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Clean up Android prebuilt artifact path and verification (#6181)
Summary:
In main branch, use dated artifacts like https://ossci-android.s3.amazonaws.com/executorch/release/executorch-241002/executorch.aar In release branch, use something like release/0.4
Automate shasum check
Pull Request resolved: #6181
Reviewed By: mergennachin
Differential Revision: D64261746
Pulled By: kirklandsign
fbshipit-source-id: 0c3e895eb44ed50cb009d8daff8b51b750a1dda5
- JNI contains the JNI binding for [NativePeer.java](https://github.com/pytorch/executorch/blob/main/extension/android/src/main/java/org/pytorch/executorch/NativePeer.java) and ExecuTorch native library, including core ExecuTorch runtime libraries, XNNPACK backend, Portable kernels, Optimized kernels, and Quantized kernels.
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- Comes with two ABI variants, arm64-v8a and x86_64.
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-`executorch_llama.aar`
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-[Java library](https://github.com/pytorch/executorch/tree/release/0.3/extension/android/src/main/java/org/pytorch/executorch) (Note: it contains the same Java classes as the previous Java, but it does not contain the JNI binding for generic Module/NativePeer Java code).
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- JNI contains the JNI binding for [LlamaModule.java](https://github.com/pytorch/executorch/blob/release/0.3/extension/android/src/main/java/org/pytorch/executorch/LlamaModule.java) and ExecuTorch native library, including core ExecuTorch runtime libraries, XNNPACK backend, Portable kernels, Optimized kernels, Quantized kernels, and LLAMA-specific Custom ops library.
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-[Java library](https://github.com/pytorch/executorch/tree/main/extension/android/src/main/java/org/pytorch/executorch) (Note: it contains the same Java classes as the previous Java, but it does not contain the JNI binding for generic Module/NativePeer Java code).
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- JNI contains the JNI binding for [LlamaModule.java](https://github.com/pytorch/executorch/blob/main/extension/android/src/main/java/org/pytorch/executorch/LlamaModule.java) and ExecuTorch native library, including core ExecuTorch runtime libraries, XNNPACK backend, Portable kernels, Optimized kernels, Quantized kernels, and LLAMA-specific Custom ops library.
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- Comes with two ABI variants, arm64-v8a and x86_64.
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