have their default values. This is different from
                `response_model_exclude_defaults` in that if the fields are set,
                they will be included in the response, even if the value is the same
                as the default.

                When `True`, default values are omitted from the response.

                Read more about it in the

        * In requests, a list will be read, eliminating duplicates and converting it to a `set`.
        * In responses, the `set` will be converted to a `list`.
        * The generated schema will specify that the `set` values are unique (using JSON Schema's `uniqueItems`).
* `bytes`:
    * Standard Python `bytes`.
    * In requests and responses will be treated as `str`.
    * The generated schema will specify that it's a `str` with `binary` "format".

    ```

Wenn Sie mit einer *Pfadoperation* kommunizieren, die zwei HTTP-Header sendet, wie:

```
X-Token: foo
X-Token: bar
```

Dann wäre die Response:

```JSON
{
    "X-Token values": [
        "bar",
        "foo"
    ]
}
```

## Zusammenfassung

Deklarieren Sie Header mittels `Header`, auf die gleiche Weise wie bei `Query`, `Path` und `Cookie`.

## Без Pydantic

The `ContextVar` has to be created at the top of the module, like:

```Python
some_var = ContextVar("some_var", default="default value")
```

To set a value used in the current "context" (e.g. for the current request) use:

```Python
some_var.set("new value")
```

To get a value anywhere inside of the context (e.g. in any part handling the current request) use:

```Python
some_var.get()
```

    ```Python hl_lines="80-83"
    {!> ../../../docs_src/security/tutorial003.py!}
    ```

#### About `**user_dict`

`UserInDB(**user_dict)` means:

*Pass the keys and values of the `user_dict` directly as key-value arguments, equivalent to:*

```Python
UserInDB(
    username = user_dict["username"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],

Common examples of CPU bound operations are things that require complex math processing.

For example:

* **Audio** or **image processing**.
* **Computer vision**: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time.

* Retrieve the stored data.
* Put that data in a Pydantic model.
* Generate a `dict` without default values from the input model (using `exclude_unset`).
    * This way you can update only the values actually set by the user, instead of overriding values already stored with default values in your model.
* Create a copy of the stored model, updating it's attributes with the received partial updates (using the `update` parameter).

                    async_exit_stack=async_exit_stack,
                )
                values, errors, background_tasks, sub_response, _ = solved_result
                if not errors:
                    raw_response = await run_endpoint_function(
                        dependant=dependant, values=values, is_coroutine=is_coroutine
                    )
                    if isinstance(raw_response, Response):

!!! note
    FastAPI will know that the value of `q` is not required because of the default value `= None`.

    The `Union` in `Union[str, None]` is not used by FastAPI, but will allow your editor to give you better support and detect errors.

## Without Pydantic