### Always `raise` in Dependencies with `yield` and `except`

If you catch an exception in a dependency with `yield`, unless you are raising another `HTTPException` or similar, you should re-raise the original exception.

                        str(additional_status_code).upper()
                    ) or http.client.responses.get(int(additional_status_code))
                    description = (
                        process_response.get("description")
                        or openapi_response.get("description")
                        or status_text
                        or "Additional Response"
                    )

            # `tests/test_infer_param_optionality.py` for an example.
            field_info = params.Path(annotation=use_annotation)
        elif is_uploadfile_or_nonable_uploadfile_annotation(
            type_annotation
        ) or is_uploadfile_sequence_annotation(type_annotation):
            field_info = params.File(annotation=use_annotation, default=default_value)

    These Pydantic models define more or less a "schema" (a valid data shape).

    So this will help us avoiding confusion while using both.

### Create initial Pydantic *models* / schemas

Create an `ItemBase` and `UserBase` Pydantic *models* (or let's say "schemas") to have common attributes while creating or reading data.

    # Contributors may wish to update or change these, e.g. to fix capitalization.
    language_names_path = Path(__file__).parent / "../docs/language_names.yml"
    local_language_names: Dict[str, str] = mkdocs.utils.yaml_load(
        language_names_path.read_text(encoding="utf-8")
    )
    for lang_path in get_lang_paths():
        if lang_path.name in {"en", "em"} or not lang_path.is_dir():
            continue

# Settings and Environment Variables

In many cases your application could need some external settings or configurations, for example secret keys, database credentials, credentials for email services, etc.

Most of these settings are variable (can change), like database URLs. And many could be sensitive, like secrets.

For this reason it's common to provide them in environment variables that are read by the application.

## Environment Variables

!!! tip

You do that with standard modern Python types.

You don't have to learn a new syntax, the methods or classes of a specific library, etc.

Just standard **Python 3.8+**.

For example, for an `int`:

```Python
item_id: int
```

or for a more complex `Item` model:

```Python
item: Item
```

...and with that single declaration you get:

    assert response.json() == {"detail": "Incorrect username or password"}


@needs_py39
def test_login_incorrect_username(client: TestClient):
    response = client.post("/token", data={"username": "foo", "password": "secret"})
    assert response.status_code == 400, response.text
    assert response.json() == {"detail": "Incorrect username or password"}


@needs_py39
def test_no_token(client: TestClient):

    assert response.json() == {"detail": "Incorrect username or password"}


@needs_py310
def test_login_incorrect_username(client: TestClient):
    response = client.post("/token", data={"username": "foo", "password": "secret"})
    assert response.status_code == 400, response.text
    assert response.json() == {"detail": "Incorrect username or password"}


@needs_py310
def test_no_token(client: TestClient):

So, if you have an object `something` (that might _not_ be a function) and you can "call" it (execute it) like:

```Python
something()
```

or

```Python
something(some_argument, some_keyword_argument="foo")
```

then it is a "callable".

## Classes as dependencies