205], "no_grad": [2, 23, 36, 47, 48, 64, 65, 84, 99, 110, 112, 113, 116, 123, 124, 137, 138, 152, 161, 172, 173, 177, 185, 190, 193, 198, 201, 210, 213, 214, 215, 219, 233, 237, 265, 272, 276], "Or": [2, 25, 27, 167, 178, 195, 214, 223, 225, 283], "eq": [2, 23, 27, 55, 138, 177, 189, 198, 213, 214, 238, 255, 283], "bool": [2, 13, 16, 17, 27, 152, 170, 187, 195, 201, 206, 217, 225, 270, 278, 281], "is_leaf": 2, "detach_": [2, 178], "register_hook": 2, "retain_grad": 2, "doc": [2, 5, 7, 8, 66, 99, 106, 107, 122, 147, 150, 157, 158, 176, 178, 187, 190, 197, 209, 222, 244, 248, 263, 281, 282, 283, 288], "calcul": [2, 14, 20, 36, 41, 48, 49, 55, 58, 62, 66, 81, 91, 94, 97, 99, 100, 102, 107, 108, 116, 123, 142, 143, 152, 161, 175, 176, 178, 179, 180, 187, 193, 198, 207, 213, 216, 217, 232, 238, 268], "penalti": [2, 168, 173, 248], "h": [2, 5, 6, 7, 9, 10, 12, 26, 27, 29, 55, 139, 152, 159, 162, 170, 194, 201, 204, 225, 230, 237, 240, 243, 264, 276, 278], "model": [2, 3, 6, 10, 13, 16, 19, 21, 26, 27, 28, 36, 38, 39, 40, 45, 46, 47, 48, 49, 52, 54, 58, 59, 60, 62, 71, 73, 74, 75, 83, 86, 96, 97, 98, 99, 101, 106, 109, 110, 111, 112, 114, 116, 118, 119, 120, 122, 123, 124, 126, 130, 133, 135, 136, 138, 141, 142, 143, 144, 147, 150, 154, 156, 157, 159, 160, 167, 168, 169, 174, 175, 176, 177, 178, 179, 181, 188, 189, 190, 192, 193, 199, 200, 202, 209, 212, 215, 216, 217, 218, 221, 222, 224, 229, 230, 231, 232, 233, 236, 240, 245, 246, 248, 253, 254, 256, 258, 261, 262, 263, 268, 269, 272, 274, 278, 279], "linear": [2, 6, 7, 11, 13, 19, 20, 23, 29, 45, 48, 50, 52, 54, 66, 74, 81, 86, 102, 106, 111, 115, 122, 123, 124, 125, 129, 131, 133, 138, 139, 142, 143, 148, 149, 152, 153, 159, 160, 164, 165, 168, 172, 176, 177, 178, 180, 185, 189, 193, 195, 197, 198, 200, 201, 205, 209, 211, 213, 214, 215, 216, 217, 219, 224, 226, 227, 228, 229, 231, 232, 235, 236, 237, 238, 241, 244, 246, 248, 251, 254, 256, 262, 263, 267, 270, 279], "loss": [2, 3, 6, 7, 9, 11, 13, 16, 19, 20, 21, 23, 36, 42, 45, 48, 54, 71, 73, 74, 75, 78, 79, 81, 83, 86, 97, 99, 102, 105, 110, 115, 116, 124, 125, 133, 136, 137, 138, 142, 149, 150, 161, 162, 164, 169, 172, 175, 177, 178, 180, 184, 185, 198, 204, 213, 214, 217, 233, 237, 238, 252, 258, 263, 272, 279], "target": [2, 3, 5, 7, 16, 19, 22, 23, 26, 27, 41, 49, 52, 55, 61, 66, 81, 86, 100, 106, 108, 110, 113, 115, 116, 127, 130, 133, 138, 142, 143, 149, 150, 151, 153, 159, 167, 169, 170, 173, 175, 176, 177, 178, 180, 185, 187, 188, 189, 190, 194, 195, 198, 204, 213, 214, 215, 216, 221, 223, 225, 237, 238, 240, 243, 244, 249, 251, 252, 272, 276], "mseloss": [2, 14, 41, 52, 71, 123, 124, 148, 149, 164, 176, 231, 279], "grad_output": [2, 10, 12, 84, 86], "ones_lik": [2, 106], "create_graph": [2, 145], "gradient_penalti": 2, "dim": [2, 5, 6, 13, 25, 37, 102, 103, 106, 115, 123, 136, 138, 149, 171, 173, 176, 177, 178, 185, 187, 189, 190, 206, 207, 223, 236, 238, 251, 276], "combined_loss": 2, "1042": 2, "0638": 2, "0103": [2, 103], "0723": 2, "2543": 2, "1222": 2, "0071": 2, "0814": 2, "1683": 2, "1052": 2, "0355": [2, 103], "document": [2, 5, 6, 7, 18, 24, 36, 52, 58, 66, 67, 87, 91, 94, 97, 99, 100, 103, 105, 106, 107, 112, 125, 127, 131, 136, 148, 150, 151, 154, 156, 158, 159, 172, 177, 178, 179, 184, 187, 188, 189, 190, 192, 193, 194, 195, 207, 213, 215, 222, 223, 226, 230, 231, 235, 237, 238, 246, 265, 270, 273, 274, 275, 276, 277, 281, 283, 284, 288], "link": [2, 5, 6, 7, 12, 14, 26, 27, 40, 42, 58, 64, 65, 91, 98, 116, 117, 120, 128, 130, 133, 150, 154, 156, 207, 221, 223, 225, 237, 281, 282, 287], "subclass": [2, 6, 7, 16, 20, 29, 37, 38, 73, 87, 99, 102, 103, 116, 124, 136, 141, 151, 171, 177, 185, 194, 207, 209, 217, 236, 247, 248, 253, 261, 269, 274, 276], "encod": [2, 9, 16, 45, 46, 47, 52, 54, 83, 84, 103, 111, 114, 127, 133, 137, 141, 151, 168, 174, 178, 187, 200, 201, 211, 216, 217, 225, 248, 270], "method": [2, 5, 6, 7, 9, 10, 12, 13, 14, 16, 19, 20, 23, 25, 27, 29, 36, 37, 43, 45, 49, 52, 55, 57, 61, 64, 65, 66, 71, 81, 87, 92, 94, 99, 100, 102, 103, 105, 106, 108, 110, 112, 116, 124, 125, 127, 129, 135, 136, 141, 145, 148, 151, 152, 154, 156, 157, 158, 159, 160, 161, 164, 168, 169, 170, 171, 172, 174, 175, 176, 177, 185, 187, 188, 189, 190, 192, 198, 199, 205, 213, 214, 216, 217, 225, 226, 230, 238, 241, 242, 243, 246, 247, 248, 251, 259, 263, 264, 265, 278, 283], "forward": [2, 3, 5, 10, 12, 13, 14, 16, 17, 19, 23, 26, 27, 29, 36, 37, 42, 48, 50, 52, 55, 58, 62, 64, 65, 66, 69, 70, 71, 78, 79, 83, 84, 94, 99, 102, 103, 106, 108, 109, 110, 111, 113, 115, 116, 124, 125, 131, 136, 137, 138, 139, 140, 142, 144, 145, 146, 148, 149, 150, 153, 154, 157, 164, 165, 167, 168, 169, 171, 172, 175, 176, 177, 178, 180, 181, 184, 185, 187, 188, 189, 193, 195, 198, 199, 201, 203, 204, 210, 211, 213, 214, 215, 221, 223, 225, 226, 228, 229, 230, 231, 232, 235, 236, 237, 238, 244, 246, 248, 251, 252, 255, 256, 265, 270, 276, 279], "detail": [2, 4, 6, 7, 10, 12, 14, 17, 18, 19, 24, 26, 27, 29, 38, 41, 48, 49, 50, 58, 59, 64, 65, 66, 81, 91, 92, 94, 103, 106, 107, 127, 130, 134, 138, 139, 141, 143, 144, 146, 148, 157, 159, 164, 165, 167, 172, 178, 179, 184, 185, 188, 189, 190, 193, 195, 201, 204, 205, 206, 207, 208, 210, 214, 215, 222, 224, 225, 228, 230, 235, 236, 237, 242, 243, 244, 246, 247, 248, 249, 252, 254, 264, 265, 270, 277], "namespac": [2, 4, 7, 10, 12, 26, 27, 37, 116, 120, 152, 170, 195, 201, 209, 225, 276], "inherit": [2, 13, 17, 26, 42, 57, 66, 94, 102, 106, 107, 110, 158, 161, 164, 174, 187, 194, 207, 209, 211, 215, 233], "linearfunct": 2, "public": [2, 10, 12, 17, 170, 225, 284], "static": [2, 10, 12, 16, 25, 64, 65, 66, 99, 119, 136, 152, 156, 157, 170, 189, 190, 197, 199, 200, 201, 212, 213, 214, 215, 216, 225, 238, 265, 281], "option": [2, 4, 6, 7, 10, 12, 13, 21, 27, 40, 49, 51, 53, 57, 59, 66, 91, 97, 98, 99, 100, 103, 106, 108, 122, 135, 136, 141, 146, 149, 151, 153, 159, 162, 164, 171, 173, 187, 189, 190, 198, 199, 200, 201, 210, 213, 214, 215, 216, 221, 226, 229, 233, 235, 245, 248, 249, 255, 265, 269, 270, 272, 273, 275, 283, 287], "autogradcontext": [2, 10, 12], "ctx": [2, 4, 6, 10, 12, 22, 145, 229], "save_for_backward": [2, 4, 6, 145], "mm": [2, 6, 65, 123, 152, 201, 202, 204, 210, 213, 223, 224, 240, 243], "unsqueez": [2, 16, 52, 64, 65, 74, 86, 106, 123, 124, 154, 165, 169, 173, 176, 178, 185, 221, 223], "expand_a": [2, 23, 198, 213, 214], "tensor_list": [2, 10, 12, 150], "get_saved_vari": 2, "grad_input": [2, 145], "grad_weight": 2, "grad_bia": 2, "Then": [2, 14, 17, 20, 24, 26, 28, 29, 49, 50, 58, 64, 65, 81, 94, 109, 110, 113, 115, 116, 128, 136, 138, 148, 149, 164, 167, 170, 171, 174, 175, 176, 178, 180, 184, 189, 190, 204, 211, 216, 217, 229, 232, 240, 242, 243, 246, 262], "5314": 2, "2807": 2, "4864": 2, "7608": 2, "9101": [2, 189], "0073": [2, 103], "mulconst": [2, 86], "object": [2, 4, 5, 6, 7, 9, 12, 13, 16, 21, 23, 26, 27, 36, 37, 38, 41, 42, 48, 55, 58, 66, 83, 99, 100, 102, 103, 106, 108, 112, 115, 116, 123, 124, 125, 130, 131, 136, 140, 144, 150, 153, 156, 158, 170, 176, 177, 178, 179, 181, 184, 185, 187, 189, 190, 193, 197, 198, 209, 213, 214, 216, 233, 237, 238, 241, 243, 249, 259, 262, 264, 265, 267, 269], "stash": 2, "saved_data": 2, "were": [2, 3, 6, 7, 11, 14, 20, 22, 27, 36, 58, 66, 92, 94, 99, 103, 106, 108, 110, 112, 115, 116, 127, 128, 141, 147, 148, 153, 162, 168, 174, 175, 177, 179, 180, 189, 190, 192, 205, 217, 221, 222, 244, 249, 252, 254, 255, 262, 278], "todoubl": 2, "On": [2, 5, 6, 7, 10, 23, 25, 26, 27, 106, 116, 129, 131, 137, 148, 150, 152, 162, 168, 171, 176, 177, 188, 193, 194, 217, 220, 225, 236, 244, 248, 265, 278], "easiest": [2, 6, 11, 27, 40, 98, 116, 136, 154, 160, 172, 174, 246, 265], "tabl": [2, 19, 25, 115, 122, 129, 136, 137, 150, 152, 159, 177, 178, 184, 189, 190, 191, 236, 249, 255, 287], "set_data": 2, "output_nr": 2, "after": [2, 3, 6, 7, 9, 11, 12, 14, 16, 17, 18, 20, 22, 23, 24, 25, 27, 28, 29, 36, 37, 38, 45, 48, 50, 52, 55, 58, 59, 64, 65, 66, 84, 87, 97, 99, 100, 103, 108, 109, 110, 113, 115, 116, 117, 123, 125, 127, 129, 130, 136, 137, 139, 142, 143, 144, 147, 148, 149, 150, 151, 152, 153, 159, 161, 162, 164, 167, 168, 170, 171, 172, 173, 174, 175, 177, 178, 180, 182, 184, 185, 189, 190, 192, 194, 195, 197, 198, 199, 201, 203, 204, 211, 213, 214, 215, 216, 217, 219, 220, 221, 223, 225, 230, 231, 241, 242, 243, 246, 248, 249, 252, 254, 255, 259, 262, 263, 265, 270, 274, 277, 279], "bug": [2, 6, 12, 27, 105, 120, 159, 202], "report": [2, 12, 20, 27, 58, 81, 97, 99, 102, 103, 107, 136, 152, 159, 176, 178, 179, 202, 217, 255], "fix": [2, 16, 20, 24, 27, 28, 36, 55, 56, 57, 58, 108, 120, 127, 140, 172, 176, 189, 190, 200, 217, 244, 264, 265, 283], "soon": [2, 6, 58, 64, 65, 137, 162, 167, 214, 237], "overview": [2, 6, 7, 13, 59, 61, 102, 127, 134, 135, 136, 142, 143, 148, 149, 150, 157, 170, 174, 176, 177, 178, 180, 184, 205, 206, 208, 212, 218, 224, 227, 245, 277], "alwai": [2, 3, 5, 7, 11, 16, 19, 22, 23, 26, 27, 46, 58, 102, 103, 107, 113, 115, 116, 120, 127, 139, 140, 144, 150, 151, 152, 154, 173, 174, 175, 176, 178, 189, 194, 201, 203, 204, 205, 211, 221, 224, 240, 270, 283], "problem": [2, 5, 7, 13, 16, 17, 22, 26, 27, 55, 57, 58, 109, 111, 115, 116, 124, 129, 131, 141, 151, 157, 159, 160, 164, 168, 172, 174, 176, 178, 184, 188, 192, 205, 207, 224, 249, 250, 254, 265, 278, 283], "question": [2, 5, 6, 7, 10, 12, 20, 26, 27, 55, 83, 100, 137, 150, 152, 158, 180, 199, 206, 216, 217, 224, 249], "forum": [2, 5, 6, 7, 26, 27, 49, 87, 123, 157, 158, 199, 224], "view": [3, 12, 13, 16, 17, 19, 23, 29, 56, 59, 61, 62, 67, 91, 100, 103, 105, 106, 112, 115, 116, 117, 123, 124, 125, 138, 139, 141, 142, 146, 147, 148, 149, 150, 157, 159, 164, 165, 170, 176, 177, 178, 179, 180, 185, 189, 190, 198, 199, 209, 213, 214, 223, 228, 231, 232, 244, 247, 256, 263, 275, 281], "prerequisit": [3, 4, 9, 18, 59, 61, 62, 111, 120, 128, 139, 146, 147, 148, 149, 150, 151, 170, 176, 177, 178, 187, 213, 231, 232], "frontend": [3, 4, 12, 93, 95, 123, 136, 193, 202, 203, 209, 215, 237, 238, 272], "semant": [3, 4, 7, 26, 55, 64, 65, 74, 106, 111, 113, 114, 124, 150, 152, 207, 208, 209, 212, 218, 222, 283], "11": [3, 6, 7, 9, 13, 20, 22, 27, 65, 103, 122, 137, 138, 153, 156, 173, 178, 187, 188, 189, 191, 206, 210, 221, 225, 232, 236, 243, 245, 249, 255, 276, 283, 287], "nvidia": [3, 6, 20, 56, 106, 144, 150, 152, 162, 188, 217, 232, 248, 265, 269, 277], "toolkit": [3, 27, 111, 157, 161, 263], "releas": [3, 5, 7, 12, 20, 24, 27, 47, 56, 117, 120, 122, 125, 137, 138, 140, 154, 157, 167, 177, 179, 184, 215, 217, 221, 225, 229, 236, 237, 238, 255, 265, 283], "greatli": [3, 7, 55, 99, 116, 175], "cpu": [3, 4, 6, 7, 10, 12, 13, 16, 17, 22, 23, 24, 27, 37, 42, 45, 47, 48, 49, 54, 58, 66, 79, 81, 89, 91, 92, 97, 106, 108, 110, 116, 117, 120, 122, 123, 131, 136, 138, 139, 148, 149, 150, 152, 162, 165, 168, 170, 172, 173, 174, 176, 177, 178, 179, 180, 181, 184, 187, 191, 194, 198, 201, 202, 203, 204, 210, 213, 214, 215, 219, 223, 229, 233, 236, 241, 244, 248, 249, 250, 252, 254, 255, 262, 269, 270, 278], "overhead": [3, 6, 7, 12, 20, 62, 91, 122, 137, 138, 139, 148, 160, 162, 164, 173, 176, 178, 179, 184, 188, 192, 193, 200, 202, 209, 215, 217, 249, 255, 265, 278], "increas": [3, 6, 7, 22, 23, 24, 28, 41, 42, 49, 81, 91, 92, 97, 100, 108, 116, 137, 138, 139, 141, 143, 146, 149, 157, 167, 173, 184, 198, 200, 209, 210, 213, 226, 236, 247, 248, 249, 252, 264, 265, 268, 269, 278], "mostli": [3, 12, 23, 94, 99, 108, 130, 142, 178, 180, 195, 213, 214, 215], "util": [3, 4, 6, 9, 12, 17, 23, 26, 27, 28, 29, 37, 38, 42, 49, 55, 57, 59, 61, 66, 91, 102, 105, 107, 116, 122, 124, 125, 129, 130, 131, 133, 136, 137, 138, 139, 149, 150, 152, 153, 156, 157, 159, 160, 162, 168, 170, 172, 173, 174, 177, 179, 180, 184, 185, 187, 188, 192, 193, 194, 198, 201, 204, 209, 210, 213, 214, 215, 216, 217, 219, 221, 223, 225, 226, 227, 233, 235, 237, 238, 240, 242, 243, 244, 247, 248, 257, 263, 264, 270, 272, 273, 274, 278], "deploy": [3, 5, 29, 47, 66, 108, 125, 126, 141, 193, 202, 215, 221, 237, 245, 246, 252, 269, 270, 277], "appear": [3, 13, 16, 26, 29, 115, 244, 252, 283], "heart": [3, 55, 127, 236, 284], "veri": [3, 4, 5, 6, 7, 10, 16, 17, 22, 23, 25, 26, 27, 28, 29, 45, 50, 52, 54, 55, 64, 65, 66, 71, 81, 83, 84, 94, 100, 103, 110, 112, 116, 124, 127, 129, 131, 138, 139, 140, 142, 149, 150, 164, 167, 168, 172, 175, 176, 178, 179, 180, 182, 184, 185, 192, 194, 198, 205, 207, 211, 214, 244, 252, 255, 264, 265, 284, 285], "time": [3, 4, 5, 6, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 23, 25, 27, 28, 29, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 78, 79, 81, 83, 84, 86, 87, 89, 92, 94, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 117, 119, 120, 124, 125, 127, 128, 129, 130, 131, 133, 134, 136, 137, 138, 140, 141, 142, 143, 144, 145, 148, 149, 150, 151, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 167, 168, 169, 171, 172, 174, 175, 176, 177, 178, 179, 180, 182, 184, 185, 187, 188, 189, 190, 192, 193, 194, 197, 198, 200, 201, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 217, 220, 221, 222, 223, 228, 229, 231, 236, 244, 246, 247, 248, 249, 250, 251, 252, 254, 256, 257, 258, 259, 260, 262, 263, 265, 266, 267, 268, 269, 274, 275, 276, 277, 281, 283], "compil": [3, 5, 7, 8, 10, 12, 18, 20, 25, 26, 29, 66, 94, 109, 120, 136, 150, 158, 181, 189, 190, 191, 200, 202, 209, 215, 221, 224, 233, 237, 241, 249, 255, 264, 265, 269, 271, 272, 276, 281], 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61, 62, 66, 81, 86, 87, 91, 97, 99, 100, 102, 103, 105, 106, 107, 108, 113, 116, 123, 125, 127, 131, 133, 135, 136, 137, 138, 139, 140, 141, 142, 146, 149, 150, 151, 152, 153, 154, 161, 162, 164, 165, 167, 169, 172, 173, 174, 175, 177, 178, 179, 181, 182, 184, 185, 187, 188, 189, 191, 193, 194, 197, 198, 200, 201, 207, 209, 212, 214, 217, 221, 222, 230, 238, 241, 248, 249, 258, 259, 260], "data_load": [3, 7, 23, 198, 213, 214, 