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Add freshness metablock to JAX OSS docs.
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docs/Custom_Operation_for_GPUs.md

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docs/_tutorials/advanced-autodiff.md

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docs/_tutorials/advanced-compilation.md

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docs/_tutorials/advanced-debugging.md

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docs/_tutorials/external-callbacks.md

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docs/_tutorials/gradient-checkpointing.md

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docs/_tutorials/jax-primitives.md

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docs/_tutorials/jaxpr.md

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docs/_tutorials/parallelism.md

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docs/_tutorials/profiling-and-performance.md

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docs/aot.md

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docs/api_compatibility.md

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docs/autodidax.ipynb

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"source": [
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"# Autodidax: JAX core from scratch\n",
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"\n",
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"<!--* freshness: { reviewed: '2024-04-08' } *-->\n",
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"\n",
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"Ever want to learn how JAX works, but the implementation seemed impenetrable?\n",
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"Well, you're in luck! By reading this tutorial, you'll learn every big idea in\n",
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"JAX's core system. You'll even get clued into our weird jargon!\n",

docs/autodidax.md

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docs/autodidax.py

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# # Autodidax: JAX core from scratch
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#
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# <!--* freshness: { reviewed: '2024-04-08' } *-->
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#
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# Ever want to learn how JAX works, but the implementation seemed impenetrable?
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# Well, you're in luck! By reading this tutorial, you'll learn every big idea in
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# JAX's core system. You'll even get clued into our weird jargon!

docs/automatic-differentiation.md

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docs/automatic-vectorization.md

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docs/building_on_jax.md

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docs/contributing.md

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docs/debugging.md

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docs/debugging/checkify_guide.md

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docs/debugging/flags.md

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docs/debugging/index.md

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docs/debugging/print_breakpoint.md

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docs/deprecation.md

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docs/developer.md

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docs/device_memory_profiling.md

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docs/distributed_data_loading.md

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docs/gpu_performance_tips.md

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docs/installation.md

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docs/investigating_a_regression.md

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docs/jax_array_migration.md

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docs/jit-compilation.md

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docs/key-concepts.md

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docs/multi_process.md

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docs/notebooks/Common_Gotchas_in_JAX.ipynb

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"source": [
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"# 🔪 JAX - The Sharp Bits 🔪\n",
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"\n",
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"<!--* freshness: { reviewed: '2024-06-03' } *-->\n",
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"\n",
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"[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/notebooks/Common_Gotchas_in_JAX.ipynb) [![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/google/jax/blob/main/docs/notebooks/Common_Gotchas_in_JAX.ipynb)"
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docs/notebooks/Common_Gotchas_in_JAX.md

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docs/notebooks/Custom_derivative_rules_for_Python_code.ipynb

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"source": [
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"# Custom derivative rules for JAX-transformable Python functions\n",
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"\n",
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"<!--* freshness: { reviewed: '2024-04-08' } *-->\n",
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"\n",
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"[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/notebooks/Custom_derivative_rules_for_Python_code.ipynb) [![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/google/jax/blob/main/docs/notebooks/Custom_derivative_rules_for_Python_code.ipynb)\n",
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"\n",
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"*mattjj@ Mar 19 2020, last updated Oct 14 2020*\n",

docs/notebooks/Custom_derivative_rules_for_Python_code.md

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docs/notebooks/Distributed_arrays_and_automatic_parallelization.ipynb

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"id": "PxHrg4Cjuapm"
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},
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"# Distributed arrays and automatic parallelization"
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"# Distributed arrays and automatic parallelization\n",
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"\n",
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"<!--* freshness: { reviewed: '2024-04-16' } *-->"
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]
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},
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{

docs/notebooks/Distributed_arrays_and_automatic_parallelization.md

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docs/notebooks/How_JAX_primitives_work.ipynb

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"source": [
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"# How JAX primitives work\n",
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"\n",
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"<!--* freshness: { reviewed: '2024-04-08' } *-->\n",
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"\n",
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"[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/notebooks/How_JAX_primitives_work.ipynb) [![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/google/jax/blob/main/docs/notebooks/How_JAX_primitives_work.ipynb)\n",
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"\n",
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"*[email protected]*, October 2019.\n",

docs/notebooks/How_JAX_primitives_work.md

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docs/notebooks/Neural_Network_and_Data_Loading.ipynb

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"source": [
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"# Training a Simple Neural Network, with PyTorch Data Loading\n",
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"\n",
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"<!--* freshness: { reviewed: '2024-05-03' } *-->\n",
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"\n",
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"[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/notebooks/Neural_Network_and_Data_Loading.ipynb) [![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/google/jax/blob/main/docs/notebooks/Neural_Network_and_Data_Loading.ipynb)\n",
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"\n",
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"**Copyright 2018 The JAX Authors.**\n",

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