```

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.

        elif isinstance(item, dict):
            assert len(item) == 1
            values = list(item.values())
            if not values:
                continue
            if isinstance(values[0], str):
                resolve_file(item=values[0], files=files, config=config)
            elif isinstance(values[0], list):
                resolve_files(items=values[0], files=files, config=config)
            else:

* 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).

import json
from pathlib import Path

file_path = Path("./openapi.json")
openapi_content = json.loads(file_path.read_text())

for path_data in openapi_content["paths"].values():
    for operation in path_data.values():
        tag = operation["tags"][0]
        operation_id = operation["operationId"]
        to_remove = f"{tag}-"
        new_operation_id = operation_id[len(to_remove) :]
        operation["operationId"] = new_operation_id

                    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):

from docs_src.query_params_str_validations.tutorial012 import app

client = TestClient(app)


def test_default_query_values():
    url = "/items/"
    response = client.get(url)
    assert response.status_code == 200, response.text
    assert response.json() == {"q": ["foo", "bar"]}


def test_multi_query_values():
    url = "/items/?q=baz&q=foobar"
    response = client.get(url)
    assert response.status_code == 200, response.text