215], "zero_grad": [3, 7, 23, 41, 52, 71, 99, 115, 116, 124, 137, 138, 148, 150, 167, 172, 176, 178, 185, 214, 231, 237, 238, 268, 272], "nll_loss": [3, 138, 150, 177, 238], "updat": [3, 7, 12, 13, 14, 15, 16, 19, 20, 23, 25, 27, 41, 47, 48, 49, 55, 58, 71, 75, 91, 99, 108, 110, 115, 116, 123, 124, 125, 137, 138, 141, 144, 151, 152, 154, 162, 167, 174, 175, 177, 178, 184, 198, 201, 205, 213, 214, 215, 216, 217, 221, 224, 227, 231, 233, 235, 238, 240, 246, 248, 258, 262, 267, 279], "captur": [3, 5, 7, 26, 27, 29, 58, 66, 99, 119, 138, 156, 187, 188, 189, 190, 202, 210, 213, 214, 216, 249], "But": [3, 7, 10, 12, 24, 47, 49, 50, 58, 81, 86, 100, 106, 112, 115, 130, 140, 162, 167, 168, 169, 175, 189, 190, 192, 194, 198, 201, 205, 208, 216, 222, 235, 238, 241, 246, 249, 270, 283], "slightli": [3, 6, 16, 27, 137, 150, 151, 173, 180, 189, 190, 208, 249, 265], "prealloc": 3, "reus": [3, 12, 71, 86, 116, 124, 145, 152, 156, 168, 175, 192, 193, 201, 203, 260, 265, 269, 278], "tensoropt": [3, 202], "floatcuda": 3, "dtype": [3, 4, 10, 12, 16, 17, 106, 112, 115, 145, 152, 165, 189, 190, 191, 195, 201, 202, 206, 208, 213, 215, 216, 223, 226, 235, 237, 241, 246, 248, 262, 270, 272], "longcuda": 3, "klong": 3, "zero": [3, 7, 13, 20, 23, 36, 41, 46, 52, 55, 71, 99, 100, 102, 103, 106, 107, 115, 116, 123, 124, 137, 138, 142, 143, 149, 150, 151, 165, 168, 170, 171, 172, 176, 178, 180, 185, 201, 205, 207, 208, 210, 216, 217, 226, 238, 241, 248, 253, 255, 261, 264, 265, 269, 270, 279], "ktrainbatchs": 3, "28": [3, 7, 9, 20, 37, 38, 103, 105, 116, 153, 169, 185, 192, 217, 221, 225, 236, 238, 241, 249, 251, 264], "copy_": [3, 45, 54, 122, 159, 176, 254, 262], "training_step": 3, "void": [3, 4, 6, 7, 17, 26, 27, 65, 159, 170, 202, 204, 225, 249, 255, 264], "cudagraph": 3, "cudastream": 3, "capturestream": 3, "getstreamfrompool": 3, "setcurrentcudastream": 3, "capture_begin": 3, "capture_end": 3, "warm": [3, 25, 77, 80, 115, 122, 184, 188, 192, 193, 209, 220, 236, 249, 255, 265], "side": [3, 24, 57, 58, 91, 115, 153, 162, 167, 169, 170, 175, 176, 184, 204, 244, 281], "prepar": [3, 13, 20, 23, 29, 49, 57, 58, 64, 65, 74, 75, 105, 113, 115, 124, 125, 130, 136, 149, 152, 153, 167, 170, 174, 176, 201, 211, 212, 215, 216, 217, 221, 226, 229, 235, 240, 245, 246, 255, 257, 269], "cach": [3, 48, 152, 159, 184, 192, 193, 200, 201, 265, 269, 278], "cubla": [3, 249], "cudnn": [3, 6, 86, 144, 151, 162, 165, 248], "warmupstream": 3, "int": [3, 4, 5, 6, 7, 11, 13, 22, 23, 26, 27, 57, 59, 61, 64, 65, 137, 138, 150, 152, 159, 170, 176, 177, 178, 184, 188, 189, 190, 194, 197, 201, 204, 223, 225, 226, 230, 232, 237, 241, 270, 276, 278, 281], "num_warmup_it": 3, "success": [3, 7, 16, 27, 81, 112, 115, 141, 159, 180, 204, 221, 223, 244], "replai": [3, 16, 29, 84, 161], "spin": [3, 66, 102, 192], "ordinari": [3, 210], "epoch": [3, 7, 9, 11, 19, 23, 38, 41, 42, 58, 59, 61, 97, 102, 105, 108, 110, 115, 116, 125, 129, 131, 133, 137, 138, 141, 150, 162, 167, 172, 174, 178, 185, 194, 214, 238, 258, 263], "59584": 3, "60000": [3, 150], "3921": 3, "2051": 3, "accuraci": [3, 11, 20, 23, 24, 28, 42, 49, 102, 107, 108, 116, 134, 135, 136, 137, 138, 141, 165, 171, 172, 173, 177, 182, 185, 187, 193, 198, 201, 210, 211, 214, 215, 217, 235, 236, 245, 246, 248, 263, 268, 269], "938": [3, 7, 162], "1826": 3, "1273": 3, "960": 3, "1796": [3, 37], "1012": [3, 162], "968": 3, "1603": 3, "0869": 3, "973": 3, "2315": 3, "0736": 3, "978": 3, "0511": [3, 201], "0704": 3, "977": [3, 162, 236], "7": [3, 6, 7, 20, 22, 23, 26, 27, 37, 40, 48, 57, 64, 65, 89, 94, 103, 122, 130, 131, 137, 138, 149, 150, 161, 162, 164, 172, 173, 187, 188, 189, 191, 193, 200, 204, 206, 215, 219, 221, 223, 224, 225, 227, 228, 232, 235, 236, 238, 240, 242, 243, 245, 246, 248, 265, 270, 287], "0802": 3, "0654": 3, "979": 3, "0774": 3, "0604": 3, "980": [3, 192], "0669": 3, "0544": [3, 103], "984": [3, 236], "0219": [3, 115], "0517": [3, 103], "983": 3, "real": [3, 7, 16, 24, 36, 42, 58, 60, 64, 99, 108, 109, 110, 111, 115, 136, 138, 142, 143, 150, 151, 164, 167, 170, 175, 180, 188, 192, 207, 209, 213, 216, 235, 236, 249, 252], "0m44": 3, "287": [3, 193, 283], "018": 3, "0m1": 3, "116": [3, 201], "produc": [3, 5, 6, 7, 13, 26, 27, 29, 66, 74, 108, 124, 127, 129, 141, 151, 153, 156, 158, 160, 162, 164, 174, 175, 180, 187, 189, 190, 195, 198, 199, 201, 213, 214, 215, 223, 231, 248, 252, 265, 283], "4092": 3, "2037": 3, "2039": 3, "1274": 3, "961": 3, "1779": 3, "1017": 3, "1559": 3, "0871": 3, "972": 3, "2240": 3, "0735": [3, 103, 217], "0520": 3, "0710": [3, 103], "0935": 3, "0666": [3, 27], "0744": 3, "0603": 3, "981": 3, "0762": 3, "0547": 3, "0207": 3, "0525": [3, 225], "0m6": 3, "952": [3, 159], "0m7": 3, "048": [3, 224], "0m0": 3, "619": 3, "gain": [3, 6, 20, 91, 184, 192, 217], "six": [3, 174, 283], "kind": [3, 6, 7, 23, 27, 41, 49, 52, 55, 64, 65, 81, 100, 107, 109, 113, 125, 134, 136, 152, 189, 193, 213, 270, 278], "larg": [3, 4, 6, 7, 8, 9, 14, 18, 19, 20, 22, 27, 41, 45, 47, 55, 67, 97, 100, 103, 105, 108, 113, 115, 116, 117, 124, 131, 135, 136, 137, 138, 141, 144, 148, 150, 153, 154, 164, 165, 174, 175, 177, 178, 179, 187, 188, 192, 193, 194, 207, 215, 217, 219, 227, 236, 241, 248, 255, 256, 265, 277, 281, 283], "improv": [3, 6, 7, 10, 12, 20, 23, 24, 25, 28, 41, 47, 55, 62, 102, 106, 108, 116, 120, 135, 136, 137, 138, 139, 144, 157, 162, 164, 168, 172, 175, 179, 180, 187, 188, 192, 193, 200, 201, 210, 213, 216, 217, 221, 224, 227, 229, 233, 235, 237, 240, 248, 249, 263, 265, 272, 274, 284], "due": [3, 6, 7, 20, 22, 26, 58, 64, 66, 91, 94, 106, 120, 137, 138, 139, 148, 167, 168, 172, 177, 188, 192, 198, 200, 207, 216, 217, 219, 228, 233, 238, 252, 278, 282, 283], "heavi": [3, 7, 55, 108, 112, 241], "impact": [3, 14, 20, 41, 97, 108, 151, 176, 179, 200, 217, 226, 247, 270, 278, 279], "smaller": [3, 11, 41, 87, 103, 108, 115, 116, 138, 139, 172, 174, 176, 187, 220, 225, 241, 249, 252, 254], "nevertheless": [3, 6, 23, 27, 162, 174], "richard": 4, "zou": 4, "integr": [4, 7, 12, 16, 18, 27, 40, 86, 97, 122, 136, 141, 154, 185, 192, 193, 195, 215, 216, 223, 236, 244, 247, 262, 267, 274, 278], "opcheck": [4, 18], "basic": [4, 10, 13, 17, 18, 19, 27, 28, 36, 37, 38, 41, 42, 43, 45, 46, 55, 60, 62, 63, 79, 83, 96, 98, 99, 102, 103, 106, 108, 111, 115, 116, 117, 119, 120, 124, 127, 129, 136, 139, 142, 144, 159, 174, 179, 206, 209, 231, 249, 263, 269, 270, 273, 284], "program": [4, 5, 6, 22, 25, 26, 27, 29, 66, 67, 103, 109, 111, 112, 115, 136, 139, 142, 158, 177, 188, 189, 193, 213, 214, 215, 216, 231, 249, 265, 276, 278], "g": [4, 6, 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59, 64, 65, 125, 134, 137, 138, 152, 204, 210, 221, 223, 225, 235, 237, 238, 240, 241, 242, 243, 246, 259, 276], "my_module_model": 5, "left": [5, 20, 36, 48, 52, 55, 58, 70, 94, 99, 105, 106, 110, 115, 124, 125, 127, 150, 152, 161, 165, 174, 175, 177, 179, 184, 185, 216, 217, 244, 252, 258, 269, 281, 283], "realm": [5, 7], "cross": [5, 9, 10, 15, 24, 49, 58, 