}
			os.Exit(2)
		}
		fatalf("parsing %s: %s", name, err)
	}
	return ast1
}

func sourceLine(n ast.Node) int {
	return fset.Position(n.Pos()).Line
}

// ParseGo populates f with information learned from the Go source code
// which was read from the named file. It gathers the C preamble
// attached to the import "C" comment, a list of references to C.xxx,
// a list of exported functions, and the actual AST, to be rewritten and

But for now, let's check these important **conceptual ideas**. These concepts also apply to any other type of web API. 💡

## Security - HTTPS

In the [previous chapter about HTTPS](./https.md){.internal-link target=_blank} we learned about how HTTPS provides encryption for your API.

We also saw that HTTPS is normally provided by a component **external** to your application server, a **TLS Termination Proxy**.

# Alternatives, Inspiration and Comparisons

What inspired **FastAPI**, how it compares to other alternatives and what it learned from them.

## Intro

**FastAPI** wouldn't exist if not for the previous work of others.

There have been many tools created before that have helped inspire its creation.

- Kubeadm: added the experimental (alpha) feature gate `EtcdLearnerMode` that allows etcd members to be joined as learner and only then promoted as voting members ([#113318](https://github.com/kubernetes/kubernetes/pull/113318), [@pacoxu](https://github.com/pacoxu))

- `kubeadm`: promoted feature gate `EtcdLearnerMode` to `beta`. Learner mode for
  joining `etcd` members is now enabled by default. ([#120228](https://github.com/kubernetes/kubernetes/pull/120228), [@pacoxu](https://github.com/pacoxu))

* Added scheduler integration test owners. ([#46930](https://github.com/kubernetes/kubernetes/pull/46930), [@k82cn](https://github.com/k82cn))
* `kubectl run` learned how to set a service account name in the generated pod spec with the `--serviceaccount` flag. ([#46318](https://github.com/kubernetes/kubernetes/pull/46318), [@liggitt](https://github.com/liggitt))

    *   Added Adafactor optimizer `tf.keras.optimizers.Adafactor`.
    *   Added `warmstart_embedding_matrix` to `tf.keras.utils`.
        This utility can be used to warmstart an embeddings matrix so you
        reuse previously-learned word embeddings when working with a new set
        of words which may include previously unseen words (the embedding
        vectors for unseen words will be randomly initialized).

*   `tf.Variable`: