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6 changes: 4 additions & 2 deletions docs/source/getting-started-faqs.md
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This page summarizes frequently asked questions and provides guidance on issues that commonly occur when adopting ExecuTorch.

If a specific issue is not covered here, consider searching for or creating an issue on GitHub under [Issues](https://github.com/pytorch/executorch/issues) or [Discussions](https://github.com/pytorch/executorch/discussions).

## Export

### Missing out variants: { _ }
Expand All @@ -10,7 +12,7 @@ The model likely contains torch custom operators. Custom ops need an Executorch

### RuntimeError: PyTorch convert function for op _ not implemented

The model likely contains an operator that is not yet supported on ExecuTorch. In this case, consider search for or creating an issue on [GitHub](https://github.com/pytorch/executorch/issues).
The model likely contains an operator that is not yet supported on ExecuTorch. In this case, consider searching for or creating an issue on [GitHub](https://github.com/pytorch/executorch/issues).

## Runtime

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### Missing Logs

ExecuTorch provides hooks to route runtime logs. By default, logs are sent to stdout/stderr, but users can override `et_pal_emit_log_message` to route logs to a custom destination. The Android and iOS extensions also provide out-of-box log routing to the appropriate platform logs. See [Runtime Platform Abstraction Layer (PAL)(https://pytorch.org/executorch/main/runtime-platform-abstraction-layer.html) for more information.
ExecuTorch provides hooks to route runtime logs. By default, logs are sent to stdout/stderr, but users can override `et_pal_emit_log_message` to route logs to a custom destination. The Android and iOS extensions also provide out-of-box log routing to the appropriate platform logs. See [Runtime Platform Abstraction Layer (PAL)](https://pytorch.org/executorch/main/runtime-platform-abstraction-layer.html) for more information.

### Error setting input: 0x10 / Attempted to resize a bounded tensor...

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