Tutorials should aim to use multi-arch container images with source code transparency

**This is a Feature Request**

**What would you like to be added**

Kubernetes documentation tutorials should integrate multi-architecture container images alongside full source code transparency.

This approach provides universal accessibility of tutorials across amd64, ARM-based, and other Kubernetes-supported architectures, therefore ensuring a consistent learning experience.

Utilising images with open access to their source code is also beneficial. It improves transparency, facilitates auditing and independent builds whilst improving trust and security within the Kubernetes community.

From an implementation perspective, automating this process would be benefical. Upon detecting an image in a request, the system could automatically verify the image's multi-arch support or prompt for additional labels or notes before approval. For instance, images could be referenced as image=linux/amd64, image=linux/arm/v7, image=linux/arm/v8, linux/arm64 and/or require the inclusion of a note for the image source code. This approach could significantly enhance future efficiency and compliance.

**Why is this needed**

Issues have emerged where tutorial examples do not work for users using architectures beyond the traditional amd64 CPU instruction set as highlighted in the issue: https://github.com/kubernetes/website/issues/45809.

In the referenced issue, the image registry.k8s.io/liveness, last updated in 2014, supports only amd64. I have also been unsuccessful in identifying the source code for this image at present.

Reports of related issues can also be found in https://github.com/kubernetes/website/issues/45420 and https://github.com/kubernetes/website/issues/45783

No specific file, test, or entry point is identified. Start by reviewing the Kubernetes documentation tutorials and linked issues 45809, 45420, and 45783; done should be an agreed, actionable scope for multi-architecture images, source transparency, and any review or automation path.

Written by the indexing model from the issue text.