102, 103, 107, 133, 139, 141, 192, 265, 268, 278, 283], "sphere": 5, "distribut": [5, 6, 7, 16, 17, 21, 23, 58, 81, 83, 87, 89, 97, 102, 106, 107, 108, 110, 115, 120, 127, 136, 137, 138, 139, 141, 146, 147, 152, 162, 164, 167, 170, 174, 176, 184, 192, 201, 212, 219, 225, 229, 232, 241, 249, 269, 278, 279], "encompass": 5, "header": [5, 6, 7, 10, 26, 27, 170, 204, 221, 225, 240, 243, 249, 281, 283, 284], "cmake": [5, 7, 204, 223, 237, 276], "futur": [5, 9, 22, 25, 26, 47, 55, 64, 65, 116, 122, 123, 133, 138, 149, 152, 156, 161, 167, 170, 172, 175, 176, 177, 178, 189, 190, 195, 197, 203, 204, 208, 213, 214, 215, 216, 221, 225, 236, 240, 267, 270], "begin": [5, 6, 7, 9, 13, 14, 20, 23, 26, 27, 29, 36, 48, 55, 56, 58, 61, 64, 65, 81, 94, 99, 102, 113, 115, 120, 127, 129, 130, 137, 139, 152, 167, 172, 175, 177, 184, 185, 207, 217, 241, 246, 248, 249, 251, 255, 258, 259, 260, 266, 267, 268, 283], "iostream": [5, 6, 7, 26, 27, 237], "argc": [5, 26, 27, 237, 276], "char": [5, 26, 27, 65, 159, 225, 237, 276], "cerr": [5, 26, 27, 237, 276], "app": [5, 27, 117, 134, 136, 154, 210, 221, 237, 240, 245, 246, 269, 270], "try": [5, 7, 14, 16, 17, 20, 23, 25, 26, 27, 29, 41, 45, 47, 49, 52, 54, 55, 58, 59, 64, 65, 67, 81, 99, 106, 107, 108, 109, 110, 111, 112, 116, 117, 122, 130, 141, 142, 143, 151, 157, 159, 161, 164, 165, 167, 170, 171, 174, 175, 180, 184, 188, 189, 190, 192, 198, 200, 203, 206, 213, 217, 225, 230, 233, 236, 237, 240, 248, 249, 252, 259, 262, 263, 276, 278, 283, 284], "deseri": [5, 7, 27, 125, 198, 213, 214, 259, 276], "catch": [5, 10, 13, 26, 64, 225, 237, 240, 276], "c10": [5, 10, 12, 17, 26, 170, 202, 204, 225, 236, 237, 249, 264, 276], "ok": [5, 115, 176, 283], "relev": [5, 7, 16, 59, 103, 109, 111, 115, 127, 128, 137, 139, 171, 187, 258, 260, 265, 267, 273], "accept": [5, 6, 24, 46, 97, 99, 108, 113, 129, 130, 139, 141, 156, 160, 165, 169, 174, 177, 184, 187, 195, 216, 219, 222, 229, 236, 255, 265, 270], "proce": [5, 13, 29, 108, 110, 159, 172, 180, 252, 265], "examin": [5, 13, 26, 29, 64, 65, 91, 99, 100, 105, 107, 108, 158], "moment": [5, 7, 13, 189, 195, 208, 223, 241], "cmakelist": [5, 7, 26, 27, 225, 237, 276], "txt": [5, 6, 7, 26, 27, 55, 130, 142, 143, 152, 159, 173, 180, 201, 225, 237, 276], "cmake_minimum_requir": [5, 7, 26, 27, 225, 237, 276], "fatal_error": [5, 7, 26, 27, 225, 237, 276], "custom_op": [5, 18, 189, 276], "find_packag": [5, 7, 26, 27, 237, 276], "add_execut": [5, 7, 26, 27, 237, 276], "target_link_librari": [5, 7, 26, 27, 225, 237, 276], "set_properti": [5, 7, 237, 276], "cxx_standard": [5, 7, 237, 276], "last": [5, 7, 13, 16, 23, 27, 37, 48, 55, 58, 59, 65, 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198, 213, 214, 225], "archiv": [5, 6, 7, 29, 162, 277], "lib": [5, 6, 7, 12, 22, 26, 27, 48, 120, 159, 203, 221, 225, 237, 264, 278], "against": [5, 18, 26, 27, 41, 42, 49, 66, 90, 103, 105, 117, 150, 162, 174, 229, 237, 249, 252], "window": [5, 6, 7, 24, 49, 57, 103, 115, 117, 148, 177, 184, 194, 223, 230, 244, 283], "debug": [5, 7, 10, 23, 29, 64, 65, 66, 67, 86, 109, 136, 140, 181, 189, 190, 202, 211, 212, 249, 264, 273], "abi": [5, 6, 7, 26, 27, 221, 223, 225, 237], "laid": 5, "within": [5, 6, 16, 22, 25, 27, 67, 94, 99, 100, 105, 115, 116, 117, 122, 123, 139, 145, 152, 157, 159, 168, 171, 175, 177, 187, 192, 193, 201, 202, 208, 209, 211, 215, 223, 225, 230, 232, 236, 249, 256, 265, 273, 278, 281, 283], "mkdir": [5, 7, 27, 184, 187, 197, 210, 225], "dcmake_prefix_path": [5, 7, 26, 27, 237, 276], "config": [5, 7, 12, 20, 24, 97, 138, 141, 152, 173, 192, 193, 195, 200, 201, 202, 213, 215, 217, 237, 238, 262, 273, 274], "someth": [5, 6, 7, 13, 16, 23, 27, 29, 49, 97, 102, 106, 110, 112, 127, 130, 150, 159, 172, 173, 180, 222, 249, 252, 264, 283], "root": [5, 6, 7, 16, 26, 27, 36, 38, 48, 57, 58, 102, 109, 123, 144, 151, 159, 177, 178, 204, 221, 230, 237, 241, 244, 263, 270, 272, 281], "4b5a67132e81": 5, "identif": [5, 7, 26, 27, 237], "gnu": [5, 6, 7, 26, 27, 237, 265, 278], "cxx": [5, 7, 26, 27, 221, 223, 225, 237], "usr": [5, 7, 22, 26, 27, 150, 210, 237], "cc": [5, 7, 26, 27, 48, 120, 133, 221, 223, 237], "detect": [5, 7, 13, 14, 22, 26, 27, 58, 83, 103, 136, 154, 173, 181, 184, 188, 237, 251, 265, 278], "info": [5, 6, 7, 26, 27, 91, 133, 147, 150, 152, 161, 187, 189, 191, 201, 224, 237, 238, 246], "pthread": [5, 6, 7, 26, 27, 225, 237], "pthread_creat": [5, 7, 26, 27, 237], "thread": [5, 6, 7, 10, 11, 25, 26, 27, 58, 122, 148, 149, 152, 153, 164, 173, 176, 177, 178, 192, 193, 197, 198, 210, 233, 237, 244, 249, 255, 264, 265, 278], "scan": [5, 7, 26, 27, 102, 103, 187], "50": [5, 7, 9, 20, 23, 25, 26, 27, 58, 59, 64, 102, 116, 151, 162, 171, 175, 178, 193, 194, 198, 201, 213, 215, 217, 220, 236, 238, 241, 246, 265], "cmakefil": [5, 7, 26, 27], "dir": [5, 7, 26, 27, 91, 105, 141, 221, 225, 241, 264], "o": [5, 6, 7, 9, 26, 27, 108, 120, 152, 165, 167, 187, 217, 249, 283], "100": [5, 7, 11, 23, 25, 26, 27, 45, 48, 54, 103, 110, 138, 148, 153, 159, 162, 164, 173, 174, 175, 178, 185, 187, 189, 192, 193, 198, 203, 213, 214, 217, 232, 236, 238, 249, 264, 277], "suppli": [5, 7, 112, 162, 173, 283], "binari": [5, 7, 24, 26, 27, 55, 58, 106, 117, 150, 162, 188, 204, 206, 212, 215, 221, 225, 229, 235, 237, 240, 241], "1d": [5, 74, 103, 124, 222, 265], "4d": [5, 52, 86, 162, 216], "path_to_model": 5, "successfulli": [5, 7, 26, 56, 64, 65, 66, 117, 134, 141, 150, 159, 177, 207, 210, 223, 236, 243, 245, 251, 257, 258, 259, 260, 266, 267, 268, 276], "coupl": [5, 16, 40, 55, 98, 106, 107, 115, 137, 139, 145, 151, 153, 167, 185, 199, 220, 265], "awai": [5, 6, 7, 27, 52, 66, 103, 105, 109, 110, 112, 127, 164, 174, 175, 176, 208, 252, 283], "ivalu": [5, 27, 64, 170, 223, 225, 237, 241, 276], "push_back": [5, 26, 27, 237, 276], "totensor": [5, 23, 27, 57, 64, 65, 102, 130, 138, 150, 154, 172, 173, 177, 185, 187, 198, 203, 204, 213, 214, 221, 223, 230, 237, 238, 241, 272, 276], "slice": [5, 6, 45, 54, 89, 113, 116, 142, 165, 171, 209, 223, 264], "eras": [5, 29], "org": [5, 7, 30, 31, 32, 33, 34, 39, 47, 48, 50, 51, 55, 64, 65, 82, 85, 93, 101, 103, 105, 107, 111, 118, 127, 137, 142, 143, 152, 155, 156, 157, 158, 167, 172, 173, 180, 183, 184, 186, 188, 190, 197, 200, 203, 204, 206, 208, 210, 212, 220, 221, 222, 223, 225, 230, 238, 240, 241, 242, 244, 248, 252, 253, 254, 276, 281, 283], "cppdoc": [5, 7], "pariti": 5, "manipul": [5, 45, 46, 66, 102, 105, 115, 124, 158, 167, 198, 201, 230], "five": [5, 11, 71, 124, 127], "ideal": [5, 7, 16, 38, 64, 65, 99, 102, 106, 108, 164, 180, 193, 213, 224], "variabl": [5, 6, 7, 9, 10, 24, 26, 27, 36, 47, 55, 66, 84, 91, 97, 99, 106, 109, 110, 112, 116, 128, 142, 147, 150, 159, 176, 179, 189, 190, 192, 200, 207, 209, 222, 223, 224, 225, 236, 240, 244, 260, 264, 270, 273, 275, 278], "kcuda": [5, 7, 202], "live": [5, 7, 12, 17, 106, 136, 140, 149, 177, 178, 208, 233, 283], "hopefulli": [5, 7, 56, 57, 81, 94, 110, 125, 266], "equip": [5, 6, 145, 151, 205, 278], "concept": [5, 7, 13, 26, 40, 