## Webhooks steps { #webhooks-steps }

The process normally is that **you define** in your code what is the message that you will send, the **body of the request**.

You also define in some way at which **moments** your app will send those requests or events.

And **your users** define in some way (for example in a web dashboard somewhere) the **URL** where your app should send those requests.

## `async` and `await` { #async-and-await }

Modern versions of Python have a very intuitive way to define asynchronous code. This makes it look just like normal "sequential" code and do the "awaiting" for you at the right moments.

When there is an operation that will require waiting before giving the results and has support for these new Python features, you can code it like:

```Python
burgers = await get_burgers(2)
```

```
mc cp ~/images.jpg myminio/images
.../images.jpg:  8.31 KB / 8.31 KB ┃▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓┃ 100.00% 59.42 KB/s 0s
```

Wait a few moments, then check the bucket’s contents with mc ls — you will see a thumbnail appear.

```
mc ls myminio/images-thumbnail
[2017-02-08 11:39:40 IST]   992B images-thumbnail.jpg
```

Então em algum momento, é finalmente sua vez. Você vai ao balcão, pega seus hambúrgueres e volta para a mesa.

Você e seu _crush_ comem os hambúrgueres e aproveitam o tempo. ✨

---

Imagine que você seja o computador / programa nessa história.

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

* **Código Asíncrono**
* **`async` y `await`**
* **Coroutines**

## Código Asíncrono

El código asíncrono simplemente significa que el lenguaje 💬 tiene una forma de decirle a la computadora / programa 🤖 que en algún momento del código, tendrá que esperar que *otra cosa* termine en otro lugar. Digamos que esa *otra cosa* se llama "archivo-lento" 📝.

Jusqu'au moment où c'est (enfin) votre tour. Vous allez au comptoir, récupérez vos burgers 🍔 et revenez à votre table.

<img src="/img/async/concurrent-burgers/concurrent-burgers-06.png" class="illustration">

Vous et votre crush 😍 mangez les burgers 🍔 et passez un bon moment ✨.

<img src="/img/async/concurrent-burgers/concurrent-burgers-07.png" class="illustration">

     *            Output stream. Must not be {@literal null}.
     * @param comments
     *            Comments.
     */
    public static void store(final Properties props, final OutputStream out, final String comments) {
        assertArgumentNotNull("props", props);
        assertArgumentNotNull("out", out);

        try {
            props.store(out, comments);
        } catch (final IOException e) {

# Sub Applications - Mounts { #sub-applications-mounts }

If you need to have two independent FastAPI applications, with their own independent OpenAPI and their own docs UIs, you can have a main app and "mount" one (or more) sub-application(s).

## Mounting a **FastAPI** application { #mounting-a-fastapi-application }

Then the Python code in your application would be equivalent to something like:

```Python
if "johndoe" == "stanleyjobson" and "love123" == "swordfish":
    ...
```