val oldReleasePattern = "release(\\d+)x".toRegex()
    return when (branchName) {
        "master" -> 0
        "release" -> 1
        else -> {
            val matchResult = oldReleasePattern.find(branchName)
            if (matchResult == null) {
                null
            } else {
                (matchResult.groupValues[1].toInt() - 4).apply {
                    require(this in 2..23)
                }

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

# WARNING: THESE OPTIONS WONT WORK IF YOU DO NOT HAVE PROPER AUTHENTICATION AND PERMISSIONS

# Use --config=tf_public_cache to try and use the TensorFlow public build cache
# to build TensorFlow. Look at ci/official/envs to find which types of jobs
# push to the cache.  For macOS, use --config=tf_public_macos_cache

* 📝 Add External Link: ML serving and monitoring with FastAPI and Evidently. PR [#9701](https://github.com/tiangolo/fastapi/pull/9701) by [@mnrozhkov](https://github.com/mnrozhkov).
* 📝 Reword in docs, from "have in mind" to "keep in mind". PR [#10376](https://github.com/tiangolo/fastapi/pull/10376) by [@malicious](https://github.com/malicious).