41, 61, 98, 111, 112, 116, 124, 128, 136, 141, 161, 176, 179, 180, 202, 213, 215, 216, 255, 264, 278], "close": [5, 7, 11, 16, 20, 23, 27, 58, 99, 103, 106, 108, 109, 137, 141, 152, 167, 185, 197, 201, 217, 263, 265, 270], "Of": [5, 16, 27, 108, 112, 116, 140, 148, 150, 185, 206, 208, 244], "cours": [5, 7, 16, 20, 23, 27, 59, 99, 100, 106, 108, 111, 112, 115, 116, 140, 148, 150, 185, 217, 230, 244], "did": [5, 7, 10, 18, 23, 27, 29, 49, 58, 66, 99, 102, 103, 106, 117, 127, 150, 156, 168, 174, 177, 180, 192, 198, 249, 283], "cover": [5, 6, 8, 16, 17, 19, 22, 26, 29, 38, 52, 64, 65, 106, 111, 120, 127, 128, 134, 136, 137, 141, 150, 170, 174, 177, 178, 185, 188, 189, 190, 191, 207, 209, 213, 216, 229, 236, 237, 248, 263, 264, 270, 278], "insid": [5, 6, 7, 12, 19, 22, 26, 27, 37, 41, 50, 120, 139, 184, 194, 211, 217, 222, 224, 241, 283], "shortli": [5, 176], "html": [5, 30, 31, 32, 33, 34, 39, 50, 51, 82, 85, 93, 101, 111, 118, 127, 128, 133, 137, 152, 155, 157, 158, 172, 183, 186, 187, 190, 197, 203, 204, 206, 208, 220, 221, 248, 252, 253, 254, 283], "peter": 6, "goldsborough": 6, "plethora": 6, "relat": [6, 13, 16, 58, 66, 99, 100, 106, 107, 112, 115, 127, 136, 139, 159, 168, 189, 190, 198, 249, 265, 283], "algebra": [6, 16, 45, 54, 102, 106, 110, 236], "wrangl": 6, "novel": [6, 100], "research": [6, 7, 20, 23, 27, 29, 55, 58, 66, 81, 83, 94, 100, 106, 110, 128, 129, 150, 152, 165, 169, 171, 187, 197, 217, 233], "power": [6, 7, 27, 48, 55, 63, 71, 81, 83, 97, 99, 102, 108, 110, 116, 124, 136, 138, 141, 150, 159, 161, 165, 170, 171, 173, 180, 187, 200, 203, 208, 210, 245, 249, 274, 278, 283], "spare": [6, 7], "deriv": [6, 7, 12, 18, 20, 27, 29, 36, 41, 45, 48, 54, 84, 86, 99, 106, 110, 112, 113, 115, 123, 124, 140, 145, 156, 160, 211, 217, 265], "express": [6, 8, 18, 27, 99, 102, 103, 106, 109, 119, 128, 139, 152, 189, 190, 207, 213, 214, 216, 226, 283], "better": [6, 7, 16, 20, 23, 25, 38, 41, 42, 49, 58, 62, 87, 99, 102, 103, 105, 108, 115, 116, 122, 127, 129, 136, 141, 142, 143, 154, 159, 160, 161, 167, 169, 172, 174, 175, 180, 184, 185, 187, 189, 190, 192, 193, 198, 200, 205, 206, 211, 213, 214, 215, 216, 217, 224, 241, 249, 265, 272, 277, 278], "frequent": [6, 26, 27, 36, 83, 91, 115, 124, 138, 191, 192, 193, 207, 237], "expens": [6, 20, 45, 108, 122, 178, 193, 200, 217, 255, 264], "plausibl": 6, "address": [6, 14, 22, 26, 28, 91, 103, 136, 150, 176, 177, 190, 192, 213, 238, 262, 278, 284], "nativ": [6, 7, 10, 17, 18, 24, 27, 47, 61, 97, 119, 120, 134, 136, 137, 139, 151, 152, 178, 192, 193, 195, 200, 205, 208, 213, 233, 236, 237, 241, 255, 264, 265, 269], "intend": [6, 7, 10, 14, 27, 99, 108, 138, 139, 159, 162, 179, 188, 189, 190, 205, 213, 229, 251, 265], "much": [6, 7, 10, 12, 14, 16, 20, 23, 27, 29, 41, 58, 62, 81, 99, 100, 108, 109, 110, 115, 116, 125, 134, 137, 140, 141, 157, 160, 161, 167, 173, 174, 177, 178, 180, 184, 185, 188, 192, 200, 205, 208, 216, 217, 235, 244, 245, 246, 248, 249, 252, 254, 263, 265, 266, 269, 278, 281, 283], "boilerpl": [6, 7, 23, 49, 110, 185, 205], "degre": [6, 41, 70, 103, 124, 141, 180, 184, 208], "organ": [6, 10, 16, 27, 57, 102, 125, 142, 164, 249, 258, 277, 278, 284], "tackl": [6, 122], "decid": [6, 7, 10, 12, 20, 22, 58, 67, 97, 99, 175, 191, 208, 212, 217, 268], "contribut": [6, 58, 67, 71, 91, 100, 106, 124, 152, 162, 184, 192], "upstream": [6, 237, 265], "rest": [6, 7, 16, 49, 131, 136, 151, 158, 172, 174, 176, 177, 181, 207, 214, 235, 252, 262, 283, 284], "chase": [6, 127], "someon": [6, 180], "fire": [6, 148], "dai": [6, 47, 115, 130, 139, 262], "head": [6, 8, 9, 20, 25, 40, 47, 83, 92, 103, 122, 127, 139, 148, 172, 179, 194, 209, 217], "straight": [6, 7, 27, 154, 180], "recurr": [6, 49, 55, 66, 87, 90, 109, 113, 123, 136, 142, 143, 168, 180, 181, 252], "unit": [6, 7, 29, 55, 103, 106, 123, 137, 138, 160, 165, 171, 174, 175, 180, 187, 192, 193, 203, 251, 265], "superior": 6, "lstm": [6, 21, 49, 55, 86, 87, 103, 111, 114, 123, 134, 136, 142, 143, 178, 197, 199, 211, 246, 252, 269], "lack": [6, 17, 91, 100, 205], "forget": [6, 7, 116, 125, 172, 188, 204], "gate": [6, 55, 103, 262], "exponenti": [6, 55, 99, 106, 110, 112, 168, 175, 200], "elu": [6, 123], "never": [6, 99, 109, 110, 111, 115, 140, 171, 182], "lltm": 6, "long": [6, 7, 12, 24, 27, 55, 56, 91, 97, 103, 106, 110, 111, 112, 114, 115, 116, 127, 137, 140, 143, 151, 152, 158, 159, 164, 168, 175, 178, 184, 201, 202, 213, 225, 241, 249, 252, 264, 265, 268, 283, 284], "term": [6, 7, 17, 45, 58, 67, 81, 99, 100, 103, 107, 110, 111, 112, 114, 122, 137, 139, 165, 171, 174, 190, 200, 208, 213, 214, 215, 216, 219, 252, 256, 278, 283, 284], "signific": [6, 7, 11, 23, 47, 58, 64, 65, 91, 100, 137, 144, 152, 158, 159, 160, 162, 167, 174, 179, 184, 188, 193, 220, 231, 232, 236, 238, 241, 248, 249, 252, 274, 283], "lstmcell": 6, "cell": [6, 25, 27, 29, 56, 66, 83, 89, 99, 100, 102, 103, 106, 107, 122, 175, 179, 187, 200, 252, 265, 284], "plain": [6, 7, 27, 55, 116, 142, 143, 180, 194, 262, 264], "input_featur": 6, "state_s": 6, "candid": [6, 159, 265], "reset_paramet": 6, "stdv": 6, "math": [6, 12, 71, 94, 99, 112, 123, 124, 165, 173, 252, 265, 287], "sqrt": [6, 94, 116, 205, 219], "uniform_": [6, 7, 178, 219], "old_h": 6, "old_cel": 6, "cat": [6, 24, 25, 45, 49, 54, 100, 102, 109, 112, 123, 149, 154, 159, 178, 180, 185, 194, 216, 247], "gate_weight": 6, "f": [6, 9, 10, 13, 14, 18, 22, 23, 26, 36, 37, 47, 48, 52, 59, 61, 71, 99, 103, 110, 115, 116, 123, 124, 125, 127, 130, 137, 138, 139, 140, 148, 149, 150, 152, 153, 159, 160, 170, 172, 173, 176, 177, 178, 185, 187, 189, 190, 198, 201, 202, 203, 204, 213, 214, 217, 221, 222, 227, 229, 230, 231, 232, 233, 236, 238, 244, 249, 251, 264, 276, 277, 279, 283], "split": [6, 9, 10, 22, 23, 24, 25, 50, 55, 58, 87, 94, 97, 102, 105, 107, 115, 127, 136, 142, 143, 148, 149, 152, 164, 174, 177, 178, 180, 194, 198, 201, 213, 214, 229, 244, 264], "input_g": 6, "sigmoid": [6, 7, 58, 103, 123, 195, 216, 265], "output_g": 6, "tanh": [6, 7, 58, 103, 110, 123, 174, 265], "candidate_cel": 6, "new_cel": 6, "hidden": [6, 9, 25, 55, 66, 100, 103, 108, 109, 113, 116, 139, 141, 142, 143, 151, 157, 178, 179, 180, 213, 247, 252, 281], "new_h": 6, "batch_siz": [6, 7, 9, 16, 23, 24, 38, 55, 57, 58, 61, 97, 123, 127, 130, 137, 138, 139, 149, 150, 152, 172, 175, 176, 177, 184, 185, 187, 191, 192, 193, 198, 201, 213, 214, 217, 237, 238, 248, 272], "rnn": [6, 25, 29, 50, 55, 66, 103, 123, 136, 149, 151, 168, 177, 180, 181, 215, 265], "new_c": 6, "intel": [6, 136, 150, 159, 162, 215, 223, 269], "mkl": [6, 159, 255, 278], "nnpack": 6, "why": [6, 7, 10, 13, 16, 18, 29, 49, 58, 94, 100, 108, 110, 115, 125, 128, 145, 148, 159, 167, 180, 188, 189, 190, 206, 208, 249, 283], "room": [6, 108, 164, 236, 283], "obviou": [6, 127, 249], "knowledg": [6, 55, 58, 81, 110, 111, 126, 128, 136, 159, 161, 283], "execut": [6, 7, 9, 10, 13, 17, 19, 21, 24, 25, 26, 27, 29, 36, 37, 44, 47, 48, 50, 53, 56, 66, 76, 80, 84, 86, 88, 91, 95, 99, 102, 104, 106, 109, 