Arm backend: Fix stale docgen generation (pytorch#19551)

Update the Ethos-U docgen template to point to the correct tutorial
path. This keeps pre-push from regenerating docs with the stale tutorial
URL.


cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils
@Sebastian-Larsson @robell @rascani

Signed-off-by: Christoffer J.L <christoffer.johanssonlundqvist@arm.com>

Author: Christoffer-JL

backends/arm/scripts/docgen/ethos-u/backends-arm-ethos-u-overview.md.in

@@ -4,7 +4,7 @@ The Arm® Ethos™-U backend targets Edge/IoT-type AI use-cases by enabli
 [Arm® Ethos™-U55 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55), [Arm® Ethos™-U65 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u65), and
 [Arm® Ethos™-U85 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85), leveraging [TOSA](https://www.mlplatform.org/tosa/) and the
 [ethos-u-vela](https://pypi.org/project/ethos-u-vela/) graph compiler. This document is a technical reference for using the Ethos-U backend, for a top level view with code examples
-please refer to the [Arm Ethos-U Backend Tutorial](https://docs.pytorch.org/executorch/stable/tutorial-arm-ethos-u.html).
+please refer to the [Arm Ethos-U Backend Tutorial](tutorials/ethos-u-getting-started.md). <!-- @lint-ignore -->
 
 ## Features
 
@@ -55,7 +55,7 @@ For more information on quantization, see [Quantization](arm-ethos-u-quantizatio
 
 ## Runtime Integration
 
-An example runtime application is available in [examples/arm/executor_runner](https://github.com/pytorch/executorch/blob/main/examples/arm/executor_runner/), and the steps requried for building and deploying it on a FVP it is explained in the previously mentioned [Arm Ethos-U Backend Tutorial](https://docs.pytorch.org/executorch/stable/tutorial-arm-ethos-u.html).
+An example runtime application is available in [examples/arm/executor_runner](https://github.com/pytorch/executorch/blob/main/examples/arm/executor_runner/), and the steps requried for building and deploying it on a FVP it is explained in the previously mentioned [Arm Ethos-U Backend Tutorial](tutorials/ethos-u-getting-started.md). <!-- @lint-ignore -->
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 For an in-depth explanation of the architecture of the executor_runner and the steps required for doing such an integration, please refer to [Ethos-U porting guide](https://github.com/pytorch/executorch/blob/main/examples/arm/ethos-u-porting-guide.md).
 

backends/arm/scripts/docgen/ethos-u/ethos-u-getting-started-tutorial.md.in

@@ -123,10 +123,14 @@ The example application is by default built with an input of ones, so the expect
 ## Takeaways
 
 In this tutorial you have learned how to use ExecuTorch to export a PyTorch model to an executable that can run on an embedded target, and then run that executable on simulated hardware.
-To learn more, check out these learning paths:
+To learn more, check out the [ExecuTorch on Arm Practical Labs](https://github.com/arm-education/executorch_on_arm_labs) series. This series provides a structured entry-point to developing with ExecuTorch on Arm, across both CPU and Ethos-U NPU.
 
-https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi-llama3/
-https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/
+For quick learning paths showcasing short tutorials:
+
+- [Run Llama3 on Raspberry Pi 5 with ExecuTorch](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi-llama3/)
+- [Visualize Ethos-U NPU Performance on FVP](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/)
+- [Image Classification with ExecuTorch on NXP i.MX 93 (Ethos-U65)](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/)
+- [Image Classification with ExecuTorch on Alif E8 DevKit (Ethos-U85)](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/alif-image-classification/)
 
 ## FAQs
 

docs/source/backends/arm-ethos-u/arm-ethos-u-overview.md

@@ -4,7 +4,7 @@ The Arm® Ethos™-U backend targets Edge/IoT-type AI use-cases by enabli
 [Arm® Ethos™-U55 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55), [Arm® Ethos™-U65 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u65), and
 [Arm® Ethos™-U85 NPU](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85), leveraging [TOSA](https://www.mlplatform.org/tosa/) and the
 [ethos-u-vela](https://pypi.org/project/ethos-u-vela/) graph compiler. This document is a technical reference for using the Ethos-U backend, for a top level view with code examples
-please refer to the [Arm Ethos-U Backend Tutorial](tutorials/ethos-u-getting-started.md).
+please refer to the [Arm Ethos-U Backend Tutorial](tutorials/ethos-u-getting-started.md). <!-- @lint-ignore -->
 
 ## Features
 
@@ -111,7 +111,7 @@ For more information on quantization, see [Quantization](arm-ethos-u-quantizatio
 
 ## Runtime Integration
 
-An example runtime application is available in [examples/arm/executor_runner](https://github.com/pytorch/executorch/blob/main/examples/arm/executor_runner/), and the steps requried for building and deploying it on a FVP it is explained in the previously mentioned [Arm Ethos-U Backend Tutorial](tutorials/ethos-u-getting-started.md).
+An example runtime application is available in [examples/arm/executor_runner](https://github.com/pytorch/executorch/blob/main/examples/arm/executor_runner/), and the steps requried for building and deploying it on a FVP it is explained in the previously mentioned [Arm Ethos-U Backend Tutorial](tutorials/ethos-u-getting-started.md). <!-- @lint-ignore -->
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 For an in-depth explanation of the architecture of the executor_runner and the steps required for doing such an integration, please refer to [Ethos-U porting guide](https://github.com/pytorch/executorch/blob/main/examples/arm/ethos-u-porting-guide.md).
 

docs/source/backends/arm-ethos-u/tutorials/ethos-u-getting-started.md

@@ -196,7 +196,6 @@ The example application is by default built with an input of ones, so the expect
 ## Takeaways
 
 In this tutorial you have learned how to use ExecuTorch to export a PyTorch model to an executable that can run on an embedded target, and then run that executable on simulated hardware.
-
 To learn more, check out the [ExecuTorch on Arm Practical Labs](https://github.com/arm-education/executorch_on_arm_labs) series. This series provides a structured entry-point to developing with ExecuTorch on Arm, across both CPU and Ethos-U NPU.
 
 For quick learning paths showcasing short tutorials: