* <a href="https://github.com/esnme/ultrajson" target="_blank"><code>ujson</code></a> - Required if you want to use `UJSONResponse`.
* `fastapi-cli` - to provide the `fastapi` command.

When you install `fastapi` it comes these standard dependencies.

## `fastapi-slim`

    response = client.put(
        "/items/bar?token=jessica", headers={"X-Token": "fake-super-secret-token"}
    )
    assert response.status_code == 403, response.text
    assert response.json() == {"detail": "You can only update the item: plumbus"}


def test_admin(client: TestClient):
    response = client.post(
        "/admin/?token=jessica", headers={"X-Token": "fake-super-secret-token"}
    )

But you still want to be able to filter and convert the data you return with a `response_model`.

For those cases, you can use a `Response` parameter.

## Use a `Response` parameter

You can declare a parameter of type `Response` in your *path operation function* (as you can do for cookies and headers).

And then you can set the `status_code` in that *temporal* response object.

```Python hl_lines="1  9  12"

If you want to use your APIs' function names as `operationId`s, you can iterate over all of them and override each *path operation's* `operation_id` using their `APIRoute.name`.

You should do it after adding all your *path operations*.

```Python hl_lines="2  12-21  24"
{!../../../docs_src/path_operation_advanced_configuration/tutorial002.py!}
```

!!! tip

If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).

Here I'll show you how to use <a href="https://gunicorn.org/" class="external-link" target="_blank">**Gunicorn**</a> with **Uvicorn worker processes**.

!!! info
    If you are using containers, for example with Docker or Kubernetes, I'll tell you more about that in the next chapter: [FastAPI in Containers - Docker](docker.md){.internal-link target=_blank}.

* Give you the received data in the parameter `item`.
    * As you declared it in the function to be of type `Item`, you will also have all the editor support (completion, etc) for all of the attributes and their types.
* Generate <a href="https://json-schema.org" class="external-link" target="_blank">JSON Schema</a> definitions for your model, you can also use them anywhere else you like if it makes sense for your project.

    And many teams use only `PUT`, even for partial updates.

    You are **free** to use them however you want, **FastAPI** doesn't impose any restrictions.

    But this guide shows you, more or less, how they are intended to be used.

### Using Pydantic's `exclude_unset` parameter

If you want to receive partial updates, it's very useful to use the parameter `exclude_unset` in Pydantic's model's `.model_dump()`.

                available, instead of erroring out, the dependency result will be
                `None`.

                This is useful when you want to have optional authentication.

                It is also useful when you want to have authentication that can be
                provided in one of multiple optional ways (for example, in a query
                parameter or in an HTTP Bearer token).

    In Pydantic version 1, you would use an internal class `Config` and `schema_extra`, as described in <a href="https://docs.pydantic.dev/1.10/usage/schema/#schema-customization" class="external-link" target="_blank">Pydantic's docs: Schema customization</a>.

    You can set `schema_extra` with a `dict` containing any additional data you would like to show up in the generated JSON Schema, including `examples`.

!!! tip