modules, and to copy and distribute the resulting executable under
terms of your choice, provided that you also meet, for each linked
independent module, the terms and conditions of the license of that
module.  An independent module is a module which is not derived from or
based on this library.  If you modify this library, you may extend this
exception to your version of the library, but you are not obligated to

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

  public static final String SEC_CH_UA_ARCH = "Sec-CH-UA-Arch";

  /**
   * The HTTP <a href="https://wicg.github.io/ua-client-hints/#sec-ch-ua-model">{@code
   * Sec-CH-UA-Model}</a> header field name.
   *
   * @since 30.0
   */
  public static final String SEC_CH_UA_MODEL = "Sec-CH-UA-Model";

  /**
   * The HTTP <a href="https://wicg.github.io/ua-client-hints/#sec-ch-ua-platform">{@code

GraalVM Native Image
--------------------

Building your native images with [GraalVM] should work automatically.

See the okcurl module for an example build.

```shell
$ ./gradlew okcurl:nativeImage
$ ./okcurl/build/graal/okcurl https://httpbin.org/get
```

License
-------

```
Copyright 2019 Square, Inc.

 * `COMPATIBLE_TLS` is a secure configuration that connects to secure–but not current–HTTPS servers.
 * `CLEARTEXT` is an insecure configuration that is used for `http://` URLs.

These loosely follow the model set in [Google Cloud Policies](https://cloud.google.com/load-balancing/docs/ssl-policies-concepts). We [track changes](../security/tls_configuration_history.md) to this policy.

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

👈 💪 ⤴️ ⚙️ 💖:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {

Temporarily adopting this point of view (of the *external developer*) can help you feel like it's more obvious where to put the parameters, the Pydantic model for the body, for the response, etc. for that *external API*.

///

### Create a callback `APIRouter` { #create-a-callback-apirouter }

First create a new `APIRouter` that will contain one or more callbacks.

Imaginemos que cargar el modelo puede **tomar bastante tiempo**, porque tiene que leer muchos **datos del disco**. Entonces no quieres hacerlo para cada request.

<span style="color: green;">INFO</span>:     Application startup complete.
```

</div>

/// note

La commande `uvicorn main:app` fait référence à :

* `main` : le fichier `main.py` (le module Python).
* `app` : l'objet créé dans `main.py` via la ligne `app = FastAPI()`.
* `--reload` : l'option disant à uvicorn de redémarrer le serveur à chaque changement du code. À ne pas utiliser en production !

///

///

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):