114, 120, 121, 126, 135, 136, 140, 141, 149, 150, 151, 158, 159, 162, 164, 170, 174, 175, 177, 178, 179, 181, 188, 189, 190, 192, 193, 198, 199, 201, 202, 203, 210, 218, 220, 221, 223, 224, 225, 229, 236, 241, 242, 243, 244, 248, 252, 261, 264, 270, 271, 276, 278], "involv": [6, 10, 11, 17, 20, 23, 27, 29, 40, 56, 66, 94, 99, 100, 106, 109, 112, 117, 125, 135, 140, 142, 147, 154, 161, 167, 178, 180, 189, 190, 198, 200, 209, 217, 256, 259, 265, 274], "launch": [6, 7, 25, 59, 67, 129, 141, 147, 148, 164, 176, 177, 178, 179, 184, 192, 223, 236, 238, 249, 255, 265, 278], "amount": [6, 23, 29, 81, 91, 125, 139, 148, 171, 188, 200, 241, 255, 259, 265], "becom": [6, 7, 13, 25, 28, 58, 81, 83, 86, 94, 100, 108, 139, 145, 160, 162, 184, 185, 192, 202, 209, 213, 227, 233, 236, 248, 270, 278], "furthermor": [6, 23, 108, 153, 160, 185, 192, 202, 216, 242, 243, 249], "interpret": [6, 7, 27, 29, 62, 91, 94, 100, 115, 136, 142, 143, 152, 180, 188, 189, 190, 202, 212, 233, 249, 253, 261, 264, 269, 278, 283], "slow": [6, 7, 10, 41, 47, 138, 192, 246, 255, 265], "down": [6, 10, 12, 13, 23, 37, 38, 47, 56, 91, 97, 99, 103, 105, 110, 116, 138, 151, 159, 160, 161, 177, 182, 185, 192, 205, 264, 265, 281], "therefor": [6, 7, 11, 17, 23, 55, 57, 66, 108, 120, 125, 127, 129, 135, 148, 149, 162, 165, 170, 171, 177, 178, 189, 190, 192, 207, 208, 216, 223, 241, 248, 257, 278, 283], "rewrit": [6, 25, 50, 66, 119, 168, 189, 190, 216, 222, 223, 270], "group": [6, 13, 22, 23, 28, 55, 67, 92, 103, 106, 122, 127, 135, 136, 137, 138, 143, 144, 146, 148, 149, 150, 159, 184, 191, 194, 231, 232, 233, 249, 278, 279, 283, 284], "profit": 6, "fewer": [6, 13, 144, 160], "visibl": [6, 26, 27, 49, 97, 105, 187, 198], "global": [6, 7, 29, 55, 62, 66, 97, 139, 153, 167, 177, 182, 189, 190, 198, 217, 227, 229, 233, 250, 262, 264, 265, 270], "aten": [6, 10, 12, 17, 27, 47, 122, 159, 184, 189, 190, 193, 198, 201, 202, 204, 213, 214, 215, 236, 237, 244, 255, 264], "translat": [6, 27, 29, 55, 66, 117, 119, 126, 130, 136, 142, 165, 181, 203, 207, 230, 265, 270], "benefit": [6, 7, 11, 20, 22, 47, 48, 94, 97, 134, 137, 156, 162, 167, 172, 179, 192, 200, 213, 217, 233, 236, 237, 248, 252, 265, 277, 278], "massiv": [6, 29, 49, 112, 115, 237], "parallel": [6, 7, 13, 19, 21, 22, 49, 51, 53, 55, 57, 61, 81, 87, 97, 102, 106, 125, 136, 141, 146, 150, 152, 165, 174, 176, 177, 178, 181, 191, 192, 231, 232, 233, 248, 255, 257, 278, 279], "lltm_cpp": 6, "conveni": [6, 10, 16, 26, 27, 49, 52, 55, 100, 112, 116, 140, 142, 143, 153, 160, 170, 174, 206, 208, 230, 237, 248, 249, 264, 270], "wrapper": [6, 7, 10, 61, 116, 125, 137, 138, 145, 151, 161, 174, 187, 212, 215, 257], "include_dir": [6, 12, 170], "include_path": 6, "manag": [6, 26, 37, 48, 55, 67, 99, 116, 120, 122, 127, 135, 139, 146, 147, 148, 164, 168, 179, 184, 193, 231, 232, 233, 248, 249, 250, 254, 255, 256, 265, 277, 278], "And": [6, 7, 12, 18, 26, 27, 28, 29, 58, 100, 112, 115, 116, 117, 127, 145, 159, 160, 162, 165, 173, 184, 185, 188, 189, 190, 192, 193, 211, 215, 216, 230, 249, 264], "overal": [6, 23, 47, 55, 103, 137, 138, 150, 164, 175, 187, 213, 216, 236, 246, 249, 265], "d_sigmoid": 6, "bit": [6, 17, 27, 29, 74, 102, 106, 122, 124, 127, 151, 173, 174, 180, 200, 205, 213, 215, 224, 238, 246, 249, 252], "pybind11": [6, 10, 26, 27, 170, 249], "datatyp": [6, 27, 45, 54, 79, 102, 106, 122, 237, 248, 252, 265], "Its": [6, 108, 110, 209, 283], "inspect": [6, 27, 86, 99, 108, 120, 137, 141, 158, 179, 182, 188, 189, 190, 198, 201, 233, 249], "notic": [6, 10, 16, 25, 26, 27, 29, 36, 47, 48, 49, 58, 66, 81, 94, 99, 102, 106, 108, 110, 125, 145, 150, 159, 161, 164, 168, 169, 172, 174, 184, 188, 189, 190, 192, 193, 204, 205, 207, 211, 259, 268, 276], "dispos": 6, "nvcc": 6, "workaround": [6, 27, 87, 94, 145, 156], "logic": [6, 7, 13, 20, 27, 41, 46, 94, 109, 138, 141, 147, 149, 171, 177, 178, 187, 193, 199, 217, 219, 231, 233, 278], "sigmoidalphablendforwardcuda": 6, "port": [6, 19, 150, 177, 230, 273], "entir": [6, 7, 16, 19, 22, 23, 29, 52, 55, 59, 66, 86, 108, 110, 113, 116, 131, 136, 137, 138, 142, 144, 149, 157, 164, 167, 169, 171, 172, 174, 178, 180, 192, 198, 205, 206, 207, 210, 213, 214, 225, 231, 248, 254, 256, 265, 269, 283], "lltm_forward": 6, "addmm": [6, 7, 122, 159, 189, 204, 213, 223, 224, 236, 255], "transpos": [6, 7, 14, 45, 54, 55, 58, 123, 159, 172, 185, 189, 209, 223], "alpha": [6, 7, 17, 161, 187, 205, 284], "respect": [6, 8, 18, 19, 36, 38, 40, 41, 42, 48, 55, 58, 69, 70, 99, 100, 110, 112, 124, 128, 139, 140, 149, 151, 156, 159, 160, 169, 174, 178, 180, 184, 189, 190, 192, 193, 214, 229, 262, 265, 267, 278], "ultim": [6, 23, 55, 58, 66, 94, 205, 224], "plop": [6, 27], "autograd": [6, 8, 14, 17, 18, 19, 25, 29, 37, 38, 40, 41, 42, 43, 45, 46, 47, 51, 52, 53, 63, 65, 68, 74, 85, 86, 88, 90, 98, 100, 101, 102, 103, 105, 106, 107, 111, 112, 116, 123, 136, 142, 143, 145, 148, 149, 159, 160, 165, 169, 175, 176, 177, 180, 181, 193, 216, 222, 225, 229, 230, 233, 244, 248, 265, 274, 276], "nice": [6, 55, 89, 116, 167, 169], "dig": [6, 100, 110, 115, 179], "deeper": [6, 13, 14, 42, 100, 106, 108, 110, 158, 159, 179, 193, 228, 276], "interest": [6, 7, 12, 16, 20, 24, 27, 29, 49, 55, 57, 64, 65, 97, 99, 100, 108, 110, 117, 119, 120, 127, 131, 137, 141, 160, 167, 168, 172, 174, 182, 189, 190, 192, 194, 217, 222, 249, 252, 264, 267, 268, 283], "alex": 6, "grave": 6, "thesi": 6, "d_tanh": 6, "relu": [6, 7, 14, 23, 27, 52, 58, 103, 110, 115, 116, 123, 125, 138, 148, 149, 153, 159, 165, 172, 173, 176, 177, 178, 185, 189, 190, 193, 197, 198, 216, 231, 232, 235, 236, 237, 238, 241, 248, 251, 265, 270], "exp": [6, 71, 109, 110, 124, 145, 207], "d_elu": 6, "mask": [6, 20, 64, 83, 100, 103, 122, 133, 136, 151, 168, 171, 179, 187, 194, 200, 205, 206, 208, 209, 212, 217, 237], "type_a": 6, "lltm_backward": 6, "grad_h": 6, "grad_cel": 6, "d_output_g": 6, "d_tanh_new_cel": 6, "d_new_cel": 6, "d_old_cel": 6, "d_candidate_cel": 6, "d_input_g": 6, "d_gate": 6, "d_weight": 6, "d_bia": 6, "keepdim": [6, 23, 138, 177, 198, 213, 214, 238], "d_x": 6, "d_old_h": 6, "d_input": 6, "span": [6, 20, 83, 109, 148, 164, 184, 217, 244, 284], "four": [6, 16, 22, 26, 94, 102, 103, 106, 120, 124, 129, 134, 137, 149, 150, 164, 185, 241, 246, 277, 283, 284], "pybind11_modul": [6, 170], "torch_extension_nam": [6, 170], "macro": [6, 7, 10, 17, 27], "maintain": [6, 12, 16, 26, 36, 37, 38, 48, 55, 81, 103, 108, 113, 120, 136, 146, 150, 161, 175, 178, 192, 193, 210, 248, 279], "mismatch": [6, 108], "nasti": [6, 262], "hard": [6, 7, 10, 17, 25, 38, 110, 141, 171, 249, 273], "At": [6, 7, 10, 16, 17, 20, 24, 27, 48, 52, 55, 56, 94, 97, 113, 116, 124, 130, 137, 138, 139, 151, 156, 161, 172, 174, 175, 176, 179, 180, 184, 205, 208, 213, 217, 223, 241, 255, 262, 265, 282], "point": [6, 7, 10, 12, 13, 16, 20, 22, 23, 24, 26, 27, 48, 52, 55, 56, 57, 58, 59, 64, 66, 91, 94, 99, 102, 103, 106, 108, 109, 111, 112, 113, 115, 116, 123, 124, 136, 138, 139, 140, 141, 145, 146, 148, 158, 161, 164, 165, 172, 174, 176, 180, 185, 187, 189, 190, 197, 198, 200, 201, 205, 207, 208, 210, 213, 216, 217, 225, 238, 241, 246, 252, 253, 261, 263, 265, 269, 281, 282, 283], "bdist_egg": 6, "egg_info": [6, 27], "egg": [6, 27], "pkg": [6, 27, 277], "dependency_link": [6, 27], "top_level": [6, 27], "manifest": [6, 27, 207, 210], "bdist": 6, "x86_64": [6, 22, 27, 221, 225], "install_lib": 6, "temp": [6, 23, 27, 152, 198, 201, 213, 214, 273], "gcc": [6, 27, 120, 159], "miniconda": [6, 22, 27], "compiler_compat": [6, 27], "wl": [6, 26, 27], "sysroot": [6, 27], "wsign": [6, 27], "dndebug": [6, 27], "fwrapv": [6, 27], "o3": [6, 27, 249], "wall": [6, 27, 109, 158, 249], "wstrict": [6, 27], "prototyp": [6, 12, 13, 17, 27, 38, 189, 190, 202, 209, 210, 216, 218, 222, 223, 229], "fpic": [6, 27, 120], "site": [6, 8, 22, 26, 27, 56, 58, 64, 65, 158, 180, 203, 230, 242, 243, 245, 255, 264, 277, 283], "csrc": [6, 26, 27, 170, 204, 240], "th": [6, 16, 27, 57, 110, 115, 150, 161], "thc": [6, 27], "7m": [6, 27, 48], "dtorch_api_include_extension_h": [6, 27], "dtorch_extension_nam": [6, 27], "d_glibcxx_use_cxx11_abi": [6, 27], "cc1plu": [6, 27], "warn": [6, 23, 27, 47, 57, 152, 159, 187, 189, 198, 201, 203, 205, 206, 207, 208, 213, 214, 233, 249, 255, 264, 270], "valid": [6, 9, 24, 27, 28, 41, 55, 97, 103, 105, 107, 108, 119, 122, 125, 127, 129, 131, 137, 138, 141, 144, 145, 170, 172, 187, 194, 198, 206, 207, 216, 217, 221, 240, 249], "objc": [6, 27], "l": [6, 9, 14, 16, 27, 48, 57, 58, 99, 110, 120, 123, 174, 175, 189, 210, 249], "rpath": [6, 27], "cpython": [6, 27, 264], "37m": [6, 27], "stub": [6, 172, 226, 241, 284], "loader": [6, 7, 28, 42, 49, 87, 107, 116, 174, 177, 194, 240, 249], "byte": [6, 152, 154, 190, 197, 225], "37": [6, 9, 20, 48, 122, 173, 178, 193, 217, 236, 255], "pyc": 6, "native_lib": 6, "zip_saf": 6, "analyz": [6, 23, 27, 66, 91, 136, 167, 174, 188, 190, 201, 264], "__pycache__": 6, "__file__": [6, 170, 249], "dist": [6, 13, 22, 123, 137, 138, 148, 150, 170, 177, 191, 231, 232, 279], "py3": 6, "remov": [6, 20, 23, 55, 58, 59, 65, 66, 92, 116, 122, 123, 128, 130, 140, 148, 152, 157, 159, 167, 173, 179, 180, 187, 188, 189, 190, 198, 199, 200, 201, 205, 206, 209, 210, 213, 214, 217, 221, 233, 246, 249, 264, 270, 281], "everyth": [6, 18, 48, 97, 106, 108, 110, 120, 141, 142, 145, 151, 154, 172, 173, 174, 175, 176, 180, 185, 203, 204, 229, 230, 241, 264], "finish": [6, 7, 19, 27, 50, 64, 102, 148, 149, 150, 164, 176, 178, 185, 204, 215, 221, 229, 235, 241, 244, 251, 265], "ubuntu": [6, 7, 184, 225, 278], "16": [6, 9, 20, 23, 27, 48, 97, 102, 103, 108, 125, 148, 151, 162, 172, 173, 178, 185, 187, 189, 193, 194, 200, 203, 210, 217, 221, 225, 231, 236, 249, 287], "04": [6, 9, 133, 184, 236, 249], "recent": [6, 55, 83, 113, 122, 127, 129, 139, 150, 152, 159, 165, 168, 172, 175, 184, 225, 278], "maco": [6, 7, 117, 150, 187], "clang": [6, 221, 223], "worst": [6, 152], "resolv": [6, 27, 108, 157, 162, 193, 207], "symbol": [6, 29, 129, 133, 157, 189, 190, 198, 216, 249, 264, 283], "linker": [6, 27, 221], "pycapsul": [6, 27], "builtin": [6, 170, 249, 264], "arg0": 6, "arg1": [6, 177], "arg2": [6, 177], "arg3": 6, "arg4": 6, "citizen": [6, 27, 205, 207], "lltmfunction": 6, "staticmethod": [6, 145, 176, 187], "benchmark": [6, 17, 20, 28, 38, 47, 122, 136, 141, 152, 153, 159, 160, 173, 179, 188, 192, 193, 200, 203, 209, 215, 217, 237, 238, 255, 261, 264, 265, 269, 278], "measur": [6, 14, 25, 41, 91, 99, 100, 102, 103, 108, 115, 138, 152, 153, 158, 160, 164, 174, 175, 179, 182, 188, 192, 193, 200, 211, 215, 220, 229, 236, 241, 248, 249, 255, 264, 269], "durat": [6, 92, 170, 175, 184, 193, 283], "32": [6, 22, 23, 25, 61, 97, 102, 103, 106, 108, 113, 122, 138, 152, 165, 169, 173, 177, 178, 184, 187, 189, 192, 193, 214, 236, 238, 246, 249, 251], "128": [6, 7, 26, 57, 58, 61, 97, 108, 115, 138, 139, 149, 150, 152, 153, 164, 173, 176, 177, 178, 201, 216, 224, 229, 237, 249, 251, 264, 272], "rang": [6, 7, 13, 14, 16, 22, 23, 25, 46, 47, 49, 57, 58, 59, 66, 71, 81, 91, 97, 100, 108, 115, 116, 117, 119, 120, 124, 130, 137, 138, 141, 149, 150, 152, 153, 172, 173, 176, 178, 180, 184, 185, 189, 190, 192, 193, 194, 195, 200, 201, 202, 210, 214, 217, 226, 227, 229, 232, 236, 238, 244, 247, 248, 252, 255, 265, 274, 278, 279], "100000": [6, 64, 65, 152, 249], "3f": [6, 23, 214, 217], "wrote": [6, 27, 154, 188, 194, 283], "post": [6, 7, 13, 24, 55, 64, 65, 108, 136, 137, 138, 141, 152, 154, 162, 164, 182, 192, 193, 199, 201, 212, 214, 216, 218, 238, 247], "my": [6, 25, 47, 56, 109, 115, 214, 220, 283], "machin": [6, 7, 22, 24, 25, 29, 40, 42, 46, 49, 55, 56, 57, 59, 60, 61, 62, 64, 65, 66, 67, 81, 97, 98, 99, 100, 117, 119, 130, 133, 136, 137, 138, 141, 146, 147, 148, 149, 150, 158, 169, 173, 177, 178, 179, 180, 181, 192, 194, 201, 210, 214, 220, 227, 236, 244, 263, 265, 273, 277, 278], "506": [6, 115], "480": [6, 255], "444": 6, "694": 6, "349": [6, 102], "335": [6, 162, 178, 279], "443": [6, 178, 255], "523": 6, "speedup": [6, 20, 25, 47, 49, 124, 136, 153, 159, 164, 169, 193, 197, 198, 200, 209, 217, 236, 241, 265], "30": [6, 7, 9, 23, 50, 91, 110, 115, 129, 136, 137, 162, 171, 176, 178, 198, 213, 217, 249, 255], "albeit": [6, 16, 246], "major": [6, 12, 13, 23, 103, 106, 115, 131, 159, 167, 179, 188, 192, 193, 208, 233, 236, 279], "particularli": [6, 15, 20, 59, 116, 168, 180, 217, 265], "engin": [6, 7, 16, 24, 36, 48, 97, 99, 119, 138, 173, 178, 187, 190, 194, 203, 222, 224, 237, 246, 281], "wonder": [6, 110, 167], "correspondingli": 6, "big": [6, 47, 58, 103, 109, 115, 124, 143, 153, 167, 174, 180, 187, 210], "win": [6, 129, 167, 201], "No": [6, 7, 59, 66, 159, 195, 221, 228], "cuda_devic": 6, "creation": [6, 7, 12, 106, 208, 219, 225, 254], "assert": [6, 13, 22, 23, 26, 120, 148, 153, 159, 165, 177, 179, 185, 210, 216, 217, 225, 226, 227], "is_avail": [6, 7, 14, 37, 47, 97, 106, 123, 170, 172, 177], "synchron": [6, 13, 19, 61, 62, 91, 148, 150, 164, 174, 176, 177, 184, 188, 192, 193, 229, 244, 248, 249, 279], "1e6": [6, 23, 152, 198, 201, 213, 214, 227, 246, 279], "1e5": 6, "again": [6, 7, 11, 25, 29, 49, 56, 66, 86, 99, 100, 103, 105, 106, 108, 109, 115, 116, 120, 127, 130, 134, 140, 144, 150, 151, 167, 176, 178, 180, 184, 187, 188, 192, 200, 213, 216, 241, 249, 259, 283], "187": [6, 249], "719": 6, "410": [6, 162], "815": 6, "149": 6, "802": [6, 159], "393": [6, 193], "458": [6, 159], "That": [6, 7, 20, 27, 48, 49, 50, 55, 99, 100, 102, 106, 110, 112, 113, 115, 116, 117, 120, 130, 139, 142, 149, 156, 158, 160, 162, 164, 165, 167, 174, 179, 184, 194, 205, 206, 208, 217, 241, 242, 252, 264, 269, 283], "great": [6, 55, 66, 117, 124, 125, 127, 207, 213, 249, 267, 283], "pull": [6, 9, 25, 100, 106, 107, 158, 189, 190, 230], "dive": [6, 7, 13, 27, 42, 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108, 116, 120, 137, 153, 167, 180, 193, 213, 219, 225, 233, 244, 249, 255, 265, 270, 278, 283], "didn": [6, 10, 26, 84, 100, 117, 158, 176, 197, 222, 283], "prospect": 