Being able to use asynchronous functions in your tests could be useful, for example, when you're querying your database asynchronously. Imagine you want to test sending requests to your FastAPI application and then verify that your backend successfully wrote the correct data in the database, while using an async database library.

Let's look at how we can make that work.

## pytest.mark.anyio

Alerting with prometheus is two step process. First we setup alerts in Prometheus server and then we need to send alerts to the AlertManager.

# Contributing guidelines

## Pull Request Checklist

Before sending your pull requests, make sure you do the following:

-   Read the [contributing guidelines](CONTRIBUTING.md).
-   Read the [Code of Conduct](CODE_OF_CONDUCT.md).
-   Ensure you have signed the
    [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-   Check if your changes are consistent with the

2. Running the CI scripts locally, as explained below
3. **Google employees only**: Google employees can use an internal-only tool
called "MLCI" that makes testing more convenient: it can execute any full CI job
against a pending change. Search for "MLCI" internally to find it.

You may invoke a CI script of your choice by following these instructions:

```bash
cd tensorflow-git-dir

2. By using a `with` block, we make sure that the file-like object is closed after the generator function is done. So, after it finishes sending the response.
3. This `yield from` tells the function to iterate over that thing named `file_like`. And then, for each part iterated, yield that part as coming from this generator function.

# OpenAPI Webhooks

There are cases where you want to tell your API **users** that your app could call *their* app (sending a request) with some data, normally to **notify** of some type of **event**.

This means that instead of the normal process of your users sending requests to your API, it's **your API** (or your app) that could **send requests to their system** (to their API, their app).

This is normally called a **webhook**.

## Webhooks steps

     * `ID <= lastGoodStreamId` can only be replayed on a new connection if they are idempotent.
     *
     * @param lastGoodStreamId the last stream ID the peer processed before sending this message. If
     *     [lastGoodStreamId] is zero, the peer processed no frames.
     * @param errorCode reason for closing the connection.
     * @param debugData only valid for HTTP/2; opaque debug data to send.

### Config Serving

Config serving is the layer that actually accepts proxy clients, connected over bidirectional gRPC streams, and serve them the required configuration.

We will have two triggers for sending config - requests and pushes.

#### Requests

  // The bookmark will report the ResourceVersion (RV) corresponding to the
  // set of objects, and be marked with `"k8s.io/initial-events-end": "true"` annotation.
  // Afterwards, the watch stream will proceed as usual, sending watch events
  // corresponding to changes (subsequent to the RV) to objects watched.
  //
  // When `sendInitialEvents` option is set, we require `resourceVersionMatch`

The process that happens when your API app calls the *external API* is named a "callback". Because the software that the external developer wrote sends a request to your API and then your API *calls back*, sending a request to an *external API* (that was probably created by the same developer).