It also re-declares `id: int`. By doing this, we are making a **contract** with the API clients, so that they can always expect the `id` to be there and to be an `int` (it will never be `None`).

/// tip

Having the return model ensure that a value is always available and always `int` (not `None`) is very useful for the API clients, they can write much simpler code having this certainty.

    * If you are comparing Uvicorn, compare it against Daphne, Hypercorn, uWSGI, etc. Application servers.
* **Starlette**:
    * Will have the next best performance, after Uvicorn. In fact, Starlette uses Uvicorn to run. So, it probably can only get "slower" than Uvicorn by having to execute more code.

    cookies: List<Cookie>,
  ) {
    val cookieStrings = mutableListOf<String>()
    for (cookie in cookies) {
      cookieStrings.add(cookieToString(cookie, true))
    }
    val multimap = mapOf("Set-Cookie" to cookieStrings)
    try {
      cookieHandler.put(url.toUri(), multimap)
    } catch (e: IOException) {
      Platform.get().log("Saving cookies failed for " + url.resolve("/...")!!, WARN, e)
    }
  }

systems is data "<abbr title="also called marshalling, conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc.

Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an `int`,...

## Parameterized dependencies { #parameterized-dependencies }

All the dependencies we have seen are a fixed function or class.

But there could be cases where you want to be able to set parameters on the dependency, without having to declare many different functions or classes.

Let's imagine that we want to have a dependency that checks if the query parameter `q` contains some fixed content.

But we want to be able to parameterize that fixed content.

/// info

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

So, you might still need to use Pydantic models.

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

## Dataclasses in `response_model` { #dataclasses-in-response-model }

You can also use `dataclasses` in the `response_model` parameter:

you can set the storage class of an object via request metadata field `x-amz-storage-class`. MinIO server then honors the storage class by
saving the object in specific number of data and parity drives.

## Storage usage

The selection of varying data and parity drives has a direct impact on the drive space usage. With storage class, you can optimize for high
redundancy or better drive space utilization.

# limitations under the License.
# ==============================================================================
#
# Sets up custom apt sources for our TF images.

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

# Fetch the NVIDIA key.
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub;

labels: [question]
body:
  - type: markdown
    attributes:
      value: |
        Thanks for your interest in FastAPI! 🚀

        Please follow these instructions, fill every question, and do every step. 🙏

        I'm asking this because answering questions and solving problems in GitHub is what consumes most of the time.

@app.get("/users/", tags=["users"])
async def get_users():
    return [{"name": "Harry"}, {"name": "Ron"}]


@app.get("/items/", tags=["items"])
async def get_items():