* Fix broken link in docs about OAuth 2.0 with scopes. PR [#275](https://github.com/tiangolo/fastapi/pull/275) by [@dmontagu](https://github.com/dmontagu).

* Refactor param extraction using Pydantic `Field`:
    * Large refactor, improvement, and simplification of param extraction from *path operations*.

as well as finishing [kubectl diff which is based on server-side dry-run feature](https://kubernetes.io/docs/concepts/overview/working-with-objects/object-management/#how-to-create-objects). We’ve continued separating kubectl code to prepare for extraction out of main repository. Finally, thanks to the awesome support and feedback from community we’ve managed to promote the new [plugin mechanism to Beta](https://kubernetes.io/docs/tasks/extend-kubectl/kubectl-plugins/).

### SIG Cloud...

([#625](https://github.com/kubernetes/enhancements/issues/625))

#### Additional Notable Feature Updates
- Support for go modules in Kubernetes Core.
- Continued preparation for cloud provider extraction and code organization.  The cloud provider code has been moved to kubernetes/legacy-cloud-providers for easier removal later and external consumption.

        If the package path doesn't include your github username, please provide some other form of proof of ownership.
    validations:
      required: true
  - type: textarea
    id: retraction-reason
    attributes:
      label: "What is the reason that you could not retract this package instead?"
      description: |
        Requesting we remove a module here only hides the generated documentation on pkg.go.dev.

     likeness depicted in a Work;
 iv. rights protecting against unfair competition in regards to a Work,
     subject to the limitations in paragraph 4(a), below;
  v. rights protecting the extraction, dissemination, use and reuse of data
     in a Work;
 vi. database rights (such as those arising under Directive 96/9/EC of the
     European Parliament and of the Council of 11 March 1996 on the legal

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.