6, "pointwis": [6, 10, 157, 162, 215, 265], "declar": [6, 7, 15, 27, 66, 81, 86, 129, 225, 241, 270], "lltm_cuda": 6, "lltm_cuda_forward": 6, "lltm_cuda_backward": 6, "check_cuda": 6, "is_cuda": 6, "check_contigu": 6, "check_input": 6, "lltm_cuda_kernel": 6, "cannot": [6, 7, 13, 16, 22, 26, 27, 55, 66, 67, 91, 106, 120, 124, 125, 127, 145, 148, 150, 151, 162, 164, 172, 174, 189, 190, 200, 211, 220, 222, 259, 274, 282], "peek": [6, 228], "cuda_runtim": 6, "templat": [6, 10, 26, 27, 65, 126, 150, 159, 225, 226, 238, 281], "typenam": [6, 225], "scalar_t": [6, 159], "__device__": 6, "__forceinline__": 6, "fmax": 6, "fmin": 6, "d_relu": 6, "explicitli": [6, 7, 22, 29, 41, 45, 48, 54, 58, 59, 66, 97, 99, 112, 139, 147, 151, 162, 178, 179, 182, 189, 192, 193, 198, 208, 209, 216, 244, 265], "zeros_lik": [6, 106, 176, 233], 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52, 58, 71, 99, 102, 107, 116, 124, 129, 150, 174, 175], "descent": [7, 9, 41, 48, 52, 71, 99, 102, 107, 116, 123, 124, 129, 150, 200, 268], "digit": [7, 52, 102, 116, 136, 137, 138, 187], "whirlwind": 7, "wet": 7, "appetit": 7, "watch": [7, 107, 127, 146, 150], "lightn": [7, 141], "talk": [7, 10, 55, 58, 61, 102, 112, 129, 150, 174, 177], "cppcon": 7, "2018": [7, 133, 152], "quick": [7, 20, 37, 54, 64, 65, 99, 108, 110, 113, 115, 134, 136, 137, 142, 148, 153, 160, 169, 200, 217, 230, 249, 252, 253, 261, 269, 276], "humor": 7, "sweep": [7, 179], "philosophi": [7, 127], "ecosystem": [7, 120], "descript": [7, 56, 137, 138, 159, 174, 176, 177, 178, 179, 187, 197, 249, 265, 275, 277, 284], "embark": 7, "excit": [7, 26, 27, 158, 167], "team": [7, 120, 129, 141, 152, 175, 187], "job": [7, 50, 58, 59, 60, 67, 91, 108, 141, 146, 147, 148, 150, 241], "reinforc": [7, 16, 107, 136, 161, 176, 177, 181], "game": [7, 49, 58, 87, 161], "tractabl": [7, 109], "multithread": [7, 48, 62, 122, 233, 244, 249, 278], "lock": [7, 16, 29, 62, 149, 150, 176, 177, 193, 233, 282], "gil": [7, 62, 148, 233], "multiprocess": [7, 13, 16, 38, 42, 57, 59, 61, 62, 123, 137, 138, 148, 149, 150, 174, 177, 178, 229, 231, 233, 278, 279], "scalabl": [7, 141, 205, 224, 236, 237, 265], "shortcom": [7, 207], "neuroevolut": 7, "owner": [7, 176, 177, 178], "anyth": [7, 15, 18, 49, 78, 79, 102, 103, 106, 109, 112, 113, 115, 116, 124, 154, 173, 197, 198, 200, 244, 262, 263, 283, 284], "serv": [7, 63, 67, 94, 102, 108, 113, 124, 136, 142, 148, 154, 158, 161, 164, 170, 177, 178, 192, 193, 207, 229, 230, 248, 249, 264, 265, 273, 277], "web": [7, 117, 230, 269, 283], "server": [7, 19, 29, 134, 135, 136, 142, 148, 164, 170, 178, 193, 195, 201, 210, 230, 231, 233, 237, 246, 269, 273, 278], "3d": [7, 67, 83, 103, 105, 112, 113, 187, 213, 216, 265], "graphic": [7, 179, 223], "photo": [7, 100], "softwar": [7, 152, 164, 170, 184, 192, 223, 230, 283], "remain": [7, 9, 97, 99, 108, 134, 150, 157, 167, 171, 180, 195, 200, 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283], "minim": [7, 12, 14, 20, 27, 41, 58, 69, 70, 73, 74, 75, 78, 79, 81, 91, 99, 110, 115, 116, 124, 136, 137, 141, 147, 152, 159, 175, 188, 198, 201, 205, 215, 217, 221, 226, 252, 265, 268], "verifi": [7, 24, 64, 65, 94, 99, 106, 120, 128, 130, 134, 145, 153, 156, 157, 160, 162, 171, 173, 192, 193, 194, 223, 236, 237, 244, 276], "zip": [7, 22, 55, 56, 58, 120, 125, 130, 152, 153, 165, 176, 178, 185, 187, 194, 197, 201, 217, 225, 233, 241], "too": [7, 12, 16, 23, 49, 70, 74, 91, 97, 102, 103, 108, 109, 115, 116, 122, 124, 139, 148, 154, 164, 167, 171, 175, 176, 178, 180, 205, 246, 259, 264, 281, 283, 284], "cu90": 7, "url": [7, 23, 133, 184, 188, 200, 225, 240, 263, 281], "wget": [7, 22, 83, 194, 197, 200, 225], "nightli": [7, 22, 83, 137, 152, 156, 188, 191, 194, 200, 203, 204, 212, 213, 215, 221, 222, 225, 238], "dep": 7, "tini": [7, 138, 164, 167], "three": [7, 12, 14, 16, 17, 38, 41, 57, 58, 70, 81, 91, 94, 99, 100, 105, 106, 109, 116, 120, 124, 125, 127, 133, 136, 139, 143, 150, 162, 168, 170, 174, 178, 179, 180, 182, 184, 185, 191, 192, 193, 200, 208, 215, 244, 246, 255, 276, 283, 284], "ey": [7, 24, 27, 115, 165, 225, 283], "fledg": 7, "visual": [7, 16, 29, 48, 58, 81, 83, 91, 92, 102, 107, 130, 136, 137, 140, 141, 145, 158, 174, 184, 187, 192, 193, 202, 207, 255, 263, 269], "studio": [7, 64, 221, 240, 242, 245], "qmake": 7, "makefil": 7, "comfort": 7, "box": [7, 10, 12, 13, 18, 20, 46, 81, 127, 136, 141, 145, 150, 173, 176, 184, 192, 193, 194, 215, 217, 222, 224, 237, 243, 244, 247, 283], "cmake_prefix_path": [7, 26, 27], "invok": [7, 10, 26, 27, 29, 108, 148, 149, 156, 158, 159, 170, 177, 178, 193, 202, 204, 207, 215, 220, 224, 244, 262, 265, 272, 278], "agre": [7, 27, 152, 279], "break": [7, 10, 12, 13, 18, 23, 37, 38, 91, 99, 103, 125, 127, 130, 154, 167, 176, 178, 188, 205, 259, 283], "unexpect": [7, 57, 207, 211, 216, 265], "pwd": [7, 225, 244], "fa350df05ecf": 7, "home": [7, 45, 54, 127, 205, 210, 221, 223, 226, 234, 240], "enter": [7, 55, 66, 137, 148, 150], "ran": [7, 115, 174, 177, 179, 189, 190, 221, 249], "me": [7, 20, 47, 55, 167, 180, 217, 283, 284], "besid": [7, 27, 102, 115, 168, 176, 238, 247, 265, 283], "encapsul": [7, 41, 52, 103, 107, 167, 176], "buffer": [7, 16, 49, 52, 65, 84, 123, 125, 137, 144, 148, 153, 156, 161, 165, 167, 168, 169, 171, 175, 189, 190, 193, 204, 219, 223, 225, 241, 254, 258, 267, 268], "nest": [7, 16, 37, 48, 102, 106, 123, 157, 174, 176, 178, 179, 212, 218, 283, 284], "similarli": [7, 11, 14, 26, 57, 66, 89, 92, 100, 106, 127, 130, 140, 141, 149, 159, 160, 167, 195, 208, 231], "w": [7, 9, 36, 41, 48, 52, 81, 84, 113, 115, 137, 152, 162, 178, 184, 194, 200, 201, 203, 240], "struct": [7, 17, 26, 225, 270], "register_paramet": [7, 219], "reflect": [7, 10, 27, 45, 54, 102, 106, 174, 211], "magic": [7, 110, 143, 150, 160], "behind": [7, 10, 20, 27, 50, 91, 108, 110, 116, 124, 144, 175, 195, 206, 208, 212, 217, 219, 283], "scene": [7, 27, 64, 65, 116, 124, 127, 219], "another_bia": 7, "recurs": [7, 19, 49, 66, 123, 219, 235, 263], "0808": 7, "8613": 7, "2017": [7, 81, 133], "5206": 7, "5353": 7, "3740": 7, "0976": 7, "4786": 7, "4928": 7, "1434": 7, "4713": 7, "1735": 7, "3293": 7, "3467": 7, "3858": 7, "1980": 7, "1986": 7, "1975": [7, 37], "4278": 7, "1831": 7, "2709": 7, "3730": 7, "4307": 7, "3236": 7, "0629": [7, 103], "2038": 7, "4638": 7, "2023": [7, 187, 189], "1230": 7, "0516": 7, "aptli": [7, 15], "register_modul": 7, "dropout": [7, 23, 24, 43, 55, 66, 81, 103, 116, 123, 125, 138, 139, 141, 143, 151, 172, 176, 178, 180, 209, 210, 214, 217, 238, 258, 259, 260, 270, 276], "conv2d": [7, 14, 23, 49, 52, 58, 86, 102, 103, 116, 125, 138, 144, 149, 165, 173, 177, 185, 193, 195, 198, 213, 214, 215, 216, 224, 235, 237, 238, 241, 244, 246, 251, 255, 265, 270], "subtleti": [7, 208], "initi": [7, 10, 13, 16, 19, 22, 23, 24, 25, 26, 27, 29, 37, 38, 41, 48, 55, 61, 66, 70, 78, 79, 81, 89, 100, 102, 106, 107, 108, 110, 113, 115, 116, 122, 124, 125, 131, 137, 138, 139, 142, 143, 151, 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